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WO2025143185A1 - Body condition display device, body condition display method, control program, body condition transmission device, and body condition display system - Google Patents

Body condition display device, body condition display method, control program, body condition transmission device, and body condition display system Download PDF

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Publication number
WO2025143185A1
WO2025143185A1 PCT/JP2024/046314 JP2024046314W WO2025143185A1 WO 2025143185 A1 WO2025143185 A1 WO 2025143185A1 JP 2024046314 W JP2024046314 W JP 2024046314W WO 2025143185 A1 WO2025143185 A1 WO 2025143185A1
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WIPO (PCT)
Prior art keywords
data
user
walking posture
posture state
aging data
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PCT/JP2024/046314
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French (fr)
Japanese (ja)
Inventor
淳貴 山村
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Suntory Holdings Ltd
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Suntory Holdings Ltd
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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/103—Measuring devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
    • A61B5/11—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor or mobility of a limb
    • 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
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/30—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indices; for individual health risk assessment

Definitions

  • Patent Document 1 discloses an exercise posture deriving device that is worn on the user's body and derives walking or running posture.
  • the body condition is preferably muscle mass, energy consumption, weight loss, cerebral flow rate, walking speed, number of steps that can be walked per unit time, or walkable distance per unit time.
  • the structure condition display device, structure condition display method, control program, structure condition transmission device, and structure condition display system can present changes in structure condition over time according to the user's condition.
  • the first communication device 103 has an antenna for transmitting and receiving wireless signals, and a wireless communication interface circuit that complies with a communication protocol such as wireless LAN.
  • the first communication device 103 is communicatively connected to the network N in accordance with a communication standard such as wireless LAN.
  • the first communication device 103 sends data received from the information processing device 300 or the server device 400 via the network N to the first processing device 120.
  • the first communication device 103 also transmits data received from the first processing device 120 to the information processing device 300 or the server device 400 via the network N.
  • the first storage device 110 is an example of a storage unit.
  • the first storage device 110 has a memory device such as a RAM (Random Access Memory) or a ROM (Read Only Memory), a fixed disk device such as a hard disk, or a portable storage device such as a flexible disk or an optical disk.
  • the first storage device 110 also stores computer programs, databases, tables, etc. used for various processes of the first wearable device 100.
  • the computer programs may be installed in the first storage device 110 from a computer-readable portable recording medium using a well-known setup program, etc.
  • the portable recording medium is, for example, a CD-ROM (compact disc read only memory), a DVD-ROM (digital versatile disc read only memory), etc.
  • the computer programs may be stored in a recording medium owned by a specific server and installed via the network N.
  • the second trained model 115 is a model for estimating aging data corresponding to the walking posture state of a user wearing the first wearable device 100 and the second wearable device 200 from the unvaccinated time aging data 113.
  • the third trained model 116 is a model for estimating aging data corresponding to the walking posture state of a user wearing the first wearable device 100 and the second wearable device 200 from the vaccinated time aging data 114.
  • the first processing device 120 operates based on a program previously stored in the first storage device 110.
  • the first processing device 120 is, for example, a CPU.
  • a DSP digital signal processor
  • an LSI large scale integration
  • an ASIC application specific integrated circuit
  • an FPGA field-programmable gate array
  • the first processing device 120 is connected to the first input device 101, the first display device 102, the first communication device 103, the first interface device 104, the first sensor 105, the first storage device 110, etc., and controls each device.
  • the first processing device 120 reads the computer program stored in the first storage device 110 and operates according to the read computer program. As a result, the first processing device 120 functions as a first acquisition unit 121, a first generation unit 122, and a display control unit 123.
  • the second wearable device 200 is a terminal device that can be worn by a user.
  • the second wearable device 200 is a wristwatch-type wearable computer worn on the user's arm.
  • the second wearable device 200 is also an earphone-type wearable computer worn on the user's ear.
  • the second wearable device 200 is also an insole-type wearable computer worn inside the user's shoe.
  • the second wearable device 200 has a second input device 201, a second display device 202, a second communication device 203, a second interface device 204, a second sensor 205, a second storage device 210, and a second processing device 220, etc.
  • the second input device 201, the second display device 202, the second communication device 203, the second interface device 204, the second sensor 205, the second storage device 210, and the second processing device 220 are connected to each other via a CPU bus or the like.
  • the second input device 201 has an input device such as a button and an interface circuit that acquires a signal from the input device, and outputs an operation signal according to an input operation by the user.
  • the second input device 201 may be omitted.
  • the second display device 202 is an example of an output unit.
  • the second display device 202 has a display including a liquid crystal, an organic electroluminescence, etc., and an interface circuit that outputs image data to the display, and displays the image data on the display.
  • the second display device 202 has an LED (Light Emitting Diode) or the like, and may notify the user by turning it on or off.
  • the second display device 202 may be omitted.
  • the second communication device 203 has an antenna for transmitting and receiving wireless signals, and a wireless communication interface circuit that complies with a communication protocol such as wireless LAN.
  • the second communication device 203 is communicatively connected to the network N in accordance with a communication standard such as wireless LAN.
  • the second communication device 203 sends data received from the information processing device 300 or the server device 400 via the network N to the second processing device 220.
  • the second communication device 203 also transmits data received from the second processing device 220 to the information processing device 300 or the server device 400 via the network N.
  • the second interface device 204 has an antenna for transmitting and receiving wireless signals, and a wireless communication interface circuit that conforms to a communication protocol such as Bluetooth (registered trademark).
  • the second interface device 204 is communicatively connected to the first wearable device 100 in accordance with a communication standard such as Bluetooth (registered trademark).
  • the second interface device 204 sends data received from the first wearable device 100 to the second processing device 220.
  • the second interface device 204 also transmits data received from the second processing device 220 to the first wearable device 100.
  • the second sensor 205 includes an acceleration sensor that measures the acceleration in three axial directions applied to the second wearable device 200, and a gyro sensor that measures the angular velocity in three axial directions applied to the second wearable device 200.
  • the acceleration sensor and the gyro sensor output measurement signals indicating the measured acceleration and angular velocity to the second processing device 220.
  • the second sensor 205 may include an insole-type pressure sensor.
  • the second sensor 205 includes a resistance change type or capacitance change type pressure sensor arranged one-dimensionally or two-dimensionally, generates sole data in which the magnitude of pressure measured by each pressure sensor is the gradation value of each pixel, and outputs the sole data to the second processing device 220.
  • the sole data is, for example, a sole image in which the magnitude of pressure measured by each pressure sensor is the gradation value of each pixel.
  • the sole data may be, for example, a sole vector whose elements are the pressures measured by each pressure sensor.
  • the second storage device 210 has a memory device such as RAM or ROM, a fixed disk device such as a hard disk, or a portable storage device such as a flexible disk or optical disk.
  • the second storage device 210 also stores computer programs, databases, tables, etc. used for various processes of the second wearable device 200.
  • the computer programs may be installed into the second storage device 210 from a computer-readable portable recording medium using a well-known setup program, etc.
  • the portable recording medium is, for example, a CD-ROM, DVD-ROM, etc.
  • the computer programs may be stored in a recording medium owned by a specific server and installed via the network N.
  • the second processing device 220 operates based on a program previously stored in the second storage device 210.
  • the second processing device 220 is, for example, a CPU.
  • a DSP, an LSI, an ASIC, an FPGA, etc. may also be used as the second processing device 220.
  • the second processing device 220 is connected to the second input device 201, the second display device 202, the second communication device 203, the second interface device 204, the second sensor 205, the second storage device 210, etc., and controls each device.
  • the second processing device 220 reads the computer program stored in the second storage device 210 and operates according to the read computer program. In this way, the second processing device 220 functions as a second acquisition unit 221 and a second transmission unit 222.
  • FIG. 4 is a schematic diagram showing an example of the data structure of data table 112.
  • the angular velocity and acceleration, geomagnetic field, air pressure (not shown), position (not shown) or sole data acquired from the first wearable device 100 or the second wearable device 200 are stored in association with the identification information (device ID) of each device and the acquisition time when each data was acquired.
  • FIG. 5 shows a sequence illustrating an example of the operation of the display process in the structure condition display system 1.
  • the second acquisition unit 221 of each second wearable device 200 acquires a measurement signal from the second sensor 205.
  • the second acquisition unit 221 acquires a measurement signal indicating the acceleration and angular velocity in three axial directions acting on the second wearable device 200 as indicated in the measurement signal, or sole of the foot data obtained by capturing an image of the sole of the user's foot, as second data obtained when the user walks (step S101).
  • the second acquisition unit 221 periodically acquires the second data.
  • the second transmission unit 222 transmits the second data acquired by the second acquisition unit 221 to the first wearable device 100 via the second interface device 204 together with the device ID of the second wearable device 200 and the acquisition time when the second data was acquired (step S102).
  • the second transmission unit 222 transmits the second data to the first wearable device 100 every time the second acquisition unit 221 acquires the second data.
  • the second transmission unit 222 may transmit multiple pieces of second data acquired by the second acquisition unit 221 together to the first wearable device 100 at any timing.
  • the first acquisition unit 121 of the first wearable device 100 acquires the second data, the device ID, and the acquisition time by receiving them from the second wearable device 200 via the first interface device 104 (step S103).
  • the first acquisition unit 121 acquires a measurement signal from the first sensor 105.
  • the first acquisition unit 121 acquires the acceleration and angular velocity in three axes directions, geomagnetism, atmospheric pressure, or position of the first wearable device 100 indicated in the measurement signal as first data obtained when the user walks (step S104).
  • the first data is different from the second data.
  • the first acquisition unit 121 periodically acquires the first data.
  • the first acquisition unit 121 stores the acquired first data and second data in the data table 112 in association with the corresponding device ID and acquisition time.
  • the input data may also be a spectral image transformed by short-time Fourier transform or wavelet transform for each angular velocity and/or acceleration in the three-axis directions included in the first data and/or second data.
  • the spectral image transformed by short-time Fourier transform or wavelet transform for each angular velocity and/or acceleration in the three-axis directions included in the first data is an example of the first image data and image data.
  • the spectral image transformed by short-time Fourier transform or wavelet transform for each angular velocity and/or acceleration in the three-axis directions included in the second data is an example of the second image data and image data.
  • a model pre-trained by a random forest or the like is used as the first trained model 111.
  • a model pre-trained by a neural network or the like may be used as the first trained model 111. This allows the structure condition display system 1 to further improve the accuracy of the first trained model 111.
  • the input data may also be a feature vector whose elements are the gradation value (pressure) of each pixel of the sole image or each element of the sole vector, and angular velocity, acceleration, geomagnetic field, atmospheric pressure, and/or position.
  • the walking posture state output from the first trained model 111 includes five elements.
  • the first element indicates the state of the stride, particularly whether the user's stride is sufficient or insufficient.
  • the second element indicates the state of the center of gravity position, particularly whether the user's center of gravity is normal, leaning forward, or leaning backward.
  • the third element indicates the state of the swinging leg, particularly whether the user's leg is not kicking, dragging, or kicking.
  • the fourth element indicates the state of the supporting leg, particularly whether the user's supporting leg is stiff or not stiff.
  • the fifth element indicates the state of the hip joint, particularly whether the user's hip joint is open or not.
  • the first element is an element related to the premise of the walking posture.
  • the second and third elements are elements related to the axis in the front-back direction.
  • the fourth and fifth elements are elements related to the axis of rotation (left-right direction).
  • the walking posture state does not have to include all of the elements from the first to the fifth elements, and it is sufficient to include at least one element.
  • the first trained model 111 includes a first trained model capable of outputting a first walking posture state estimated based on first image data based on first data acquired from the first wearable device 100, and a second trained model capable of outputting a second walking posture state estimated based on second image data based on second data acquired from each of the second wearable devices 200.
  • the body state display system 1 can estimate the walking posture state for each body part on which the wearable device is attached, and can comprehensively estimate the walking posture state of the user based on the walking posture state estimated for each body part.
  • the first acquisition unit 121 may acquire the walking posture state by inputting input data based on the first data into the first layer of the first trained model, and may acquire the walking posture state by inputting input data based on the second data into the second layer of the second trained model.
  • This first layer is a specific layer of the input layer or the intermediate layer.
  • This second layer is a layer different from the first layer, and is a specific layer of the input layer or the intermediate layer. This allows the structure condition display system 1 to flexibly change the number of processing layers (number of processing steps) in each trained model, and suppress increases in processing time and processing load.
  • the first trained model 111 may include any combination of the trained models described above.
  • the first trained model 111 includes a first upstream trained model, a second upstream trained model, a third upstream trained model, and a downstream trained model.
  • the first upstream trained model is trained to output a predetermined feature amount when a first principal component of sole data is input.
  • the second upstream trained model is trained to output a predetermined feature amount when a second principal component of sole data is input.
  • the third upstream trained model is trained to output a predetermined feature amount when a first principal component of angular velocity or acceleration is input.
  • the first acquisition unit 121 converts the first data and second data acquired in steps S103 and S104 into a format corresponding to the first trained model 111 and inputs the converted data to the first trained model 111.
  • the first acquisition unit 121 estimates the walking posture state of the user wearing the first wearable device 100 and the second wearable device 200 based on the information output from the first trained model 111.
  • the third acquisition unit 321 estimates the walking posture state of the user for each element included in the walking posture state.
  • the first wearable device 100 stores in advance in the first storage device 110 a table or formula indicating the relationship between the feature and the walking posture state.
  • the first acquisition unit 121 refers to the table or formula stored in the first storage device 110 and identifies the walking posture state of the user that corresponds to the feature output from the first trained model 111.
  • the first acquisition unit 121 estimates that the walking posture state output from the greatest number of trained models among the walking posture states output from each trained model is the walking posture state of the user.
  • the first acquisition unit 121 estimates the walking posture state of the user based on the acquired first data and second data, using the first trained model 111. That is, the first acquisition unit 121 acquires the walking posture state estimated based on the acquired first data and second data, using the first trained model 111.
  • the first generating unit 122 modifies the unvaccinated aging data 113 based on the walking posture state acquired by the first acquiring unit 121, and generates modified unvaccinated aging data corresponding to the user's walking posture state (step S106).
  • the modified unvaccinated aging data is an example of the fourth aging data.
  • the modified unvaccinated aging data indicates, for example, the change over time in the body condition estimated for a user who does not take a specified supplement and has the walking posture state acquired by the first acquiring unit 121.
  • the first generating unit 122 estimates the modified unvaccinated aging data using, for example, the second trained model 115.
  • the second trained model 115 includes one or more trained models.
  • the second trained model 115 is generated in advance by the information processing device 300 or the server device 400, etc.
  • the second trained model 115 is trained in advance by supervised learning such as deep learning and support vector machines.
  • supervised learning such as deep learning and support vector machines.
  • the second trained model 115 is trained to output time-course data estimated for a user having that walking posture state.
  • the second trained model 115 is trained in advance using the unvaccinated time-course data 113, multiple sets of the walking posture state measured for each of multiple users, and the time-course data measured for each user.
  • the second trained model 115 includes trained models corresponding to various types of body conditions (muscle mass, energy consumption, weight loss, cerebral blood flow, possible walking speed, possible number of steps, possible walking distance, etc.), for example.
  • the second trained model 115 may also include trained models corresponding to each element included in the walking posture state.
  • the first generation unit 122 inputs the unvaccinated time-course data 113 and the walking posture state into the second trained model 115, and obtains the time-course data output from the second trained model 115 as corrected unvaccinated time-course data. In this way, the first generation unit 122 generates (estimates) corrected unvaccinated time-course data.
  • the first generating unit 122 may generate the corrected unvaccinated aging data without using the second trained model 115.
  • the first wearable device 100 stores in advance in the first storage device 110 a table showing the relationship between the walking posture state of a user who does not take a specified supplement and the degree of change (difference or percentage, etc.) in the aging data based on past measurement results for multiple users.
  • the first generating unit 122 refers to the table to identify the degree corresponding to the walking posture state of each user, corrects the unvaccinated aging data 113 based on the identified degree, and generates the corrected unvaccinated aging data.
  • the first generating unit 122 modifies the vaccination time aging data 114 based on the walking posture state acquired by the first acquiring unit 121, and generates modified vaccination time aging data corresponding to the user's walking posture state (step S107).
  • the modified vaccination time aging data is an example of second aging data.
  • the modified vaccination time aging data indicates, for example, the change over time in the body condition estimated for a user who has taken a specified supplement and has the walking posture state acquired by the first acquiring unit 121.
  • the first generating unit 122 estimates the modified vaccination time aging data using, for example, the third trained model 116.
  • the third trained model 116 includes one or more trained models.
  • the third trained model 116 is generated in advance by the information processing device 300 or the server device 400, etc.
