WO2024261936A1 - Disease risk estimation device, disease risk estimation system, disease risk estimation method, and recording medium - Google Patents

Abstract

Provided is a disease risk estimation device which, in order to use sensor data measured in response to a foot movement to estimate a disease risk reflecting a risk of each disease, comprises: an acquisition unit that acquires sensor data measured in response to a foot movement of a target person who is an estimation target of the disease risk; a risk estimation unit that uses the acquired sensor data to estimate the disease risk reflecting the risk of each disease; and an output unit that outputs disease risk information according to the estimated disease risk.

Description

DISEASE RISK ESTIMATION DEVICE, DISEASE RISK ESTIMATION SYSTEM, DISEASE RISK ESTIMATION METHOD, AND RECORDING MEDIUM

The present disclosure relates to a disease risk estimation device, a disease risk estimation system, a disease risk estimation method, and a recording medium.

As interest in healthcare grows, attention is being focused on services that provide information based on gait. For example, technology is being developed that analyzes gait using sensor data measured by sensors mounted in footwear such as shoes. Time-series sensor data contains characteristics associated with walking events related to physical conditions. If a subject's disease risk can be estimated based on the characteristics associated with walking events, information based on disease risk can be provided to specialized institutions that handle health insurance and life insurance.

Patent Document 1 discloses an insurance proposal system that proposes a wide variety of insurance plans based on details arising from lifestyle habits in daily life. The system of Patent Document 1 includes a pedestrian database in which pedestrian information is accumulated, including gait information for multiple pedestrians and injury/illness history information indicating past injuries and illnesses that is stored in association with the gait information. The system of Patent Document 1 acquires the user's gait information from a footwear module that has a sensor unit that detects movement. The system of Patent Document 1 refers to the pedestrian information and calculates an index value that serves as an indicator for insurance premiums according to the user's gait information.

JP 2020-197948 A

The method of Patent Document 1 refers to pedestrian information stored in a database to calculate an index value corresponding to the user's gait information. According to the method of Patent Document 1, if past injuries and illnesses associated with gait information are stored in the database, an index value corresponding to gait information can be calculated. The method of Patent Document 1 also discloses an example of weighting the walking risk value according to the severity of injuries and illnesses such as sprains and fractures. However, the method of Patent Document 1 was not able to estimate disease risk that reflected the risk for each disease.

The objective of the present disclosure is to provide a disease risk estimation device, a disease risk estimation system, a disease risk estimation method, and a recording medium that can estimate disease risk that reflects the risk of each disease using sensor data measured according to foot movement.

A disease risk estimation device according to one embodiment of the present disclosure includes an acquisition unit that acquires sensor data measured according to foot movements of a subject for whom disease risk is to be estimated, a risk estimation unit that uses the acquired sensor data to estimate a disease risk that reflects the risk for each disease, and an output unit that outputs disease risk information according to the estimated disease risk.

In one embodiment of the disease risk estimation method disclosed herein, sensor data measured according to the foot movements of a subject for whom disease risk is to be estimated is acquired, the acquired sensor data is used to estimate disease risk reflecting the risk for each disease, and disease risk information corresponding to the estimated disease risk is output.

A program according to one embodiment of the present disclosure causes a computer to execute the following processes: acquiring sensor data measured according to the foot movements of a subject for whom a disease risk is to be estimated; estimating a disease risk that reflects the risk for each disease using the acquired sensor data; and outputting disease risk information according to the estimated disease risk.

According to the present disclosure, it is possible to provide a disease risk estimation device, a disease risk estimation system, a disease risk estimation method, and a recording medium that can estimate disease risk that reflects the risk of each disease using sensor data measured according to foot movements.

1 is a block diagram showing an example of the configuration of a disease risk estimation system according to the present disclosure. 1 is a block diagram showing an example of the configuration of a measurement device provided in a disease risk estimation system in the present disclosure. FIG. 1 is a conceptual diagram showing an example of the arrangement of measurement devices in a disease risk estimation system according to the present disclosure. 1 is a conceptual diagram showing an example of a coordinate system set in a measurement device of a disease risk estimation system in the present disclosure. FIG. FIG. 2 is a conceptual diagram showing an example of a human body surface used in the description of the present disclosure. 1 is a block diagram showing an example of the configuration of a disease risk estimation device provided in a disease risk estimation system in the present disclosure. FIG. FIG. 1 is a conceptual diagram showing an example of a walking cycle used in the description of the present disclosure. 1 is a conceptual diagram showing an example of an estimation of a physical ability score in a disease risk estimation system in the present disclosure. FIG. 1 is a conceptual diagram showing an example of a disease risk score estimation in the disease risk estimation system of the present disclosure. 1 is a conceptual diagram showing an example of a disease risk score estimation in the disease risk estimation system of the present disclosure. 1 is a conceptual diagram showing an example of a disease risk score estimation in the disease risk estimation system of the present disclosure. 1 is a flowchart showing an example of the operation of the disease risk estimation system in the present disclosure. 1 is a conceptual diagram for explaining an application example of a disease risk estimation system in the present disclosure. 1 is a conceptual diagram for explaining an application example of a disease risk estimation system in the present disclosure. 1 is a block diagram showing an example of the configuration of a disease risk estimation system according to the present disclosure. 1 is a block diagram showing an example of the configuration of a disease risk estimation device provided in a disease risk estimation system in the present disclosure. FIG. 1 is a graph showing an example of time series data of disease risk scores estimated by the disease risk estimation system in the present disclosure. 1 is a graph showing an example of time series data of disease risk scores estimated by the disease risk estimation system in the present disclosure. 1 is a graph showing an example of time series data of disease risk scores estimated by the disease risk estimation system in the present disclosure. 1 is a flowchart showing an example of the operation of the disease risk estimation system in the present disclosure. 1 is a conceptual diagram for explaining an application example of a disease risk estimation system in the present disclosure. 1 is a conceptual diagram for explaining an application example of a disease risk estimation system in the present disclosure. 1 is a block diagram showing an example of the configuration of a disease risk estimation system according to the present disclosure. 1 is a block diagram showing an example of the configuration of a disease risk estimation device provided in a disease risk estimation system in the present disclosure. FIG. 1 is a flowchart showing an example of the operation of the disease risk estimation system in the present disclosure. 1 is a conceptual diagram for explaining an application example of a disease risk estimation system in the present disclosure. 1 is a conceptual diagram for explaining an application example of a disease risk estimation system in the present disclosure. 1 is a conceptual diagram for explaining an application example of a disease risk estimation system in the present disclosure. 1 is a conceptual diagram for explaining an application example of a disease risk estimation system in the present disclosure. 1 is a conceptual diagram for explaining an application example of a disease risk estimation system in the present disclosure. 1 is a conceptual diagram for explaining an application example of a disease risk estimation system in the present disclosure. 1 is a block diagram showing an example of the configuration of a disease risk estimation device provided in a disease risk estimation system in the present disclosure. FIG. 11 is a flowchart for explaining an example of the operation of a disease risk estimation device provided in a disease risk estimation system in the present disclosure. FIG. 2 is a block diagram showing an example of a hardware configuration for executing control and processing in the present disclosure.

Below, the embodiments for implementing the present disclosure are described with reference to the drawings. In this disclosure, the drawings used in the description of each embodiment relate to one or more embodiments. Furthermore, the elements included in each drawing may apply to one or more embodiments. The embodiments described below are limited in a way that is technically preferable for implementing the present disclosure, but the scope of the disclosure is not limited to the following. In all drawings used in the description of the embodiments below, similar parts are given the same reference numerals unless there is a special reason. In the embodiments below, repeated description of similar configurations and operations may be omitted. The direction of the arrows in the drawings is an example and does not limit the direction of data, signals, etc.

First Embodiment
First, an example of a disease risk estimation system according to the present disclosure will be described with reference to the drawings. The disease risk estimation system of this embodiment estimates the disease risk of a specific disease using sensor data related to foot movements according to the walking of a subject (user) whose disease risk is to be estimated. In this embodiment, an example of estimating a disease risk reflecting the risk for each disease will be given.

(composition)
1 is a block diagram showing an example of the configuration of a disease risk estimation system 1 in the present disclosure. The disease risk estimation system 1 includes a measurement device 10 and a disease risk estimation device 13. For example, the measurement device 10 is installed in the footwear of a subject (user) whose disease risk is to be estimated. For example, the function of the disease risk estimation device 13 is installed in a mobile terminal carried by the subject (user). Below, the configurations of the measurement device 10 and the disease risk estimation device 13 will be described individually.

[Measuring equipment]
2 is a block diagram showing an example of the configuration of the measurement device 10. The measurement device 10 has a sensor 110, a control unit 113, a communication unit 115, and a power source 117. The sensor 110 has an acceleration sensor 111 and an angular velocity sensor 112. The sensor 110 may include sensors other than the acceleration sensor 111 and the angular velocity sensor 112. Descriptions of sensors other than the acceleration sensor 111 and the angular velocity sensor 112 that may be included in the sensor 110 will be omitted.

The acceleration sensor 111 is a sensor that measures acceleration in three axial directions (also called spatial acceleration). The acceleration sensor 111 measures acceleration (also called spatial acceleration) as a physical quantity related to foot movement. The acceleration sensor 111 outputs the measured acceleration to the control unit 113. For example, the acceleration sensor 111 can be a piezoelectric type, a piezo-resistive type, a capacitance type, or other type of sensor. There are no limitations on the sensor used as the acceleration sensor 111 as long as it can measure acceleration.

Angular velocity sensor 112 is a sensor that measures angular velocity (also called spatial angular velocity) around three axes. Angular velocity sensor 112 measures angular velocity (also called spatial angular velocity) as a physical quantity related to foot movement. Angular velocity sensor 112 outputs the measured angular velocity to control unit 113. For example, a vibration type, capacitance type, or other type of sensor can be used as angular velocity sensor 112. There are no limitations on the sensor used as angular velocity sensor 112 as long as it can measure angular velocity.

The sensor 110 is realized, for example, by an inertial measurement unit that measures acceleration and angular velocity. An example of an inertial measurement unit is an IMU (Inertial Measurement Unit). The IMU includes an acceleration sensor 111 that measures acceleration in three axial directions and an angular velocity sensor 112 that measures angular velocity around three axes. The sensor 110 may be realized by an inertial measurement unit such as a VG (Vertical Gyro) or an AHRS (Attitude Heading Reference System). The sensor 110 may also be realized by a GPS/INS (Global Positioning System/Inertial Navigation System). The sensor 110 may be realized by a device other than an inertial measurement unit as long as it can measure physical quantities related to foot movement.

3 is a conceptual diagram showing an example of the measurement device 10 being placed in the shoes 100 of both feet. In the example of FIG. 3, the measurement device 10 is placed at a position that corresponds to the back side of the arch of the foot. For example, the measurement device 10 is placed in an insole inserted into the shoe 100. For example, the measurement device 10 may be placed on the bottom surface of the shoe 100. For example, the measurement device 10 may be embedded in the body of the shoe 100. The measurement device 10 may be detachable from the shoe 100, or may not be detachable from the shoe 100. The measurement device 10 may be placed at a position other than the back side of the arch of the foot, as long as it can measure sensor data related to foot movement. The measurement device 10 may also be placed in socks worn by the user or in an accessory such as an anklet worn by the user. The measurement device 10 may also be attached directly to the foot or embedded in the foot. The measurement device 10 may also be placed in one of the shoes 100, as long as it can measure data that can be used to estimate disease risk.

In the example of FIG. 3, a local coordinate system is set with the measuring device 10 (sensor 110) as the reference, including an x-axis in the left-right direction, a y-axis in the front-back direction, and a z-axis in the up-down direction. FIG. 3 shows an example in which the same coordinate system is set for the left foot and the right foot. For example, when sensors 110 manufactured with the same specifications are placed in the left and right shoes 100, the up-down orientation (Z-axis orientation) of the sensors 110 placed in the left and right shoes 100 is the same. In this case, the three axes of the local coordinate system set for the sensor data derived from the left foot and the three axes of the local coordinate system set for the sensor data derived from the right foot are the same for the left and right. In this disclosure, the x-axis is positive to the left, the y-axis is positive backward, and the z-axis is positive upward.

FIG. 4 is a conceptual diagram for explaining the local coordinate system (x-axis, y-axis, z-axis) set in the measuring device 10 (sensor 110) installed on the back side of the arch, and the world coordinate system (x-axis, y-axis, z-axis) set with respect to the ground. FIG. 4 shows an example in which different coordinate systems are set for the left foot and the right foot. In the world coordinate system (x-axis, y-axis, z-axis), the user's lateral direction is set to the x-axis direction, the direction of the user's back is set to the y-axis direction, and the direction of gravity is set to the z-axis direction when the user is standing upright facing the direction of travel. Note that the example in FIG. 4 conceptually shows the relationship between the local coordinate system (x-axis, y-axis, z-axis) and the world coordinate system (x-axis, y-axis, z-axis), and does not accurately show the relationship between the local coordinate system and the world coordinate system, which changes according to the user's walking.

FIG. 5 is a conceptual diagram for explaining the planes (also called human body planes) set for the human body. In this embodiment, a sagittal plane that divides the body into left and right, a coronal plane that divides the body into front and back, and a horizontal plane that divides the body horizontally are defined. As shown in FIG. 5, when the user stands upright with the center line of the foot facing the direction of travel, the world coordinate system and the local coordinate system are assumed to match. FIG. 5 shows an example in which different coordinate systems are set for the left and right feet. In this embodiment, the rotation in the sagittal plane around the X-axis (x-axis) as the rotation axis is defined as roll, the rotation in the coronal plane around the Y-axis (y-axis) as the rotation axis is defined as pitch, and the rotation in the horizontal plane around the Z-axis (z-axis) as the rotation axis is defined as yaw. In addition, the rotation angle in the sagittal plane around the X-axis (x-axis) as the rotation axis is defined as roll angle, the rotation angle in the coronal plane around the Y-axis (y-axis) as the rotation axis is defined as pitch angle, and the rotation angle in the horizontal plane around the Z-axis (z-axis) as the rotation axis is defined as yaw angle.

The control unit 113 (control means) causes the acceleration sensor 111 and the angular velocity sensor 112 to measure sensor data. For example, the control unit 113 causes the acceleration sensor 111 and the angular velocity sensor 112 to start measurement in response to a measurement start signal transmitted from the disease risk estimation device 13. For example, the control unit 113 may cause the acceleration sensor 111 and the angular velocity sensor 112 to start measurement in response to detection of the user walking. For example, the control unit 113 starts measuring the step width starting from the point in time when it is detected that either the left or right foot has started to move in the forward direction after the vertical heights of both feet have remained the same for a predetermined period of time. The control unit 113 may also be configured to start measuring the step width at a predetermined timing.

The control unit 113 acquires the acceleration in three axial directions from the acceleration sensor 111. The control unit 113 also acquires the angular velocity around three axes from the angular velocity sensor 112. For example, the control unit 113 performs analog-to-digital conversion (ADC) of the acquired physical quantities (analog data) such as angular velocity and acceleration. The physical quantities (analog data) measured by the acceleration sensor 111 and the angular velocity sensor 112 may be converted to digital data in each of the acceleration sensor 111 and the angular velocity sensor 112. For example, an ADC circuit that performs ADC of the physical quantities (analog data) such as angular velocity and acceleration may be provided. The control unit 113 outputs the converted digital data (also called sensor data) to the communication unit 115. For example, the control unit 113 may temporarily store the sensor data in a storage unit (not shown).

The sensor data includes at least acceleration data converted into digital data and angular velocity data converted into digital data. The acceleration data includes acceleration vectors in three axial directions. The angular velocity data includes angular velocity vectors about three axes. The acceleration data and angular velocity data are linked to the time at which they were acquired. The control unit 113 may also apply corrections such as corrections for mounting errors, temperature corrections, and linearity corrections to the acceleration data and angular velocity data.

For example, the control unit 113 may calculate at least one of the gait indices described below. In that case, the measurement device 10 outputs the calculated gait indices to the disease risk estimation device 13. For example, the control unit 113 may calculate a feature amount used to estimate physical ability described below. In that case, the measurement device 10 outputs the calculated feature amount to the disease risk estimation device 13.

For example, the control unit 113 is realized by a microcomputer or microcontroller that performs overall control of the measuring device 10 and performs data processing. For example, the control unit 113 has a CPU (Central Processing Unit), RAM (Random Access Memory), ROM (Read Only Memory), flash memory, etc.

The communication unit 115 (communication means) acquires sensor data from the control unit 113. The communication unit 115 transmits the acquired sensor data to the disease risk estimation device 13. The sensor data transmitted from the communication unit 115 is received by the disease risk estimation device 13. There are no particular limitations on the timing of transmitting the sensor data. For example, the communication unit 115 transmits the sensor data at a preset transmission timing. For example, the communication unit 115 transmits the sensor data in real time according to the measurement of the sensor data. For example, the communication unit 115 may store sensor data measured over a predetermined period and transmit the stored sensor data all at once at a preset timing. For example, the communication unit 115 (communication means) may be configured to receive a measurement start signal from the disease risk estimation device 13. In this case, the communication unit 115 outputs the received measurement start signal to the control unit 113.

