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WO2025022479A1 - Dispositif de fourniture d'informations, système de fourniture d'informations, procédé de fourniture d'informations et support d'enregistrement - Google Patents

Dispositif de fourniture d'informations, système de fourniture d'informations, procédé de fourniture d'informations et support d'enregistrement Download PDF

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Publication number
WO2025022479A1
WO2025022479A1 PCT/JP2023/026720 JP2023026720W WO2025022479A1 WO 2025022479 A1 WO2025022479 A1 WO 2025022479A1 JP 2023026720 W JP2023026720 W JP 2023026720W WO 2025022479 A1 WO2025022479 A1 WO 2025022479A1
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WIPO (PCT)
Prior art keywords
mental
physical state
advice information
index
estimation model
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Pending
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PCT/JP2023/026720
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English (en)
Japanese (ja)
Inventor
洵 安川
晨暉 黄
史行 二瓶
浩司 梶谷
善喬 野崎
康介 西原
謙一郎 福司
謙太郎 中原
裕明 中野
あずさ 古川
和也 尾崎
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NEC Corp
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NEC Corp
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Priority to PCT/JP2023/026720 priority Critical patent/WO2025022479A1/fr
Publication of WO2025022479A1 publication Critical patent/WO2025022479A1/fr
Pending legal-status Critical Current
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    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H20/00—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance

Definitions

  • This disclosure relates to an information providing device, an information providing system, an information providing method, and a recording medium.
  • Patent Document 1 discloses a walking training support device that measures the walking state of a person undergoing walking training and visualizes the foot movements.
  • Patent Document 1 With the method of Patent Document 1, it was difficult to measure the walking state of a person undergoing walking training in daily life. Therefore, with the method of Patent Document 1, it was not possible to monitor the gait of the person being managed in daily life and provide appropriate advice according to the gait of the person being managed.
  • the purpose of this disclosure is to provide an information provision device, an information provision system, an information provision method, and a recording medium that can monitor the gait of a person to be managed in daily life and provide advice according to the mental and physical state of the person to be managed.
  • An information providing device includes an acquisition unit that acquires sensor data including acceleration and angular velocity measured by a measuring device mounted on the footwear of the person to be managed, an estimation unit that estimates the mental and physical state of the person to be managed using the acquired sensor data, an advice information generation unit that generates advice information according to the mental and physical state of the person to be managed, and an output unit that outputs the generated advice information.
  • sensor data including acceleration and angular velocity measured by a measuring device mounted on the footwear of the person to be managed is acquired, the acquired sensor data is used to estimate the mental and physical state of the person to be managed, advice information corresponding to the mental and physical state of the person to be managed is generated, and the generated advice information is output.
  • a program causes a computer to execute the following processes: acquiring sensor data including acceleration and angular velocity measured by a measuring device mounted on the footwear of the person to be managed; estimating the mental and physical state of the person to be managed using the acquired sensor data; generating advice information according to the mental and physical state of the person to be managed; and outputting the generated advice information.
  • This disclosure makes it possible to provide an information provision device, information provision system, information provision method, and recording medium that can monitor the gait of a person to be managed in daily life and provide advice according to the mental and physical state of the person to be managed.
  • FIG. 1 is a block diagram showing an example of a configuration of an information providing system according to the present disclosure.
  • 1 is a block diagram showing an example of a configuration of a measurement device included in an information providing system according to the present disclosure.
  • 1 is a conceptual diagram showing an example of the arrangement of measuring devices provided in an information providing system according to the present disclosure.
  • 1 is a conceptual diagram showing an example of a coordinate system set in a measurement device included in an information providing system in the present disclosure.
  • FIG. 1 is a conceptual diagram for explaining a human body surface.
  • 2 is a block diagram showing an example of a configuration of an information providing device included in the information providing system according to the present disclosure.
  • FIG. FIG. 1 is a conceptual diagram for explaining a walking cycle.
  • FIG. 1 is a conceptual diagram for explaining an example of estimation of mental and physical states by an information providing device included in an information providing system in the present disclosure.
  • FIG. 1 is a conceptual diagram for explaining an example of generation of advice information by an information providing device included in an information providing system in the present disclosure.
  • FIG. 11 is a flowchart for explaining an example of an operation of the information providing system in the present disclosure. 11 is a flowchart for illustrating an example of a gait index calculation process performed by the information provision system in the present disclosure.
  • 1 is a conceptual diagram showing the correlation between businesses, managers (guardians), managed persons (children), and experts in the present disclosure.
  • 1 is a conceptual diagram showing a display example of advice information output from an information providing system in the present disclosure.
  • 11 is a conceptual diagram showing a display example of reservation information corresponding to advice information output from the information providing system in the present disclosure.
  • 1 is a conceptual diagram showing a display example of advice information output from an information providing system in the present disclosure.
  • 11 is a conceptual diagram showing a display example of reservation information corresponding to advice information output from the information providing system in the present disclosure.
  • 1 is a block diagram showing an example of a configuration of an information providing device according to the present disclosure.
  • 10 is a flowchart for explaining an example of an operation of an information providing device in the present disclosure.
  • FIG. 2 is a block diagram showing an example of a hardware configuration for executing processes in the present disclosure.
  • the information provision system of this embodiment estimates the mental and physical state of the managed person by using sensor data related to foot movements measured according to the walking of the managed person.
  • the information provision system of this embodiment provides advice (advice information) according to the estimated mental and physical state to the manager of the managed person.
  • a child is taken as an example of the managed person.
  • the manager is the guardian of the child.
  • the method of this embodiment can be applied not only to children but also to any person. For example, the method of this embodiment can be applied to the management of patients in hospitals and the like, and the management of residents in nursing homes and the like.
  • the information providing system 1 includes a measuring device 10 and an information providing device 12.
  • the measuring device 10 is installed in footwear of a child (person to be managed).
  • the function of the information providing device 12 is implemented in a mobile terminal carried by a guardian (person to be managed) or in a server or cloud connected to the mobile terminal carried by the guardian via a network.