  • the third trained model 116 is trained in advance by supervised learning such as deep learning and support vector machines.
  • supervised learning such as deep learning and support vector machines.
  • the third trained model 116 is trained to output aging data estimated for a user having that walking posture state.
  • the third trained model 116 is trained in advance using vaccination time-based aging data 114, multiple sets of walking posture states measured for each of multiple users, and aging data measured for each user.
  • the third trained model 116 includes trained models corresponding to various types of body conditions (muscle mass, energy consumption, weight loss, cerebral blood flow, possible walking speed, possible number of steps, possible walking distance, etc.), for example.
  • the third trained model 116 may also include trained models corresponding to each element included in the walking posture state.
  • the first generation unit 122 inputs the user's vaccination time aging data 114 and walking posture state into the third trained model 116, and obtains the aging data output from the third trained model 116 as modified vaccination time aging data. In this way, the first generation unit 122 generates (estimates) modified vaccination time aging data.
  • the first generating unit 122 may generate the modified vaccination time aging data without using the third trained model 116.
  • the first wearable device 100 stores in advance in the first storage device 110 a table showing the relationship between the walking posture state of a user who takes a specified supplement and the degree of change (difference or percentage, etc.) of the aging data based on past measurement results for multiple users.
  • the first generating unit 122 refers to the table to identify the degree corresponding to the walking posture state of each user, corrects the vaccination time aging data 114 based on the identified degree, and generates the modified vaccination time aging data.
  • the display control unit 123 outputs the unvaccinated time aging data 113, the vaccinated time aging data 114, the corrected unvaccinated time aging data, and the corrected vaccinated time aging data by displaying them on the first display device 102, notifying the user (step S108), and ends the series of steps. This allows the user to recognize changes in his or her physical condition over time.
  • steps S101 to S103 and S104 may be omitted, and the first acquisition unit 121 may estimate the walking posture state based on only one of the first data and the second data.
  • the first wearable device 100 can estimate the walking posture state at low cost and with low load without using the second wearable device 200 by estimating the walking posture state based on the first data.
  • the first wearable device 100 can increase the degree of freedom of the body part from which the second data is acquired by estimating the walking posture state based on the second data, and can estimate the walking posture state with high accuracy.
  • steps S106 and S107 may be omitted, and the first generation unit 122 may generate only one of the modified unvaccinated time aging data and the modified vaccinated time aging data.
  • step S108 the display control unit 123 displays only one of the modified unvaccinated time aging data and the modified vaccinated time aging data. Also, in step S108, the display control unit 123 does not have to display the non-vaccination time-varying data 113 and/or the vaccination time-varying data 114.
  • the first learned model 111 may be stored in the server device 400 instead of the first storage device 110.
  • the first acquisition unit 121 acquires the walking posture state estimated by the first learned model 111 by accessing the server device 400.
  • the first acquisition unit 121 transmits a request signal requesting transmission of the user's walking posture state to the server device 400 via the first communication device 103.
  • the request signal includes the first data and/or the second data converted into a format corresponding to the first learned model 111.
  • the server device 400 receives the request signal from the first wearable device 100.
  • the server device 400 inputs the first data and/or the second data included in the received request signal to the first learned model 111 and acquires output information output from the first learned model 111.
  • the server device 400 transmits the acquired output information to the first wearable device 100.
  • the first acquisition unit 121 acquires output information by receiving it from the server device 400 via the first communication device 103, and estimates the walking posture state of the user wearing the first wearable device 100 and the second wearable device 200 based on the acquired output information.
  • the second trained model 115 and/or the third trained model 116 may be stored in the server device 400 instead of the first storage device 110.
  • the first generation unit 122 accesses the server device 400 to obtain the modified unvaccinated time aging data and/or the modified vaccinated time aging data estimated by the second trained model 115 and/or the third trained model 116.
  • the first generation unit 122 transmits a request signal to the server device 400 via the first communication device 103, requesting the transmission of the modified unvaccinated time aging data and/or the modified vaccinated time aging data.
  • the request signal includes the unvaccinated time aging data 113 and/or the vaccinated time aging data 114 and the walking posture state.
  • the server device 400 receives the request signal from the first wearable device 100.
  • the server device 400 inputs the unvaccinated time aging data 113 or the vaccinated time aging data 114 contained in the received request signal and the walking posture state into the second trained model 115 or the third trained model 116, and acquires output information output from the second trained model 115 or the third trained model 116.
  • the server device 400 transmits the acquired output information to the first wearable device 100.
  • the first acquisition unit 121 acquires the output information by receiving it from the server device 400 via the first communication device 103, and estimates the modified unvaccinated time aging data and/or the modified vaccinated time aging data based on the acquired output information.
  • the body condition display system 1 can estimate the walking posture state or aging data by using the latest learned model updated by the server device 400, by utilizing the trained model stored in the server device 400. Furthermore, the body condition display system 1 can reduce the storage capacity of the first wearable device 100 by utilizing the trained model stored in the server device 400. On the other hand, the first wearable device 100 can estimate the walking posture state or aging data even when the communication connection with the server device 400 is disconnected, by utilizing the trained model stored in the first wearable device 100. Furthermore, the first wearable device 100 can reduce the amount of communication between the first wearable device 100 and the server device 400 by utilizing the trained model stored in the first wearable device 100.
  • Figure 6 is a graph showing the changes in the physical condition of a specific user over time.
  • the horizontal axis indicates the user's age
  • the vertical axis indicates the user's muscle mass.
  • Graphs 601 and 602 respectively show unvaccinated and vaccinated data over time for a typical person
  • graphs 603 and 604 respectively show corrected unvaccinated and corrected vaccinated data over time for a user with a specific walking posture.
  • a person's muscle mass decreases with age.
  • the walking posture of the user illustrated in FIG. 6 is not good, and the degree of decrease in the user's muscle mass is greater than that of a typical person.
  • the changes over time in energy consumption, weight loss, cerebral flow, walking speed, number of steps, and walking distance also have similar characteristics to the changes over time in muscle mass.
  • the user can recognize the change over time in the body condition when the specified supplement is not taken and the walking posture is not improved in the future.
  • the user can clearly recognize the difference in the change over time in the body condition when the walking posture is improved and not improved in the state of not taking the specified supplement.
  • the user can recognize the change over time in the body condition when the specified supplement is taken but the walking posture is not improved in the future.
  • the user can clearly recognize the difference in the change over time in the body condition when the specified supplement is taken and the walking posture is improved and not improved.
  • the body condition display system 1 can increase the user's motivation to take prescribed supplements and improve their walking posture.
  • the inventors have evaluated the first trained model 111 for all of the first to fifth elements of the walking posture state, and have confirmed that the accuracy rate of the information output from the first trained model 111 is 93% or higher, indicating that the accuracy is sufficiently high. In other words, it has been confirmed that the structure condition display system 1 can use the first trained model 111 to more accurately estimate the user's walking posture state, and more accurately estimate the changes in the structure condition over time.
  • the structural body condition display system 1 can accurately and efficiently calculate the change over time in the walking posture state or structural body state for various combinations of various types of parameters. Furthermore, by using the trained model, the structural body condition display system 1 can identify the change over time in the walking posture state or structural body state without storing the change over time in the walking posture state or structural body state corresponding to various combinations of various types of parameters, thereby reducing the storage capacity of the storage device. Furthermore, by using the trained model, the structural body condition display system 1 can identify the change over time in the walking posture state or structural body state without performing complex judgments according to various combinations of various types of parameters, thereby reducing the processing time and processing load of the display process.
  • the main source of power for walking is pulling up the thighs, and the main source of power for running is kicking with the toes. Therefore, walking and running, i.e. running as slowly as possible, are fundamentally different movements, and clearly changing the movements between walking and running leads to the health of the user.
  • the ball of the foot In order to kick with the toes, the ball of the foot must be placed on the floor, causing the foot to roll inward and eliminating the arch. This loosens the bones of the foot overall, making it easier for impacts on the foot to be transmitted to the knee or hip. Running puts a strain on the feet, so it is not advisable to perform running, which puts a strain on the feet, while walking.
  • the body state display system 1 estimates each of the first to fifth elements as the walking posture state of the user, and can identify whether the user is walking appropriately without running.
  • each of the first to fifth elements is interrelated, and as the user's posture improves in the order of the first to fifth elements, the user can easily improve their walking posture.
  • the walking posture state includes, as a premise, a first element that indicates whether the user's stride length is sufficient or insufficient, so that the body state display system 1 can guarantee that the user's stride length is at least a certain level.
  • the walking posture state can allow the body state display system 1 to check whether the user's posture is being kept straight.
  • the body state display system 1 can check whether the user is pulling up the thighs correctly.
  • the walking motion will not be performed properly. If the knee is turned inward relative to the toes, the knee will get in the way and the opposite leg will not come out correctly. In other words, if the supporting leg is stiff or the hip joint is not open, the leg that is to be put out will not rise correctly. On the other hand, if the user rotates the hip joint, space is secured for the opposite leg to come out, making it easier to lift the leg and correctly pulling up the thigh.
  • the body state display system 1 can confirm whether the axis of rotation is correct or not.
  • the structural body condition display system 1 acquires the walking posture state of the user based on data obtained when the user walks, and estimates and displays the change over time in the structural body condition of the user according to the acquired walking posture state. This enables the structural body condition display system 1 to present the change over time in the structural body condition according to the user's condition.
  • FIG. 7 is a diagram showing the schematic configuration of an information processing device 300 in a structure condition display system 1 according to another embodiment.
  • the information processing device 300 is an example of a body state transmission device.
  • the information processing device 300 has a third communication device 301, a third storage device 310, a third processing device 320, etc.
  • the third communication device 301, the third storage device 310, and the third processing device 320 are connected to each other via a CPU bus or the like.
  • the third communication device 301 is an example of a transmitter.
  • the third communication device 301 has a wired communication interface circuit that complies with a communication protocol such as TCP/IP.
  • the third communication device 301 is communicatively connected to the network N in accordance with a communication standard such as Ethernet (registered trademark).
  • the third communication device 301 sends data received from the first wearable device 100, the second wearable device 200, the server device 400, etc. via the network N to the third processing device 320.
  • the third communication device 301 transmits data received from the third processing device 320 to the first wearable device 100, the second wearable device 200, the server device 400, etc. via the network N.
  • the third communication device 301 may have an antenna for transmitting and receiving wireless signals and a wireless communication interface circuit that complies with a communication protocol such as wireless LAN, and may be communicatively connected to the network N in accordance with a communication standard such as wireless LAN.
  • a communication protocol such as wireless LAN
  • the third storage device 310 has a memory device such as RAM or ROM, a fixed disk device such as a hard disk, or a portable storage device such as a flexible disk or optical disk.
  • the third storage device 310 also stores computer programs, databases, tables, etc. used for various processes of the information processing device 300.
  • the computer programs may be installed in the third storage device 310 from a computer-readable portable recording medium such as a CD-ROM or DVD-ROM using a known setup program, etc.
  • the computer programs may be stored in a recording medium owned by a specific server and installed via the network N.
  • the third storage device 310 stores data such as a first trained model 311, a data table 312, unvaccinated time aging data 313, vaccination time aging data 314, a second trained model 315, and a third trained model 316.
  • the first trained model 311, the data table 312, unvaccinated time aging data 313, vaccination time aging data 314, the second trained model 315, and the third trained model 316 are models, tables, or data similar to the first trained model 111, the data table 112, unvaccinated time aging data 113, vaccination time aging data 114, the second trained model 115, and the third trained model 116, respectively.
  • FIG. 8 is a sequence showing an example of the operation of the display process in the structure condition display system 1 according to this embodiment.
  • the first acquisition unit 121 of the first wearable device 100 transmits the acquired first data and second data together with the device ID and acquisition time corresponding to each data to the information processing device 300 via the first communication device 103 (step S205).
  • the first acquisition unit 121 transmits the first data and the second data to the information processing device 300, for example, for a predetermined number of steps of the user.
  • the first acquisition unit 121 may transmit the first data or the second data to the information processing device 300 each time it acquires the first data or the second data.
  • the first acquisition unit 121 may also transmit the acquired multiple pieces of first data and second data to the information processing device 300 at any timing.
  • the third acquisition unit 321 of the information processing device 300 acquires the first data, the second data, the device ID, and the acquisition time from the first wearable device 100 by receiving them via the third communication device 301 (step S206).
  • the third acquisition unit 321 stores the acquired first data and second data in the data table 312 in association with the corresponding device ID and acquisition time.
  • the estimation unit 322 estimates the walking posture state of the user wearing the first wearable device 100 and the second wearable device 200 based on the first data and the second data acquired by the third acquisition unit 321, similar to the processing of step S105 in FIG. 6 (step S207).
  • the third acquisition unit 321 estimates the walking posture state of the user using the first trained model 311.
  • the third generation unit 323 corrects the unvaccinated time aging data 313 based on the walking posture state estimated by the estimation unit 322, in a manner similar to the processing of step S106 in FIG. 6, and generates corrected unvaccinated time aging data corresponding to the user's walking posture state (step S208).
  • the third generation unit 323 estimates the corrected unvaccinated time aging data using, for example, the second trained model 315.
  • the third generation unit 323 corrects the vaccination time aging data 314 based on the walking posture state estimated by the estimation unit 322, in a manner similar to the processing of step S106 in FIG. 6, and generates corrected vaccination time aging data according to the user's walking posture state (step S209).
  • the third generation unit 323 estimates the corrected vaccination time aging data using, for example, the third trained model 316.
  • the transmission control unit 324 outputs the unvaccinated time aging data 313, the vaccinated time aging data 314, the corrected unvaccinated time aging data, and the corrected vaccinated time aging data by transmitting them to the first wearable device 100 via the third communication device 301 (step S210).
  • the first acquisition unit 121 of the first wearable device 100 acquires the unvaccinated time aging data 313, the vaccinated time aging data 314, the corrected unvaccinated time aging data, and the corrected vaccinated time aging data by receiving them from the information processing device 300 via the first communication device 103 (step S211).
  • steps S201 to S203 and S204 may be omitted, and the estimation unit 322 may estimate the walking posture state based on only one of the first data and the second data.
  • the information processing device 300 can estimate the walking posture state at low cost and with low load without using the second wearable device 200.
  • the information processing device 300 can increase the degree of freedom of the body part from which the second data is obtained, and can estimate the walking posture state with high accuracy.
  • steps S208 and S209 may be omitted, and the third generation unit 323 may generate only one of the modified unvaccinated time aging data and the modified vaccinated time aging data.
  • step S212 the display control unit 123 displays only one of the modified unvaccinated time aging data and the modified vaccinated time aging data. Also, in step S212, the display control unit 123 does not have to display the non-vaccination time chronological data 313 and/or the vaccination time chronological data 314.
  • the transmission control unit 324 may transmit each piece of aging data to an administrator device owned by an administrator who manages the user's health.
  • the administrator device notifies the administrator by displaying the received aging data.
  • the administrator can recognize changes in the user's physical condition over time and suggest improvements to the user's walking posture.
  • the first learned model 311 may be stored in the server device 400, not in the third storage device 310.
  • the estimation unit 322 obtains the walking posture state estimated by the first learned model 311 by accessing the server device 400.
  • the second learned model 315 and/or the third learned model 316 may be stored in the server device 400, not in the third storage device 310.
  • the third generation unit 323 obtains each aging data estimated by the second learned model 315 and/or the third learned model 316 by accessing the server device 400.
  • the information processing device 300 may estimate the walking posture state, and the first wearable device 100 may estimate each piece of aging data.
  • the estimation unit 322 transmits the walking posture state to the first wearable device 100 via the third communication device 301.
  • the first acquisition unit 121 of the first wearable device 100 acquires the walking posture state by receiving it from the information processing device 300 via the first communication device 103.
  • the first generation unit 122 generates each piece of aging data based on the walking posture state acquired by the first acquisition unit 121, in a manner similar to the processing of step S106 or S107 in FIG. 5.
  • the first wearable device 100 may estimate the walking posture state, and the information processing device 300 may estimate each piece of aging data.
  • the first acquisition unit 121 acquires the walking posture state by estimating it in the same manner as in the processing of step S105, and transmits the acquired walking posture state to the information processing device 300 via the first communication device 103.
  • the estimation unit 322 of the information processing device 300 acquires the walking posture state by receiving it from the first wearable device 100 via the third communication device 301.
  • the third generation unit 323 generates each piece of aging data based on the walking posture state acquired by the estimation unit 322.
  • multiple first wearable devices 100 and/or multiple information processing devices 300 may cooperate to share each step of the above-mentioned processes.
  • Body condition display system 100 First wearable device, 102 First display device, 105 First sensor, 110 First storage device, 111 First trained model, 121 First acquisition unit, 122 First generation unit, 200 Second wearable device, 300 Information processing device, 301 Third communication device, 310 Third storage device, 311 First trained model, 322 Estimation unit, 323 Third generation unit