For example, the communication unit 115 transmits the sensor data to the disease risk estimation device 13 via wireless communication. For example, the communication unit 115 transmits the sensor data to the disease risk estimation device 13 via a wireless communication function (not shown) that complies with standards such as Bluetooth (registered trademark) or WiFi (registered trademark). The communication function of the communication unit 115 may be in accordance with standards other than Bluetooth (registered trademark) or WiFi (registered trademark). The communication unit 115 may transmit the sensor data to the disease risk estimation device 13 via a wired connection such as a cable.

The power source 117 is a battery that supplies power for the measurement device 10 to operate. For example, the power source 117 is realized by a thin battery such as a coin type or button type. For example, the power source 117 is realized by a primary battery such as a lithium primary battery, a silver oxide battery, an alkaline button battery, or an air zinc battery. When realized by a primary battery, the power source 117 is preferably realized by a long-life battery. The power source 117 may also be realized by a rechargeable secondary battery. When realized by a secondary battery, the power source 117 may be a battery that can be charged via a wired connection or a battery that can be wirelessly powered. If the power source 117 is capable of wireless power supply, a wireless power supply device may be placed in a place where footwear is placed, such as an entrance or a shoe cupboard. If footwear equipped with the measurement device 10 is placed on the wireless power supply device, the measurement device 10 can be appropriately charged when not in use.

[Disease risk estimation device]
6 is a block diagram showing an example of the configuration of the disease risk estimation device 13. The disease risk estimation device 13 has an acquisition unit 131, a waveform processing unit 132, a gait index calculation unit 133, a storage unit 134, a physical ability estimation unit 135, a disease risk estimation unit 136, and an output unit 137. The waveform processing unit 132, the gait index calculation unit 133, the physical ability estimation unit 135, and the disease risk estimation unit 136 constitute the risk estimation unit 15. The waveform processing unit 132 and the gait index calculation unit 133 constitute the calculation unit 130. The physical ability estimation unit 135 and the disease risk estimation unit 136 constitute the estimation unit 140.

The acquisition unit 131 (acquisition means) acquires sensor data from the measurement device 10. The acquisition unit 131 receives the sensor data from the measurement device 10 via wireless communication. For example, the acquisition unit 131 receives the sensor data from the measurement device 10 via a wireless communication function (not shown) conforming to a standard such as Bluetooth (registered trademark) or WiFi (registered trademark). Note that the communication function of the acquisition unit 131 may conform to a standard other than Bluetooth (registered trademark) or WiFi (registered trademark) as long as it can communicate with the measurement device 10. The acquisition unit 131 may receive the sensor data from the measurement device 10 via a wired connection such as a cable. For example, the acquisition unit 131 may acquire gait indices and feature amounts calculated by the measurement device 10.

The acquisition unit 131 also acquires physical information (attributes) of the user. The physical information includes gender, date of birth, height, and weight. The date of birth is converted to age. For example, the physical information is input via an input device (not shown). For example, the physical information is input via a mobile terminal used by the user. For example, the physical information may be stored in advance in the storage unit 134. The physical information may be updated at any time in response to input by the user.

The waveform processing unit 132 (waveform processing means) acquires sensor data from the acquisition unit 131. The waveform processing unit 132 extracts time series data for one walking cycle from the time series data of acceleration in three axial directions and angular velocity around three axes contained in the sensor data. The time series data for one walking cycle is also called walking waveform data. The waveform processing unit 132 extracts walking waveform data based on the timing of walking events detected from the time series data of the sensor data. For example, the waveform processing unit 132 extracts walking waveform data that starts at the timing of a heel strike and ends at the timing of the next heel strike.

Figure 7 is a conceptual diagram for explaining a step cycle based on the right foot. The step cycle based on the left foot is the same as that of the right foot. The horizontal axis of Figure 7 shows one walking cycle of the right foot, starting from the point when the heel of the right foot lands on the ground and ending at the point when the heel of the right foot lands on the ground. The horizontal axis of Figure 7 is normalized with the step cycle as 100%. Normalizing one walking cycle to 100% is called the first normalization. One walking cycle of one foot is broadly divided into a stance phase in which at least a part of the sole of the foot is in contact with the ground and a swing phase in which the sole of the foot is off the ground. The stance phase is a period in which at least a part of the sole of the foot is in contact with the ground. The stance phase is further divided into an early stance phase T1, a mid stance phase T2, a final stance phase T3, and an early swing phase T4. The swing phase is a period in which the sole of the foot is off the ground. The swing phase is further divided into early swing T5, mid swing T6, and final swing T7. The horizontal axis in FIG. 7 is normalized so that the stance phase is 60% and the swing phase is 40%. Normalizing the gait waveform data so that the stance phase is 60% and the swing phase is 40% is called second normalization. Note that the periods shown in FIG. 7 are merely examples, and do not limit the periods that make up a step cycle or the names of these periods.

As shown in Figure 7, multiple events occur during walking. Multiple events during walking are also called walking events. P1 represents the event of the heel of the right foot touching the ground (heel strike) (HS: Heel Strike). P2 represents the event of the toe of the left foot lifting off the ground (opposite toe off) while the sole of the right foot is on the ground (OTO: Opposite Toe Off). P3 represents the event of the right heel lifting off the ground (heel rise) while the sole of the right foot is on the ground (HR: Heel Rise). P4 represents the event of the left heel touching the ground (opposite heel strike) (OHS: Opposite Heel Strike). P5 represents the event of the right toe lifting off the ground (toe off) while the sole of the left foot is on the ground (TO: Toe Off). P6 represents an event in which the left and right feet cross (foot crossing) with the sole of the left foot touching the ground (FA: Foot Adjacent). P7 represents an event in which the tibia of the right foot is nearly perpendicular to the ground with the sole of the left foot touching the ground (TV: Tibia Vertical). P8 represents an event in which the heel of the right foot touches the ground (heel strike) (HS: Heel Strike). P8 corresponds to the end of the walking cycle that begins with P1, and corresponds to the starting point of the next walking cycle. Note that the walking events shown in Figure 7 are merely examples, and do not limit the events that occur during walking or the names of those events.

The timing of heel strike is the timing of the minimum peak immediately after the maximum peak that appears in the time series data of forward acceleration (Y-direction acceleration). The maximum peak that marks the timing of heel strike corresponds to the maximum peak of the gait waveform data for one step cycle. The section between successive heel strikes corresponds to one step cycle. The timing of toe off is the timing of the rise of the maximum peak that appears after the stance phase period in which no fluctuations appear in the time series data of forward acceleration (Y-direction acceleration). The midpoint between the timing of the minimum roll angle and the timing of the maximum roll angle corresponds to the mid-stance phase.

The waveform processing unit 132 normalizes (first normalization) the time of the extracted walking waveform data for one step cycle to a walking cycle of 0 to 100% (percent). The timing of 1%, 10%, etc. included in the 0 to 100% walking cycle is also called a walking phase. The waveform processing unit 132 also normalizes (second normalization) the first normalized walking waveform data for one step cycle so that the stance phase is 60% and the swing phase is 40%. By second normalizing the walking waveform data, it is possible to reduce the deviation in the walking phase from which feature values are extracted. The waveform processing unit 132 outputs the normalized walking waveform data to the gait index calculation unit 133.

For example, the waveform processing unit 132 extracts and normalizes walking waveform data for one step cycle using the forward acceleration (Y-direction acceleration). For accelerations/angular velocities other than the forward acceleration (Y-direction acceleration), the waveform processing unit 132 extracts and normalizes walking waveform data for one step cycle in accordance with the walking cycle of the forward acceleration (Y-direction acceleration). The waveform processing unit 132 may also generate time series data of angles around three axes by integrating time series data of angular velocities around three axes. In that case, the waveform processing unit 132 extracts and normalizes walking waveform data for one step cycle in accordance with the walking cycle of the forward acceleration (Y-direction acceleration) for angles around three axes as well.

The waveform processing unit 132 may extract/normalize the walking waveform data for one step cycle using acceleration/angular velocity other than the forward acceleration (Y-direction acceleration). For example, the waveform processing unit 132 may detect heel strike and toe lift from the time series data of vertical acceleration (Z-direction acceleration) (not shown in the drawing). The timing of heel strike is the timing of a steep minimum peak that appears in the time series data of vertical acceleration (Z-direction acceleration). At the timing of the steep minimum peak, the value of the vertical acceleration (Z-direction acceleration) becomes almost 0. The minimum peak that marks the timing of heel strike corresponds to the minimum peak of the walking waveform data for one step cycle. The section between successive heel strikes is the one step cycle. The timing of toe lift is the timing of an inflection point in the middle of the time series data of vertical acceleration (Z-direction acceleration) gradually increasing after a section of small fluctuation following the maximum peak immediately after heel strike. The waveform processing unit 132 may also extract/normalize the walking waveform data for one step cycle using both the forward acceleration (Y-direction acceleration) and the vertical acceleration (Z-direction acceleration). The waveform processing unit 132 may also extract/normalize the walking waveform data for one step cycle using acceleration, angular velocity, angle, etc. other than the forward acceleration (Y-direction acceleration) and the vertical acceleration (Z-direction acceleration).

The waveform processing unit 132 extracts features (physical ability features) used to estimate physical abilities from the walking waveform data. The waveform processing unit 132 extracts physical ability features used to estimate at least one physical ability. For example, the waveform processing unit 132 extracts physical ability features used to estimate at least one of physical abilities such as grip strength (total muscle strength of the entire body), dynamic balance, lower limb muscle strength, mobility, and static balance. For example, the waveform processing unit 132 extracts physical ability features for each walking phase cluster according to preset conditions. A walking phase cluster is a cluster that integrates walking phases that are consecutive in time. A walking phase cluster includes at least one walking phase. A walking phase cluster also includes a single walking phase. The waveform processing unit 132 outputs the extracted physical ability features to the physical ability estimation unit 135.

The gait index calculation unit 133 (gait index calculation means) acquires normalized gait waveform data from the waveform processing unit 132. The gait index calculation unit 133 uses the normalized gait waveform data to calculate gait indices used to estimate physical ability. There are no particular limitations on the gait indices to be calculated, so long as they can be calculated using normalized gait waveform data. For example, the gait index calculation unit 133 calculates gait indices related to distance, height, angle, speed, time, frailty level, CPEI (Center of Pressure Exclusion Index), etc. Representative gait indices are listed below. Specific calculation methods for the following gait indices will be omitted.

For example, the gait index calculation unit 133 calculates indices related to distance and height as gait indices. For example, the gait index calculation unit 133 calculates stride length, turning distance, foot lift height, FTC (Foot Clearance), and MTC (Minimum Toe Clearance). Stride length indicates the distance between the front foot and the rear foot while walking. Turning distance indicates the maximum distance that the foot is moved outward in the direction of travel during the swing phase. Foot lift height indicates the maximum distance between the measuring device 10 (sensor 110) and the ground during the swing phase. FTC indicates the maximum distance between the heel and the ground during the swing phase. MTC indicates the minimum distance between the toe and the ground during the swing phase.

For example, the gait index calculation unit 133 calculates indexes related to angles as gait indices. For example, the gait index calculation unit 133 calculates the contact angle, the take-off angle, the toe direction, the heel contact roll angle, the toe off roll angle, the swing leg peak angular velocity, and the big toe angle. The contact angle indicates the maximum angle between the sole of the foot and the ground at heel contact. The take-off angle indicates the angle between the sole of the foot and the ground during the swing phase. The toe direction indicates the average value of the direction of the toe relative to the direction of travel during the swing phase. The heel contact roll angle is the angle between the ankle and the ground at heel contact as viewed from a rear perspective. The toe off roll angle is the angle between the ankle and the ground at push-off as viewed from a rear perspective. The swing leg peak angular velocity is the angular velocity in the ankle dorsiflexion direction in the section from immediately after push-off until the toe comes closest to the ground. The hallux angle indicates the angle at which the big toe is tilted toward the index toe. Specifically, the hallux angle is the angle between the center line of the first metatarsal bone and the center line of the first proximal phalanx.

For example, the gait index calculation unit 133 calculates an index related to speed as a gait index. For example, the gait index calculation unit 133 calculates walking speed, cadence, and maximum swing speed. Walking speed indicates the walking speed. Cadence indicates the number of steps per minute. Maximum swing speed indicates the speed at which the leg is swung out during the swing phase.

For example, the gait index calculation unit 133 calculates time-related indices as gait indices. For example, the gait index calculation unit 133 calculates stance time, load time, sole contact time, push-off time, swing time, and DST (Double Support Time). Stance time indicates the time that the foot is on the ground while walking. Stance time is the sum of load time, sole contact time, and push-off time. Load time is the time from when the heel touches the ground until the toe touches the ground during the stance phase. Sole contact time is the time during the stance phase when the entire sole of the foot is on the ground and the sole of the foot is horizontal to the ground. Push-off time is the time from when the sole of the foot is on the ground until the toe pushes off the ground during the stance phase. Swing time indicates the time that the foot is off the ground while walking. DST is divided into DST1 and DST2. DST1 indicates the time during which the foot on which the measuring device 10 (sensor 110) is mounted is in front of the other foot during a period when both feet are on the ground at the same time. DST2 indicates the time during which the foot on which the measuring device 10 (sensor 110) is mounted is behind the other foot during a period when both feet are on the ground at the same time.

For example, the gait index calculation unit 133 calculates a frailty level and a center of pressure exclusion index (CPEI) as the gait index. The frailty level is an estimated value of a frailty state according to a walking state. For example, the gait index calculation unit 133 estimates an index such as a judgment result _R1 indicating health, a judgment result _R2 indicating a possibility of frailty, and a judgment result _R3 indicating a high possibility of frailty as the frailty level. The CPEI indicates an estimated value of a swelling rate of the movement of the center of foot pressure applied to the ground during the stance phase.

The memory unit 134 (storage means) stores a physical ability estimation model (described later) that estimates physical ability using physical ability features extracted from the walking waveform data. For example, the physical ability is at least one of grip strength, dynamic balance, lower limb muscle strength, mobility, and static balance. The physical ability may include other than grip strength, dynamic balance, lower limb muscle strength, mobility, and static balance. The memory unit 134 stores physical ability estimation models trained for multiple subjects. For example, the physical ability estimation model outputs an index of physical ability (physical ability score) in response to input of physical ability features extracted from the walking waveform data.

The memory unit 134 also stores a disease risk estimation model (described later) that estimates disease risk using physical information, gait index, and physical ability score. The disease risk indicates the risk of contracting a specific disease. For example, specific diseases include gout, diabetes, hypertension, nephrolithiasis, liver cirrhosis, arteriosclerosis, thromboembolism, dyslipidemia, hypercholesterolemia, and hyperlipidemia. For example, specific diseases include lower back pain, sleep apnea syndrome, insomnia, depression, osteoarthritis of the knee, and Parkinson's syndrome. Specific diseases may include diseases other than those mentioned above. The memory unit 134 stores a disease risk estimation model trained on multiple subjects. For example, the disease risk estimation model outputs an index related to disease risk (disease risk score) in response to input of physical information, gait index, and physical ability score.

For example, the physical ability estimation model and disease risk estimation model may be stored in the memory unit 134 when the product is shipped from the factory. The physical ability estimation model and disease risk estimation model may also be stored in the memory unit 134 at a timing such as at the time of calibration before the disease risk estimation device 13 is used by a user. For example, a physical ability estimation model and disease risk estimation model stored in a storage device (not shown) such as an external server may be used. In that case, it is sufficient if the physical ability estimation model and disease risk estimation model can be accessed via an interface (not shown) connected to the storage device.

The storage unit 134 also stores the user's physical information (attributes). The physical information includes gender, date of birth, height, and weight. The date of birth is converted to age. The physical information may be updated at any time.

The physical ability estimation unit 135 (physical ability estimation means) acquires the physical ability feature extracted from the walking waveform data from the waveform processing unit 132. The physical ability estimation unit 135 also acquires physical information (attributes) stored in the memory unit 134. The physical ability estimation unit 135 estimates a physical ability score using the physical ability feature and the physical information (attributes). The physical ability estimation unit 135 inputs the physical ability feature and the user's physical information (attributes) to a physical ability estimation model stored in the memory unit 134. For example, the physical ability estimation unit 135 estimates a physical ability score related to at least one of the physical abilities of grip strength (total muscle strength of the entire body), dynamic balance, lower limb muscle strength, mobility, and static balance. The estimation of the physical ability score by the physical ability estimation unit 135 will be described later. The physical ability estimation unit 135 outputs the physical ability score output from the physical ability estimation model to the disease risk estimation unit 136.

For example, the physical ability estimation unit 135 may estimate the physical information (attributes) using the gait index calculated by the gait index calculation unit 133. For example, the physical ability estimation unit 135 estimates the physical information (attributes) using a gait index that has a correlation with the physical information (attributes). For example, when muscle strength decreases due to aging, a decrease is observed in walking speed and cadence. Therefore, if walking speed and cadence are used, it is possible to estimate the age, even if the exact age cannot be estimated. For example, there is a correlation between height and stride length. Therefore, if stride length is used, it is possible to estimate the age, even if the exact age cannot be estimated. In addition, it is possible to estimate the age more accurately by combining specific gait indices. With this configuration, it is possible to estimate the physical information using the sensor data measured by the measurement device 10 without inputting any of the physical information such as height, weight, age, and sex. It is also assumed that there are users who do not want to input information such as age, weight, BMI (Body Mass Index), and shoe size. In order to estimate the disease risk of such a user, it is useful to estimate physical information (attributes) using gait indices. For example, the physical ability estimation unit 135 may compare the input value of the physical information (attributes) with the estimated value. If there is a large discrepancy between the input value of the physical information (attributes) and the estimated value, there is a possibility that the input value entered by the user contains an error. In such a case, a notification or warning may be sent to the user's terminal device, etc., to prompt the user to check the input value of the physical information (attributes).