  • the configurations of the measuring device 10 and the information providing device 12 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 as a physical quantity related to foot movement.
  • the acceleration sensor 111 outputs the measured acceleration to the control unit 113.
  • 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 as a physical quantity related to foot movement. Angular velocity sensor 112 outputs the measured angular velocity to control unit 113.
  • angular velocity sensor 112 For example, a vibration type, capacitance type, or other type of sensor can be used as angular velocity sensor 112.
  • 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.
  • the measurement device 10 is placed at a position that corresponds to the back side of the arch of the foot.
  • the measurement device 10 is placed in an insole inserted into the shoe 100.
  • the measurement device 10 may be placed on the bottom surface of the shoe 100.
  • the measurement device 10 may be embedded in 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 person to be managed, or in an accessory such as an anklet worn by the person to be managed.
  • 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 used for estimation.
  • a local coordinate system is set with the measuring device 10 (sensor 110) as a reference.
  • the local coordinate system includes 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.
  • 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 underside of the arch, and the world coordinate system (x-axis, y-axis, z-axis) set with respect to the ground.
  • the world coordinate system x-axis, y-axis, z-axis
  • the lateral direction of the managed person is set as the x-axis direction
  • the direction of the managed person's back is set as the y-axis direction
  • the direction of gravity is set as the z-axis direction. Note that the example in FIG.
  • 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
  • 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) is a controller that controls the measuring device 10.
  • the control unit 113 is realized by a microcomputer or microcontroller that performs overall control of the measuring device 10 and data processing.
  • the control unit 113 has a CPU (Central Processing Unit), RAM (Random Access Memory), ROM (Read Only Memory), flash memory, etc.
  • the control unit 113 acquires the acceleration in three axial directions (spatial acceleration) from the acceleration sensor 111.
  • the control unit 113 also acquires the angular velocity around three axes (spatial angular velocity) from the angular velocity sensor 112.
  • the control unit 113 performs AD (Analog-to-Digital) conversion of the acquired physical quantities (analog data) such as acceleration and angular velocity.
  • AD conversion circuit (not shown) that performs AD conversion of the physical quantities (analog data) such as angular velocity and acceleration is provided in the control unit 113.
  • the sensor data includes 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.
  • the control unit 113 may calculate any of the gait indices and feature amounts described below. In this case, the measurement device 10 outputs the calculated gait indices and feature amounts to the information providing device 12.
  • the communication unit 115 acquires sensor data from the control unit 113.
  • the communication unit 115 transmits the acquired sensor data to the information providing device 12.
  • the timing of transmitting the sensor data There are no particular limitations on the timing of transmitting the sensor data.
  • the communication unit 115 transmits the sensor data at a preset transmission timing.
  • the communication unit 115 transmits the sensor data in real time according to the measurement of the sensor data.
  • the communication unit 115 may store sensor data measured over a predetermined period in a storage unit (not shown), and transmit the sensor data stored in the storage unit all at once at a preset timing.
  • the communication unit 115 may be configured to receive a measurement start signal from the information providing device 12. In this case, the communication unit 115 outputs the received measurement start signal to the control unit 113.
  • the communication unit 115 transmits the sensor data to the information providing device 12 via wireless communication.
  • the communication unit 115 transmits the sensor data to the information providing device 12 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 be configured to transmit the sensor data to the information providing device 12 via a wired connection such as a cable.
  • a wireless power supply device may be placed in a place where footwear is placed, such as an entrance or a shoe cupboard. In this case, 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.
  • [Information Providing Device] 6 is a block diagram showing an example of the configuration of the information providing device 12.
  • the information providing device 12 has an acquisition unit 121, a waveform processing unit 122, a gait index calculation unit 123, a storage unit 124, a mental and physical state estimation unit 125, an advice information generation unit 126, and an output unit 127.
  • the waveform processing unit 122, the gait index calculation unit 123, and the mental and physical state estimation unit 125 constitute the estimation unit 15.
  • the waveform processing unit 122 and the gait index calculation unit 123 constitute the calculation unit 13.
  • the acquisition unit 121 acquires sensor data from the measuring device 10 mounted on the footwear of the managed person.
  • the acquisition unit 121 receives the sensor data from the measuring device 10 via wireless communication.
  • the acquisition unit 121 receives the sensor data from the measuring device 10 via a wireless communication function (not shown) conforming to a standard such as Bluetooth (registered trademark) or WiFi (registered trademark).
  • the communication function of the acquisition unit 121 may conform to a standard other than Bluetooth (registered trademark) or WiFi (registered trademark) as long as it can communicate with the measuring device 10.
  • the acquisition unit 121 may receive the sensor data from the measuring device 10 via a wired connection such as a cable.
  • the acquisition unit 121 may acquire gait indices and feature amounts calculated by the measuring device 10.
  • the acquisition unit 121 may add location information of the managed person's mobile terminal (not shown), which is the source of the sensor data, to the sensor data.
  • location information is measured using the Global Positioning System (GPS) function installed in a mobile device.
  • GPS Global Positioning System
  • the acquisition unit 121 also acquires attribute data of the managed person.
  • the attribute data includes gender, date of birth, height, and weight.
  • the date of birth is converted to age.
  • the gender, date of birth (age), height, and weight contained in the attribute data are also called physical information.
  • the attribute data is input via an input device (not shown).
  • the attribute data is input via a terminal device used by the administrator.
  • the attribute data may be input via a mobile terminal used by the managed person.
  • the attribute data may be stored in advance in the storage unit 124.
  • the attribute data may be updated at any time in response to input by the managed person or the administrator.
  • the waveform processing unit 122 acquires sensor data from the acquisition unit 121.
  • the waveform processing unit 122 extracts time series data for one walking cycle from the time series data of acceleration in three axial directions (spatial acceleration) and angular velocity around three axes (spatial angular velocity) contained in the sensor data.