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Abstract

Provided are a body condition display device, a body condition display method, a control program, a body condition transmission device, and a body condition display system that allow a temporal change in a body condition corresponding to the condition of a user to be presented. The body condition display device comprises: a storage unit that stores first temporal data indicating a temporal change in the body condition of a person that occurs when the person has taken a prescribed supplement; an acquisition unit that acquires the walking posture condition of the user on the basis of data acquired when the user walks; a generation unit that corrects the first temporal data on the basis of the walking posture condition, and generates second temporal data corresponding to the walking posture condition; and a display unit that displays the second temporal data.

Description

躯体状態表示装置、躯体状態表示方法、制御プログラム、躯体状態送信装置及び躯体状態表示システムStructure status display device, structure status display method, control program, structure status transmission device, and structure status display system

　本開示は、躯体状態表示装置、躯体状態表示方法、制御プログラム、躯体状態送信装置及び躯体状態表示システムに関する。 This disclosure relates to a structure status display device, a structure status display method, a control program, a structure status transmission device, and a structure status display system.

　従来、ユーザがサプリメントを摂取した際における躯体状態の経年変化を提示するシステムが開発されている。 　Previously, systems have been developed that show changes in the physical condition of users over time when they take supplements.

　特許文献１には、ユーザの身体に着用されて、歩行または走行姿勢を導出する運動姿勢導出装置が開示されている。 Patent Document 1 discloses an exercise posture deriving device that is worn on the user's body and derives walking or running posture.

特開２０２１－９８０２７号公報JP 2021-98027 A

　ユーザの躯体状態の経年変化を提示するシステムにおいて、ユーザの状態に応じた躯体状態の経年変化を提示することが要求されている。 In a system that shows the changes over time in the condition of a user's building structure, there is a demand to show changes over time in the building structure condition that correspond to the user's condition.

　躯体状態表示装置、躯体状態表示方法、制御プログラム、躯体状態送信装置及び躯体状態表示システムの目的は、ユーザの状態に応じた躯体状態の経年変化を提示することを可能とすることにある。 The purpose of the structural body condition display device, structural body condition display method, control program, structural body condition transmission device, and structural body condition display system is to make it possible to present changes in the structural body condition over time according to the user's condition.

　実施形態に係る躯体状態表示装置は、人物が所定のサプリメントを摂取した際における人物の躯体状態の経年変化を示す第１経年データを記憶する記憶部と、ユーザが歩行した際に得られるデータに基づいてユーザの歩行姿勢状態を取得する取得部と、歩行姿勢状態に基づいて、第１経年データを修正し、歩行姿勢状態に応じた第２経年データを生成する生成部と、第２経年データを表示する表示部と、を有する。 The body condition display device according to the embodiment has a storage unit that stores first aging data indicating changes in a person's body condition over time when the person takes a specified supplement, an acquisition unit that acquires the walking posture state of the user based on data obtained when the user walks, a generation unit that modifies the first aging data based on the walking posture state and generates second aging data corresponding to the walking posture state, and a display unit that displays the second aging data.

　実施形態に係る躯体状態表示装置において、記憶部は、人物が所定のサプリメントを摂取しなかった際における人物の躯体状態の経年変化を示す第３経年データを記憶し、表示部は、第１経年データ、第２経年データ、及び、第３経年データを表示することが好ましい。 In the physical body condition display device according to the embodiment, it is preferable that the memory unit stores third aging data indicating changes over time in the physical body condition of the person when the person does not take a specified supplement, and the display unit displays the first aging data, the second aging data, and the third aging data.

　実施形態に係る躯体状態表示装置において、生成部は、歩行姿勢状態に基づいて、第３経年データを修正し、歩行姿勢状態に応じた第４経年データを生成し、表示部は、第１経年データ、第２経年データ、第３経年データ、及び、第４経年データを表示することが好ましい。 In the body condition display device according to the embodiment, it is preferable that the generation unit modifies the third aging data based on the walking posture state and generates fourth aging data corresponding to the walking posture state, and the display unit displays the first aging data, the second aging data, the third aging data, and the fourth aging data.

　実施形態に係る躯体状態表示装置において、躯体状態は、筋肉量、エネルギー消費量、体重減少量、脳内流量、歩行可能速度、単位時間あたりの歩行可能歩数、又は、単位時間あたりの歩行可能距離であることが好ましい。 In the body condition display device according to the embodiment, the body condition is preferably muscle mass, energy consumption, weight loss, cerebral flow rate, walking speed, number of steps that can be walked per unit time, or walkable distance per unit time.

　実施形態に係る躯体状態表示装置において、取得部は、ユーザに装着可能で且つユーザが歩行した際に得られるデータを取得するウェアラブル装置からユーザが歩行した際に得られるデータを受信することが好ましい。 In the body condition display device according to the embodiment, it is preferable that the acquisition unit receives data obtained when the user walks from a wearable device that can be worn by the user and acquires data obtained when the user walks.

　実施形態に係る躯体状態表示装置において、躯体状態表示装置は、ユーザに装着可能であり、ユーザが歩行した際に得られるデータを計測するセンサをさらに有することが好ましい。 In the body condition display device according to the embodiment, the body condition display device is wearable by the user and preferably further includes a sensor that measures data obtained when the user walks.

　実施形態に係る躯体状態表示装置において、記憶部は、ユーザが歩行した際に得られるデータに基づいて歩行姿勢状態を出力可能な学習済みモデルを記憶し、取得部は、学習済みモデルを用いて推定された歩行姿勢状態を取得することが好ましい。 In the body state display device according to the embodiment, it is preferable that the storage unit stores a trained model capable of outputting a walking posture state based on data obtained when the user walks, and the acquisition unit acquires the walking posture state estimated using the trained model.

　実施形態に係る躯体状態表示装置において、取得部は、ユーザが歩行した際に得られるデータに基づいて歩行姿勢状態を出力可能な学習済みモデルを記憶する外部装置にアクセスすることによって、学習済みモデルによって推定された歩行姿勢状態を取得することが好ましい。 In the body state display device according to the embodiment, it is preferable that the acquisition unit acquires the walking posture state estimated by the trained model by accessing an external device that stores the trained model capable of outputting the walking posture state based on data obtained when the user walks.

　実施形態に係る躯体状態表示方法は、ユーザが所定のサプリメントを摂取した際におけるユーザの躯体状態の経年変化を示す第１経年データを記憶部に記憶し、ユーザが歩行した際に得られるデータに基づいてユーザの歩行姿勢状態を取得し、歩行姿勢状態に基づいて、第１経年データを修正し、ユーザの歩行姿勢状態に応じた第２経年データを生成し、第２経年データを表示部に表示する。 The physical body condition display method according to the embodiment stores in a storage unit first aging data indicating changes in the physical body condition of a user over time when the user takes a specified supplement, acquires the walking posture state of the user based on data obtained when the user walks, modifies the first aging data based on the walking posture state, generates second aging data corresponding to the walking posture state of the user, and displays the second aging data on a display unit.

　実施形態に係る制御プログラムは、記憶部及び表示部を有する躯体状態表示装置の制御プログラムであって、ユーザが所定のサプリメントを摂取した際におけるユーザの躯体状態の経年変化を示す第１経年データを記憶部に記憶し、ユーザが歩行した際に得られるデータに基づいてユーザの歩行姿勢状態を取得し、歩行姿勢状態に基づいて、第１経年データを修正し、ユーザの歩行姿勢状態に応じた第２経年データを生成し、第２経年データを表示部に表示することを躯体状態表示装置に実行させる。 The control program according to the embodiment is a control program for a body condition display device having a memory unit and a display unit, and causes the body condition display device to store in the memory unit first aging data indicating changes in the user's body condition over time when the user takes a specified supplement, acquire the user's walking posture state based on data obtained when the user walks, modify the first aging data based on the walking posture state, generate second aging data corresponding to the user's walking posture state, and display the second aging data on the display unit.

　実施形態に係る躯体状態送信装置は、ユーザが所定のサプリメントを摂取した際におけるユーザの躯体状態の経年変化を示す第１経年データを記憶する記憶部と、ユーザが歩行した際に得られるデータに基づいてユーザの歩行姿勢状態を推定する推定部と、歩行姿勢状態に基づいて、第１経年データを修正し、ユーザの歩行姿勢状態に応じた第２経年データを生成する生成部と、第２経年データを送信する送信部と、を有する。 The body condition transmission device according to the embodiment has a storage unit that stores first aging data indicating changes in the user's body condition over time when the user takes a specified supplement, an estimation unit that estimates the user's walking posture condition based on data obtained when the user walks, a generation unit that modifies the first aging data based on the walking posture condition and generates second aging data corresponding to the user's walking posture condition, and a transmission unit that transmits the second aging data.