For example, the estimated values of the physical information (attributes) are stored in the memory unit 134. The physical information (attributes) may be estimated by any one of the waveform processing unit 132, the gait index calculation unit 133, the physical ability estimation unit 135, and the disease risk estimation unit 136 constituting the risk estimation unit 15. For example, a component that estimates the physical information (attributes) may be added to the disease risk estimation device 13. For example, the acquisition unit 131 may acquire the estimated values of the physical information (attributes) estimated by an external estimation device (not shown).

Next, an example of the estimation of the physical ability score by the physical ability estimation unit 135 will be described. Here, an example of the feature values used to estimate grip strength (total muscle strength of the entire body), dynamic balance, lower limb muscle strength, mobility, and static balance will be described. Note that the examples given below do not limit the physical abilities estimated by the physical ability estimation unit 135. The physical abilities estimated by the physical ability estimation unit 135 may be appropriately selected depending on the disease for which the disease risk is to be estimated.

<Grip strength (total muscle strength of the whole body)>
There is a correlation between grip strength, which is one of the physical abilities, and the total muscle strength of the whole body. Grip strength is also correlated with knee extension strength. For example, an estimated value of grip strength is an index of total muscle strength. For example, a score according to an estimated value of grip strength (also called a total muscle strength score) is an index of total muscle strength. The total muscle strength score is a value obtained by scoring grip strength, which is an index of total muscle strength, according to a preset criterion. Grip strength is affected by attributes such as gender, age, and height. Therefore, the total muscle strength score may be scored according to a criterion for each attribute. In particular, grip strength is affected by gender. Therefore, the total muscle strength score may be scored according to different criteria depending on gender. Note that the index of total muscle strength is not limited to grip strength as long as the total muscle strength can be scored.

The walking phase from which the features used to estimate grip strength are extracted differs depending on gender. For men, there is a correlation between quadriceps activity and grip strength. Therefore, to estimate men's grip strength, features extracted from walking phases in which the characteristics of quadriceps activity are apparent are used. For women, there is a correlation between grip strength and activity of the vastus lateralis, vastus intermedius, and vastus medialis muscles of the quadriceps. Therefore, to estimate women's grip strength, features extracted from walking phases in which the characteristics of vastus lateralis, vastus intermedius, and vastus medialis muscles are apparent are used.

Features AM1, AM2, AM3, and AM4 are used to estimate the grip strength of a man. Feature AM1 is extracted from the 3% walking phase section of the walking waveform data related to the time series data of the acceleration in the forward direction (acceleration in the Y direction). The 3% walking phase is included in the initial stance phase T1. Feature AM1 mainly includes features related to the movement of the vastus lateralis, vastus intermedius, and vastus medialis, which are among the quadriceps muscles. Feature AM2 is extracted from the 59-62% walking phase section of the walking waveform data related to the time series data of the acceleration in the forward direction (acceleration in the Y direction). The 59-62% walking phase is included in the early swing phase T4. Feature AM2 mainly includes features related to the movement of the rectus femoris, which is among the quadriceps muscles. Feature AM3 is extracted from the 59-62% walking phase section of the walking waveform data related to the time series data of the acceleration in the vertical direction (acceleration in the Z direction). 59-62% of the walking phase is included in the early swing phase T4. Feature AM3 mainly includes features related to the movement of the rectus femoris, which is one of the quadriceps muscles. Feature AM4 is the proportion of the period from heel-contact to toe-off of the opposite foot during the period when both feet are simultaneously on the ground (DST1). DST1 is the proportion of the period from heel-contact to toe-off of the opposite foot during one stride cycle. Feature AM4 mainly includes features attributable to the quadriceps muscles.

Feature AF1, feature AF2, and feature AF3 are used to estimate the grip strength of women. Feature AF1 is extracted from a 13% section of the walking phase of the walking waveform data related to the time series data of lateral acceleration (X-direction acceleration). The 13% walking phase is included in the mid-stance phase T2. Feature AF1 mainly includes features related to the movement of the vastus lateralis, vastus intermedius, and vastus medialis of the quadriceps. Feature AF2 is extracted from a 7-10% section of the walking phase of the walking waveform data related to the time series data of the angular velocity (pitch angular velocity) in the coronal plane (around the Y-axis). The 7-10% walking phase is included in the early stance phase T1. Feature AF2 mainly includes features related to the movement of the vastus lateralis, vastus intermedius, and vastus medialis. Feature AF3 is the proportion of the period from heel contact to toe-off of the opposite foot to the period during which both feet are simultaneously on the ground (DST2). DST2 is the ratio of the period from heel contact to toe-off of the opposite foot in a gait cycle. The sum of DST1 and DST2 corresponds to the period during which both feet are simultaneously in contact with the ground in a gait cycle. Feature AF3 mainly includes features related to the movements of the vastus lateralis, vastus intermedius, and vastus medialis.

<Dynamic balance>
Dynamic balance, which is one of the physical abilities, can be evaluated by the results of a Functional Reach Test (FRT). In the present disclosure, the results of the FRT are evaluated by the distance between the fingertips (also called the functional reach distance) when the upper limbs are moved forward as far as possible from a standing position with both hands raised at 90 degrees relative to the horizontal plane. The functional reach distance (hereinafter, called the FR distance) is the FRT performance value. The larger the FR distance, the higher the FRT performance. The dynamic balance may be evaluated by something other than the FRT performed with both hands. For example, the dynamic balance may be evaluated by the performance of the FRT performed with one hand or other variations of the FRT.

The index of dynamic balance is the FR distance. For example, an estimated value of the FR distance is the index of dynamic balance. For example, a score according to the estimated value of the FR distance (also called the dynamic balance score) is the index of dynamic balance. The dynamic balance score is a value obtained by scoring the FR distance, which is an index of dynamic balance, using a preset criterion. Dynamic balance is affected by attributes such as height. Therefore, the dynamic balance score may be scored using a criterion for each attribute. Note that the index of dynamic balance is not limited to the FR distance as long as dynamic balance can be scored. The FR distance is correlated with the activity of the gluteus medius, iliac muscle, hamstrings (long head of biceps femoris), tibialis anterior muscle, etc., and the magnitude of the compensatory movement of turning the toes outward. Therefore, the feature quantity extracted from the walking phase in which these features appear is used to estimate the FR distance.

Features B1, B2, B3, B4, and B5 are used to estimate the FR distance. Feature B1 is extracted from the 75-79% walking phase of the gait waveform data related to the time series data of the acceleration in the forward direction (acceleration in the Y direction). The 75-79% walking phase is included in the mid-swing phase T6. Feature B1 mainly includes features related to the movement of the tibialis anterior and the short head of the biceps femoris. Feature B2 is extracted from the 62% walking phase of the gait waveform data related to the time series data of the acceleration in the vertical direction (acceleration in the Z direction). The 62% walking phase is included in the early swing phase T5. Feature B2 mainly includes features related to the movement of the iliacus. Feature B3 is extracted from the 7-8% walking phase of the gait waveform data related to the time series data of the angular velocity in the coronal plane (around the Y axis). The 7-8% walking phase is included in the early stance phase T1. The feature B3 mainly includes features related to the movement of the gluteus medius. The feature B4 is extracted from the section of the walking phase 57-58% of the walking waveform data related to the time series data of the angle (posture angle) in the horizontal plane (around the Z axis). The walking phase 57-58% is included in the early swing phase T4. The feature B4 mainly includes features related to the compensatory movement. The compensatory movement is a movement to change the foot angle to obtain stability in order to compensate for the deterioration of balance ability and muscle function that occurs with aging. The feature B5 is the average value of the foot angle in the horizontal plane during the swing phase. For example, the feature B5 is the average value in the swing phase of the walking waveform data. In other words, the feature B5 is the integral value of the walking waveform data related to the time series data of the angular velocity in the horizontal plane (around the Z axis). The feature B5 mainly includes features related to the compensatory movement.

<Lower limb strength>
Lower limb muscle strength, which is one of the physical abilities, can be evaluated by the results of a chair stand test. In the present disclosure, the results of the 5-times chair stand test, in which the person stands up and sits down on a chair five times, are evaluated. The 5-times chair stand test is also called the SS-5 (Sit to Stand-5) test. The results of the 5-times chair stand test are evaluated based on the time it takes to stand up and sit down on a chair five times (also called the sit-to-stand time). The sit-to-stand time is the score value of the SS-5 test. The shorter the sit-to-stand time, the higher the score of the SS-5 test. The results may also be evaluated based on the results of a 30-second chair stand (CS-30) test, which measures the number of times the person stands up and sits down on a chair in 30 seconds.

The index of lower limb muscle strength is the sit-stand time. For example, an estimate of the sit-stand time five times is an index of lower limb muscle strength. For example, a score according to the estimate of the sit-stand time (also called the lower limb muscle strength score) is an index of lower limb muscle strength. The lower limb muscle strength score is a value obtained by scoring the sit-stand time, which is an index of lower limb muscle strength, using a preset criterion. Lower limb muscle strength is affected by attributes such as age. Therefore, the lower limb muscle strength score may be scored using a criterion for each attribute. Note that the index of lower limb muscle strength is not limited to the sit-stand time, as long as the lower limb muscle strength can be scored. The sit-stand time is correlated with the quadriceps, hamstrings, tibialis anterior, and gastrocnemius. Therefore, feature values extracted from the walking phase in which these features appear are used to estimate the sit-stand time.

The estimation of lower limb muscle strength includes feature C1, feature C2, feature C3, and feature C4. Feature C1 is extracted from the section of walking phase 42-54% of the walking waveform data related to the time series data of angular velocity in the sagittal plane (around the X-axis). Walking phase 42-54% is the section from the end of stance phase T3 to the early swing phase T4. Feature C1 mainly includes features related to the movement of the gastrocnemius. Feature C2 is extracted from the section of walking phase 99-100% of the walking waveform data related to the time series data of angular velocity in the coronal plane (around the Y-axis). Walking phase 99-100% is the end of the end of swing phase T7. Feature C2 mainly includes features related to the movement of the quadriceps, hamstrings, and tibialis anterior. Feature C3 is extracted from the 10% to 12% walking phase section of the walking waveform data related to the time series data of angular velocity in the coronal plane (around the Y-axis). The 10% to 12% walking phase is the beginning of mid-stance phase T2. Feature C3 mainly includes features related to the movement of the quadriceps, hamstrings, and gastrocnemius. Feature C4 is extracted from the 99% walking phase section of the walking waveform data related to the time series data of angles (posture angles) in the horizontal plane (around the Z-axis). The 99% walking phase is the end of end-swing phase T7. Feature C4 mainly includes features related to the movement of the quadriceps, hamstrings, and tibialis anterior.

<Movement Ability>
Mobility, which is one of the physical abilities, can be evaluated by the results of a TUG (Time Up and Go) test. In the present disclosure, the results of the TUG test are evaluated based on the time it takes to stand up from a chair, walk to a landmark 3 meters away, change direction, and sit back down on the chair (also called the TUG time). The TUG time is the score value of the TUG test. The shorter the TUG time, the higher the score of the TUG test. Mobility may be evaluated by the score of a test related to mobility other than the TUG test.

The index of mobility is the time required for TUG. For example, an estimate of the time required for TUG is an index of mobility. For example, a score according to the estimate of the time required for TUG (also called a mobility score) is an index of mobility. The mobility score is a value obtained by scoring the time required for TUG, which is an index of mobility, using a preset criterion. Mobility is affected by attributes such as age. Therefore, the mobility score may be scored using a criterion for each attribute. Note that the index of mobility is not limited to the time required for TUG, as long as mobility can be scored. The time required for TUG is correlated with the quadriceps, gluteus medius, and tibialis anterior. Therefore, feature quantities extracted from the walking phase in which these features appear are used to estimate the time required for TUG.

Feature amount D1, feature amount D2, feature amount D3, feature amount D4, feature amount D5, and feature amount D6 are used to estimate mobility. Feature amount D1 is extracted from the section of walking phase 64-65% of walking waveform data related to time series data of lateral acceleration (X-direction acceleration). Walking phase 64-65% is included in early swing phase T5. Feature amount D1 mainly includes features related to the movement of the quadriceps in the standing and sitting movements. Feature amount D2 is extracted from the section of walking phase 57-58% of walking waveform data related to time series data of angular velocity in the sagittal plane (around the X-axis). Walking phase 57-58% is included in early swing phase T4. Feature amount D2 mainly includes features related to the movement of the quadriceps related to the kicking speed of the foot. The feature amount D3 is extracted from a section of the walking phase 19-20% of the walking waveform data related to the time series data of the angular velocity in the coronal plane (around the Y axis). The walking phase 19-20% is included in the mid-stance phase T2. The feature amount D3 mainly includes features related to the movement of the gluteus medius muscle in the change of direction. The feature amount D4 is extracted from a section of the walking phase 12-13% of the walking waveform data related to the time series data of the angular velocity in the horizontal plane (around the Z axis). The walking phase 12-13% is the beginning of the mid-stance phase T2. The feature amount D4 mainly includes features related to the movement of the gluteus medius muscle in the change of direction. The feature amount D5 is extracted from a section of the walking phase 74-75% of the walking waveform data related to the time series data of the angular velocity in the horizontal plane (around the Z axis). The walking phase 74-75% is the beginning of the mid-swing phase T6. Feature D5 mainly includes features related to the movement of the tibialis anterior muscle when standing up, sitting down, and changing direction. Feature D6 is extracted from the section of the walking phase 76-80% of the walking waveform data related to the time series data of the angle (posture angle) in the coronal plane (around the Y axis). The walking phase 76-80% is included in the mid-swing phase T6. Feature D6 mainly includes features related to the movement of the tibialis anterior muscle when standing up, sitting down, and changing direction.

<Static balance>
Static balance, which is one of the physical abilities, can be evaluated by the performance of a one-leg standing test. In the present disclosure, the performance of the one-leg standing test is evaluated based on the time (also called one-leg standing time) during which the eyes are closed and one leg is raised 5 cm (centimeters) from the ground. The one-leg standing time is a performance value of static balance. The longer the one-leg standing time, the higher the performance of static balance. Static balance may be evaluated by a performance other than the one-leg standing test with eyes closed. For example, static balance may be evaluated by a one-leg standing test with eyes open (one-leg standing test with eyes open) or other variations of the one-leg standing test.

The static balance index is the single leg standing time. For example, an estimate of the single leg standing time is an index of static balance. For example, a score according to the estimate of the single leg standing time (also called the static balance score) is an index of static balance. The static balance score is a value obtained by scoring the single leg standing time, which is an index of static balance, using a preset criterion. Static balance is affected by attributes such as age and height. Therefore, the static balance score may be scored using a criterion for each attribute. Note that the static balance index is not limited to the single leg standing time as long as the static balance can be scored. The single leg standing time is correlated with the gluteus medius, adductor longus, sartorius, and abductor and adductor muscles. Therefore, the feature values extracted from the walking phase in which these features appear are used to estimate the single leg standing time.

Features E1, E2, E3, E4, E5, E6, and E7 are used to estimate static balance. Feature E1 is extracted from the 13-19% gait phase section of the gait waveform data related to the time series data of lateral acceleration (X-direction acceleration). The 13-19% gait phase is included in the mid-stance phase T2. Feature E1 mainly includes features related to the movement of the gluteus medius. Feature E2 is extracted from the 95% gait phase section of the gait waveform data related to the time series data of vertical acceleration (Z-direction acceleration). The 95% gait phase is the end of the end-swing phase T7. Feature E2 mainly includes features related to the movement of the gluteus medius. Feature E3 is extracted from the 64-65% gait phase section of the gait waveform data related to the time series data of angular velocity in the coronal plane (around the Y-axis). The walking phase 64-65% is included in the early swing phase T5. The feature amount E3 mainly includes features related to the movement of the adductor longus and sartorius. The feature amount E4 is extracted from the section of the walking phase 11-16% of the walking waveform data related to the time series data of the angular velocity in the horizontal plane (around the Z axis). The walking phase 11-16% is included in the mid-stance phase T2. The feature amount E4 mainly includes features related to the movement of the gluteus medius. The feature amount E5 is extracted from the section of the walking phase 57-58% of the walking waveform data related to the time series data of the angular velocity in the horizontal plane (around the Z axis). The walking phase 57-58% is included in the early swing phase T4. The feature amount E5 mainly includes features related to the movement of the adductor longus and sartorius. The feature amount E6 is extracted from the section of the walking phase 100% of the walking waveform data related to the time series data of the angle (posture angle) in the horizontal plane (around the Z axis). The 100% walking phase corresponds to the timing of heel contact when switching from the final swing phase T7 to the initial stance phase T1. The feature value of the gait waveform data in the 100% walking phase corresponds to the foot angle when the sole of the foot is in contact with the ground. Feature value E6 mainly includes features related to the movement of the gluteus medius. Feature value E7 is the distance between the axis of motion and the foot (circumflex over). Feature value E7 is the amount of circumflex over normalized by the subject's height. Feature value E7 mainly includes features related to the movement of the abductor and adductor muscles.