  • the time series data for one walking cycle is also called walking waveform data.
  • the waveform processing unit 122 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 122 extracts time series data for one walking cycle that starts at the timing of a heel strike and ends at the timing of the next heel strike as walking waveform data.
  • 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 similar to that of the right foot.
  • the horizontal axis of Figure 7 shows one step 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 set to 100% (percent). Normalizing one step cycle to 100% is called the first normalization.
  • One step cycle of one leg is broadly divided into a stance phase and a swing phase.
  • the stance phase is a period during 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 during 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.
  • 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 while the sole of the right foot is on the ground (Opposite Toe Off) (OTO: Opposite Toe Off).
  • P3 represents the event of the right heel lifting off the ground while the sole of the right foot is on the ground (Heel Rise) (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 while the sole of the left foot is on the ground (Toe Off) (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 122 normalizes the time of the extracted walking waveform data for one step cycle to a walking cycle of 0 to 100% (first normalization). The timing of 1%, 10%, etc. included in the 0 to 100% walking cycle is also called a walking phase.
  • the waveform processing unit 122 also normalizes the first normalized walking waveform data for one step cycle to 60% in the stance phase and 40% in the swing phase (second normalization). By subjecting the walking waveform data to the second normalization, it is possible to reduce the deviation in the walking phase from which features are extracted.
  • the waveform processing unit 122 outputs the normalized walking waveform data to the gait index calculation unit 123.
  • the waveform processing unit 122 extracts walking waveform data for one step cycle using the forward acceleration (Y-direction acceleration).
  • the waveform processing unit 122 normalizes the extracted walking waveform data for one step cycle.
  • the waveform processing unit 122 extracts/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 122 may also generate time series data of angles around three axes by integrating time series data of angular velocities around three axes. In this case, the waveform processing unit 122 extracts/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 122 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 122 may detect heel strike and toe lift from the time series data of vertical acceleration (Z-direction acceleration) (not shown).
  • 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 122 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 122 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 122 may extract a feature value (physical ability feature value) used to estimate physical ability from the walking waveform data.
  • the waveform processing unit 122 extracts a physical ability feature value 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. A description of specific physical ability feature values is omitted.
  • the waveform processing unit 122 extracts a physical ability feature value for each walking phase cluster according to a preset condition.
  • 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 gait index calculation unit 123 acquires normalized gait waveform data from the waveform processing unit 122.
  • the gait index calculation unit 123 uses the normalized gait waveform data to calculate gait indices used to estimate physical ability.
  • gait indices used to estimate physical ability.
  • the gait index calculation unit 123 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.
  • the gait index calculation unit 123 calculates indices related to distance and height as gait indices. For example, the gait index calculation unit 123 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.
  • 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 gait index calculation unit 123 calculates indexes related to angles as gait indices. For example, the gait index calculation unit 123 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.
  • the gait index calculation unit 123 calculates an index related to speed as a gait index. For example, the gait index calculation unit 123 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.
  • the gait index calculation unit 123 calculates time-related indices as gait indices. For example, the gait index calculation unit 123 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.
  • 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
  • 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.
  • the gait index calculation unit 123 calculates the frailty level and the Center of Pressure Exclusion Index (CPEI) as gait indices.
  • the frailty level is an estimate of the frailty state according to the walking condition.
  • the gait index calculation unit 123 estimates an index as the frailty level according to the judgment result such as not frail, possibly frail, or highly likely frail.
  • the CPEI indicates an estimate of the rate at which the center of foot pressure acting on the ground expands during the stance phase.
  • the memory unit 124 stores a mental and physical state estimation model (described later) that estimates the mental and physical state of the managed person using gait indices extracted from the walking waveform data.
  • the mental and physical state includes physical conditions such as flat feet and floating toes.
  • the mental and physical state includes mental conditions such as manic depression.
  • the memory unit 124 stores a mental and physical state estimation model that has learned the mental and physical states of multiple subjects.
  • the mental and physical state estimation model outputs an index indicating the mental and physical state (mental and physical state index) in response to an input of a gait indices extracted from the walking waveform data.
  • the mental and physical state index is an expression that represents the physical or mental state.
  • the mental and physical state estimation model may output a mental and physical state score indicating the degree of a specific mental and physical state as an index regarding the mental and physical state in response to an input of a gait indices extracted from the walking waveform data.
  • the mental and physical state score is a score expressed by a numerical value or a symbol indicating the degree of a physical condition such as flat feet or floating toes or a mental condition such as manic depression.
  • the mental and physical state estimation model may be configured to output a mental and physical state score related to the mental and physical state, including attribute data of the managed person.
  • the mental and physical state estimation model may also be configured to output a mental and physical state score related to the mental and physical state, including physical ability features extracted from the walking waveform data.
  • the storage unit 124 also stores an advice information generation model (described later) that outputs advice information including advice to the administrator who manages the managed person in response to an input of an index output from the mental and physical state estimation model.
  • the advice information generation model is a model that is trained using a data set of mental and physical state indexes and advice corresponding to the indexes as teacher data.
  • the advice information generation model is a model trained to output advice information regarding the mental and physical state in response to an input of a mental and physical state index.
  • the advice information generation model outputs advice information including an introduction to an expert on the mental and physical state in response to an input of a mental and physical state index.
  • the advice information generation model outputs advice information including advice regarding the mental and physical state in response to an input of a mental and physical state index.
  • the advice information generation model may be a model customized for each type of mental and physical state.
  • the advice information generation model may be a model that generates advice information including advice on a rule basis in response to an input of a mental and physical state index related to the managed person.
  • the advice information generation model may include a large-scale language model that generates sentences including advice information in response to an input of a mental and physical state index related to the managed person.
  • the memory unit 124 stores the mental and physical state estimation model and the advisory information generation model learned for multiple subjects.
  • the mental and physical state estimation model and the advisory information generation model may be stored in the memory unit 124 when the product is shipped from the factory.