　実施形態に係る躯体状態表示システムは、躯体状態送信装置と、躯体状態表示装置と、を含む躯体状態表示システムであって、躯体状態送信装置は、ユーザが所定のサプリメントを摂取した際におけるユーザの躯体状態の経年変化を示す第１経年データと、ユーザが歩行した際に得られるデータに基づいて歩行姿勢状態を出力可能な学習済みモデルとを記憶する記憶部と、ユーザが歩行した際に得られるデータに基づき、学習済みモデルを用いてユーザの歩行姿勢状態を推定する推定部と、歩行姿勢状態に基づいて、第１経年データを修正し、ユーザの歩行姿勢状態に応じた第２経年データを生成する生成部と、第２経年データを送信する送信部と、を有し、躯体状態表示装置は、第２経年データを表示する表示部を有する。 The structural body condition display system according to the embodiment includes a structural body condition transmission device and a structural body condition display device. The structural body condition transmission device has a memory unit that stores first aging data indicating changes in the structural body condition of a user over time when the user takes a specified supplement and a trained model capable of outputting a walking posture state based on data obtained when the user walks, an estimation unit that estimates the walking posture state of the user using the trained model based on data obtained when the user walks, a generation unit that corrects the first aging data based on the walking posture state and generates second aging data corresponding to the walking posture state of the user, and a transmission unit that transmits the second aging data. The structural body condition display device has a display unit that displays the second aging data.

　躯体状態表示装置、躯体状態表示方法、制御プログラム、躯体状態送信装置及び躯体状態表示システムは、ユーザの状態に応じた躯体状態の経年変化を提示することができる。 The structure condition display device, structure condition display method, control program, structure condition transmission device, and structure condition display system can present changes in structure condition over time according to the user's condition.

　本発明の目的及び効果は、特に請求項において指摘される構成要素及び組み合わせを用いることによって認識され且つ得られるだろう。前述の一般的な説明及び後述の詳細な説明の両方は、例示的及び説明的なものであり、特許請求の範囲に記載されている本発明を制限するものではない。 The objects and advantages of the invention will be realized and obtained by means of the elements and combinations particularly pointed out in the claims. Both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention as claimed.

実施形態に係る躯体状態表示システム１の概略構成を示す図である。1 is a diagram showing a schematic configuration of a structure condition display system 1 according to an embodiment. 第１ウェアラブル装置１００の概略構成を示す図である。A diagram showing a schematic configuration of a first wearable device 100. 第２ウェアラブル装置２００の概略構成を示す図である。A diagram showing a schematic configuration of a second wearable device 200. データテーブル１１２のデータ構造の一例を示す模式図である。2 is a schematic diagram showing an example of the data structure of a data table 112. FIG. 表示処理の動作の例を示すシーケンスである。11 is a sequence illustrating an example of an operation of a display process. 特定のユーザの躯体状態の経年変化を示すグラフである。1 is a graph showing changes over time in the physical condition of a specific user. 他の情報処理装置３００の概略構成を示す図である。FIG. 13 is a diagram showing a schematic configuration of another information processing device 300. 他の表示処理の動作の例を示すシーケンスである。13 is a sequence showing an example of another operation of the display process.

　以下、実施形態の一側面に係る躯体状態表示装置、躯体状態表示方法、制御プログラム、躯体状態送信装置及び躯体状態表示システムについて図を参照しつつ説明する。但し、本発明の技術的範囲はそれらの実施の形態に限定されず、特許請求の範囲に記載された発明とその均等物に及ぶ点に留意されたい。 Below, a structural body status display device, structural body status display method, control program, structural body status transmission device, and structural body status display system according to one aspect of the embodiment will be described with reference to the drawings. However, please note that the technical scope of the present invention is not limited to these embodiments, but extends to the inventions described in the claims and their equivalents.

　図１は、実施形態に係る躯体状態表示システム１の概略構成を示す図である。 FIG. 1 is a diagram showing the general configuration of a structure condition display system 1 according to an embodiment.

　図１に示すように、躯体状態表示システム１は、第１ウェアラブル装置１００、一又は複数の第２ウェアラブル装置２００、情報処理装置３００及びサーバ装置４００等を有する。第１ウェアラブル装置１００及び各第２ウェアラブル装置２００は、ユーザに装着されて使用される。情報処理装置３００は、ユーザの健康を管理する管理者等により使用される。サーバ装置４００は、例えばクラウドネットワーク上に配置されたサーバ等である。サーバ装置４００は、外部装置の一例である。 As shown in FIG. 1, the body condition display system 1 includes a first wearable device 100, one or more second wearable devices 200, an information processing device 300, and a server device 400. The first wearable device 100 and each second wearable device 200 are worn by a user for use. The information processing device 300 is used by an administrator or the like who manages the health of the user. The server device 400 is, for example, a server disposed on a cloud network. The server device 400 is an example of an external device.

　第１ウェアラブル装置１００と、各第２ウェアラブル装置２００とは、Ｂｌｕｅｔｏｏｔｈ（登録商標）、無線ＬＡＮ（Local Area Network）等の無線ネットワークを介して相互に通信可能に接続される。また、第１ウェアラブル装置１００と、情報処理装置３００と、サーバ装置４００とは、ネットワークＮを介して相互に通信可能に接続される。ネットワークＮは、インターネット又はイントラネット等の有線ネットワークである。ネットワークＮは、無線ＬＡＮ（Local Area Network）等の無線ネットワークでもよい。また、各第２ウェアラブル装置２００と、情報処理装置３００と、サーバ装置４００とは、ネットワークＮを介して相互に通信可能に接続されてもよい。 The first wearable device 100 and each second wearable device 200 are connected to each other so as to be able to communicate with each other via a wireless network such as Bluetooth (registered trademark) or a wireless LAN (Local Area Network). The first wearable device 100, the information processing device 300, and the server device 400 are connected to each other so as to be able to communicate with each other via a network N. The network N is a wired network such as the Internet or an intranet. The network N may be a wireless network such as a wireless LAN (Local Area Network). The second wearable devices 200, the information processing device 300, and the server device 400 may be connected to each other so as to be able to communicate with each other via the network N.

　図２は、第１ウェアラブル装置１００の概略構成を示す図である。 FIG. 2 is a diagram showing the general configuration of the first wearable device 100.

　第１ウェアラブル装置１００は、躯体状態表示装置の一例である。第１ウェアラブル装置１００は、多機能携帯電話（いわゆるスマートフォン）、タブレットＰＣ等の、ユーザに装着可能な端末装置である。第１ウェアラブル装置１００は、第１入力装置１０１、第１表示装置１０２、第１通信装置１０３、第１インタフェース装置１０４、第１センサ１０５、第１記憶装置１１０及び第１処理装置１２０等を有する。第１入力装置１０１、第１表示装置１０２、第１通信装置１０３、第１インタフェース装置１０４、第１センサ１０５、第１記憶装置１１０及び第１処理装置１２０は、ＣＰＵ（Central Processing Unit）バス等を介して相互に接続される。 The first wearable device 100 is an example of a body state display device. The first wearable device 100 is a terminal device that can be worn by a user, such as a multi-function mobile phone (a so-called smartphone) or a tablet PC. The first wearable device 100 has a first input device 101, a first display device 102, a first communication device 103, a first interface device 104, a first sensor 105, a first storage device 110, and a first processing device 120, etc. The first input device 101, the first display device 102, the first communication device 103, the first interface device 104, the first sensor 105, the first storage device 110, and the first processing device 120 are connected to each other via a CPU (Central Processing Unit) bus or the like.

　第１入力装置１０１は、タッチパネル式等の入力デバイス及び入力デバイスから信号を取得するインタフェース回路を有し、利用者の入力操作に応じた操作信号を出力する。 The first input device 101 has an input device such as a touch panel and an interface circuit that acquires signals from the input device, and outputs operation signals in response to the user's input operations.

　第１表示装置１０２は、表示部の一例である。第１表示装置１０２は、液晶、有機ＥＬ（Electro-Luminescence）等を含むディスプレイ及びディスプレイに画像データを出力するインタフェース回路を有し、画像データをディスプレイに表示する。 The first display device 102 is an example of a display unit. The first display device 102 has a display including a liquid crystal, an organic EL (Electro-Luminescence), or the like, and an interface circuit that outputs image data to the display, and displays the image data on the display.

　第１通信装置１０３は、無線信号を送受信するアンテナと、無線ＬＡＮ等の通信プロトコルに従った無線通信インタフェース回路を有する。第１通信装置１０３は、無線ＬＡＮ等の通信規格に従って、ネットワークＮと通信接続する。第１通信装置１０３は、ネットワークＮを介して情報処理装置３００又はサーバ装置４００等から受信したデータを第１処理装置１２０に送る。また、第１通信装置１０３は、第１処理装置１２０から受け取ったデータを、ネットワークＮを介して情報処理装置３００又はサーバ装置４００等に送信する。 The first communication device 103 has an antenna for transmitting and receiving wireless signals, and a wireless communication interface circuit that complies with a communication protocol such as wireless LAN. The first communication device 103 is communicatively connected to the network N in accordance with a communication standard such as wireless LAN. The first communication device 103 sends data received from the information processing device 300 or the server device 400 via the network N to the first processing device 120. The first communication device 103 also transmits data received from the first processing device 120 to the information processing device 300 or the server device 400 via the network N.

　第１インタフェース装置１０４は、無線信号を送受信するアンテナと、Ｂｌｕｅｔｏｏｔｈ（登録商標）等の通信プロトコルに従った無線通信インタフェース回路を有する。第１インタフェース装置１０４は、Ｂｌｕｅｔｏｏｔｈ（登録商標）等の通信規格に従って、各第２ウェアラブル装置２００と通信接続する。第１インタフェース装置１０４は、各第２ウェアラブル装置２００から受信したデータを第１処理装置１２０に送る。また、第１インタフェース装置１０４は、第１処理装置１２０から受け取ったデータを、各第２ウェアラブル装置２００に送信する。 The first interface device 104 has an antenna for transmitting and receiving wireless signals, and a wireless communication interface circuit that complies with a communication protocol such as Bluetooth (registered trademark). The first interface device 104 is communicatively connected to each second wearable device 200 in accordance with a communication standard such as Bluetooth (registered trademark). The first interface device 104 sends data received from each second wearable device 200 to the first processing device 120. The first interface device 104 also transmits data received from the first processing device 120 to each second wearable device 200.

　第１センサ１０５は、第１ウェアラブル装置１００にかかる３軸方向の加速度を計測する加速度センサ、及び、第１ウェアラブル装置１００にかかる３軸方向の角速度を計測するジャイロセンサ等を含む。加速度センサ及びジャイロセンサは、計測した加速度及び角速度を示す計測信号を第１処理装置１２０に出力する。 The first sensor 105 includes an acceleration sensor that measures the acceleration in three axial directions applied to the first wearable device 100, and a gyro sensor that measures the angular velocity in three axial directions applied to the first wearable device 100. The acceleration sensor and gyro sensor output measurement signals indicating the measured acceleration and angular velocity to the first processing device 120.

　なお、第１センサ１０５は、第１ウェアラブル装置１００にかかる地磁気、即ち第１ウェアラブル装置１００を装着する利用者の移動を検知する地磁気センサ等を含んでもよい。また、第１センサ１０５は、第１ウェアラブル装置１００の周囲の気圧、即ち第１ウェアラブル装置１００を装着する利用者の鉛直方向における移動を検知する気圧センサを含んでもよい。また、第１センサ１０５は、第１ウェアラブル装置１００の位置、即ち第１ウェアラブル装置１００を装着する利用者の移動を検知するＧＰＳ（Global Positioning System）センサを含んでもよい。その場合、第１センサ１０５は、検知した地磁気、気圧又は位置を示す計測信号を第１処理装置１２０に出力する。 The first sensor 105 may include a geomagnetic sensor that detects the geomagnetic field acting on the first wearable device 100, i.e., the movement of the user wearing the first wearable device 100. The first sensor 105 may also include an air pressure sensor that detects the air pressure around the first wearable device 100, i.e., the vertical movement of the user wearing the first wearable device 100. The first sensor 105 may also include a GPS (Global Positioning System) sensor that detects the position of the first wearable device 100, i.e., the movement of the user wearing the first wearable device 100. In this case, the first sensor 105 outputs a measurement signal indicating the detected geomagnetic field, air pressure, or position to the first processing device 120.

　第１記憶装置１１０は、記憶部の一例である。第１記憶装置１１０は、ＲＡＭ（Random Access Memory）、ＲＯＭ（Read Only Memory）等のメモリ装置、ハードディスク等の固定ディスク装置、又はフレキシブルディスク、光ディスク等の可搬用の記憶装置等を有する。また、第１記憶装置１１０には、第１ウェアラブル装置１００の各種処理に用いられるコンピュータプログラム、データベース、テーブル等が格納される。コンピュータプログラムは、コンピュータ読み取り可能な可搬型記録媒体から公知のセットアッププログラム等を用いて第１記憶装置１１０にインストールされてもよい。可搬型記録媒体は、例えばＣＤ－ＲＯＭ（compact disc read only memory）、ＤＶＤ－ＲＯＭ（digital versatile disc read only memory）等である。コンピュータプログラムは、所定のサーバが有する記録媒体に記憶され、ネットワークＮを介してインストールされてもよい。 The first storage device 110 is an example of a storage unit. The first storage device 110 has a memory device such as a RAM (Random Access Memory) or a ROM (Read Only Memory), a fixed disk device such as a hard disk, or a portable storage device such as a flexible disk or an optical disk. The first storage device 110 also stores computer programs, databases, tables, etc. used for various processes of the first wearable device 100. The computer programs may be installed in the first storage device 110 from a computer-readable portable recording medium using a well-known setup program, etc. The portable recording medium is, for example, a CD-ROM (compact disc read only memory), a DVD-ROM (digital versatile disc read only memory), etc. The computer programs may be stored in a recording medium owned by a specific server and installed via the network N.