8 is a conceptual diagram showing an example of a physical ability estimation model 150 that estimates physical ability. The feature values extracted from the walking waveform data are input to the physical ability estimation model 150 that estimates physical ability. In addition to the feature value data extracted from the walking waveform data, the user's physical information (attributes) is input. In FIG. 8, the physical information (attributes) input to the physical ability estimation model 150 is omitted. In response to the input of the physical ability feature values extracted from the walking waveform data, the physical ability estimation model 150 outputs a physical ability score related to the physical ability. In the example of FIG. 8, the physical ability estimation model 150 includes a grip strength estimation model 151, a dynamic balance estimation model 152, a lower limb muscle strength estimation model 153, a mobility estimation model 154, and a static balance estimation model 155. Each of the grip strength estimation model 151, the dynamic balance estimation model 152, the lower limb muscle strength estimation model 153, the mobility estimation model 154, and the static balance estimation model 155 outputs a score for each estimation target of the model. The physical ability estimation model 150 may be configured by a single model, not by a model for each physical ability. Also, the physical ability estimation model 150 may be a physical ability value such as grip strength, FR distance, standing and sitting time, TUG time, and one-legged standing time, instead of a physical ability score.

The grip strength estimation model 151 outputs a grip strength score S1 related to grip strength (total muscle strength of the whole body) in response to the input of the feature amounts AM1 to AM4 or the feature amounts AF1 to AF3. For example, the grip strength estimation model 151 may be a model that outputs grip strength in response to the input of the feature amounts AM1 to AM4 or the feature amounts AF1 to AF3. For example, the grip strength estimation model 151 may be a different model for men and women. There are no limitations on the estimation result of the grip strength estimation model 151 as long as an estimation result related to a grip strength index is output in response to the input of a physical ability feature amount for estimating total muscle strength. For example, the grip strength estimation model 151 may be a model that outputs grip strength in response to the input of the feature amounts AM1 to AM4 or the feature amounts AF1 to AF3. For example, the grip strength estimation model 151 may be a model that estimates grip strength using attribute data such as age and height in addition to the feature amounts AM1 to AM4 or the feature amounts AF1 to AF3.

The dynamic balance estimation model 152 outputs a dynamic balance score S2 related to dynamic balance in response to the input of the features B1 to B5. There are no limitations on the estimation results of the dynamic balance estimation model 152, so long as an estimation result related to a dynamic balance index is output in response to the input of the physical ability features for estimating dynamic balance. For example, the dynamic balance estimation model 152 may be a model that outputs the FR distance in response to the input of the features B1 to B5. For example, the dynamic balance estimation model 152 may be a model that estimates dynamic balance using attribute data such as height in addition to the features B1 to B5.

The lower limb muscle strength estimation model 153 outputs a lower limb muscle strength score S3 related to lower limb muscle strength in response to input of the features C1 to C4. There are no limitations on the estimation result of the lower limb muscle strength estimation model 153, so long as an estimation result related to an index of lower limb muscle strength is output in response to input of the physical ability features for estimating lower limb muscle strength. For example, the lower limb muscle strength estimation model 153 may be a model that outputs a lower limb muscle strength score S3 related to lower limb muscle strength in response to input of the features C1 to C4. For example, the lower limb muscle strength estimation model 153 may be a model that estimates dynamic balance using attribute data such as age in addition to the features C1 to C4.

The mobility estimation model 154 outputs a mobility score S4 related to mobility in response to the input of the features D1 to D6. There are no limitations on the estimation results of the mobility estimation model 154, so long as an estimation result related to a mobility index is output in response to the input of the physical ability features for estimating mobility. For example, the mobility estimation model 154 may be a model that outputs the TUG required time in response to the input of the features D1 to D6. For example, the mobility estimation model 154 may be a model that estimates mobility using attribute data such as age in addition to the features D1 to D6.

The static balance estimation model 155 outputs a static balance score S5 related to static balance in response to the input of the features E1 to E7. There are no limitations on the estimation results of the static balance estimation model 155, so long as an estimation result related to a static balance index is output in response to the input of the physical ability features for estimating static balance. For example, the static balance estimation model 155 may be a model that outputs one-leg standing time in response to the input of the features E1 to E7. For example, the static balance estimation model 155 may be a model that estimates static balance using attribute data such as age and height in addition to the features E1 to E7.

The physical ability estimation model 150 may be stored in an external storage device constructed in a cloud or a server. In this case, the physical ability estimation unit 135 uses the physical ability estimation model 150 via an interface (not shown) connected to the storage device. The physical ability estimation model 150 is a machine learning model. For example, the physical ability estimation model 150 is a model trained on a data set using as teacher data a data set in which physical information (attributes) and gait indices related to multiple subjects are explanatory variables and a score related to physical ability is an objective variable. The physical ability estimation model 150 may be a model trained on a data set using as teacher data a data set in which physical information (attributes) and gait waveform data related to multiple subjects are explanatory variables and a score related to physical ability is an objective variable. For example, the physical ability estimation model 150 may be a model trained on teacher data in which gait waveform data of acceleration in three axial directions, angular velocity around three axes, and angle (posture angle) around three axes are included in explanatory variables.

For example, the physical ability estimation model 150 may be generated by learning using a linear regression algorithm. For example, the physical ability estimation model 150 may be generated by learning using a support vector machine (SVM) algorithm. For example, the physical ability estimation model 150 may be generated by learning using a Gaussian process regression (GPR) algorithm. For example, the physical ability estimation model 150 may be generated by learning using a random forest (RF) algorithm. For example, the physical ability estimation model 150 may be generated by unsupervised learning that classifies the subject from which the physical ability feature was generated according to the input of the physical ability feature. There are no particular limitations on the algorithm used to train the physical ability estimation model 150.

The disease risk estimation unit 136 (disease risk estimation) acquires the estimation result of the physical ability (physical ability score) estimated by the physical ability estimation unit 135. In addition, the disease risk estimation unit 136 acquires the gait index from the gait index calculation unit 133. In addition, the disease risk estimation unit 136 acquires the user's physical information (attributes) from the storage unit 134. The disease risk estimation unit 136 estimates a disease risk reflecting the risk for each disease using the physical ability score, the gait index, and the physical information (attributes). For example, the disease risk estimation unit 136 may be configured to estimate a disease risk reflecting the risk for each disease using at least the gait index. For example, the disease risk estimation unit 136 generates disease risk information including advice corresponding to the disease risk generated by applying it to a preset document format. For example, the disease risk information may be generated using a large-scale language model.

9 is a conceptual diagram showing an example of disease risk estimation by the disease risk estimation unit 136. The disease risk estimation unit 136 inputs physical information, gait index, and physical ability score used to estimate the disease risk for a specific disease to the disease risk estimation model 160. The disease risk estimation model 160 receives the physical information, gait index, and physical ability score used to estimate the disease risk for a specific disease. In response to the input of the physical information, gait index, and physical ability score, the disease risk estimation model 160 outputs a disease risk score for a specific disease. In the example of FIG. 9, a disease risk score is estimated for each of a plurality of diseases. The disease risk estimation model 160 may be configured with a model for each disease, or may be configured with a single model. As the amount of data used for estimation increases, the accuracy of the disease risk score estimation by the disease risk estimation model 160 improves.

For example, the disease risk estimation model 160 outputs a disease risk score for a specific disease such as a lifestyle-related disease. For example, the disease risk estimation model 160 outputs a disease risk score for a specific disease such as gout, diabetes, hypertension, nephrolithiasis, liver cirrhosis, arteriosclerosis, thromboembolism, dyslipidemia, hypercholesterolemia, and hyperlipidemia. For example, the disease risk estimation model 160 includes lower back pain, sleep apnea syndrome, insomnia, depression, osteoarthritis of the knee, and Parkinson's syndrome. The disease risk estimation model 160 may be configured to output a disease risk score for a disease other than those mentioned above. For example, the disease risk estimation model 160 may be configured to estimate a disease risk score including test item data from a health checkup.

The disease risk estimation model 160 may be stored in an external storage device constructed in a cloud or a server. In this case, the disease risk estimation unit 136 uses the disease risk estimation model 160 via an interface (not shown) connected to the storage device. The disease risk estimation model 160 is a machine learning model. For example, the disease risk estimation model 160 is a model trained using a data set that uses physical information (attributes), gait indices, and physical abilities of multiple subjects as explanatory variables and a disease risk score for a specific disease as a target variable as training data. For example, the disease risk estimation model 160 may be a model trained using training data in which gait waveform data of acceleration in three axial directions, angular velocity around three axes, and angles around three axes (posture angles) are included as explanatory variables.

For example, the disease risk estimation model 160 is generated by learning using a linear regression algorithm. For example, the disease risk estimation model 160 is generated by learning using a support vector machine (SVM) algorithm. For example, the disease risk estimation model 160 is generated by learning using a Gaussian process regression (GPR) algorithm. For example, the disease risk estimation model 160 is generated by learning using a random forest (RF) algorithm. For example, the disease risk estimation model 160 may be generated by unsupervised learning that classifies the subject from which the feature data was generated according to the feature data. There are no particular limitations on the algorithm used to train the disease risk estimation model 160.

For example, the disease risk estimation model 160 may be a machine learning model such as an incomplete heterogeneous variational autoencoder or a random forest. If an incomplete heterogeneous variational autoencoder is used, the disease risk of the subject (user) can be estimated even if there is some loss in features such as physical information (attributes), gait indicators, and physical information.

In the example of FIG. 9, the disease risk estimation unit 136 calculates a disease risk score reflecting the risk of each disease by multiplying the disease risk score output from the disease risk estimation model 160 by the weight for each disease. The weight for each disease is a value reflecting the risk of the disease. For example, the weight for each disease is set to a value according to the rank indicating the risk of the disease. For example, the weight for a disease with a high rank is set to a large value compared to the weight for a disease with a low rank. In the example of FIG. 9, weights a, b, ..., and z are set for each of disease A, disease B, ..., and disease Z. For example, when disease A is at a higher risk than disease B, weight a is set to a larger value than weight b. In the example of FIG. 9, the disease risk score for disease A is a x R _A , the disease risk score for disease B is b x R _B , and the disease risk score for disease Z is z x R _Z. The disease risk estimation model 160 may be configured to output a disease risk score reflecting the risk of each disease according to input of physical information, gait index, and physical ability score.

The weight for each disease is set according to the indicators for when one contracts the disease. For example, the weight for each disease is set according to indicators such as the mortality rate, life expectancy, and medical costs when one contracts the disease. For example, the higher the mortality rate, the higher the weight is set. For example, the shorter the life expectancy, the higher the weight is set. For example, the higher the medical costs, the higher the weight is set. The indicators for when one contracts the disease are not limited to those given here, and can be any value that corresponds to the risk of the disease.

FIG. 10 is a conceptual diagram showing an example of disease risk estimation by the disease risk estimation unit 136. In the example of FIG. 10, the disease risk estimation unit 136 calculates a disease risk score according to the danger of the combination of diseases. For example, the combination of diabetes and heart disease is high risk. Therefore, the disease risk score for such combinations will be a large value.

In the example of FIG. 10, the disease risk estimation unit 136 inputs the physical information, gait index, and physical ability score used to estimate the disease risk for a specific disease to the disease risk estimation model 160. For example, the disease risk estimation unit 136 multiplies the disease risk score output from the disease risk estimation model 160 by a weight for each disease combination to calculate a disease risk score reflecting the risk for each disease combination. The weight for each disease combination is a value reflecting the risk according to the disease combination. For example, the weight for each disease combination is set to a value according to the rank indicating the risk of the disease combination to be estimated. For example, the weight for a disease combination with a high rank is set to a large value compared to the weight for a disease combination with a low rank. For example, the disease risk estimation unit 136 calculates the product of the disease risk scores reflecting the risk of each combined disease as the disease risk score for each disease combination. The disease risk estimation unit 136 may calculate a disease risk score for a combination of three or more diseases. 10, the disease risk score for the combination of disease A and disease B is _RAB , and the disease risk score for the combination of disease B and disease C is _RBC . Moreover, the disease risk score for the combination of disease Y and disease Z is _RYZ . Note that the disease risk estimation model 160 may be configured to output a disease risk score reflecting the risk for each combination of diseases in response to input of physical information, gait index, and physical ability score.

The weighting for each combination of diseases is set according to the indicators when one contracts those diseases. For example, the weighting for each combination of diseases is set according to indicators such as mortality rate, life expectancy, and medical costs when one contracts those diseases. For example, the higher the mortality rate, the higher the weighting for each combination of diseases is set. For example, the shorter the life expectancy, the higher the weighting for each combination of diseases is set. For example, the higher the medical costs, the higher the weighting for each combination of diseases is set. The indicators when one contracts two or more diseases at the same time are not limited to those given here, and may be values according to the risk of the diseases.

For example, the disease risk estimation model may be a model that outputs the average number of receipts issued per year in response to inputs of physical information, gait index, and physical ability score. The average number of receipts issued per year corresponds to the number of times an individual visits the hospital per year for treatment of a specific disease. In this case, the disease risk estimation unit 136 calculates the disease risk score using the average number of receipts issued per year. For example, the disease risk estimation model is generated by learning using a data set in which physical information (attributes), gait index, and physical ability of multiple subjects are explanatory variables, and the average number of receipts issued per year for a specific disease is the objective variable, as training data.

FIG. 11 is a conceptual diagram showing an example of a disease risk estimation model 165 that estimates the average annual number of receipts issued. The disease risk estimation unit 136 inputs physical information, gait index, and physical ability score to the disease risk estimation model 165. The disease risk estimation model 165 receives the physical information, gait index, and physical ability score used to estimate the disease risk for a specific disease. In response to the input of the physical information, gait index, and physical ability score, the disease risk estimation model 165 outputs the average annual number of receipts issued for a specific disease. In the example of FIG. 11, the average annual number of receipts issued is estimated for each of a plurality of diseases. The disease risk estimation unit 136 calculates the disease risk score using the average annual number of receipts issued output from the disease risk estimation model 165.

Here, an example will be described in which the disease risk estimation unit 136 calculates a disease risk score using the average annual number of medical receipts issued. Three calculation examples will be given below. It is assumed that the average annual number of medical receipts issued for a standard person μ ₀ has been obtained in advance. The disease risk estimation model 165 outputs the average annual number of medical receipts issued μ for a specific disease in response to input of physical information, gait index, and physical ability score for a person whose disease risk is to be estimated.

In the first method, the disease risk estimation unit 136 calculates, as the disease risk score, the ratio of the average annual number of medical receipts issued for a standard person μ ₀ to the average annual number of medical receipts issued μ estimated for the user. The disease risk estimation unit 136 calculates the disease risk score RS ₁ using the following formula 1.

第２の手法において、疾病リスク推定部１３６は、特定疾病に関する年平均レセプト発行数がポアソン分布に従うという仮定の下で、疾病リスクスコアを計算する。第２の手法において、疾病リスク推定部１３６は、標準的な人の年平均レセプト発行数の確率質量関数Ｐ₀（Ｘ＝ｋ）と、ユーザに関して推定された年平均レセプト発行数の確率質量関数Ｐ（Ｘ＝ｋ）との比を、疾病リスクスコアとして計算する（ｋは自然数）。疾病リスク推定部１３６は、以下の式２を用いて、疾病リスクスコアＲＳ₂を計算する。

In the second method, the disease risk estimation unit 136 calculates the disease risk score under the assumption that the annual average number of receipts issued for a specific disease follows a Poisson distribution. In the second method, the disease risk estimation unit 136 calculates the disease risk score as the ratio of the probability mass function _P0 (X=k) of the annual average number of receipts issued for a standard person to the probability mass function P(X=k) of the annual average number of receipts issued estimated for the user (k is a natural number). The disease risk estimation unit 136 calculates the disease risk score _RS2 using the following formula 2.

第３の手法において、疾病リスク推定部１３６は、特定疾病に関する年平均レセプト発行数のオッズ比を計算する。疾病リスク推定部１３６は、以下の式３を用いて、疾病リスクスコアＲＳ₃を計算する。

In the third method, the disease risk estimation unit 136 calculates the odds ratio of the annual average number of receipts issued for a specific disease. The disease risk estimation unit 136 calculates a disease risk score _RS3 using the following formula 3.

なお、上記の３通りの計算例は、一例であって、年平均レセプト発行数を用いた疾病リスクスコアの計算方法を限定するものではない。疾病リスク推定部１３６は、年平均レセプト発行数以外の指標を用いて、疾病リスクスコアを計算するように構成されてもよい。

The above three calculation examples are merely examples, and do not limit the method of calculating the disease risk score using the annual average number of medical receipts issued. The disease risk estimation unit 136 may be configured to calculate the disease risk score using an index other than the annual average number of medical receipts issued.

In the example of FIG. 11, the disease risk estimation unit 136 multiplies the disease risk score RS for each disease calculated using the annual average number of receipts issued by the weight for each disease to calculate a disease risk score reflecting the risk for each disease. As in the example of FIG. 9, in the example of FIG. 11, a weight set to a value corresponding to the rank indicating the risk of the disease to be estimated is used. In the example of FIG. 11, the disease risk score for disease A is a×RS _A , the disease risk score for disease B is b×RS _B , and the disease risk score for disease Z is z×RS _Z. The disease risk estimation model 160 may be configured to output a score reflecting the risk for each disease in response to input of physical information, gait index, and physical ability score. The disease risk estimation unit 136 may also be configured to output a score reflecting the risk for each combination of diseases.