  • the mental and physical state estimation model and the advisory information generation model may also be stored in the memory unit 124 at the timing of calibration of the information providing device 12.
  • the mental and physical state estimation model and the advisory information generation model saved in a storage device (not shown) such as an external server may be used. In that case, it is sufficient if the mental and physical state estimation model and the advisory information generation model can be accessed via an interface (not shown) connected to the storage device.
  • the storage unit 124 also stores attribute data of the managed person.
  • the attribute data includes gender, date of birth (age), height, and weight.
  • the attribute data may be updated at any time.
  • the storage unit 124 may store medical examination data of the managed person.
  • the medical examination data can be a factor that improves the estimation accuracy of mental and physical condition indicators and advice information.
  • the medical examination data of the managed person includes at least any of the examination items specified in Article 6 of the Enforcement Regulations of the School Health and Safety Act.
  • the medical examination data of the managed person may also include examination items not specified in Article 6 of the Enforcement Regulations of the School Health and Safety Act.
  • the mental and physical state estimation unit 125 (mental and physical state estimation means) acquires a gait index generated from walking waveform data from the waveform processing unit 122. If necessary, the mental and physical state estimation unit 125 acquires attribute data stored in the memory unit 124. The mental and physical state estimation unit 125 estimates a mental and physical state index using the gait index. The mental and physical state estimation unit 125 inputs the gait index of the managed person to the mental and physical state estimation model stored in the memory unit 124. For example, the mental and physical state estimation unit 125 estimates a mental and physical state index related to a physical condition such as flat feet or floating toes, or a mental condition such as manic depression. The estimation of the mental and physical state index by the mental and physical state estimation unit 125 will be described later. The mental and physical state estimation unit 125 outputs the mental and physical state index output from the mental and physical state estimation model to the advice information generation unit 126.
  • FIG. 8 is a conceptual diagram showing an example of mental and physical state index estimation by the mental and physical state estimation unit 125.
  • the mental and physical state estimation unit 125 inputs attribute data and gait index used to estimate the mental and physical state of the managed person to the mental and physical state estimation model 150.
  • the mental and physical state estimation model 150 receives input of attribute data and gait index used to estimate a specific mental and physical state.
  • the mental and physical state estimation model 150 outputs a mental and physical state index related to a specific mental and physical state.
  • the mental and physical state estimation model 150 may include multiple models that estimate a mental and physical state index for each mental and physical state.
  • the mental and physical state estimation model 150 may be a single model that collectively outputs mental and physical state indexes related to multiple mental and physical states.
  • the mental and physical state estimation model 150 estimates a mental and physical state index related to flatfoot.
  • the angle of the foot when the sole of the foot touches the ground is twisted toward the arch. That is, the symptoms of flatfoot are reflected in the angle around the axis of travel (in the coronal plane) when the sole of the foot touches the ground.
  • the foot pressure center trajectory becomes closer to the central axis of the foot, so the CPEI becomes smaller.
  • the mental and physical state estimation model 150 can estimate a mental and physical state index related to flatfoot.
  • the mental and physical state estimation model 150 that estimates a mental and physical state index related to flatfoot may be a model trained with one of the angle around the axis of travel (in the coronal plane) when the sole of the foot touches the ground and the CPEI as an explanatory variable.
  • the mental and physical state estimation model 150 estimates a mental and physical state index related to floating toes.
  • the toes leave the ground early, so the plantar flexion angle at the time of toe-off becomes shallow.
  • the ground cannot be pushed off strongly, so the forward acceleration and vertical acceleration at the time of foot push-off become small. Therefore, if the mental and physical state estimation model 150 is made to learn teacher data in which the plantar flexion angle at the time of toe-off, the forward acceleration and vertical acceleration at the time of push-off are used as explanatory variables, and the degree of floating foot is used as the objective variable, a mental and physical state index related to floating foot can be estimated.
  • the mental and physical state estimation model 150 related to floating foot may be a model that learns any one of the plantar flexion angle at the time of toe-off, the forward acceleration at the time of push-off, and the vertical acceleration at the time of push-off as explanatory variables.
  • the mental and physical state estimation model 150 that estimates the mental and physical state index related to flat feet may be a model that has been trained using data other than the plantar flexion angle at the timing of toe-off and the forward acceleration and vertical acceleration at the timing of push-off as explanatory variables.
  • the mental and physical state estimation model 150 outputs a mental and physical state index that indicates that the person to be managed may have floating feet.
  • the mental and physical state estimation model 150 estimates a mental and physical state index related to a mental state such as manic depression.
  • a mental state such as manic depression is reflected in the way one walks and posture. For example, when one is walking listlessly in a manic depression state, one tends to look down and one's walking speed and stride length become smaller. Also, when one walks in such a manner, the heel rocker time and DST become shorter, and the vertical acceleration and angular velocity around the left and right axis become smaller.
  • the mental and physical state estimation model 150 is made to learn teaching data in which the walking speed, stride length, heel rocker time, DST, vertical acceleration, and angular velocity around the left and right axis are explanatory variables, and the degree of manic depression is the objective variable, a mental and physical state index related to manic depression can be estimated.
  • the mental and physical state estimation model 150 that estimates a mental and physical state index related to manic depression may be a model that has learned any one of the walking speed, stride length, heel rocker time, DST, vertical acceleration, and angular velocity around the left and right axis as explanatory variables.
  • the mental and physical state estimation model 150 that estimates the mental and physical state index related to bipolar disorder may be a model that has been trained using data other than walking speed, stride length, heel rocker time, DST, vertical acceleration, and angular velocity around the left and right axes as explanatory variables.
  • the mental and physical state estimation model 150 outputs a mental and physical state index that indicates that the person to be managed may be bipolar.
  • the mental and physical state estimation model 150 may be configured to output a mental and physical state index in response to input of health check data, attribute data, and a gait index. If the health check data items include attribute data, the mental and physical state estimation model 150 may be configured to output a mental and physical state index in response to input of health check data and a gait index.