　第１記憶装置１１０には、データとして、第１学習済みモデル１１１、データテーブル１１２、未接種時経年データ１１３、接種時経年データ１１４、第２学習済みモデル１１５及び第３学習済みモデル１１６等が記憶される。第１学習済みモデル１１１は、第１ウェアラブル装置１００及び第２ウェアラブル装置２００を装着したユーザの歩行姿勢状態を推定するためのモデルである。データテーブル１１２には、第１ウェアラブル装置１００の第１センサ１０５又は第２ウェアラブル装置２００の第２センサ２０５により測定された各情報が記憶される。データテーブル１１２の詳細については後述する。 The first storage device 110 stores data such as a first trained model 111, a data table 112, unvaccinated time aging data 113, vaccinated time aging data 114, a second trained model 115, and a third trained model 116. The first trained model 111 is a model for estimating the walking posture state of a user wearing the first wearable device 100 and the second wearable device 200. The data table 112 stores information measured by the first sensor 105 of the first wearable device 100 or the second sensor 205 of the second wearable device 200. Details of the data table 112 will be described later.

　未接種時経年データ１１３は、第３経年データの一例である。未接種時経年データ１１３は、人物が所定のサプリメントを摂取しなかった際における人物の躯体状態の経年変化を示す。所定のサプリメントは、例えばＤＨＡ、ＥＰＡ、ビタミンＤ、ビタミンＥ、オリザプラス及び／又はＤＰＡ等の成分を含むサプリメントである。躯体状態は、筋肉量、エネルギー消費量、体重減少量、脳内流量、歩行可能速度、単位時間（例えば１日）あたりの歩行可能歩数、又は、単位時間（例えば１日）あたりの歩行可能距離等である。経年データは、所定タイミング毎（例えば一カ月毎又は一年毎等）の躯体状態を示す。未接種時経年データ１１３は、所定のサプリメントを摂取していない一般的な人物について測定した躯体状態の経年変化に基づいて予め設定される。 The unvaccinated longitudinal data 113 is an example of the third longitudinal data. The unvaccinated longitudinal data 113 indicates the change in the physical condition of a person over time when the person does not take a specified supplement. The specified supplement is, for example, a supplement containing ingredients such as DHA, EPA, vitamin D, vitamin E, Oryza Plus, and/or DPA. The physical condition is muscle mass, energy consumption, weight loss, brain flow rate, walking speed, number of steps that can be walked per unit time (e.g., one day), or distance that can be walked per unit time (e.g., one day). The longitudinal data indicates the physical condition at a specified timing (e.g., one month or one year). The unvaccinated longitudinal data 113 is preset based on the change in the physical condition of a general person who does not take a specified supplement over time.

　接種時経年データ１１４は、第１経年データの一例である。接種時経年データ１１４は、人物が所定のサプリメントを摂取した際における人物の躯体状態の経年変化を示す。接種時経年データ１１４は、所定のサプリメントを摂取している一般的な人物について測定した躯体状態の経年変化に基づいて予め設定される。 The vaccination time aging data 114 is an example of the first aging data. The vaccination time aging data 114 indicates the change over time in the physical condition of a person when the person takes a specified supplement. The vaccination time aging data 114 is set in advance based on the change over time in the physical condition measured for a typical person who takes a specified supplement.

　第２学習済みモデル１１５は、未接種時経年データ１１３から、第１ウェアラブル装置１００及び第２ウェアラブル装置２００を装着したユーザの歩行姿勢状態に応じた経年データを推定するためのモデルである。第３学習済みモデル１１６は、接種時経年データ１１４から、第１ウェアラブル装置１００及び第２ウェアラブル装置２００を装着したユーザの歩行姿勢状態に応じた経年データを推定するためのモデルである。 The second trained model 115 is a model for estimating aging data corresponding to the walking posture state of a user wearing the first wearable device 100 and the second wearable device 200 from the unvaccinated time aging data 113. The third trained model 116 is a model for estimating aging data corresponding to the walking posture state of a user wearing the first wearable device 100 and the second wearable device 200 from the vaccinated time aging data 114.

　第１処理装置１２０は、予め第１記憶装置１１０に記憶されているプログラムに基づいて動作する。第１処理装置１２０は、例えばＣＰＵである。第１処理装置１２０として、ＤＳＰ（digital signal processor）、ＬＳＩ（large scale integration）、ＡＳＩＣ（Application Specific Integrated Circuit）、ＦＰＧＡ（Field-Programmable Gate Array）等が用いられてもよい。第１処理装置１２０は、第１入力装置１０１、第１表示装置１０２、第１通信装置１０３、第１インタフェース装置１０４、第１センサ１０５及び第１記憶装置１１０等と接続され、各装置を制御する。 The first processing device 120 operates based on a program previously stored in the first storage device 110. The first processing device 120 is, for example, a CPU. A DSP (digital signal processor), an LSI (large scale integration), an ASIC (application specific integrated circuit), an FPGA (field-programmable gate array), etc. may be used as the first processing device 120. The first processing device 120 is connected to the first input device 101, the first display device 102, the first communication device 103, the first interface device 104, the first sensor 105, the first storage device 110, etc., and controls each device.

　第１処理装置１２０は、第１記憶装置１１０に記憶されたコンピュータプログラムを読み取り、読み取ったコンピュータプログラムに従って動作する。これにより、第１処理装置１２０は、第１取得部１２１、第１生成部１２２及び表示制御部１２３として機能する。 The first processing device 120 reads the computer program stored in the first storage device 110 and operates according to the read computer program. As a result, the first processing device 120 functions as a first acquisition unit 121, a first generation unit 122, and a display control unit 123.

　図３は、第２ウェアラブル装置２００の概略構成を示す図である。 FIG. 3 is a diagram showing the general configuration of the second wearable device 200.

　第２ウェアラブル装置２００は、ユーザに装着可能な端末装置である。例えば、第２ウェアラブル装置２００は、ユーザの腕に装着される腕時計型ウェアラブルコンピュータである。また、第２ウェアラブル装置２００は、ユーザの耳に装着されるイヤフォン型ウェアラブルコンピュータである。また、第２ウェアラブル装置２００は、ユーザの靴内部に装着されるインソール（中敷き）型ウェアラブルコンピュータである。第２ウェアラブル装置２００は、第２入力装置２０１、第２表示装置２０２、第２通信装置２０３、第２インタフェース装置２０４、第２センサ２０５、第２記憶装置２１０及び第２処理装置２２０等を有する。第２入力装置２０１、第２表示装置２０２、第２通信装置２０３、第２インタフェース装置２０４、第２センサ２０５、第２記憶装置２１０及び第２処理装置２２０は、ＣＰＵバス等を介して相互に接続される。 The second wearable device 200 is a terminal device that can be worn by a user. For example, the second wearable device 200 is a wristwatch-type wearable computer worn on the user's arm. The second wearable device 200 is also an earphone-type wearable computer worn on the user's ear. The second wearable device 200 is also an insole-type wearable computer worn inside the user's shoe. The second wearable device 200 has a second input device 201, a second display device 202, a second communication device 203, a second interface device 204, a second sensor 205, a second storage device 210, and a second processing device 220, etc. The second input device 201, the second display device 202, the second communication device 203, the second interface device 204, the second sensor 205, the second storage device 210, and the second processing device 220 are connected to each other via a CPU bus or the like.

　第２入力装置２０１は、ボタン等の入力デバイス及び入力デバイスから信号を取得するインタフェース回路を有し、利用者の入力操作に応じた操作信号を出力する。第２入力装置２０１は、省略されてもよい。 The second input device 201 has an input device such as a button and an interface circuit that acquires a signal from the input device, and outputs an operation signal according to an input operation by the user. The second input device 201 may be omitted.

　第２表示装置２０２は、出力部の一例である。第２表示装置２０２は、液晶、有機ＥＬ等を含むディスプレイ及びディスプレイに画像データを出力するインタフェース回路を有し、画像データをディスプレイに表示する。第２表示装置２０２は、ＬＥＤ（Light Emitting Diode）等を有し、点灯又は消灯によりユーザに対する通知を行ってもよい。第２表示装置２０２は、省略されてもよい。 The second display device 202 is an example of an output unit. The second display device 202 has a display including a liquid crystal, an organic electroluminescence, etc., and an interface circuit that outputs image data to the display, and displays the image data on the display. The second display device 202 has an LED (Light Emitting Diode) or the like, and may notify the user by turning it on or off. The second display device 202 may be omitted.

　第２通信装置２０３は、無線信号を送受信するアンテナと、無線ＬＡＮ等の通信プロトコルに従った無線通信インタフェース回路を有する。第２通信装置２０３は、無線ＬＡＮ等の通信規格に従って、ネットワークＮと通信接続する。第２通信装置２０３は、ネットワークＮを介して情報処理装置３００又はサーバ装置４００等から受信したデータを第２処理装置２２０に送る。また、第２通信装置２０３は、第２処理装置２２０から受け取ったデータを、ネットワークＮを介して情報処理装置３００又はサーバ装置４００等に送信する。 The second communication device 203 has an antenna for transmitting and receiving wireless signals, and a wireless communication interface circuit that complies with a communication protocol such as wireless LAN. The second communication device 203 is communicatively connected to the network N in accordance with a communication standard such as wireless LAN. The second communication device 203 sends data received from the information processing device 300 or the server device 400 via the network N to the second processing device 220. The second communication device 203 also transmits data received from the second processing device 220 to the information processing device 300 or the server device 400 via the network N.

　第２インタフェース装置２０４は、無線信号を送受信するアンテナと、Ｂｌｕｅｔｏｏｔｈ（登録商標）等の通信プロトコルに従った無線通信インタフェース回路を有する。第２インタフェース装置２０４は、Ｂｌｕｅｔｏｏｔｈ（登録商標）等の通信規格に従って、第１ウェアラブル装置１００と通信接続する。第２インタフェース装置２０４は、第１ウェアラブル装置１００から受信したデータを第２処理装置２２０に送る。また、第２インタフェース装置２０４は、第２処理装置２２０から受け取ったデータを、第１ウェアラブル装置１００に送信する。 The second interface device 204 has an antenna for transmitting and receiving wireless signals, and a wireless communication interface circuit that conforms to a communication protocol such as Bluetooth (registered trademark). The second interface device 204 is communicatively connected to the first wearable device 100 in accordance with a communication standard such as Bluetooth (registered trademark). The second interface device 204 sends data received from the first wearable device 100 to the second processing device 220. The second interface device 204 also transmits data received from the second processing device 220 to the first wearable device 100.

　第２センサ２０５は、第２ウェアラブル装置２００にかかる３軸方向の加速度を計測する加速度センサ、及び、第２ウェアラブル装置２００にかかる３軸方向の角速度を計測するジャイロセンサ等を含む。加速度センサ及びジャイロセンサは、計測した加速度及び角速度を示す計測信号を第２処理装置２２０に出力する。第２センサ２０５は、インソール型圧力センサを含んでもよい。その場合、第２センサ２０５は、１次元又は２次元に配置された抵抗変化型又は静電容量変化型の圧力センサを含み、各圧力センサにより測定された圧力の大きさを各画素の階調値とする足裏データを生成して、第２処理装置２２０に出力する。足裏データは、例えば各圧力センサにより測定された圧力の大きさを各画素の階調値とする足裏画像である。足裏データは、例えば各圧力センサにより測定された圧力を要素とする足裏ベクトルでもよい。 The second sensor 205 includes an acceleration sensor that measures the acceleration in three axial directions applied to the second wearable device 200, and a gyro sensor that measures the angular velocity in three axial directions applied to the second wearable device 200. The acceleration sensor and the gyro sensor output measurement signals indicating the measured acceleration and angular velocity to the second processing device 220. The second sensor 205 may include an insole-type pressure sensor. In this case, the second sensor 205 includes a resistance change type or capacitance change type pressure sensor arranged one-dimensionally or two-dimensionally, generates sole data in which the magnitude of pressure measured by each pressure sensor is the gradation value of each pixel, and outputs the sole data to the second processing device 220. The sole data is, for example, a sole image in which the magnitude of pressure measured by each pressure sensor is the gradation value of each pixel. The sole data may be, for example, a sole vector whose elements are the pressures measured by each pressure sensor.

　第２記憶装置２１０は、ＲＡＭ、ＲＯＭ等のメモリ装置、ハードディスク等の固定ディスク装置、又はフレキシブルディスク、光ディスク等の可搬用の記憶装置等を有する。また、第２記憶装置２１０には、第２ウェアラブル装置２００の各種処理に用いられるコンピュータプログラム、データベース、テーブル等が格納される。コンピュータプログラムは、コンピュータ読み取り可能な可搬型記録媒体から公知のセットアッププログラム等を用いて第２記憶装置２１０にインストールされてもよい。可搬型記録媒体は、例えばＣＤ－ＲＯＭ、ＤＶＤ－ＲＯＭ等である。コンピュータプログラムは、所定のサーバが有する記録媒体に記憶され、ネットワークＮを介してインストールされてもよい。 The second storage device 210 has a memory device such as RAM or ROM, a fixed disk device such as a hard disk, or a portable storage device such as a flexible disk or optical disk. The second storage device 210 also stores computer programs, databases, tables, etc. used for various processes of the second wearable device 200. The computer programs may be installed into the second storage device 210 from a computer-readable portable recording medium using a well-known setup program, etc. The portable recording medium is, for example, a CD-ROM, DVD-ROM, etc. The computer programs may be stored in a recording medium owned by a specific server and installed via the network N.

　第２処理装置２２０は、予め第２記憶装置２１０に記憶されているプログラムに基づいて動作する。第２処理装置２２０は、例えばＣＰＵである。第２処理装置２２０として、ＤＳＰ、ＬＳＩ、ＡＳＩＣ、ＦＰＧＡ等が用いられてもよい。第２処理装置２２０は、第２入力装置２０１、第２表示装置２０２、第２通信装置２０３、第２インタフェース装置２０４、第２センサ２０５及び第２記憶装置２１０等と接続され、各装置を制御する。 The second processing device 220 operates based on a program previously stored in the second storage device 210. The second processing device 220 is, for example, a CPU. A DSP, an LSI, an ASIC, an FPGA, etc. may also be used as the second processing device 220. The second processing device 220 is connected to the second input device 201, the second display device 202, the second communication device 203, the second interface device 204, the second sensor 205, the second storage device 210, etc., and controls each device.