The output unit 137 (output means) outputs disease risk information according to the disease risk score estimated by the disease risk estimation unit 136. For example, the output unit 137 displays the disease risk information on the screen of the subject (user)'s mobile terminal. For example, the output unit 137 outputs the disease risk information to an external system or the like that uses the disease risk information. There are no particular limitations on the use of the output disease risk information. For example, the disease risk information is used for statistical analysis, research into disease prevention, and the like.

For example, the disease risk estimation device 13 is connected to an external system built on a cloud or a server via a mobile terminal (not shown) carried by the subject (user). The mobile terminal (not shown) is a portable communication device. For example, the mobile terminal is a mobile communication device having a communication function such as a smartphone, a smart watch, or a mobile phone. For example, the disease risk estimation device 13 is connected to the mobile terminal via wireless communication. For example, the disease risk estimation device 13 is connected to the mobile terminal via a wireless communication function (not shown) conforming to a standard such as Bluetooth (registered trademark) or WiFi (registered trademark). Note that the communication function of the disease risk estimation device 13 may conform to a standard other than Bluetooth (registered trademark) or WiFi (registered trademark). For example, the disease risk estimation device 13 may be connected to the mobile terminal via a wire such as a cable. The disease risk information may be used by an application installed on the mobile terminal. In that case, the mobile terminal executes processing using the disease risk information by application software or the like installed on the mobile terminal.

(Operation)
Next, the operation of the disease risk estimation system 1 will be described with reference to the drawings. The operation of the disease risk estimation device 13 included in the disease risk estimation system 1 will be described below. FIG. 12 is a flowchart for explaining an example of the operation of the disease risk estimation device 13. In explaining the processing according to the flowchart of FIG. 12, the components of the disease risk estimation device 13 will be described as the subject of the operations. The subject of the processing according to the flowchart of FIG. 12 may be the disease risk estimation device 13.

In FIG. 12, first, the acquisition unit 131 acquires time series data of sensor data measured by the measurement device 10 mounted on the footwear (step S11). The sensor data includes acceleration in three axial directions and angular velocity around three axes.

Next, the waveform processing unit 132 extracts walking waveform data from the time series data of the sensor data (step S12). The walking waveform data corresponds to the time series data of the sensor data for one walking cycle.

Next, the waveform processing unit 132 normalizes the extracted walking waveform data (step S13). The waveform processing unit 132 performs first normalization on the walking waveform data so that the step period is 100%. The waveform processing unit 132 also performs second normalization on the walking waveform data so that the stance phase is 60% and the swing phase is 40%.

Next, the gait index calculation unit 133 uses the normalized walking waveform data to calculate gait indices used to estimate physical ability (step S14). For example, the gait index calculation unit 133 calculates gait indices related to distance, height, angle, speed, time, frailty level, CPEI, etc.

Next, the physical ability estimation unit 135 estimates physical ability using the physical information and gait indices (step S15). For example, the physical ability estimation unit 135 estimates physical ability scores such as grip strength (total muscle strength of the entire body), dynamic balance, lower limb muscle strength, mobility, and static balance.

Next, the disease risk estimation unit 136 estimates a disease risk that reflects the risk for each disease using the physical information, gait index, and physical ability (step S16). The disease risk estimation unit 136 estimates a disease risk score that reflects the risk for each disease. For example, the disease risk estimation unit 136 estimates a disease risk score that reflects the risk for each disease, such as gout, diabetes, hypertension, nephrolithiasis, liver cirrhosis, arteriosclerosis, thromboembolism, dyslipidemia, hypercholesterolemia, and hyperlipidemia. For example, the disease risk estimation unit 136 estimates a disease risk score that reflects the risk for each disease, such as lower back pain, sleep apnea syndrome, insomnia, depression, osteoarthritis of the knee, and Parkinson's syndrome.

Next, the output unit 137 outputs disease risk information related to the estimated disease risk (step S17). For example, the output unit 137 displays the disease risk information on the screen of the subject (user)'s mobile terminal. For example, the output unit 137 outputs the disease risk information to an external system or the like that uses the disease risk information.

(Application example)
Next, application examples according to the present embodiment will be described with reference to the drawings. In the following application examples, an example is shown in which disease risk is estimated using feature amount data measured by a measuring device 10 placed on a shoe. For example, the function of the disease risk estimation device 13 is installed in a mobile terminal carried by a user. The function of the disease risk estimation device 13 may be implemented in a server or cloud connected to the mobile terminal carried by the user so as to be capable of data communication.

13 and 14 are conceptual diagrams showing an example of displaying disease risk information estimated by the disease risk estimation device 13 on the screen of a mobile terminal 170 carried by a user walking while wearing shoes 100 in which a measuring device 10 is placed. In the example of FIGS. 13 and 14, disease risk information estimated using sensor data measured while the user is walking is displayed on the screen of the mobile terminal 170. Disease risk information estimated for each user is optimized for each user and displayed on the screen of the mobile terminal 170. For example, the disease risk information includes advice corresponding to the disease risk generated by fitting it to a preset document format. For example, advice corresponding to the disease risk may be generated using a large-scale language model.

FIG. 13 is an example in which disease risk information including a disease risk score according to a rank indicating the risk of the disease is displayed on the screen of the mobile terminal 170. In the example of FIG. 13, disease risk scores reflecting the risk of each disease, such as "Disease (Rank 1): Y1, Disease (Rank 2): Y2, Disease (Rank 3): Y3, ..., Disease (Rank Q): YQ", are displayed on the screen of the mobile terminal 170. Also, in the example of FIG. 13, disease risk information including advice optimized for each user according to the disease risk, such as "Your risk of disease (Rank 1) is high. We recommend that you receive a medical examination at YY Hospital," is displayed on the screen of the mobile terminal 170 according to the disease risk score.

FIG. 14 shows an example of disease risk information including a disease risk score according to the risk of disease combinations displayed on the screen of the mobile terminal 170. In the example of FIG. 14, disease risk scores reflecting the risk of each disease combination, such as "Disease A + Disease B: X1, Disease B + Disease C: X2, Disease B + Disease C: X3, ..., Disease X + Disease Y: XZ," are displayed on the screen of the mobile terminal 170. Also, in the example of FIG. 14, disease risk information including advice according to the disease risk, such as "The combination of disease A and disease B is high risk. We recommend that you receive a checkup at XX Hospital," is displayed on the screen of the mobile terminal 170 according to the disease risk score.

With regard to the above-mentioned application example, a user who checks the information on disease risk reflecting the risk for each disease displayed on the display unit of the mobile terminal 170 can recognize his/her own disease risk. The disease risk information may be provided to a party other than the user. For example, the disease risk information may be output to a terminal device (not shown) used by a doctor or trainer who manages the user's physical condition, or by the user's family, etc. For example, the disease risk information may be recorded in a database (not shown) constructed for the purpose of health management, etc. There are no particular limitations on the output destination or use of the disease risk information.

As described above, the disease risk estimation system of this embodiment includes a measurement device and a disease risk estimation device. The measurement device is installed on the footwear of a subject for whom disease risk information is to be estimated. The measurement device measures spatial acceleration and spatial angular velocity. The measurement device generates sensor data using the measured spatial acceleration and spatial angular velocity. The measurement device transmits the generated sensor data to the disease risk estimation device. The disease risk estimation device includes an acquisition unit, a risk estimation unit, and an output unit. The acquisition unit acquires sensor data measured according to the movement of the feet of a subject for whom disease risk is to be estimated. The risk estimation unit has a calculation unit and an estimation unit. The calculation unit calculates a gait index using the sensor data. The estimation unit inputs data including the gait index calculated using the sensor data to the disease risk estimation model. The disease risk estimation model outputs a disease risk score indicating the degree of disease risk related to a disease in response to the input of data including the gait index. The estimation unit estimates a disease risk reflecting the risk for each disease in response to the disease risk score output from the disease risk estimation model. The output unit outputs disease risk information according to the estimated disease risk.

As described above, the disease risk estimation device of this embodiment estimates disease risk reflecting the risk for each disease using sensor data measured according to sensor data related to the subject's foot movements. In other words, according to this embodiment, it is possible to estimate disease risk reflecting the risk for each disease using sensor data measured according to foot movements.

In one aspect of this embodiment, the estimation unit calculates a disease risk score that reflects the risk of each disease by multiplying the disease risk score by a weight for each disease that corresponds to the rank indicating the risk of the disease. According to this aspect, it is possible to estimate a disease risk that reflects the risk of each disease according to the rank indicating the risk of the disease.

In one aspect of this embodiment, the estimation unit calculates a disease risk score that reflects the risk of each disease combination by multiplying the disease risk score by a weight for each disease combination. According to this aspect, it is possible to estimate a disease risk score that reflects the risk of each disease combination.

In one aspect of this embodiment, the disease risk estimation device displays information optimized for the subject on the screen of a terminal device that can be viewed by the user. According to this aspect, information can be provided that is optimized for the subject.

In one aspect of this embodiment, the disease risk estimation model is a model trained using a machine learning technique. For example, the disease risk estimation model includes an incomplete heterogeneous variational autoencoder. According to this aspect, even if there is some loss of data such as gait indicators, the subject's disease risk can be estimated.

Second Embodiment
Next, a disease risk estimation device according to a second embodiment will be described with reference to the drawings. The disease risk estimation device according to this embodiment outputs disease risk information including suggested information according to a change trend of the disease risk.

(composition)
FIG. 15 is a block diagram showing an example of the configuration of a disease risk estimation system 2 in the present disclosure. The disease risk estimation system 2 includes a measurement device 20 and a disease risk estimation device 23. For example, the measurement device 20 is installed in the footwear of a subject (user) whose disease risk is to be estimated. For example, the function of the disease risk estimation device 23 is installed in a mobile terminal carried by the subject (user). The measurement device 20 has the same configuration as the measurement device 10 of the first embodiment. In the following, a description of the measurement device 20 will be omitted, and only the disease risk estimation device 23 will be described. Note that the main configuration of the disease risk estimation device 23 is similar to the configuration of the disease risk estimation device 13 of the first embodiment, and therefore, the description may be omitted.

[Disease risk estimation device]
16 is a block diagram showing an example of the configuration of the disease risk estimation device 23. The disease risk estimation device 23 has an acquisition unit 231, a calculation unit 230, an estimation unit 240, a storage unit 234, a change tendency determination unit 245, and an output unit 237. The calculation unit 230 and the estimation unit 240 constitute the risk estimation unit 25.

The acquisition unit 231 (acquisition means) has the same configuration as the acquisition unit 131 of the first embodiment. The acquisition unit 231 acquires sensor data from the measurement device 20. The acquisition unit 231 receives the sensor data from the measurement device 20 via wireless communication. For example, the acquisition unit 231 receives the sensor data from the measurement device 20 via a wireless communication function (not shown) conforming to a standard such as Bluetooth (registered trademark) or WiFi (registered trademark). Note that the communication function of the acquisition unit 231 may conform to a standard other than Bluetooth (registered trademark) or WiFi (registered trademark) as long as it can communicate with the measurement device 20. The acquisition unit 231 may receive the sensor data from the measurement device 20 via a wired connection such as a cable. For example, the acquisition unit 231 may acquire a gait index or a feature amount calculated by the measurement device 20.

The acquisition unit 231 also acquires the user's physical information (attributes). The physical information includes gender, date of birth, height, and weight. The date of birth is converted to age. For example, the physical information is input via an input device (not shown). For example, the physical information is input via a mobile terminal used by the user. For example, the physical information may be stored in advance in the storage unit 234. The physical information may be updated at any time in response to input by the user.

The calculation unit 230 (calculation means) has the same configuration as the calculation unit 130 of the first embodiment. The calculation unit 230 has the functions of the waveform processing unit 132 and gait index calculation unit 133 of the first embodiment. The calculation unit 230 acquires sensor data from the acquisition unit 231. The calculation unit 230 extracts time series data for one walking cycle (gait waveform data) from the time series data of acceleration in three axial directions and angular velocity about three axes included in the sensor data. The calculation unit 230 extracts gait waveform data based on the timing of walking events detected from the time series data of the sensor data. For example, the calculation unit 230 extracts gait waveform data that starts from the timing of a heel strike and ends with the timing of the next heel strike.

The calculation unit 230 normalizes (first normalization) the time of the extracted walking waveform data for one step cycle to a walking cycle of 0 to 100% (percent). The calculation unit 230 also normalizes (second normalization) the first normalized walking waveform data for one step cycle so that the stance phase is 60% and the swing phase is 40%.

The calculation unit 230 extracts features (physical ability features) used to estimate physical abilities from the walking waveform data. The calculation unit 230 extracts physical ability features used to estimate at least one physical ability. For example, the calculation unit 230 extracts physical ability features used to estimate at least one of physical abilities such as grip strength (total muscle strength of the entire body), dynamic balance, lower limb muscle strength, mobility, and static balance. For example, the calculation unit 230 extracts physical ability features for each walking phase cluster according to preset conditions. The calculation unit 230 outputs the extracted physical ability features to the estimation unit 240.

The calculation unit 230 uses the normalized walking waveform data to calculate gait indices used to estimate physical ability. For example, the calculation unit 230 calculates gait indices related to distance, height, angle, speed, time, frailty level, CPEI (Center of Pressure Exclusion Index), etc.

The memory unit 234 (storage means) has the same configuration as the memory unit 134 of the first embodiment. The memory unit 234 stores a physical ability estimation model that estimates physical ability using physical ability features extracted from gait waveform data. For example, the physical ability estimation model outputs an index related to physical ability (physical ability score) in response to input of physical ability features extracted from gait waveform data. The memory unit 234 also stores a disease risk estimation model that estimates disease risk using physical information, gait index, and physical ability score. For example, the disease risk estimation model outputs an index related to disease risk (disease risk score) in response to input of physical information, gait index, and physical ability score.

The memory unit 234 stores the physical ability estimation model and disease risk estimation model learned for multiple subjects. For example, the physical ability estimation model and disease risk estimation model may be stored in the memory unit 234 when the product is shipped from the factory. The physical ability estimation model and disease risk estimation model may also be stored in the memory unit 234 at a timing such as at the time of calibration before the disease risk estimation device 23 is used by a user. For example, a physical ability estimation model and disease risk estimation model saved in a storage device (not shown) such as an external server may be used. In that case, it is sufficient if the physical ability estimation model and disease risk estimation model can be accessed via an interface (not shown) connected to the storage device.

The storage unit 234 also stores the user's physical information (attributes). The physical information includes gender, date of birth, height, and weight. The date of birth is converted to age. The physical information may be updated at any time.

The estimation unit 240 (estimation means) has the same configuration as the estimation unit 140 of the first embodiment. The estimation unit 240 includes the functions of the physical ability estimation unit 135 and the disease risk estimation unit 136 of the first embodiment. The estimation unit 240 acquires the physical ability feature extracted from the walking waveform data from the calculation unit 230. The estimation unit 240 also acquires the physical information (attributes) stored in the memory unit 234. The estimation unit 240 estimates a physical ability score using the physical ability feature and the physical information (attributes). The estimation unit 240 inputs the physical ability feature and the user's physical information (attributes) to the physical ability estimation model stored in the memory unit 234. For example, the estimation unit 240 estimates a physical ability score related to at least one of the physical abilities of grip strength (total muscle strength of the entire body), dynamic balance, lower limb muscle strength, mobility, and static balance. The estimation unit 240 uses the physical ability score, the gait index, and the physical information (attributes) to estimate a disease risk score that reflects the risk of each disease. The estimation unit 240 outputs the estimated disease risk score.

The change trend determination unit 245 acquires time series data of disease risk scores that reflect the risk for each disease. The change trend determination unit 245 determines the change trend for each disease risk according to changes in the time series data of the disease risk score. The change trend determination unit 245 determines whether there is a change trend, such as an increasing trend, a decreasing trend, or a stagnant trend, in the time series data of the disease risk score. The change trend determination unit 245 determines the disease risk for each disease according to the change trend for each disease risk.

FIG. 17 is a graph showing an example of time series data of disease risk scores for one user. The graph in FIG. 17 relates to three diseases with different change trends in disease risk scores. For example, the change trend determination unit 245 determines the change trend according to the slope of the disease risk score in a specific period. For example, the change trend determination unit 245 may determine the change trend according to the slope of a tangent line fitted to the curve of the time series data of the disease risk score.

In the example of FIG. 17, a stagnation trend is observed for the time series data of the disease risk score for disease A (dashed line). An increase trend is observed for the time series data of the disease risk score for disease B (dash-dotted line). A decrease trend is observed for the time series data of the disease risk score for disease C (dash-dotted line). For example, the change trend determination unit 245 determines that there is no change in the risk of disease A in response to the stagnation trend of the disease risk score for disease A. For example, the change trend determination unit 245 determines that there is a change in the risk of disease B in response to the increase trend of the disease risk score for disease B. For example, the change trend determination unit 245 determines that the risk of disease C has decreased in response to the decrease trend of the disease risk score for disease C.

18 is a graph showing an example of time series data of disease risk scores for multiple users. The graph in FIG. 18 relates to three users with different change trends of the same disease risk score. A stagnation trend is observed in the time series data of the disease risk score for user 1 (dashed line). An increase trend is observed in the time series data of the disease risk score for user 2 (dash-dotted line). A decrease trend is observed in the time series data of the disease risk score for user 3 (dash-dotted line). For example, the change trend determination unit 245 determines that there is no change in the disease risk in response to the stagnation trend of the disease risk score for user 1. For example, the change trend determination unit 245 determines that there is a change in the disease risk in response to the increase trend of the disease risk score for user 2. For example, the change trend determination unit 245 determines that the disease risk has decreased in response to the decrease trend of the disease risk score for user 3.