  • the mental and physical state estimation model 150 may be stored in an external storage device constructed in a cloud, a server, or the like. In this case, the mental and physical state estimation unit 125 uses the mental and physical state estimation model 150 via an interface (not shown) connected to the storage device.
  • the mental and physical state estimation model 150 is a machine learning model.
  • the mental and physical state estimation model 150 is a model trained using a data set as training data in which attribute data and gait indices relating to multiple subjects are used as explanatory variables and mental and physical state indices relating to a specific mental and physical state are used as objective variables.
  • the mental and physical state estimation model 150 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.
  • the mental and physical state estimation model 150 is generated by learning using a linear regression algorithm.
  • the mental and physical state estimation model 150 is generated by learning using a support vector machine (SVM) algorithm.
  • the mental and physical state estimation model 150 is generated by learning using a Gaussian process regression (GPR) algorithm.
  • the mental and physical state estimation model 150 is generated by learning using a random forest (RF) algorithm.
  • the mental and physical state estimation model 150 may be generated by unsupervised learning that classifies the mental and physical state of the managed person according to attribute data and gait indices. There are no particular limitations on the algorithm used to train the mental and physical state estimation model 150.
  • the mental and physical state estimation model 150 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 mental and physical state of the managed individual can be estimated even if there are some gaps in the attribute data or gait indicators.
  • the advice information generating unit 126 acquires the mental and physical state index estimated by the mental and physical state estimating unit 125.
  • the advice information generating unit 126 estimates advice information corresponding to the mental and physical state index.
  • the advice information generating unit 126 may be configured to estimate advice information corresponding to the mental and physical state index, including health checkup data.
  • the advice information generating unit 126 outputs the estimated advice information.
  • the advice information generating unit 126 inputs mental and physical state indexes used to estimate advice information to the advice information estimating model 160.
  • the advice information estimating model 160 outputs advice information related to a specific mental and physical state.
  • a single piece of advice information is output from the advice information estimating model 160 in response to the input of a single mental and physical state index.
  • the advice information estimating model 160 may be configured to output multiple pieces of advice information in response to the input of a single mental and physical state index.
  • the advice information estimating model 160 may also be configured to output a single piece of advice information in response to the input of multiple mental and physical state indexes.
  • the advice information estimating model 160 may also be configured to output multiple pieces of advice information in response to the input of multiple mental and physical state indexes.
  • the advice information estimation model 160 When a mental and physical condition index indicating that the managed person may have flat feet, floating gait, or the like is input, the advice information estimation model 160 outputs advice information including advice regarding flat feet, floating gait, or the like. For example, the advice information estimation model 160 outputs advice information including advice indicating that the managed person may have flat feet, floating gait, or the like to a terminal device carried by the administrator of the managed person. For example, the advice information estimation model 160 may output advice information including advice that there is an abnormality in the managed person's foot to a terminal device carried by the administrator of the managed person. For example, the advice information estimation model 160 outputs advice information including contact information for a foot care specialist. For example, the advice information estimation model 160 may output advice information including contact information for an osteopathic clinic or specialty store that can arrange for special insoles to be placed in shoes.
  • the advice information estimation model 160 may be a model that outputs advice information including advice from an expert in response to an input of a mental and physical state index.
  • the advice information estimation model 160 may be a model that has been trained with teacher data in which a mental and physical state index is an explanatory variable and expert advice corresponding to the mental and physical state index is an objective variable.
  • the advice information estimation model 160 may be a model that generates advice information including advice from experts such as psychiatrists, orthopedic surgeons, certified psychologists, and physical therapists in a rule-based manner in response to an input of a mental and physical state index.
  • the advice information estimation model 160 is generated by learning using a linear regression algorithm.
  • the advice information estimation model 160 is generated by learning using a support vector machine (SVM) algorithm.
  • the advice information estimation model 160 is generated by learning using a Gaussian process regression (GPR) algorithm.
  • the advice information estimation model 160 is generated by learning using a random forest (RF) algorithm.
  • the advice information estimation model 160 may be generated by unsupervised learning that classifies the advice information of the managed person according to attribute data, gait index, and mental and physical condition index. There are no particular limitations on the algorithm used to train the advice information estimation model 160.
  • the advice information 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, advice information regarding the managed person can be estimated even if there is some loss in the attribute data or gait indicators.
  • the output unit 127 outputs the advice information estimated by the advice information generation unit 126.
  • the output unit 127 outputs the advice information to a mobile terminal carried by an administrator who manages the managed users.
  • the output unit 127 may display the advice information of the managed users on the screen of the administrator's mobile terminal.
  • the output unit 127 may output the advice information to an external system or the like that uses the advice information. There are no particular limitations on the use of the output advice information.
  • the information providing device 12 may be connected to the mobile terminal via a wire such as a cable.
  • the advice information may be used by an application installed on the mobile terminal.
  • the mobile terminal executes a process using the advice information by application software or the like installed on the mobile terminal.
  • the calculation unit 13 executes a gait index calculation process using the acquired sensor data (step S12).
  • the calculation unit 13 calculates a gait index used to estimate physical ability. Details of the gait index calculation process in step S12 will be described later ( FIG. 11 ).
  • the mental and physical state estimation unit 125 estimates the physical state of the managed person using the attribute data and gait index (step S13).
  • the advice information generating unit 126 generates advice information according to the estimated mental and physical state of the managed person (step S14).
  • the output unit 127 outputs the generated advice information (step S15).
  • the output unit 127 outputs the advice information to a mobile terminal (not shown) carried by the administrator of the managed entity.
  • the output unit 127 may output the advice information to an external system that uses the advice information.
  • the output unit 127 may display the advice information on the screen of the managed entity's mobile terminal.
  • FIG. 11 is a flowchart for explaining an example of the operation of the calculation unit 13.
  • the components of the calculation unit 13 will be described as the subject of the operation.
  • the subject of the operation of the process according to the flowchart in FIG. 11 may be the information providing device 12 or the calculation unit 13.