　第２処理装置２２０は、第２記憶装置２１０に記憶されたコンピュータプログラムを読み取り、読み取ったコンピュータプログラムに従って動作する。これにより、第２処理装置２２０は、第２取得部２２１及び第２送信部２２２として機能する。 The second processing device 220 reads the computer program stored in the second storage device 210 and operates according to the read computer program. In this way, the second processing device 220 functions as a second acquisition unit 221 and a second transmission unit 222.

　図４は、データテーブル１１２のデータ構造の一例を示す模式図である。 FIG. 4 is a schematic diagram showing an example of the data structure of data table 112.

　データテーブル１１２には、第１ウェアラブル装置１００又は第２ウェアラブル装置２００から取得した角速度及び加速度、地磁気、気圧（不図示）、位置（不図示）又は足裏データが、各装置の識別情報（装置ＩＤ）及び各データが取得された取得時刻と関連付けて記憶される。 In the data table 112, the angular velocity and acceleration, geomagnetic field, air pressure (not shown), position (not shown) or sole data acquired from the first wearable device 100 or the second wearable device 200 are stored in association with the identification information (device ID) of each device and the acquisition time when each data was acquired.

　図５は、躯体状態表示システム１における表示処理の動作の例を示すシーケンスである。 FIG. 5 shows a sequence illustrating an example of the operation of the display process in the structure condition display system 1.

　以下、図５示したフローチャートを参照しつつ、表示処理の動作の例を説明する。なお、以下に説明する動作のフローは、予め躯体状態表示システム１が有する各装置の各記憶装置に記憶されているプログラムに基づき主に各装置の各処理装置により各装置の各要素と協働して実行される。 Below, an example of the display processing operation will be described with reference to the flowchart shown in Figure 5. Note that the flow of the operation described below is executed mainly by each processing device of each device in cooperation with each element of each device, based on a program that has been stored in advance in each storage device of each device of the structure condition display system 1.

　最初に、各第２ウェアラブル装置２００の第２取得部２２１は、第２センサ２０５から計測信号を取得する。第２取得部２２１は、計測信号に示される第２ウェアラブル装置２００にかかる３軸方向の加速度及び角速度を示す計測信号又はユーザの足裏が撮像された足裏データを、ユーザが歩行した際に得られる第２データとして取得する（ステップＳ１０１）。第２取得部２２１は、定期的に第２データを取得する。 First, the second acquisition unit 221 of each second wearable device 200 acquires a measurement signal from the second sensor 205. The second acquisition unit 221 acquires a measurement signal indicating the acceleration and angular velocity in three axial directions acting on the second wearable device 200 as indicated in the measurement signal, or sole of the foot data obtained by capturing an image of the sole of the user's foot, as second data obtained when the user walks (step S101). The second acquisition unit 221 periodically acquires the second data.

　次に、第２送信部２２２は、第２取得部２２１が取得した第２データを、第２ウェアラブル装置２００の装置ＩＤ及び第２データを取得した取得時刻とともに、第２インタフェース装置２０４を介して第１ウェアラブル装置１００に送信する（ステップＳ１０２）。第２送信部２２２は、第２取得部２２１が第２データを取得するたびに、第２データを第１ウェアラブル装置１００に送信する。第２送信部２２２は、第２取得部２２１が取得した複数の第２データをまとめて任意のタイミングに第１ウェアラブル装置１００に送信してもよい。 Then, the second transmission unit 222 transmits the second data acquired by the second acquisition unit 221 to the first wearable device 100 via the second interface device 204 together with the device ID of the second wearable device 200 and the acquisition time when the second data was acquired (step S102). The second transmission unit 222 transmits the second data to the first wearable device 100 every time the second acquisition unit 221 acquires the second data. The second transmission unit 222 may transmit multiple pieces of second data acquired by the second acquisition unit 221 together to the first wearable device 100 at any timing.

　次に、第１ウェアラブル装置１００の第１取得部１２１は、第１インタフェース装置１０４を介して第２ウェアラブル装置２００から第２データ、装置ＩＤ及び取得時刻を受信することにより取得する（ステップＳ１０３）。 Next, the first acquisition unit 121 of the first wearable device 100 acquires the second data, the device ID, and the acquisition time by receiving them from the second wearable device 200 via the first interface device 104 (step S103).

　次に、第１取得部１２１は、第１センサ１０５から計測信号を取得する。第１取得部１２１は、計測信号に示される第１ウェアラブル装置１００にかかる３軸方向の加速度及び角速度、地磁気、気圧又は位置を、ユーザが歩行した際に得られる第１データとして取得する（ステップＳ１０４）。第１データは、第２データとは異なるデータである。第１取得部１２１は、定期的に第１データを取得する。第１取得部１２１は、取得した第１データ及び第２データを対応する装置ＩＤ及び取得時刻と関連付けてデータテーブル１１２に記憶する。 Next, the first acquisition unit 121 acquires a measurement signal from the first sensor 105. The first acquisition unit 121 acquires the acceleration and angular velocity in three axes directions, geomagnetism, atmospheric pressure, or position of the first wearable device 100 indicated in the measurement signal as first data obtained when the user walks (step S104). The first data is different from the second data. The first acquisition unit 121 periodically acquires the first data. The first acquisition unit 121 stores the acquired first data and second data in the data table 112 in association with the corresponding device ID and acquisition time.

　次に、第１取得部１２１は、取得した第１データ及び第２データに基づいて、第１ウェアラブル装置１００及び第２ウェアラブル装置２００を装着したユーザの歩行姿勢状態を推定することにより取得する（ステップＳ１０５）。第１取得部１２１は、第１学習済みモデル１１１を用いて、ユーザの歩行姿勢状態を推定する。 Next, the first acquisition unit 121 estimates and acquires the walking posture state of the user wearing the first wearable device 100 and the second wearable device 200 based on the acquired first data and second data (step S105). The first acquisition unit 121 estimates the walking posture state of the user using the first trained model 111.

　第１学習済みモデル１１１は、一又は複数の学習済みモデルを含む。第１学習済みモデル１１１は、情報処理装置３００又はサーバ装置４００等により事前に生成される。第１学習済みモデル１１１は、ディープラーニング、サポートベクターマシン等の教師あり学習により事前学習される。第１学習済みモデル１１１は、ランダムフォレスト等により事前学習されてもよい。第１学習済みモデル１１１は、入力データが入力された場合に、その入力データを生成したウェアラブル装置を装着したユーザの歩行姿勢状態を出力するように学習される。 The first trained model 111 includes one or more trained models. The first trained model 111 is generated in advance by the information processing device 300, the server device 400, or the like. The first trained model 111 is trained in advance by supervised learning such as deep learning and support vector machines. The first trained model 111 may be trained in advance by random forests, or the like. The first trained model 111 is trained to output, when input data is input, the walking posture state of a user wearing the wearable device that generated the input data.

　入力データは、第１ウェアラブル装置１００により取得された第１データ、及び／又は、第２ウェアラブル装置２００により取得された第２データである。入力データは、第１データ及び／又は第２データに基づいて生成されたデータでもよい。例えば、入力データは、連続する所定歩数（例えば１０歩）分又は所定期間（例えば５秒間）分の第１データ及び／又は第２データのセット（各データを要素として一次元又は二次元に並べたベクトル）である。第１取得部１２１は、連続して計測された複数の第１データ及び第２データを解析して、時系列に並んだ第１データ及び第２データにおける極大値及び極小値を検出する。第１取得部１２１は、検出した極大値及び極小値に基づいて、第１ウェアラブル装置１００及び第２ウェアラブル装置２００の振動周期を検出する。第１取得部１２１は、検出した振動周期に基づいて、第１ウェアラブル装置１００及び第２ウェアラブル装置２００を装着したユーザの脚の振り出し間隔を特定する。そして、第１取得部１２１は、ユーザの所定歩数分ずつの第１データ及び第２データを特定する。入力データは、連続する所定歩数分又は所定期間分の第１データ及び／又は第２データの平均値、中央値、最大値又は最小値等の統計値でもよい。また、入力データは、第１データ及び／又は第２データに含まれる角速度、加速度又は地磁気から算出されたクォータニオンでもよい。 The input data is the first data acquired by the first wearable device 100 and/or the second data acquired by the second wearable device 200. The input data may be data generated based on the first data and/or the second data. For example, the input data is a set of first data and/or second data (a vector in which each data is arranged in one dimension or two dimensions as an element) for a predetermined number of consecutive steps (e.g., 10 steps) or a predetermined period (e.g., 5 seconds). The first acquisition unit 121 analyzes a plurality of first data and second data measured continuously to detect maximum and minimum values in the first data and second data arranged in time series. The first acquisition unit 121 detects the vibration period of the first wearable device 100 and the second wearable device 200 based on the detected maximum and minimum values. The first acquisition unit 121 identifies the leg swing interval of the user wearing the first wearable device 100 and the second wearable device 200 based on the detected vibration period. Then, the first acquisition unit 121 identifies the first data and the second data for each predetermined number of steps of the user. The input data may be a statistical value such as an average value, a median value, a maximum value, or a minimum value of the first data and/or the second data for a predetermined number of consecutive steps or a predetermined period. The input data may also be a quaternion calculated from the angular velocity, acceleration, or geomagnetism included in the first data and/or the second data.

　また、入力データは、第１データ及び／又は第２データに含まれる３軸方向の角速度もしくは加速度又は足裏データに対して主成分分析によって算出された所定成分（第一主成分及び／又は第二主成分等）でもよい。その場合、第一主成分の寄与率が所定比率（例えば８０％）より大きい場合、第一主成分が入力データとして使用され、第一主成分の寄与率が所定比率以下である場合、第二主成分が入力データとして使用されてもよい。また、主成分分析は、公知のノイズ処理技術を利用して、ノイズが除去されてから実行されてもよい。これらの場合、第１学習済みモデル１１１として、ランダムフォレスト等により事前学習されたモデルが使用される。これらの場合、第１学習済みモデル１１１として、ニューラルネットワーク等により事前学習されたモデルが使用されてもよい。これにより、躯体状態表示システム１は、第１学習済みモデル１１１の精度をより向上させることができる。 The input data may also be a predetermined component (such as the first principal component and/or the second principal component) calculated by principal component analysis of the angular velocity or acceleration in three axial directions or the sole data included in the first data and/or the second data. In this case, if the contribution rate of the first principal component is greater than a predetermined ratio (e.g., 80%), the first principal component may be used as the input data, and if the contribution rate of the first principal component is equal to or less than the predetermined ratio, the second principal component may be used as the input data. Furthermore, the principal component analysis may be performed after noise is removed using a known noise processing technique. In these cases, a model pre-trained by a random forest or the like is used as the first trained model 111. In these cases, a model pre-trained by a neural network or the like may be used as the first trained model 111. This allows the structure condition display system 1 to further improve the accuracy of the first trained model 111.

　また、入力データは、第１データ及び／又は第２データに含まれる３軸方向の各角速度及び／又は加速度に対して短時間フーリエ変換又はウェーブレット変換によって変換されたスペクトルの画像でもよい。第１データに含まれる３軸方向の各角速度及び／又は加速度に対して短時間フーリエ変換又はウェーブレット変換によって変換されたスペクトルの画像は、第１画像データ及び画像データの一例である。第２データに含まれる３軸方向の各角速度及び／又は加速度に対して短時間フーリエ変換又はウェーブレット変換によって変換されたスペクトルの画像は、第２画像データ及び画像データの一例である。これらの場合、第１学習済みモデル１１１として、ランダムフォレスト等により事前学習されたモデルが使用される。これらの場合、第１学習済みモデル１１１として、ニューラルネットワーク等により事前学習されたモデルが使用されてもよい。これにより、躯体状態表示システム１は、第１学習済みモデル１１１の精度をより向上させることができる。 The input data may also be a spectral image transformed by short-time Fourier transform or wavelet transform for each angular velocity and/or acceleration in the three-axis directions included in the first data and/or second data. The spectral image transformed by short-time Fourier transform or wavelet transform for each angular velocity and/or acceleration in the three-axis directions included in the first data is an example of the first image data and image data. The spectral image transformed by short-time Fourier transform or wavelet transform for each angular velocity and/or acceleration in the three-axis directions included in the second data is an example of the second image data and image data. In these cases, a model pre-trained by a random forest or the like is used as the first trained model 111. In these cases, a model pre-trained by a neural network or the like may be used as the first trained model 111. This allows the structure condition display system 1 to further improve the accuracy of the first trained model 111.

　また、入力データは、足裏画像の各画素の階調値（圧力）もしくは足裏ベクトルの各要素と、角速度、加速度、地磁気、気圧及び／又は位置とを要素とする特徴ベクトルでもよい。 The input data may also be a feature vector whose elements are the gradation value (pressure) of each pixel of the sole image or each element of the sole vector, and angular velocity, acceleration, geomagnetic field, atmospheric pressure, and/or position.

　第１学習済みモデル１１１から出力される歩行姿勢状態は、五つの要素を含む。第１要素は、歩幅の状態、特にユーザの歩幅が十分であるか不十分であるかを示す。第２要素は、重心位置の状態、特にユーザの重心が正常であるか前傾であるか後傾であるかを示す。第３要素は、振出脚の状態、特にユーザが出す足を蹴っていないか引きずっているか蹴っているかを示す。第４要素は、支持脚の状態、特にユーザの軸足が突っ張っているか突っ張っていないかを示す。第５要素は、股関節の状態、特にユーザの股関節が開いているか開いていないかを示す。第１要素は、歩行姿勢の前提に関する要素である。第２要素及び第３要素は、前後方向の軸に関する要素である。第４要素及び第５要素は、回転（左右方向の）軸に関する要素である。歩行姿勢状態は、第１要素から第５要素までの全ての要素を含んでいなくてもよく、少なくとも一つの要素を含んでいればよい。 The walking posture state output from the first trained model 111 includes five elements. The first element indicates the state of the stride, particularly whether the user's stride is sufficient or insufficient. The second element indicates the state of the center of gravity position, particularly whether the user's center of gravity is normal, leaning forward, or leaning backward. The third element indicates the state of the swinging leg, particularly whether the user's leg is not kicking, dragging, or kicking. The fourth element indicates the state of the supporting leg, particularly whether the user's supporting leg is stiff or not stiff. The fifth element indicates the state of the hip joint, particularly whether the user's hip joint is open or not. The first element is an element related to the premise of the walking posture. The second and third elements are elements related to the axis in the front-back direction. The fourth and fifth elements are elements related to the axis of rotation (left-right direction). The walking posture state does not have to include all of the elements from the first to the fifth elements, and it is sufficient to include at least one element.