FIG. 19 is a graph showing an example of time series data of disease risk scores for multiple users. The graph in FIG. 19 is for three users with different change trends of the same disease risk score. FIG. 19 is an example of determining the change trend of a disease risk score according to a threshold set for the disease risk score. In the example of FIG. 19, a lower threshold T _L and an upper threshold T _U are set. The time series data of the disease risk score of user 1 (dashed line) is below the lower threshold T _L. The time series data of the disease risk score of user 2 (dotted line) exceeds the lower threshold T _L and also exceeds the upper threshold T _U. The time series data of the disease risk score of user 3 (dotted line) exceeded the lower threshold T _L , but fell below the lower threshold T _L over time. For example, the change trend determination unit 245 determines that the risk of disease is low for user 1 because the disease risk score is below the lower threshold T _L. For example, the change trend determination unit 245 determines that the risk of disease is high for user 2 because the disease risk score has exceeded the upper threshold value T _U. For example, the change trend determination unit 245 determines that the risk of disease is low for user 3 because the disease risk score has fallen below the lower threshold value T _L. The example of FIG. 19 shows an example in which two threshold values are set for the disease risk score. Only one threshold value or three or more threshold values may be set for the disease risk score.

The change trend determination unit 245 generates information according to the determination result regarding the disease risk for each disease. For example, the change trend determination unit 245 generates suggested information according to the determination result regarding the disease risk for each disease. For example, the change trend determination unit 245 generates suggested information including advice according to the disease risk generated by applying it to a preset document format. For example, advice according to the disease risk may be generated using a large-scale language model.

The output unit 237 (output means) has the same configuration as the output unit 137 of the first embodiment. The output unit 237 outputs disease risk information according to the disease risk score estimated by the estimation unit 240. The output unit 237 also outputs suggested information according to the determination result by the change trend determination unit 245. For example, the output unit 237 displays the disease risk information and suggested information on the screen of the mobile terminal of the subject (user). For example, the output unit 237 outputs the disease risk information and suggested information to an external system or the like that uses the disease risk information and suggested information. There are no particular limitations on the use of the output disease risk information and suggested information. For example, the disease risk information and suggested information are used for statistical analysis, research on disease prevention, and the like.

(Operation)
Next, the operation of the disease risk estimation system 2 will be described with reference to the drawings. The operation of the disease risk estimation device 23 included in the disease risk estimation system 2 will be described below. FIG. 20 is a flowchart for explaining an example of the operation of the disease risk estimation device 23. In explaining the processing according to the flowchart of FIG. 20, the components of the disease risk estimation device 23 will be described as the subjects of operation. The subject of operation of the processing according to the flowchart of FIG. 20 may be the disease risk estimation device 23.

In FIG. 20, first, the acquisition unit 231 acquires time series data of sensor data measured by the measurement device 20 mounted on the footwear (step S21). The sensor data includes acceleration in three axial directions and angular velocity around three axes.

Next, the calculation unit 230 extracts walking waveform data from the time series data of the sensor data (step S22). The walking waveform data corresponds to the time series data of the sensor data for one walking cycle.

Next, the calculation unit 230 normalizes the extracted walking waveform data (step S23). The calculation unit 230 performs first normalization on the walking waveform data so that the stride cycle is 100%. The calculation unit 230 also performs second normalization on the walking waveform data so that the stance phase is 60% and the swing phase is 40%.

Next, the calculation unit 230 uses the normalized walking waveform data to calculate gait indices used to estimate physical ability (step S24). For example, the calculation unit 230 calculates gait indices related to distance, height, angle, speed, time, frailty level, CPEI, etc.

Next, the estimation unit 240 estimates physical ability using the physical information and gait indices (step S25). For example, the estimation unit 240 estimates physical ability scores such as grip strength (total muscle strength of the entire body), dynamic balance, lower limb muscle strength, mobility, and static balance.

Next, the estimation unit 240 estimates a disease risk that reflects the risk of each disease using the physical information, gait index, and physical ability (step S26). The estimation unit 240 estimates a disease risk score that reflects the risk of each disease. For example, the estimation unit 240 estimates a disease risk score that reflects the risk of each disease, such as gout, diabetes, hypertension, nephrolithiasis, liver cirrhosis, arteriosclerosis, thromboembolism, dyslipidemia, hypercholesterolemia, and hyperlipidemia. For example, the estimation unit 240 estimates a disease risk score that reflects the risk of each disease, such as lower back pain, sleep apnea syndrome, insomnia, depression, osteoarthritis of the knee, and Parkinson's syndrome.

Next, the change trend determination unit 245 determines the change trend of the estimated disease risk (step S27). The change trend determination unit 245 generates proposal information according to the change trend of the time series data of the disease risk score.

Next, the output unit 237 outputs disease risk information related to the estimated disease risk and suggested information according to the determination result (step S28). For example, the output unit 237 displays the disease risk information and suggested information on the screen of the subject (user)'s mobile terminal. For example, the output unit 237 outputs the disease risk information and suggested information to an external system or the like that uses the disease risk information and suggested information.

(Application example)
Next, application examples according to the present embodiment will be described with reference to the drawings. In the following application examples, an example is shown in which disease risk is estimated using feature amount data measured by a measuring device 20 placed on a shoe. For example, the function of the disease risk estimation device 23 is installed in a mobile device carried by the user. The function of the disease risk estimation device 23 may be implemented in a server or cloud connected to the mobile device carried by the user so as to be capable of data communication.

21 and 22 are conceptual diagrams showing an example of displaying disease risk information estimated by the disease risk estimation device 23 on the screen of a mobile device 270 carried by a user walking while wearing shoes 200 in which a measuring device 20 is placed. In the example of FIGS. 21 and 22, disease risk information estimated using sensor data measured while the user is walking is displayed on the screen of the mobile device 270. Disease risk information estimated for each user is displayed on the screen of the mobile device 270 in an optimized manner for each user. For example, the disease risk information includes advice corresponding to the disease risk generated by fitting it to a preset document format. For example, advice corresponding to the disease risk may be generated using a large-scale language model.

21 is an example of disease risk information including a change trend of disease risk and advice displayed on the screen of the mobile terminal 270. In the example of FIG. 21, information according to the change trend of disease risk, such as "Disease (rank 1): examination required, Disease (rank 2): caution required, ..., Disease (rank Q): ..." is displayed on the screen of the mobile terminal 270. Also, in the example of FIG. 21, disease risk information including advice according to the change trend of disease risk, such as "The score for disease (rank 1) has exceeded the upper threshold. Please see a doctor at a hospital," is displayed on the screen of the mobile terminal 270 in accordance with the change trend of disease risk that requires examination. Furthermore, in the example of FIG. 21, disease risk information including advice according to disease risk, such as "The score for disease (rank 2) has exceeded the lower threshold. Please review your diet," is displayed on the screen of the mobile terminal 270 in accordance with the change trend of disease risk that requires caution.

FIG. 22 is an example of disease risk information including the changing trend of disease risk and advice displayed on the screen of the mobile device 270. In the example of FIG. 22, information according to the changing trend of disease risk, such as "Disease (rank 1): rising trend, Disease (rank 2): rising trend, ..., Disease (rank Q): ...", is displayed on the screen of the mobile device 270. Also, in the example of FIG. 22, disease risk information including advice according to the disease risk, such as "Disease risk for disease (rank 1) and disease (rank 2) is on the rise. We recommend that you receive a checkup at YY Hospital," is displayed on the screen of the mobile device 270 according to the changing trend of disease risk.

Regarding the above application example, a user who checks the disease risk information according to the changing trend of disease risk displayed on the display unit of the mobile terminal 270 can recognize his/her own disease risk. The disease risk information may be provided to a person other than the user. For example, the disease risk information may be output to a terminal device (not shown) used by a doctor or trainer who manages the user's physical condition, or by the user's family, etc. For example, the disease risk information may be recorded in a database (not shown) constructed for the purpose of health management, etc. There are no particular limitations on the output destination or use of the disease risk information.

As described above, the disease risk estimation system of this embodiment includes a measurement device and a disease risk estimation device. The measurement device is installed on the footwear of a subject for whom disease risk information is to be estimated. The measurement device measures spatial acceleration and spatial angular velocity. The measurement device generates sensor data using the measured spatial acceleration and spatial angular velocity. The measurement device transmits the generated sensor data to the disease risk estimation device. The disease risk estimation device includes an acquisition unit, a risk estimation unit, a change trend determination unit, and an output unit. The acquisition unit acquires sensor data measured according to the movement of the feet of a subject for whom disease risk is to be estimated. The risk estimation unit has a calculation unit and an estimation unit. The calculation unit calculates a gait index using the sensor data. The estimation unit inputs data including the gait index calculated using the sensor data to the disease risk estimation model. The disease risk estimation model outputs a disease risk score indicating the degree of disease risk related to the disease in response to the input of data including the gait index. The estimation unit estimates a disease risk reflecting the risk for each disease in response to the disease risk score output from the disease risk estimation model. The change trend determination unit determines the disease risk for each disease according to the change trend of the disease risk score. The output unit outputs disease risk information according to the estimated disease risk.

As described above, the disease risk estimation device of this embodiment estimates disease risk reflecting the risk for each disease using sensor data measured according to sensor data related to the subject's foot movements. The disease risk estimation device of this embodiment determines the disease risk for each disease according to the change trend of the disease risk score. In other words, according to this embodiment, it is possible to estimate disease risk reflecting the risk for each disease according to the change trend of the disease risk score.

In one aspect of this embodiment, the change trend determination unit determines that the disease risk is high for a disease whose disease risk score is on an increasing trend. The change trend determination unit determines that the disease risk is low for a disease whose disease risk score is on either a decreasing trend or a stagnant trend. According to this aspect, the disease risk can be determined according to the increasing, decreasing, and stagnant trend of the disease risk score.

In one aspect of this embodiment, the change trend determination unit determines that the disease risk is high for a disease whose disease risk score exceeds the threshold. The change trend determination unit determines that the disease risk is low for a disease whose disease risk score falls below the threshold. According to this aspect, the disease risk can be determined based on the relationship between the disease risk score and the threshold.

Third Embodiment
Next, a disease risk estimation device according to a third embodiment will be described with reference to the drawings. The disease risk estimation device according to this embodiment outputs disease risk information including suggested information according to disease risk. The disease risk estimation device according to this embodiment outputs suggested information for insurance-related institutions such as health insurance associations and life insurance companies.

(composition)
FIG. 23 is a block diagram showing an example of the configuration of a disease risk estimation system 3 in the present disclosure. The disease risk estimation system 3 includes a measurement device 30 and a disease risk estimation device 33. For example, the measurement device 30 is installed in the footwear of a subject whose disease risk is to be estimated. For example, the function of the disease risk estimation device 33 is installed in a mobile terminal carried by the subject. The measurement device 30 has the same configuration as the measurement device 10 of the first embodiment. In the following, a description of the measurement device 30 will be omitted, and only the disease risk estimation device 33 will be described. Note that the main configuration of the disease risk estimation device 33 is similar to the configuration of the disease risk estimation device 13 of the first embodiment, and therefore may be omitted from the description.

[Disease risk estimation device]
Fig. 24 is a block diagram showing an example of the configuration of a disease risk estimation device 33. The disease risk estimation device 33 has an acquisition unit 331, a calculation unit 330, an estimation unit 340, a storage unit 334, a proposed information generation unit 345, and an output unit 337. The calculation unit 330 and the estimation unit 340 constitute a risk estimation unit 35. Fig. 24 shows a configuration in which a proposed information generation unit 345 is added to the configuration of the first embodiment. The proposed information generation unit 345 may be added to the configuration of the second embodiment.

The acquisition unit 331 (acquisition means) has the same configuration as the acquisition unit 131 of the first embodiment. The acquisition unit 331 acquires sensor data from the measurement device 30. The acquisition unit 331 receives the sensor data from the measurement device 30 via wireless communication. For example, the acquisition unit 331 receives the sensor data from the measurement device 30 via a wireless communication function (not shown) conforming to a standard such as Bluetooth (registered trademark) or WiFi (registered trademark). Note that the communication function of the acquisition unit 331 may conform to a standard other than Bluetooth (registered trademark) or WiFi (registered trademark) as long as it can communicate with the measurement device 30. The acquisition unit 331 may receive the sensor data from the measurement device 30 via a wired connection such as a cable. For example, the acquisition unit 331 may acquire gait indices and feature amounts calculated by the measurement device 30.

The acquisition unit 331 also acquires the user's physical information (attributes). The physical information includes gender, date of birth, height, and weight. The date of birth is converted to age. For example, the physical information is input via an input device (not shown). For example, the physical information is input via a mobile terminal used by the user. For example, the physical information may be stored in advance in the storage unit 334. The physical information may be updated at any time in response to input by the user.

The calculation unit 330 (calculation means) has the same configuration as the calculation unit 130 of the first embodiment. The calculation unit 330 has the functions of the waveform processing unit 132 and gait index calculation unit 133 of the first embodiment. The calculation unit 330 acquires sensor data from the acquisition unit 331. The calculation unit 330 extracts time series data for one walking cycle (gait waveform data) from the time series data of acceleration in three axial directions and angular velocity about three axes included in the sensor data. The calculation unit 330 extracts gait waveform data based on the timing of walking events detected from the time series data of the sensor data. For example, the calculation unit 330 extracts gait waveform data that starts at the timing of a heel strike and ends at the timing of the next heel strike.

The calculation unit 330 normalizes (first normalization) the time of the extracted walking waveform data for one step cycle to a walking cycle of 0 to 100% (percent). The calculation unit 330 also normalizes (second normalization) the first normalized walking waveform data for one step cycle so that the stance phase is 60% and the swing phase is 40%.

The calculation unit 330 extracts features (physical ability features) used to estimate physical abilities from the walking waveform data. The calculation unit 330 extracts physical ability features used to estimate at least one physical ability. For example, the calculation unit 330 extracts physical ability features used to estimate at least one of physical abilities such as grip strength (total muscle strength of the entire body), dynamic balance, lower limb muscle strength, mobility, and static balance. For example, the calculation unit 330 extracts physical ability features for each walking phase cluster according to preset conditions. The calculation unit 330 outputs the extracted physical ability features to the estimation unit 340.

The calculation unit 330 uses the normalized walking waveform data to calculate gait indices used to estimate physical ability. For example, the calculation unit 330 calculates gait indices related to distance, height, angle, speed, time, frailty level, CPEI (Center of Pressure Exclusion Index), etc.

The memory unit 334 (storage means) has the same configuration as the memory unit 134 of the first embodiment. The memory unit 334 stores a physical ability estimation model that estimates physical ability using physical ability features extracted from gait waveform data. For example, the physical ability estimation model outputs an index related to physical ability (physical ability score) in response to input of physical ability features extracted from gait waveform data. The memory unit 334 also stores a disease risk estimation model that estimates disease risk using physical information, gait index, and physical ability score. For example, the disease risk estimation model outputs an index related to disease risk (disease risk score) in response to input of physical information, gait index, and physical ability score.

The memory unit 334 stores the physical ability estimation model and disease risk estimation model learned for multiple subjects. For example, the physical ability estimation model and disease risk estimation model may be stored in the memory unit 334 when the product is shipped from the factory. The physical ability estimation model and disease risk estimation model may also be stored in the memory unit 334 at a timing such as at the time of calibration before the disease risk estimation device 33 is used by the subject. For example, a physical ability estimation model and disease risk estimation model saved in a storage device (not shown) such as an external server may be used. In that case, it is sufficient if the physical ability estimation model and disease risk estimation model can be accessed via an interface (not shown) connected to the storage device.

The storage unit 334 also stores the subject's physical information (attributes). The physical information includes gender, date of birth, height, and weight. The date of birth is converted to age. The physical information may be updated at any time.

The estimation unit 340 (estimation means) has the same configuration as the estimation unit 140 of the first embodiment. The estimation unit 340 includes the functions of the physical ability estimation unit 135 and the disease risk estimation unit 136 of the first embodiment. The estimation unit 340 acquires the physical ability feature extracted from the walking waveform data from the calculation unit 330. The estimation unit 340 also acquires the physical information (attributes) stored in the memory unit 334. The estimation unit 340 estimates a physical ability score using the physical ability feature and the physical information (attributes). The estimation unit 340 inputs the physical ability feature and the subject's physical information (attributes) to the physical ability estimation model stored in the memory unit 334. For example, the estimation unit 340 estimates a physical ability score related to at least one of the physical abilities of grip strength (total muscle strength of the whole body), dynamic balance, lower limb muscle strength, mobility, and static balance. The estimation unit 340 uses the physical ability score, the gait index, and the physical information (attributes) to estimate a disease risk score that reflects the risk of each disease. The estimation unit 340 outputs the estimated disease risk score.

The proposed information generation unit 345 acquires a disease risk score that reflects the risk for each disease. The proposed information generation unit 345 generates proposed information for insurance-related institutions such as health insurance associations and life insurance companies based on the disease risk score. For example, the proposed information generation unit 345 generates disease risk information including proposed information regarding the insured person for insurance-related institutions such as health insurance associations and life insurance companies. The health insurance associations and life insurance companies can take action for the insured person based on the proposed information in response to acquiring the disease risk information.