  • the waveform processing unit 122 extracts walking waveform data from the time series data of the sensor data (step S121).
  • the walking waveform data corresponds to the time series data of the sensor data for one walking cycle.
  • the waveform processing unit 122 normalizes the extracted walking waveform data (step S122).
  • the waveform processing unit 122 performs first normalization on the walking waveform data so that the step period is 100%.
  • the waveform processing unit 122 also performs second normalization on the walking waveform data so that the stance phase is 60% and the swing phase is 40%.
  • FIG. 12 is a conceptual diagram showing the correlation between the business operator, administrator (guardian), person under management (child), and expert.
  • the business operator provides the administrator (guardian) with a service using the information provision system 1.
  • the business operator Based on a contract concluded with the guardian, the business operator provides the guardian with advice information estimated according to the child's physical condition.
  • the guardian pays the business operator a fee for the service using the information provision system 1. If the child's health checkup data is used to estimate the advice information, the guardian provides the child's health checkup data to the business operator.
  • rules regarding the handling of personal information and appropriate data management are clarified. The business operator clearly explains that the advice information is for reference only and does not guarantee medical accuracy or completeness.
  • a child is a child being raised by a guardian.
  • the child is loaned or provided with a special insole equipped with a measuring device 10 by a business that has a contract with the guardian.
  • the child wears shoes equipped with the special insole and carries a mobile terminal (not shown) that can communicate with the measuring device 10 while moving around.
  • the mobile terminal uploads the sensor data measured by the measuring device 10 to the business's cloud server.
  • the sensor data uploaded to the cloud server is used to estimate the child's physical and mental state and generate advisory information.
  • the expert pays a registration fee to the business to receive recommendations for consultations and examinations for children with physical and mental conditions in the expert's area of expertise.
  • the expert provides advice tailored to the child's physical condition in response to consultations from parents who contact the expert in response to the advice information.
  • the expert also examines children whom the expert visits in response to the advice information.
  • the expert examines the child and provides treatment tailored to the results of the examination.
  • the expert also provides appropriate advice to the parents based on the results of the child's examination.
  • FIG. 13 is an example in which advice information according to the child's physical and mental condition is displayed on the screen of the mobile terminal 170A of the guardian (Mr. A).
  • advice information such as "Your child's feet may be abnormal. We suggest that you consult a foot care specialist" is displayed on the screen of the mobile terminal 170A.
  • Advice information optimized for the managed person is displayed on the screen of the mobile terminal 170A.
  • an address for accessing information on foot care specialists is displayed on the screen of the mobile terminal 170A.
  • the guardian can access information on foot care specialists according to the content of the advice information.
  • the advice information is information that supports the administrator's decision-making regarding the managed person.
  • FIG. 14 shows an example in which reservation information made by a parent in response to advice information is displayed on the screen of a terminal device 180A used by a foot care specialist.
  • the screen of terminal device 180A displays reservation information stating, "A reservation has been accepted from Mr. A. He would like to visit around 14:00 on July 14th. Would you like to confirm the reservation?"
  • the foot care specialist selects the reservation confirmation button, a reservation confirmation email is sent to the mobile device 170A of the parent who made the reservation.
  • FIG. 15 is an example in which advice information according to the child's mental and physical condition is displayed on the screen of the mobile terminal 170B of the guardian (Mr. B).
  • advice information stating "Your child does not seem to be in good spirits. We suggest that you consult with a mental health professional" is displayed on the screen of the mobile terminal 170B.
  • Advice information optimized for the managed person is displayed on the screen of the mobile terminal 170B.
  • an address for accessing information on the mental health professional is displayed on the screen of the mobile terminal 170B.
  • the guardian can access information on the mental health professional according to the content of the advice information.
  • the advice information is information that supports the administrator in making decisions regarding the managed person.
  • the information provision system of this embodiment includes a measuring device and an information provision device.
  • the measuring device is installed in the footwear of at least one of the subjects.
  • the measuring device measures acceleration and angular velocity.
  • the measuring device generates sensor data using the measured acceleration and angular velocity.
  • the measuring device transmits the generated sensor data to the information provision device.
  • the information provision device includes an acquisition unit, an estimation unit, an advice information generation unit, and an output unit.
  • the acquisition unit acquires sensor data including acceleration and angular velocity measured by a measuring device mounted on the footwear of the managed person.
  • the estimation unit estimates the mental and physical state of the managed person using the acquired sensor data.
  • the advice information generation unit generates advice information including advice information corresponding to the mental and physical state of the managed person.
  • the output unit outputs the generated advice information.
  • the information provision device of this embodiment uses sensor data including acceleration and angular velocity measured by a measuring device mounted on the footwear of the managed person to provide advice information according to the managed person's mental and physical state. Therefore, according to this embodiment, it is possible to monitor the daily behavior of the managed person and provide advice according to the managed person's mental and physical state.
  • the estimation unit has a calculation unit and a mental and physical state estimation unit.
  • the calculation unit calculates a gait index using sensor data.
  • the mental and physical state estimation unit inputs data including the gait index calculated using the sensor data to the mental and physical state estimation model.
  • the mental and physical state estimation model outputs a mental and physical state index indicating the degree of the mental and physical state in response to the input of the data including the gait index.
  • the mental and physical state estimation unit estimates a mental and physical state in response to the mental and physical state index output from the mental and physical state estimation model.
  • a mental and physical state in response to the mental and physical state index can be estimated by inputting data including the gait index calculated using sensor data to the mental and physical state estimation model.
  • the advice information generation unit estimates advice information according to the mental and physical state index of the managed person using an advice information estimation model.
  • the advice information estimation model outputs advice information in response to an input of a mental and physical state index.
  • the advice information of the managed person can be estimated by inputting a mental and physical state index into the advice information generation model.
  • the mental and physical state estimation unit estimates a mental and physical state including a mental and physical state index related to the foot condition using a mental and physical state estimation model.