　第１学習済みモデル１１１から出力される歩行姿勢状態は、第１要素から第５要素までの各要素を直接示す情報に限定されず、第１要素から第５要素までの各要素に関する所定の特徴量でもよい。 The walking posture state output from the first trained model 111 is not limited to information directly indicating each of the first to fifth elements, but may be a predetermined feature amount relating to each of the first to fifth elements.

　第１学習済みモデル１１１は、例えば、第１ウェアラブル装置１００及び各第２ウェアラブル装置２００のそれぞれに対応する学習済みモデルを含む。即ち、第１学習済みモデル１１１は、第１ウェアラブル装置１００から取得した第１データに基づいて推定された第１歩行姿勢状態を出力可能な第１学習済みモデルと、それぞれ各第２ウェアラブル装置２００から取得した第２データに基づいて推定された第２歩行姿勢状態を出力可能な第２学習済みモデルとを含む。 The first trained model 111 includes, for example, trained models corresponding to the first wearable device 100 and each of the second wearable devices 200. That is, the first trained model 111 includes a first trained model capable of outputting a first walking posture state estimated based on first data acquired from the first wearable device 100, and a second trained model capable of outputting a second walking posture state estimated based on second data acquired from each of the second wearable devices 200.

　または、第１学習済みモデル１１１は、第１ウェアラブル装置１００から取得した第１データに基づく第１画像データに基づいて推定された第１歩行姿勢状態を出力可能な第１学習済みモデルと、それぞれ各第２ウェアラブル装置２００から取得した第２データに基づく第２画像データに基づいて推定された第２歩行姿勢状態を出力可能な第２学習済みモデルとを含む。 Alternatively, the first trained model 111 includes a first trained model capable of outputting a first walking posture state estimated based on first image data based on first data acquired from the first wearable device 100, and a second trained model capable of outputting a second walking posture state estimated based on second image data based on second data acquired from each of the second wearable devices 200.

　これらにより、躯体状態表示システム１は、ウェアラブル装置が装着される体の部位毎に歩行姿勢状態を推定し、部位毎に推定した歩行姿勢状態に基づいて、ユーザの歩行姿勢状態を総合的に推定することができる。 As a result, the body state display system 1 can estimate the walking posture state for each body part on which the wearable device is attached, and can comprehensively estimate the walking posture state of the user based on the walking posture state estimated for each body part.

　なお、これらの場合、第１取得部１２１は、第１データに基づく入力データを第１学習済みモデルの第１階層に入力することにより歩行姿勢状態を取得し、第２データに基づく入力データを第２学習済みモデルの第２階層に入力することにより歩行姿勢状態を取得してもよい。この第１階層は、入力層又は中間層のうちの特定の層である。この第２階層は、第１階層と異なる層であり、入力層又は中間層のうちの特定の層である。これにより、躯体状態表示システム１は、各学習済みモデルにおける処理階層数（処理ステップ数）を柔軟に変更でき、処理時間及び処理負荷の増大を抑制することができる。 In these cases, the first acquisition unit 121 may acquire the walking posture state by inputting input data based on the first data into the first layer of the first trained model, and may acquire the walking posture state by inputting input data based on the second data into the second layer of the second trained model. This first layer is a specific layer of the input layer or the intermediate layer. This second layer is a layer different from the first layer, and is a specific layer of the input layer or the intermediate layer. This allows the structure condition display system 1 to flexibly change the number of processing layers (number of processing steps) in each trained model, and suppress increases in processing time and processing load.

　また、第１取得部１２１は、第１データに基づく入力データを第１学習済みモデルに入力した際に第１学習済みモデルの第１階層から出力される情報を歩行姿勢状態として取得し、第２データに基づく入力データを第２学習済みモデルに入力した際に第２学習済みモデルの第２階層から出力される情報を歩行姿勢状態として取得してもよい。この第１階層は、中間層又は出力層のうちの特定の層である。この第２階層は、第１階層と異なる層であり、中間層又は出力層のうちの特定の層である。これにより、躯体状態表示システム１は、各学習済みモデルにおける処理階層数（処理ステップ数）を柔軟に変更でき、処理時間及び処理負荷の増大を抑制することができる。 The first acquisition unit 121 may also acquire, as the walking posture state, information output from the first layer of the first trained model when input data based on the first data is input to the first trained model, and acquire, as the walking posture state, information output from the second layer of the second trained model when input data based on the second data is input to the second trained model. This first layer is a specific layer of the intermediate layer or the output layer. This second layer is a layer different from the first layer and is a specific layer of the intermediate layer or the output layer. This allows the structure condition display system 1 to flexibly change the number of processing layers (number of processing steps) in each trained model, and suppress increases in processing time and processing load.

　なお、第１学習済みモデル１１１は、複数の第１ウェアラブル装置１００及び第２ウェアラブル装置２００に対応するように生成されてもよい。特に、第１学習済みモデル１１１は、全ての第１ウェアラブル装置１００及び第２ウェアラブル装置２００に対応するように生成されてもよい。即ち、第１学習済みモデル１１１は、第１ウェアラブル装置１００から取得した第１データ及び各第２ウェアラブル装置２００から取得した第２データに基づいて推定された歩行姿勢状態を出力可能な単一の学習済みモデルを含む。 In addition, the first trained model 111 may be generated to correspond to a plurality of first wearable devices 100 and second wearable devices 200. In particular, the first trained model 111 may be generated to correspond to all of the first wearable devices 100 and second wearable devices 200. That is, the first trained model 111 includes a single trained model capable of outputting a walking posture state estimated based on the first data acquired from the first wearable device 100 and the second data acquired from each second wearable device 200.

　または、第１学習済みモデル１１１は、第１ウェアラブル装置１００及び各第２ウェアラブル装置２００から取得した第１データ及び各第２データに基づく画像データに基づいて推定された歩行姿勢状態を出力可能な単一の学習済みモデルを含む。 Alternatively, the first trained model 111 includes a single trained model capable of outputting an estimated walking posture state based on image data based on the first data and each second data acquired from the first wearable device 100 and each second wearable device 200.

　これらの場合、第１学習済みモデル１１１は、相互に対応する時刻（略一致する時刻）に複数のウェアラブル装置から取得された第１データ及び第２データを要素とするベクトルが入力された場合に、歩行姿勢状態を出力するように学習される。 In these cases, the first trained model 111 is trained to output a walking posture state when a vector whose elements are the first data and the second data acquired from multiple wearable devices at corresponding times (approximately the same times) is input.

　これにより、躯体状態表示システム１は、複数のウェアラブル装置から取得したデータに基づいて、複合的に歩行姿勢状態を推定することができる。 As a result, the body condition display system 1 can estimate the walking posture condition in a comprehensive manner based on data acquired from multiple wearable devices.

　また、第１学習済みモデル１１１は、第１要素から第５要素までの各要素に対応する学習済みモデルを含む。即ち、第１学習済みモデル１１１は、第１要素から第５要素までの各要素に関する情報を出力可能な複数の学習済みモデルを含む。第１学習済みモデル１１１は、第１要素から第５要素までの各要素に関する情報をまとめて出力可能な単一の学習済みモデルを含んでもよい。 The first trained model 111 also includes trained models corresponding to each of the first to fifth elements. That is, the first trained model 111 includes a plurality of trained models capable of outputting information relating to each of the first to fifth elements. The first trained model 111 may also include a single trained model capable of collectively outputting information relating to each of the first to fifth elements.

　また、第１学習済みモデル１１１は、直列に配列される上流側学習済みモデル及び下流側学習済みモデルを含んでもよい。その場合、上流側学習済みモデルは、入力データが入力された場合に所定の特徴量を出力するように学習され、下流側学習済みモデルは、上流側学習済みモデルの中間層又は出力層から出力された特徴量が入力された場合に、歩行姿勢状態を出力するように学習される。所定の特徴量は、入力データに関する特徴量、又は、歩行姿勢状態に関する特徴量である。 The first trained model 111 may also include an upstream trained model and a downstream trained model arranged in series. In this case, the upstream trained model is trained to output a predetermined feature when input data is input, and the downstream trained model is trained to output a walking posture state when a feature output from an intermediate layer or an output layer of the upstream trained model is input. The predetermined feature is a feature related to the input data or a feature related to the walking posture state.

　また、第１学習済みモデル１１１は、上記した各学習済みモデルの任意の組み合わせを含んでもよい。例えば、第１学習済みモデル１１１は、第１上流側学習済みモデル、第２上流側学習済みモデル、第３上流側学習済みモデル及び下流側学習済みモデルを含む。第１上流側学習済みモデルは、足裏データの第一主成分が入力された場合に所定の特徴量を出力するように学習される。第２上流側学習済みモデルは、足裏データの第二主成分が入力された場合に所定の特徴量を出力するように学習される。第３上流側学習済みモデルは、角速度又は加速度の第一主成分が入力された場合に所定の特徴量を出力するように学習される。または、第３上流側学習済みモデルは、角速度及び／又は加速度に対して短時間フーリエ変換又はウェーブレット変換によって変換されたスペクトルの画像が入力された場合に所定の特徴量を出力するように学習される。下流側学習済みモデルは、第１上流側学習済みモデル、第２上流側学習済みモデル及び第３上流側学習済みモデルの出力層又は中間層から出力されたデータを要素とする特徴ベクトルが入力された場合に、歩行姿勢状態を出力するように学習される。これにより、躯体状態表示システム１は、第１学習済みモデル１１１の精度をより向上させることができる。 Furthermore, the first trained model 111 may include any combination of the trained models described above. For example, the first trained model 111 includes a first upstream trained model, a second upstream trained model, a third upstream trained model, and a downstream trained model. The first upstream trained model is trained to output a predetermined feature amount when a first principal component of sole data is input. The second upstream trained model is trained to output a predetermined feature amount when a second principal component of sole data is input. The third upstream trained model is trained to output a predetermined feature amount when a first principal component of angular velocity or acceleration is input. Or, the third upstream trained model is trained to output a predetermined feature amount when an image of a spectrum transformed by short-time Fourier transform or wavelet transform for angular velocity and/or acceleration is input. The downstream trained model is trained to output a walking posture state when a feature vector whose elements are data output from the output layer or intermediate layer of the first upstream trained model, the second upstream trained model, and the third upstream trained model is input. This allows the body state display system 1 to further improve the accuracy of the first trained model 111.

　または、第１学習済みモデル１１１は、第１上流側学習済みモデル、第２上流側学習済みモデル及び下流側学習済みモデルを含む。第１上流側学習済みモデルは、足裏データが入力された場合に所定の特徴量を出力するように学習される。第２上流側学習済みモデルは、角速度又は加速度が入力された場合に所定の特徴量を出力するように学習される。下流側学習済みモデルは、第１上流側学習済みモデル及び第２上流側学習済みモデルの出力層又は中間層から出力されたデータを要素とする特徴ベクトルが入力された場合に、歩行姿勢状態を出力するように学習される。これにより、躯体状態表示システム１は、第１学習済みモデル１１１の精度をより向上させることができる。 Alternatively, the first trained model 111 includes a first upstream trained model, a second upstream trained model, and a downstream trained model. The first upstream trained model is trained to output a predetermined feature value when sole data is input. The second upstream trained model is trained to output a predetermined feature value when angular velocity or acceleration is input. The downstream trained model is trained to output a walking posture state when a feature vector whose elements are data output from the output layer or intermediate layer of the first upstream trained model and the second upstream trained model is input. This enables the body state display system 1 to further improve the accuracy of the first trained model 111.

　このように、第１学習済みモデル１１１は、第１ウェアラブル装置１００及び第２ウェアラブル装置２００を装着したユーザの推定された歩行姿勢状態を出力可能に設けられる。 In this way, the first trained model 111 is configured to be able to output an estimated walking posture state of a user wearing the first wearable device 100 and the second wearable device 200.

　第１学習済みモデル１１１の学習用データは、事前に生成された入力データと、その入力データを生成したウェアラブル装置を装着したユーザの歩行姿勢状態との組合せを含む。各学習用データは、各ウェアラブル装置により第１データ又は第２データが生成された時に、第１データ又は第２データを生成したウェアラブル装置を装着したユーザを撮像した動画像を見た専門家により作成される。 The learning data of the first trained model 111 includes a combination of pre-generated input data and the walking posture state of a user wearing the wearable device that generated the input data. Each learning data is created by an expert who views a video image of a user wearing the wearable device that generated the first data or second data when the first data or second data is generated by each wearable device.

　各学習用データは、例えば連続する所定歩数分又は所定期間分の第１データ又は第２データのセットと、歩行姿勢状態との組合せとで構成される。その場合、各学習用データは、各学習用データに含まれる第１データ又は第２データの一部が重複するように生成されてもよい。即ち、第１の学習用データには、第１時刻から所定歩数分又は所定期間分のデータが含まれ、第２の学習用データには、第１時刻の直後（例えば１秒後）の第２時刻から所定歩数分又は所定期間分のデータが含まれてもよい。これにより、躯体状態表示システム１は、少ないサンプルデータから、第１学習済みモデル１１１を効率良く生成することができ、第１学習済みモデル１１１の生成に要する時間及び手間を低減させることができる。 Each learning data is composed of, for example, a set of first data or second data for a predetermined number of consecutive steps or a predetermined period of time, in combination with the walking posture state. In this case, each learning data may be generated such that a portion of the first data or second data contained in each learning data overlaps. That is, the first learning data may include data for a predetermined number of steps or a predetermined period of time from a first time, and the second learning data may include data for a predetermined number of steps or a predetermined period of time from a second time immediately after the first time (e.g., one second later). This allows the structure state display system 1 to efficiently generate the first trained model 111 from a small amount of sample data, and reduces the time and effort required to generate the first trained model 111.