The proposed information generating unit 345 generates proposed information according to the disease risk score for each disease. For example, the proposed information generating unit 345 may generate proposed information according to the change trend of the disease risk score for each disease. For example, the proposed information generating unit 345 generates proposed information including advice according to the disease risk score generated by applying it to a preset document format. For example, advice according to the disease risk score may be generated using a large-scale language model.

The output unit 337 (output means) has the same configuration as the output unit 137 of the first embodiment. The output unit 337 outputs disease risk information according to the disease risk score estimated by the estimation unit 340. The output unit 337 also outputs proposed information according to the disease risk score by the proposed information generation unit 345. For example, the output unit 337 outputs the disease risk information and the proposed information to an external system or the like that uses the disease risk information and the proposed information. For example, the output unit 337 outputs the disease risk information and the proposed information to a terminal device (not shown) used by an insurance-related institution such as a health insurance association or a life insurance company. In this case, the insurance-related institution such as a health insurance association or a life insurance company corresponds to a user who views the disease risk information and the proposed information. There are no particular limitations on the use of the output disease risk information and the proposed information. For example, the disease risk information and the proposed information are used by the health insurance association to determine the timing of providing specific health guidance to the subject. For example, the disease risk information and the proposed information are used by the life insurance company to determine the timing of providing incentives such as insurance premium discounts and benefits to the subject. For example, disease risk information and recommendation information may be used for statistical analysis and research into disease prevention.

(Operation)
Next, the operation of the disease risk estimation system 3 will be described with reference to the drawings. The operation of the disease risk estimation device 33 included in the disease risk estimation system 3 will be described below. FIG. 25 is a flowchart for explaining an example of the operation of the disease risk estimation device 33. In explaining the processing according to the flowchart of FIG. 25, the components of the disease risk estimation device 33 will be described as the subjects of operation. The subject of operation of the processing according to the flowchart of FIG. 25 may be the disease risk estimation device 33.

In FIG. 25, first, the acquisition unit 331 acquires time series data of sensor data measured by the measurement device 30 mounted on the footwear (step S31). The sensor data includes acceleration in three axial directions and angular velocity around three axes.

Next, the calculation unit 330 extracts walking waveform data from the time series data of the sensor data (step S32). The walking waveform data corresponds to the time series data of the sensor data for one walking cycle.

Next, the calculation unit 330 normalizes the extracted walking waveform data (step S33). The calculation unit 330 performs first normalization on the walking waveform data so that the stride cycle is 100%. The calculation unit 330 also performs second normalization on the walking waveform data so that the stance phase is 60% and the swing phase is 40%.

Next, the calculation unit 330 uses the normalized walking waveform data to calculate gait indices used to estimate physical ability (step S34). For example, the calculation unit 330 calculates gait indices related to distance, height, angle, speed, time, frailty level, CPEI, etc.

Next, the estimation unit 340 estimates physical ability using the physical information and gait indices (step S35). For example, the estimation unit 340 estimates physical ability scores such as grip strength (total muscle strength of the entire body), dynamic balance, lower limb muscle strength, mobility, and static balance.

Next, the estimation unit 340 estimates a disease risk that reflects the risk of each disease using the physical information, gait index, and physical ability (step S36). The estimation unit 340 estimates a disease risk score that reflects the risk of each disease. For example, the estimation unit 340 estimates a disease risk score that reflects the risk of each disease, such as gout, diabetes, hypertension, nephrolithiasis, liver cirrhosis, arteriosclerosis, thromboembolism, dyslipidemia, hypercholesterolemia, and hyperlipidemia. For example, the estimation unit 340 estimates a disease risk score that reflects the risk of each disease, such as lower back pain, sleep apnea syndrome, insomnia, depression, osteoarthritis of the knee, and Parkinson's syndrome.

Next, the proposed information generation unit 345 generates proposed information for insurance-related institutions such as health insurance associations and life insurance companies based on the estimated disease risk (step S37).

Next, the output unit 337 outputs disease risk information related to disease risk and proposal information for insurance-related institutions (step S38). For example, the disease risk information and proposal information are used by a health insurance association to determine the timing for providing specific health guidance to the subject. For example, the disease risk information and proposal information are used by a life insurance company to determine the timing for providing incentives such as insurance premium discounts and special benefits to the subject. For example, the disease risk information and proposal information may be used for statistical analysis and research into disease prevention.

(Application example)
Next, application examples according to the present embodiment will be described with reference to the drawings. In the following application examples, the relationship between a business providing a service using the disease risk estimation system 3, an insurance-related institution using the service, and a recipient who receives benefits from the insurance-related institution is shown. Below, examples of a health insurance association and a life insurance company are described separately.

[Health Insurance Association]
FIG. 26 shows an example in which the insurance-related institution is a health insurance association. The business provides the health insurance association with a service using a disease risk estimation system 3. Based on a contract concluded with the health insurance association, the business provides the health insurance association with disease risk information and proposal information regarding the insured person. The health insurance association pays the business a usage fee for the service using the disease risk estimation system 3. The contract between the business and the health insurance association clarifies rules regarding the handling of personal information and appropriate data management. The business clearly explains that the disease risk score is reference information and does not guarantee medical accuracy or completeness.

The health insurance society will fully explain the details of the personal information protection policy and data management to the insured person and obtain their consent. If there are any changes to the personal information protection policy or data management, the health insurance society will explain the changes to the insured person and obtain their consent. For example, consent from the insured person will be obtained electronically. The health insurance society will provide specific health guidance to the insured person depending on the disease risk information provided by the business operator.

The insured person is the entity that pays health insurance premiums to the health insurance association. The insured person is loaned or provided with a dedicated insole equipped with a measuring device 30 by a business that has a contract with the health insurance association. The business provides support regarding the installation and operation of a dedicated app having the function of the disease risk estimation device 33, and the wearing of the dedicated insole. The business cooperates with the health insurance association to add new functions and perform updates to provide appropriate support to the insured person. The insured person installs a dedicated app having the function of the disease risk estimation device 33 on his/her mobile device. The function of the disease risk estimation device 33 may be installed on a cloud server managed by the business. The insured person wears shoes equipped with the dedicated insole and walks while carrying a mobile device on which the dedicated app is installed. The dedicated app estimates a disease risk score using sensor data measured by the measuring device 30 according to the insured person's walking. The dedicated app uploads data including disease risk information according to the disease risk score to the business's cloud server. The dedicated app may upload the sensor data measured by the measuring device 30 to a cloud server managed by the business operator. For example, the dedicated app displays disease risk information corresponding to the disease risk score estimated by the disease risk estimation device 33 on the screen of a mobile device carried by the insured person. For example, the insured person improves their lifestyle habits in accordance with the improvement advice included in the disease risk information.

The terminal device used by the health insurance association downloads the disease risk information of the insured from the carrier's cloud server. The health insurance association refers to the disease risk information. The health insurance association provides specific health guidance to the insured according to the disease risk score included in the disease risk information. The specific health guidance is provided by experts with specialized knowledge. The health insurance association periodically refers to the disease risk score of the insured, and provides health support and counseling by experts according to changes in the score. For example, the health insurance association holds regular consultation sessions and events, and provides information on maintaining health using the measuring device 30. The health insurance association also takes into account the opinions and requests of the insured. The health insurance association periodically verifies and evaluates whether the application of the disease risk estimation system 3 has resulted in improvements in the insured's health and reductions in medical expenses.

27 is an example of disease risk information for an insured person displayed on the screen of a terminal device 380A used by a health insurance association. The screen of the terminal device 380A displays information according to the disease risk score, such as "The disease risk of disease (rank 1) for insured person K has exceeded the standard for specific health guidance." The screen of the terminal device 380A also displays suggested information, such as "We recommend that you send a notice of specific health guidance to insured person K." This suggested information is displayed at an appropriate time for the health insurance association to notify the insured person of the specific health guidance. Furthermore, the screen of the terminal device 380A displays suggested information, such as "Do you want to send a notice of specific health guidance to insured person K?" A staff member who has checked the disease risk information displayed on the screen of the terminal device 380A can notify the insured person of the specific health guidance. For example, when the "YES" button displayed on the screen of the terminal device 380A is pressed, a notice of specific health guidance is sent to the mobile device carried by the insured person.

28 shows an example in which information in response to a notification of specific health guidance sent from a terminal device 380A of a health insurance association is displayed on the screen of a mobile terminal 370 carried by an insured person walking while wearing shoes 300 in which a measuring device 30 is placed. In the example of FIG. 28, a notice stating "Your disease risk for disease (rank 1) is high. Please receive specific health guidance" is displayed on the screen of the mobile terminal 370. Also, in the example of FIG. 28, disease risk information including advice regarding specific health guidance, such as "You are a candidate for specific health guidance. Please make an appointment with a health management center immediately," is displayed on the screen of the mobile terminal 370 according to the disease risk score. Furthermore, the disease risk information may include statistical information of the entire insured person. For example, the percentage of insured persons whose disease risk scores have improved as a result of receiving specific health guidance among other insured persons with disease risk scores similar to the insured person who was the target of the notification of specific health guidance may be presented. Specifically, advice regarding specific health guidance will include information such as, "70% of people who have a similar disease risk to you have improved their disease risk by receiving specific health guidance." This will allow insured persons to manage their own health while referring to the trends of people in a similar situation to themselves.

The insured person who checks the information corresponding to the specific health guidance notification displayed on the display unit of the mobile terminal 370 can recognize the need to receive specific health guidance. The specific health guidance notification may be provided to a person other than the insured person. For example, the specific health guidance notification may be sent to a terminal device (not shown) used by a doctor or trainer who manages the insured person's physical condition, a family member of the insured person, or a superior at the insured person's company. For example, the specific health guidance notification may be recorded in a database (not shown) constructed for the purpose of health management, etc. There are no particular limitations on the destination or use of the specific health guidance notification.

[Life Insurance Companies]
FIG. 29 shows an example in which the insurance-related institution is a life insurance company. The business provides the life insurance company with a service using the disease risk estimation system 3. Based on a contract concluded with the life insurance company, the business provides the life insurance company with disease risk information and proposal information regarding the policyholder. The life insurance company pays the business a fee for the service using the disease risk estimation system 3. The contract between the business and the life insurance company clarifies rules regarding the handling of personal information and appropriate data management. The business clearly explains that the disease risk score is reference information and does not guarantee medical accuracy or completeness.

Life insurance companies will fully explain the details of their personal information protection policies and data management to policyholders and obtain their consent. If there are any changes to the details of their personal information protection policies or data management, the life insurance companies will explain the changes to their policyholders and obtain their consent. For example, consent from policyholders will be obtained electronically. Life insurance companies will provide incentives to policyholders depending on the content of disease risk information provided by carriers.

The policyholder is the entity that pays the insurance premium to the life insurance company. The policyholder is loaned or provided with a dedicated insole equipped with the measuring device 30 by a business that has a contract with the life insurance company. The business provides support regarding the installation and operation of a dedicated app having the function of the disease risk estimation device 33, and the wearing of the dedicated insole. The business cooperates with the health insurance association to add new functions and perform updates to provide appropriate support to the policyholder. The policyholder installs a dedicated app having the function of the disease risk estimation device 33 on his/her mobile device. The function of the disease risk estimation device 33 may be installed on a cloud server managed by the business. The policyholder wears shoes equipped with the dedicated insole and walks while carrying a mobile device equipped with the dedicated app. The dedicated app estimates a disease risk score using sensor data measured by the measuring device 30 according to the policyholder's walking. The dedicated app uploads data including disease risk information according to the disease risk score to the cloud server of the business. The dedicated app may upload the sensor data measured by the measuring device 30 to a cloud server managed by the business operator. For example, the dedicated app displays disease risk information corresponding to the disease risk score estimated by the disease risk estimation device 33 on the screen of a mobile device carried by the policyholder. For example, the policyholder improves their lifestyle habits in accordance with the improvement advice included in the disease risk information.

The terminal device used by the life insurance company downloads the policyholder's disease risk information from the operator's cloud server. The health insurance association refers to the disease risk information. The life insurance company provides incentives to the policyholder according to the disease risk score included in the disease risk information. For example, the life insurance company provides incentives such as discounts on insurance premiums and special benefits. For example, the life insurance company sets rewards for achievement and gradual goals to motivate the policyholder to take action. For example, the life insurance company provides insurance product plans based on the policyholder's health improvement status. The health insurance association periodically refers to the policyholder's disease risk score and provides health support and counseling by experts according to changes in the score. For example, the life insurance company holds regular consultations and events and provides information on maintaining health using the measuring device 30. The life insurance company also takes into account the opinions and requests of the policyholder. The health insurance association periodically verifies and evaluates whether the application of the disease risk estimation system 3 has resulted in the policyholder's health improvement and reduction in medical expenses.

FIG. 30 shows an example of disease risk information for a policyholder displayed on the screen of a terminal device 380B used by a health insurance association. The screen of the terminal device 380B displays information according to the disease risk score, such as "The disease risk score for disease (rank 2) for policyholder L has fallen below the standard for granting incentives." The screen of the terminal device 380B also displays suggested information, such as "We recommend that you send a notice of incentive grant to policyholder L." This suggested information is displayed at an appropriate time for the life insurance company to grant an incentive to the policyholder. The screen of the terminal device 380B also displays suggested information, such as "Would you like to send a notice of incentive grant to policyholder L?" A staff member who has checked the disease risk information displayed on the screen of the terminal device 380B can notify the policyholder of the grant of an incentive. For example, when the "YES" button displayed on the screen of the terminal device 380B is pressed, a notice of incentive grant is sent to the mobile terminal carried by the policyholder.

Figure 31 shows an example in which information in response to a notification of incentive provision sent from the life insurance company's terminal device 380B is displayed on the screen of a mobile terminal 370 carried by a policyholder walking in shoes 300 with a measuring device 30 placed on them. In the example of Figure 31, a notice stating "Your disease risk for disease A has fallen below the standard. An incentive has been provided by the life insurance company" is displayed on the screen of the mobile terminal 370. Also, in the example of Figure 31, information regarding incentive provision, stating "Your insurance premiums will be discounted by 10% for six months starting in June," is displayed on the screen of the mobile terminal 370 according to the disease risk score.

The policyholder who checks the information regarding the grant of the incentive displayed on the display unit of the mobile terminal 370 can recognize that he or she will receive an incentive. The notification of the grant of the incentive may be provided to a person other than the policyholder. For example, the notification of the grant of the incentive may be sent to a terminal device (not shown) used by a doctor or trainer who manages the policyholder's physical condition, a family member of the policyholder, or the policyholder's superiors at work. For example, the notification of the grant of the incentive may be recorded in a database (not shown) constructed for the purpose of health management, etc. There are no particular limitations on the destination or use of the notification of the grant of the incentive.

As described above, the disease risk estimation system of this embodiment includes a measurement device and a disease risk estimation device. The measurement device is installed on the footwear of a subject for whom disease risk information is to be estimated. The measurement device measures spatial acceleration and spatial angular velocity. The measurement device generates sensor data using the measured spatial acceleration and spatial angular velocity. The measurement device transmits the generated sensor data to the disease risk estimation device. The disease risk estimation device includes an acquisition unit, a risk estimation unit, a proposed information generation unit, and an output unit. The acquisition unit acquires sensor data measured according to the movement of the feet of a subject for whom disease risk is to be estimated. The risk estimation unit has a calculation unit and an estimation unit. The calculation unit calculates a gait index using the sensor data. The estimation unit inputs data including the gait index calculated using the sensor data to the disease risk estimation model. The disease risk estimation model outputs a disease risk score indicating the degree of disease risk related to the disease in response to the input of data including the gait index. The estimation unit estimates a disease risk reflecting the risk for each disease in response to the disease risk score output from the disease risk estimation model. The change trend determination unit determines the disease risk for each disease according to the change trend of the disease risk score. The proposal information generation unit generates proposal information for insurance-related institutions according to the disease risk reflecting the risk for each disease. The output unit outputs disease risk information according to the estimated disease risk.

As described above, the disease risk estimation device of this embodiment estimates a disease risk that reflects the risk for each disease, using sensor data measured according to sensor data related to the subject's foot movements. The disease risk estimation device of this embodiment generates proposal information for insurance-related institutions according to the disease risk that reflects the risk for each disease. In other words, according to this embodiment, proposal information for insurance-related institutions can be generated using sensor data measured related to the subject.

In one aspect of this embodiment, the insurance-related institution is a health insurance association. The acquisition unit acquires sensor data measured according to the walking of an insured person of the health insurance association. The risk estimation unit estimates a disease risk reflecting the risk for each disease using the acquired sensor data. The proposed information generation unit generates proposed information including the timing for notifying the insured person of specific health guidance according to the disease risk reflecting the estimated risk for each disease. The output unit transmits the proposed information including the timing for notifying the insured person of specific health guidance to a terminal device used by the health insurance association. According to this aspect, proposed information including the timing for notifying the insured person of specific health guidance can be generated using sensor data measured on the insured person.

In one aspect of this embodiment, the insurance-related institution is a life insurance company. The acquisition unit acquires sensor data measured according to the walking of a policyholder of the life insurance company. The risk estimation unit estimates a disease risk reflecting the risk for each disease using the acquired sensor data. The proposal information generation unit generates proposal information including the timing for granting an incentive to the policyholder according to the disease risk reflecting the estimated risk for each disease. The output unit transmits the proposal information including the timing for granting the incentive to a terminal device used by the life insurance company. According to this aspect, proposal information including the timing for granting an incentive to the policyholder can be generated using sensor data measured on the policyholder.