  • the mental and physical state estimation model outputs a mental and physical state index related to the foot condition in response to input of data including a gait index.
  • the advice information generation unit estimates advice information corresponding to the mental and physical state index of the managed person using the advice information estimation model.
  • the advice information estimation model outputs advice information including information about a foot specialist in response to input of a mental and physical state index related to the foot condition.
  • advice information corresponding to the managed person's foot condition can be estimated by using an advice information generation model that outputs a mental and physical state index related to the foot condition.
  • the mental and physical state estimation unit estimates a mental and physical state including a mental and physical state index related to the mental state using a mental and physical state estimation model.
  • the mental and physical state estimation model outputs a mental and physical state index related to the mental state in response to input of data including a gait index.
  • the advice information generation unit estimates advice information corresponding to the mental and physical state index of the managed person using the advice information estimation model.
  • the advice information estimation model outputs advice information including information about a mental care professional in response to input of a mental and physical state index related to the mental state.
  • advice information corresponding to the mental state of the managed person can be estimated by using an advice information generation model that outputs a mental and physical state index related to the mental state.
  • the mental and physical state estimation model and the advice information generation model are models trained using machine learning techniques.
  • the mental and physical state estimation model includes an incomplete heterogeneous variational autoencoder. According to this aspect, even if there is some loss of data such as gait indicators, advice information according to the mental and physical state of the person to be managed can be estimated.
  • the information providing device displays advice information optimized for the managed individual on the screen of a terminal device used by the administrator of the managed individual.
  • advice information estimated according to the mental and physical state of the managed individual can be provided in an optimized manner for the managed individual.
  • the information providing device has a simplified configuration of the information providing device included in the information providing system according to the first embodiment.
  • composition 17 is a block diagram showing an example of a configuration of the information providing device 20 in the present disclosure.
  • the information providing device 20 includes an acquisition unit 21, an estimation unit 25, an advice information generation unit 26, and an output unit 27.
  • the acquisition unit 21 acquires sensor data including acceleration and angular velocity measured by a measuring device mounted on the footwear of the managed person.
  • the estimation unit 25 estimates the mental and physical state of the managed person using the acquired sensor data.
  • the advice information generation unit 26 generates advice information according to the mental and physical state of the managed person.
  • the output unit 27 outputs the generated advice information.
  • Fig. 18 is a flowchart for explaining an example of the operation of the information providing device 20.
  • the components of the information providing device 20 will be described as the subject of the operation.
  • the subject of the process according to the flowchart of Fig. 18 may be the information providing device 20.
  • the acquisition unit 21 acquires sensor data including acceleration and angular velocity measured by a measuring device mounted on the footwear of the person to be managed (step S21).
  • the estimation unit 25 estimates the mental and physical state of the managed person using the acquired sensor data (step S22).
  • the advice information generation unit 26 generates advice information including advice information according to the mental and physical state of the person to be managed (step S23).
  • the output unit 27 outputs the generated advice information (step S24).
  • the information provision device of this embodiment uses sensor data including acceleration and angular velocity measured by a measuring device mounted on the footwear of the managed person to provide advice information according to the managed person's mental and physical state. Therefore, according to this embodiment, it is possible to monitor the daily behavior of the managed person and provide advice according to the managed person's mental and physical state.
  • an information processing device 90 (computer) in Fig. 19 is given as an example of such a hardware configuration.
  • the information processing device 90 in Fig. 19 is an example of a configuration for executing the processes according to each embodiment, and does not limit the scope of the present disclosure.
  • 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.
  • 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.
  • 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.
  • the program is a software program for executing the processing of each embodiment.
  • the processor 91 executes the program expanded into the main storage device 92.
  • the processor 91 executes the program to execute the processing of each embodiment.
  • 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).
  • 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.
  • 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.
  • 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 processing according to each embodiment of the present invention.
  • the hardware configuration in FIG. 19 is an example of a hardware configuration for executing the processing according to each embodiment, and does not limit the scope of the present invention. Programs that cause a computer to execute the processing according to each embodiment are also included in the scope of the present invention.
  • the scope of the present invention also includes a program recording medium on which a program for executing the processing in this embodiment is recorded.
  • the program recording medium is a computer-readable, non-transient recording medium.
  • the recording medium can be realized, for example, as an optical recording medium such as a CD (Compact Disc) or a DVD (Digital Versatile Disc).
  • the recording medium may also be realized as a semiconductor recording medium such as a USB (Universal Serial Bus) memory or an SD (Secure Digital) card.
  • the recording medium may also be realized as a magnetic recording medium such as a flexible disk, or other recording medium.
  • the components of each embodiment may be combined in any manner.
  • the components of each embodiment may be realized by software.
  • the components of each embodiment may be realized by circuitry.
  • An acquisition unit that acquires sensor data including acceleration and angular velocity measured by a measuring device mounted on the footwear of the person to be managed; an estimation unit that estimates a mental and physical state of the managed person by using the acquired sensor data; an advice information generating unit that generates advice information according to the mental and physical state of the person to be managed; and an output unit that outputs the generated advice information.
  • the estimation unit is a calculation unit that calculates a gait index using the sensor data; and a mental/physical state estimation unit that inputs data including the gait index calculated using the sensor data into a mental/physical state estimation model that outputs a mental/physical state index indicating a degree of the mental/physical state in response to input of data including the gait index, and estimates the mental/physical state of the person to be managed in accordance with the mental/physical state index output from the mental/physical state estimation model.
  • the advice information generation unit 3 The information providing device according to claim 2, which estimates the advice information corresponding to the mental and physical state index of the managed person using an advice information estimation model that outputs the advice information in response to an input of the mental and physical state index.
  • the mental and physical state estimation unit is estimating a mental and physical state including the mental and physical state index related to a foot condition using the mental and physical state estimation model, which outputs the mental and physical state index related to a foot condition in response to input of data including the gait index;
  • the advice information generation unit An information providing device as described in Appendix 3, which estimates the advice information corresponding to the mental and physical condition index of the managed person using an advice information estimation model that outputs the advice information including information about a foot expert in response to input of the mental and physical condition index related to the foot condition.
  • the mental and physical state estimation unit is estimating a mental and physical state including the mental and physical state index related to a mental state using the mental and physical state estimation model, which outputs the mental and physical state index related to a mental state in response to input of data including the gait index;
  • the advice information generation unit An information providing device as described in Appendix 3, which estimates advice information corresponding to the mental and physical state index of the managed person using an advice information estimation model that outputs advice information including information regarding mental care professionals in response to input of the mental and physical state index related to the mental state.
  • the mental and physical state estimation model and the advice information estimation model are models trained using a machine learning technique,
  • the mental and physical state estimation model is 4.
  • the information providing apparatus of claim 3, comprising an incomplete heterogeneous variational autoencoder.
  • Appendix 7 An information providing device according to any one of Supplementary Notes 1 to 6;
  • the measuring device includes: An information provision system that is installed on the footwear of the person to be managed, measures acceleration and angular velocity, generates the sensor data using the measured acceleration and angular velocity, and transmits the generated sensor data to the information provision device.
  • the information providing device includes: 8.
  • the computer Acquire sensor data including acceleration and angular velocity measured by a measuring device mounted on the footwear of the person to be managed, Using the acquired sensor data, estimate a mental and physical state of the person to be managed; generating advice information according to the mental and physical state of the person to be managed; The information providing method outputs the generated advice information.
  • the computer Calculating a gait index using the sensor data; inputting data including the gait index calculated using the sensor data into a mental/physical state estimation model that outputs a mental/physical state index indicating a degree of the mental/physical state in response to input of data including the gait index; 10.
  • the information providing method further comprising estimating the mental and physical state of the person to be managed according to the mental and physical state index output from the mental and physical state estimation model.
  • the computer The information providing method according to claim 10, further comprising estimating the advice information corresponding to the mental and physical state index of the managed person using an advice information estimation model that outputs the advice information in response to an input of the mental and physical state index.
  • Appendix 12 The computer estimating a mental and physical state including the mental and physical state index related to a foot condition using the mental and physical state estimation model, which outputs the mental and physical state index related to a foot condition in response to input of data including the gait index;
  • the computer estimating a mental and physical state including the mental and physical state index related to a mental state using the mental and physical state estimation model, which outputs the mental and physical state index related to a mental state in response to input of data including the gait index;
  • the mental and physical state estimation model and the advice information estimation model are models trained using a machine learning technique, The mental and physical state estimation model is 12.
  • Appendix 18 a process of estimating a mental and physical state including the mental and physical state index related to a foot condition, using the mental and physical state estimation model which outputs the mental and physical state index related to a foot condition in response to input of data including the gait index;
  • the mental and physical state estimation model and the advice information estimation model are models trained using a machine learning technique,
  • the mental and physical state estimation model is 20.

Landscapes

  • Health & Medical Sciences (AREA)
  • Engineering & Computer Science (AREA)
  • Epidemiology (AREA)
  • General Health & Medical Sciences (AREA)
  • Medical Informatics (AREA)
  • Primary Health Care (AREA)
  • Public Health (AREA)
  • Measurement Of The Respiration, Hearing Ability, Form, And Blood Characteristics Of Living Organisms (AREA)

Abstract

L'invention concerne un dispositif de fourniture d'informations comprenant : une unité d'acquisition qui acquiert des données de capteur comprenant une accélération et une vitesse angulaire mesurées par un dispositif de mesure monté sur une chaussure d'une personne à prendre en charge afin de surveiller la démarche de la personne à prendre en charge sur une base quotidienne et de fournir des conseils correspondant à l'état psychosomatique de la personne à prendre en charge ; une unité d'estimation qui utilise les données de capteur acquises pour estimer l'état psychosomatique de la personne à prendre en charge ; une unité de génération d'informations de conseils qui génère des informations de conseils en réponse à l'état psychosomatique de la personne à prendre en charge ; et une unité de sortie qui délivre les informations de conseils générées.
PCT/JP2023/026720 2023-07-21 2023-07-21 Dispositif de fourniture d'informations, système de fourniture d'informations, procédé de fourniture d'informations et support d'enregistrement Pending WO2025022479A1 (fr)

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Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20200204954A1 (en) * 2017-08-04 2020-06-25 Your Data Consulting Computer system for displaying the logistical path of entities over time
JP2021053255A (ja) * 2019-10-01 2021-04-08 ローム株式会社 歩行特徴量検出装置
JP2021193526A (ja) * 2020-06-08 2021-12-23 株式会社Agoop 情報処理装置、プログラム、及び、情報処理方法
WO2022244222A1 (fr) * 2021-05-21 2022-11-24 日本電気株式会社 Dispositif d'estimation, système d'estimation, procédé d'estimation et support d'enregistrement
JP2023507730A (ja) * 2019-12-17 2023-02-27 マハナ セラピューティクス、インコーポレイテッド 平均ユーザ対話データに基づいてアプリケーション・ユーザの心理状態をリモートでモニタするための方法及びシステム

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20200204954A1 (en) * 2017-08-04 2020-06-25 Your Data Consulting Computer system for displaying the logistical path of entities over time
JP2021053255A (ja) * 2019-10-01 2021-04-08 ローム株式会社 歩行特徴量検出装置
JP2023507730A (ja) * 2019-12-17 2023-02-27 マハナ セラピューティクス、インコーポレイテッド 平均ユーザ対話データに基づいてアプリケーション・ユーザの心理状態をリモートでモニタするための方法及びシステム
JP2021193526A (ja) * 2020-06-08 2021-12-23 株式会社Agoop 情報処理装置、プログラム、及び、情報処理方法
WO2022244222A1 (fr) * 2021-05-21 2022-11-24 日本電気株式会社 Dispositif d'estimation, système d'estimation, procédé d'estimation et support d'enregistrement

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