Fourth Embodiment
Next, a disease risk estimation device according to a fourth embodiment will be described with reference to the drawings. The disease risk estimation device of this embodiment has a simplified configuration of the disease risk estimation device included in the disease risk estimation systems of the first to third embodiments.

(composition)
32 is a block diagram showing an example of the configuration of a disease risk estimation device 40 in the present disclosure. The disease risk estimation device 40 includes an acquisition unit 41, a risk estimation unit 45, and an output unit 47.

The acquisition unit 41 acquires sensor data measured according to the foot movements of a subject for whom disease risk is to be estimated. The risk estimation unit 45 uses the acquired sensor data to estimate a disease risk that reflects the risk for each disease. The output unit 47 outputs disease risk information according to the estimated disease risk.

(Operation)
Next, the operation of the disease risk estimation device 40 will be described with reference to the drawings. Fig. 33 is a flowchart for explaining an example of the operation of the disease risk estimation device 40. In explaining the processing according to the flowchart of Fig. 33, the components of the disease risk estimation device 40 will be described as the subject of the operations. The subject of the processing according to the flowchart of Fig. 33 may be the disease risk estimation device 40.

In FIG. 33, first, the acquisition unit 41 acquires sensor data measured according to the foot movements of a subject whose disease risk is to be estimated (step S41).

The risk estimation unit 45 uses the acquired sensor data to estimate disease risk that reflects the risk for each disease (step S42).

The output unit 47 outputs disease risk information according to the estimated disease risk (step S43).

As described above, the disease risk estimation device of this embodiment estimates disease risk reflecting the risk for each disease using sensor data measured according to sensor data related to the subject's foot movements. In other words, according to this embodiment, it is possible to estimate disease risk reflecting the risk for each disease using sensor data measured according to foot movements.

(Hardware)
Next, a hardware configuration for executing the control and processing in the present disclosure will be described with reference to the drawings. Here, an information processing device 90 (computer) in Fig. 34 is given as an example of such a hardware configuration. The information processing device 90 in Fig. 34 is an example of a configuration for executing the control and processing in the present disclosure, and does not limit the scope of the present disclosure.

As shown in FIG. 34, the information processing device 90 includes a processor 91, a main memory device 92, an auxiliary memory device 93, an input/output interface 95, and a communication interface 96. In FIG. 34, the interface is abbreviated as I/F (Interface). The processor 91, the main memory device 92, the auxiliary memory device 93, the input/output interface 95, and the communication interface 96 are connected to each other via a bus 98 so as to be able to communicate data with each other. In addition, the processor 91, the main memory device 92, the auxiliary memory device 93, and the input/output interface 95 are connected to a network such as the Internet or an intranet via the communication interface 96.

The processor 91 expands a program (instructions) stored in the auxiliary storage device 93 or the like into the main storage device 92. For example, the program is a software program for executing the control and processing in this disclosure. The processor 91 executes the program expanded into the main storage device 92. The processor 91 executes the program to execute the control and processing in this disclosure.

The main memory 92 has an area in which programs are expanded. Programs stored in the auxiliary memory 93 or the like are expanded in the main memory 92 by the processor 91. The main memory 92 is realized by a volatile memory such as a DRAM (Dynamic Random Access Memory). In addition, a non-volatile memory such as an MRAM (Magneto-resistive Random Access Memory) may be configured/added to the main memory 92.

The auxiliary storage device 93 stores various data such as programs. The auxiliary storage device 93 is realized by a local disk such as a hard disk or flash memory. Note that it is also possible to omit the auxiliary storage device 93 by configuring the various data to be stored in the main storage device 92.

The input/output interface 95 is an interface for connecting the information processing device 90 to peripheral devices based on standards and specifications. The communication interface 96 is an interface for connecting to external systems and devices via a network such as the Internet or an intranet based on standards and specifications. The input/output interface 95 and the communication interface 96 may be a common interface for connecting to external devices.

If necessary, input devices such as a keyboard, mouse, or touch panel may be connected to the information processing device 90. These input devices are used to input information and settings. When a touch panel is used as the input device, a screen having the function of a touch panel becomes the interface. The processor 91 and the input devices are connected via an input/output interface 95.

The information processing device 90 may be equipped with a display device for displaying information. If a display device is equipped, the information processing device 90 is equipped with a display control device (not shown) for controlling the display of the display device. The information processing device 90 and the display device are connected via an input/output interface 95.

The information processing device 90 may be equipped with a drive device. The drive device acts as an intermediary between the processor 91 and a recording medium (program recording medium) to read data and programs stored on the recording medium and to write the processing results of the information processing device 90 to the recording medium. The information processing device 90 and the drive device are connected via an input/output interface 95.

The above is an example of a hardware configuration for enabling the control and processing in this disclosure. The hardware configuration in FIG. 34 is an example of a hardware configuration for executing the control and processing in this disclosure, and does not limit the scope of this disclosure. Programs that cause a computer to execute the control and processing in this disclosure are also included in the scope of this disclosure.

　Program recording media on which the programs of the present disclosure are recorded are also included within the scope of the present disclosure. The recording media can be realized, for example, as optical recording media such as CDs (Compact Discs) and DVDs (Digital Versatile Discs). The recording media may also be realized as semiconductor recording media such as USB (Universal Serial Bus) memory and SD (Secure Digital) cards. The recording media may also be realized as magnetic recording media such as flexible disks, or other recording media. When the programs executed by the processor are recorded on a recording medium, the recording medium corresponds to a program recording medium.

The components in this disclosure may be combined in any manner. The components in this disclosure may be implemented by software. The components in this disclosure may be implemented by circuits.

The present disclosure has been described above with reference to the embodiments, but the present disclosure is not limited to the above-mentioned embodiments. Various modifications that can be understood by those skilled in the art can be made to the configuration and details of the present disclosure within the scope of the present disclosure. Furthermore, each embodiment can be combined with other embodiments as appropriate.

A part or all of the above-described embodiments can be described as, but is not limited to, the following supplementary notes.
(Appendix 1)
An acquisition unit that acquires sensor data measured according to foot movements of a subject who is a subject for whom a disease risk is to be estimated;
a risk estimation unit that estimates a disease risk reflecting the risk for each disease by using the acquired sensor data;
An output unit that outputs disease risk information corresponding to the estimated disease risk.
(Appendix 2)
The risk estimation unit is
a calculation unit that calculates a gait index using the sensor data;
a disease risk estimation model that outputs a disease risk score indicating the degree of disease risk related to the disease in response to input of data including the gait index, the disease risk estimation model inputting data including the gait index calculated using the sensor data, and estimating disease risk information according to the disease risk score output from the disease risk estimation model.
(Appendix 3)
The estimation unit is
A disease risk estimation device as described in Appendix 2, which calculates the disease risk score reflecting the risk of each disease by multiplying the disease risk score by a weight for each disease corresponding to a rank indicating the risk of the disease.
(Appendix 4)
The estimation unit is
A disease risk estimation device as described in Appendix 2, which calculates the disease risk score reflecting the risk of each combination of diseases by multiplying the disease risk score by a weight for each combination of diseases.
(Appendix 5)
A disease risk estimation device as described in Appendix 2, comprising a change trend determination unit that determines the disease risk for each disease based on the change trend of the disease risk score.
(Appendix 6)
The change tendency determination unit
Determining that the disease risk is high for a disease whose disease risk score is on the rise;
A disease risk estimation device as described in Appendix 5, which determines that the disease risk is low for any disease for which the disease risk score is on a downward trend or a stagnant trend.
(Appendix 7)
The change tendency determination unit
A disease risk is determined to be high for a disease whose disease risk score exceeds a threshold value;
A disease risk estimation device as described in Appendix 5, which determines that the disease risk is low for a disease whose disease risk score is below the threshold.
(Appendix 8)
A disease risk estimation device as described in Appendix 2, comprising a proposal information generation unit that generates proposal information for insurance-related institutions in accordance with the disease risk reflecting the risk for each disease.
(Appendix 9)
The insurance-related institution is a health insurance association,
The acquisition unit is
Acquire the sensor data measured according to the walking of the insured person of the health insurance association;
The risk estimation unit is
Using the acquired sensor data, a disease risk reflecting a risk for each disease is estimated;
The proposal information generation unit,
generating the proposal information including a timing for notifying the insured person of specific health guidance in accordance with the disease risk reflecting the estimated risk for each disease;
The output unit is
A disease risk estimation device as described in Appendix 8, which transmits the proposal information, including the timing for notifying the specific health guidance, to a terminal device used by the health insurance association.
(Appendix 10)
The insurance-related institution is a life insurance company;
The acquisition unit is
acquiring the sensor data measured according to the walking of a policyholder of the life insurance company;
The risk estimation unit is
Using the acquired sensor data, a disease risk reflecting a risk for each disease is estimated;
The proposal information generation unit,
generating the proposal information including a timing for granting an incentive to the policyholder according to the disease risk reflecting the estimated risk for each disease;
The output unit is
A disease risk estimation device as described in Appendix 8, which transmits the proposal information, including the timing of granting the incentive, to a terminal device used by the life insurance company.
(Appendix 11)
The disease risk estimation model is
This is a model trained using machine learning techniques.
3. The disease risk estimation apparatus of claim 2, comprising an incomplete heterogeneous variational autoencoder.
(Appendix 12)
A disease risk estimation device according to any one of appendix 1 to 11,
A disease risk estimation system comprising: a measuring device that is installed in the footwear of the subject whose disease risk information is to be estimated, measures spatial acceleration and spatial angular velocity, generates the sensor data using the measured spatial acceleration and spatial angular velocity, and transmits the generated sensor data to the disease risk estimation device.
(Appendix 13)
The disease risk estimation device comprises:
A disease risk estimation system as described in Appendix 12, which displays the disease risk information optimized for the subject on a screen of a terminal device that can be viewed by a user.
(Appendix 14)
The computer
Acquire sensor data measured according to foot movements of a subject who is a subject for whom a disease risk is to be estimated;
Using the acquired sensor data, a disease risk reflecting the risk for each disease is estimated;
A disease risk estimation method that outputs disease risk information corresponding to the estimated disease risk.
(Appendix 15)
The computer
Calculating a gait index using the sensor data;
inputting data including the gait index calculated using the sensor data into a disease risk estimation model that outputs a disease risk score indicating a degree of the disease risk related to the disease in response to input of data including the gait index;
A disease risk estimation method as described in Appendix 14, which estimates disease risk information according to the disease risk score output from the disease risk estimation model.
(Appendix 16)
The computer
A disease risk estimation method as described in Appendix 15, in which the disease risk score is multiplied by a weight for each disease corresponding to a rank indicating the risk of the disease, thereby calculating the disease risk score reflecting the risk of each disease.
(Appendix 17)
The computer
A disease risk estimation method as described in Appendix 15, in which the disease risk score reflecting the risk of each combination of diseases is calculated by multiplying the disease risk score by a weight for each combination of diseases.
(Appendix 18)
The computer
A disease risk estimation method described in Appendix 15, in which the disease risk for each disease is determined based on the trend of change in the disease risk score.
(Appendix 19)
The computer
A disease risk estimation method as described in Appendix 15, which generates proposal information for insurance-related institutions based on the disease risk reflecting the risk for each disease.
(Appendix 20)
A process of acquiring sensor data measured according to foot movements of a subject who is a subject for whom a disease risk is to be estimated;
A process of estimating a disease risk reflecting the risk for each disease using the acquired sensor data;
A non-transitory computer-readable recording medium having recorded thereon a program for causing a computer to execute the process of: outputting disease risk information corresponding to the estimated disease risk.

1, 2, 3 Disease risk estimation system 10, 20, 30 Measurement device 13, 23, 33, 40 Disease risk estimation device 15 Risk estimation unit 41 Acquisition unit 45 Risk estimation unit 47 Output unit 110 Sensor 111 Acceleration sensor 112 Angular velocity sensor 113 Control unit 115 Communication unit 117 Power supply 130, 230, 330 Calculation unit 131, 231, 331 Acquisition unit 132 Waveform processing unit 133 Gait index calculation unit 134, 234, 334 Storage unit 135 Physical ability estimation unit 136 Disease risk estimation unit 137, 237, 337 Output unit 140, 240 Estimation unit 245 Change tendency determination unit 345 Proposal information generation unit

Claims (20)

An acquisition unit that acquires sensor data measured according to foot movements of a subject who is a subject for whom a disease risk is to be estimated;
a risk estimation unit that estimates a disease risk reflecting the risk for each disease by using the acquired sensor data;
An output unit that outputs disease risk information corresponding to the estimated disease risk. The risk estimation unit is
a calculation unit that calculates a gait index using the sensor data;
a disease risk estimation model that outputs a disease risk score indicating the degree of disease risk related to the disease in response to input of data including the gait index, the disease risk estimation model inputting data including the gait index calculated using the sensor data, and estimating disease risk information corresponding to the disease risk score output from the disease risk estimation model. The estimation unit is
3. A disease risk estimation device as described in claim 2, wherein the disease risk score reflecting the risk of each disease is calculated by multiplying the disease risk score by a weight for each disease corresponding to a rank indicating the risk of the disease. The estimation unit is
The disease risk estimation device according to claim 2 , wherein the disease risk score reflecting the risk of each combination of diseases is calculated by multiplying the disease risk score by a weight for each combination of diseases. The disease risk estimation device according to claim 2, further comprising a change trend determination unit that determines the disease risk for each disease according to the change trend of the disease risk score. The change tendency determination unit
Determining that the disease risk is high for a disease whose disease risk score is on the rise;
The disease risk estimation device according to claim 5 , wherein the disease risk score is determined to be low for any of diseases for which the disease risk score shows a declining or stagnant trend. The change tendency determination unit
A disease risk is determined to be high for a disease whose disease risk score exceeds a threshold value;
The disease risk estimation device according to claim 5 , wherein the disease risk is determined to be low for a disease whose disease risk score is below the threshold. The disease risk estimation device according to claim 2, further comprising a proposal information generation unit that generates proposal information for insurance-related institutions in accordance with the disease risk that reflects the risk for each disease. The insurance-related institution is a health insurance association,
The acquisition unit is
Acquire the sensor data measured according to the walking of the insured person of the health insurance association;
The risk estimation unit is
Using the acquired sensor data, a disease risk reflecting a risk for each disease is estimated;
The proposal information generation unit,
generating the proposal information including a timing for notifying the insured person of specific health guidance in accordance with the disease risk reflecting the estimated risk for each disease;
The output unit is
The disease risk estimation device according to claim 8 , wherein the proposal information including the timing of notifying the specific health guidance is transmitted to a terminal device used by the health insurance association. The insurance-related institution is a life insurance company;
The acquisition unit is
acquiring the sensor data measured according to the walking of a policyholder of the life insurance company;
The risk estimation unit is
Using the acquired sensor data, a disease risk reflecting a risk for each disease is estimated;
The proposal information generation unit,
generating the proposal information including a timing for granting an incentive to the policyholder according to the disease risk reflecting the estimated risk for each disease;
The output unit is
9. The disease risk estimation device according to claim 8, wherein the proposal information including the timing of granting the incentive is transmitted to a terminal device used by the life insurance company. The disease risk estimation model is
This is a model trained using machine learning techniques.
The disease risk estimation apparatus of claim 2 , comprising an incomplete heterogeneous variational autoencoder. A disease risk estimation device according to any one of claims 1 to 11,
A disease risk estimation system comprising: a measuring device that is installed in the footwear of the subject whose disease risk information is to be estimated, measures spatial acceleration and spatial angular velocity, generates the sensor data using the measured spatial acceleration and spatial angular velocity, and transmits the generated sensor data to the disease risk estimation device. The disease risk estimation device comprises:
The disease risk estimation system according to claim 12 , wherein the disease risk information optimized for the subject is displayed on a screen of a terminal device viewable by a user. The computer
Acquire sensor data measured according to foot movements of a subject who is a subject for whom a disease risk is to be estimated;
Using the acquired sensor data, a disease risk reflecting the risk for each disease is estimated;
A disease risk estimation method that outputs disease risk information corresponding to the estimated disease risk. The computer
Calculating a gait index using the sensor data;
inputting data including the gait index calculated using the sensor data into a disease risk estimation model that outputs a disease risk score indicating a degree of the disease risk related to the disease in response to input of data including the gait index;
The disease risk estimation method according to claim 14, further comprising estimating disease risk information according to the disease risk score output from the disease risk estimation model. The computer
The disease risk estimation method according to claim 15, wherein the disease risk score reflecting the risk of each disease is calculated by multiplying the disease risk score by a weight for each disease corresponding to a rank indicating the risk of the disease. The computer
The disease risk estimation method according to claim 15, wherein the disease risk score reflecting the risk of each combination of diseases is calculated by multiplying the disease risk score by a weight for each combination of diseases. The computer
The disease risk estimation method according to claim 15, wherein the disease risk for each disease is determined according to a change trend of the disease risk score. The computer
The disease risk estimation method according to claim 15 , further comprising generating proposal information for insurance-related institutions in accordance with the disease risk reflecting the risk for each disease. A process of acquiring sensor data measured according to foot movements of a subject who is a subject for whom a disease risk is to be estimated;
A process of estimating a disease risk reflecting the risk for each disease using the acquired sensor data;
A non-transitory computer-readable recording medium having recorded thereon a program for causing a computer to execute the process of: outputting disease risk information corresponding to the estimated disease risk.

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