　第１学習済みモデル１１１がランダムフォレスト等により事前学習される場合、各学習用データは、例えば連続する所定歩数分又は所定期間分の第１データ又は第２データを一次元に並べたベクトルと、歩行姿勢状態との組合せとで構成される。各学習用データは、例えば第１データ又は第２データのうち、３軸方向の角速度のみを含んでもよい。躯体状態表示システム１は、角速度と加速度の両方に基づいて第１学習済みモデル１１１を生成することにより、第１学習済みモデル１１１の精度を向上させることができる。一方、躯体状態表示システム１は、角速度のみに基づいて第１学習済みモデル１１１を生成することにより、第１学習済みモデル１１１の生成に要する時間及び手間を低減させることができる。これらの場合も、各学習用データは、各学習用データに含まれる第１データ又は第２データの一部が重複するように生成されてもよい。 When the first trained model 111 is pre-trained by a random forest or the like, each training data is composed of a combination of a vector in which the first data or the second data for a predetermined number of consecutive steps or a predetermined period of time is arranged in one dimension, and a walking posture state. Each training data may include, for example, only the angular velocities in the three axial directions of the first data or the second data. The structure status display system 1 can improve the accuracy of the first trained model 111 by generating the first trained model 111 based on both the angular velocities and the accelerations. On the other hand, the structure status display system 1 can reduce the time and effort required to generate the first trained model 111 by generating the first trained model 111 based only on the angular velocities. In these cases, each training data may be generated such that a portion of the first data or the second data included in each training data overlaps.

　第１取得部１２１は、ステップＳ１０３及びＳ１０４で取得した第１データ及び第２データを、第１学習済みモデル１１１に対応する形式に変換して、第１学習済みモデル１１１に入力する。第１取得部１２１は、第１学習済みモデル１１１から出力された情報に基づいて、第１ウェアラブル装置１００及び第２ウェアラブル装置２００を装着したユーザの歩行姿勢状態を推定する。第３取得部３２１は、歩行姿勢状態に含まれる要素毎に、ユーザの歩行姿勢状態を推定する。 The first acquisition unit 121 converts the first data and second data acquired in steps S103 and S104 into a format corresponding to the first trained model 111 and inputs the converted data to the first trained model 111. The first acquisition unit 121 estimates the walking posture state of the user wearing the first wearable device 100 and the second wearable device 200 based on the information output from the first trained model 111. The third acquisition unit 321 estimates the walking posture state of the user for each element included in the walking posture state.

　第１学習済みモデル１１１から出力される情報が特徴量である場合、第１ウェアラブル装置１００は、特徴量と歩行姿勢状態との関係を示すテーブル又は式を予め第１記憶装置１１０に記憶しておく。第１取得部１２１は、第１記憶装置１１０に記憶されたテーブル又は式を参照し、第１学習済みモデル１１１から出力される特徴量に対応するユーザの歩行姿勢状態を特定する。 If the information output from the first trained model 111 is a feature, the first wearable device 100 stores in advance in the first storage device 110 a table or formula indicating the relationship between the feature and the walking posture state. The first acquisition unit 121 refers to the table or formula stored in the first storage device 110 and identifies the walking posture state of the user that corresponds to the feature output from the first trained model 111.

　また、第１学習済みモデル１１１が各ウェアラブル装置に対応する複数の学習済みモデルを含む場合、第１取得部１２１は、各学習済みモデルから出力された歩行姿勢状態のうち、最も多くの学習済みモデルから出力された歩行姿勢状態が、そのユーザの歩行姿勢状態であると推定する。 Furthermore, if the first trained model 111 includes multiple trained models corresponding to each wearable device, the first acquisition unit 121 estimates that the walking posture state output from the greatest number of trained models among the walking posture states output from each trained model is the walking posture state of the user.

　このように、第１取得部１２１は、第１学習済みモデル１１１を利用して、取得した第１データ及び第２データに基づいて、ユーザの歩行姿勢状態を推定する。即ち、第１取得部１２１は、第１学習済みモデル１１１を利用し、取得した第１データ及び第２データに基づいて推定された歩行姿勢状態を取得する。 In this way, the first acquisition unit 121 estimates the walking posture state of the user based on the acquired first data and second data, using the first trained model 111. That is, the first acquisition unit 121 acquires the walking posture state estimated based on the acquired first data and second data, using the first trained model 111.

　次に、第１生成部１２２は、第１取得部１２１が取得した歩行姿勢状態に基づいて、未接種時経年データ１１３を修正し、ユーザの歩行姿勢状態に応じた修正未接種時経年データを生成する（ステップＳ１０６）。修正未接種時経年データは、第４経年データの一例である。修正未接種時経年データは、例えば、所定のサプリメントを摂取せず且つ第１取得部１２１が取得した歩行姿勢状態を有するユーザについて推定される躯体状態の経年変化を示す。第１生成部１２２は、例えば第２学習済みモデル１１５を用いて、修正未接種時経年データを推定する。 Then, the first generating unit 122 modifies the unvaccinated aging data 113 based on the walking posture state acquired by the first acquiring unit 121, and generates modified unvaccinated aging data corresponding to the user's walking posture state (step S106). The modified unvaccinated aging data is an example of the fourth aging data. The modified unvaccinated aging data indicates, for example, the change over time in the body condition estimated for a user who does not take a specified supplement and has the walking posture state acquired by the first acquiring unit 121. The first generating unit 122 estimates the modified unvaccinated aging data using, for example, the second trained model 115.

　第２学習済みモデル１１５は、一又は複数の学習済みモデルを含む。第２学習済みモデル１１５は、情報処理装置３００又はサーバ装置４００等により事前に生成される。第２学習済みモデル１１５は、ディープラーニング、サポートベクターマシン等の教師あり学習により事前学習される。第２学習済みモデル１１５は、未接種時経年データ１１３及び歩行姿勢状態が入力された場合に、その歩行姿勢状態を有するユーザについて推定される経年データを出力するように学習される。第２学習済みモデル１１５は、未接種時経年データ１１３と、複数のユーザのそれぞれについて測定された歩行姿勢状態と、各ユーザについて測定された経年データとの複数のセットを用いて事前学習される。 The second trained model 115 includes one or more trained models. The second trained model 115 is generated in advance by the information processing device 300 or the server device 400, etc. The second trained model 115 is trained in advance by supervised learning such as deep learning and support vector machines. When the unvaccinated time-course data 113 and a walking posture state are input, the second trained model 115 is trained to output time-course data estimated for a user having that walking posture state. The second trained model 115 is trained in advance using the unvaccinated time-course data 113, multiple sets of the walking posture state measured for each of multiple users, and the time-course data measured for each user.

　第２学習済みモデル１１５は、例えば、躯体状態の各種別（筋肉量、エネルギー消費量、体重減少量、脳血流量、歩行可能速度、歩行可能歩数、歩行可能距離等）に対応する学習済みモデルを含む。また、第２学習済みモデル１１５は、歩行姿勢状態に含まれる各要素に対応する学習済みモデルを含んでもよい。 The second trained model 115 includes trained models corresponding to various types of body conditions (muscle mass, energy consumption, weight loss, cerebral blood flow, possible walking speed, possible number of steps, possible walking distance, etc.), for example. The second trained model 115 may also include trained models corresponding to each element included in the walking posture state.

　第１生成部１２２は、未接種時経年データ１１３及び歩行姿勢状態を第２学習済みモデル１１５に入力し、第２学習済みモデル１１５から出力された経年データを修正未接種時経年データとして取得する。これにより、第１生成部１２２は、修正未接種時経年データを生成（推定）する。 The first generation unit 122 inputs the unvaccinated time-course data 113 and the walking posture state into the second trained model 115, and obtains the time-course data output from the second trained model 115 as corrected unvaccinated time-course data. In this way, the first generation unit 122 generates (estimates) corrected unvaccinated time-course data.

　なお、第１生成部１２２は、第２学習済みモデル１１５を用いずに、修正未接種時経年データを生成してもよい。その場合、第１ウェアラブル装置１００は、複数のユーザに対する過去の測定結果に基づいて、所定のサプリメントを摂取しないユーザの歩行姿勢状態と、経年データの変化の度合い（差分又は割合等）との関係を示すテーブルを第１記憶装置１１０に予め記憶しておく。第１生成部１２２は、そのテーブルを参照して各ユーザの歩行姿勢状態に対応する度合いを特定し、特定した度合いに基づいて未接種時経年データ１１３を修正し、修正未接種時経年データを生成する。 The first generating unit 122 may generate the corrected unvaccinated aging data without using the second trained model 115. In this case, the first wearable device 100 stores in advance in the first storage device 110 a table showing the relationship between the walking posture state of a user who does not take a specified supplement and the degree of change (difference or percentage, etc.) in the aging data based on past measurement results for multiple users. The first generating unit 122 refers to the table to identify the degree corresponding to the walking posture state of each user, corrects the unvaccinated aging data 113 based on the identified degree, and generates the corrected unvaccinated aging data.

　次に、第１生成部１２２は、第１取得部１２１が取得した歩行姿勢状態に基づいて、接種時経年データ１１４を修正し、ユーザの歩行姿勢状態に応じた修正接種時経年データを生成する（ステップＳ１０７）。修正接種時経年データは、第２経年データの一例である。修正接種時経年データは、例えば、所定のサプリメントを摂取し且つ第１取得部１２１が取得した歩行姿勢状態を有するユーザについて推定される躯体状態の経年変化を示す。第１生成部１２２は、例えば第３学習済みモデル１１６を用いて、修正接種時経年データを推定する。 Next, the first generating unit 122 modifies the vaccination time aging data 114 based on the walking posture state acquired by the first acquiring unit 121, and generates modified vaccination time aging data corresponding to the user's walking posture state (step S107). The modified vaccination time aging data is an example of second aging data. The modified vaccination time aging data indicates, for example, the change over time in the body condition estimated for a user who has taken a specified supplement and has the walking posture state acquired by the first acquiring unit 121. The first generating unit 122 estimates the modified vaccination time aging data using, for example, the third trained model 116.

　第３学習済みモデル１１６は、一又は複数の学習済みモデルを含む。第３学習済みモデル１１６は、情報処理装置３００又はサーバ装置４００等により事前に生成される。第３学習済みモデル１１６は、ディープラーニング、サポートベクターマシン等の教師あり学習により事前学習される。第３学習済みモデル１１６は、接種時経年データ１１４及び歩行姿勢状態が入力された場合に、その歩行姿勢状態を有するユーザについて推定される経年データを出力するように学習される。第３学習済みモデル１１６は、接種時経年データ１１４と、複数のユーザのそれぞれについて測定された歩行姿勢状態と、各ユーザについて測定された経年データとの複数のセットを用いて事前学習される。 The third trained model 116 includes one or more trained models. The third trained model 116 is generated in advance by the information processing device 300 or the server device 400, etc. The third trained model 116 is trained in advance by supervised learning such as deep learning and support vector machines. When vaccination time-based aging data 114 and a walking posture state are input, the third trained model 116 is trained to output aging data estimated for a user having that walking posture state. The third trained model 116 is trained in advance using vaccination time-based aging data 114, multiple sets of walking posture states measured for each of multiple users, and aging data measured for each user.

　第３学習済みモデル１１６は、例えば、躯体状態の各種別（筋肉量、エネルギー消費量、体重減少量、脳血流量、歩行可能速度、歩行可能歩数、歩行可能距離等）に対応する学習済みモデルを含む。また、第３学習済みモデル１１６は、歩行姿勢状態に含まれる各要素に対応する学習済みモデルを含んでもよい。 The third trained model 116 includes trained models corresponding to various types of body conditions (muscle mass, energy consumption, weight loss, cerebral blood flow, possible walking speed, possible number of steps, possible walking distance, etc.), for example. The third trained model 116 may also include trained models corresponding to each element included in the walking posture state.

　第１生成部１２２は、ユーザの接種時経年データ１１４及び歩行姿勢状態を第３学習済みモデル１１６に入力し、第３学習済みモデル１１６から出力された経年データを修正接種時経年データとして取得する。これにより、第１生成部１２２は、修正接種時経年データを生成（推定）する。 The first generation unit 122 inputs the user's vaccination time aging data 114 and walking posture state into the third trained model 116, and obtains the aging data output from the third trained model 116 as modified vaccination time aging data. In this way, the first generation unit 122 generates (estimates) modified vaccination time aging data.

　なお、第１生成部１２２は、第３学習済みモデル１１６を用いずに、修正接種時経年データを生成してもよい。その場合、第１ウェアラブル装置１００は、複数のユーザに対する過去の測定結果に基づいて、所定のサプリメントを摂取するユーザの歩行姿勢状態と、経年データの変化の度合い（差分又は割合等）との関係を示すテーブルを第１記憶装置１１０に予め記憶しておく。第１生成部１２２は、そのテーブルを参照して各ユーザの歩行姿勢状態に対応する度合いを特定し、特定した度合いに基づいて接種時経年データ１１４を修正し、修正接種時経年データを生成する。 The first generating unit 122 may generate the modified vaccination time aging data without using the third trained model 116. In this case, the first wearable device 100 stores in advance in the first storage device 110 a table showing the relationship between the walking posture state of a user who takes a specified supplement and the degree of change (difference or percentage, etc.) of the aging data based on past measurement results for multiple users. The first generating unit 122 refers to the table to identify the degree corresponding to the walking posture state of each user, corrects the vaccination time aging data 114 based on the identified degree, and generates the modified vaccination time aging data.

　次に、表示制御部１２３は、未接種時経年データ１１３、接種時経年データ１１４、修正未接種時経年データ及び修正接種時経年データを第１表示装置１０２に表示することにより出力してユーザに通知し（ステップＳ１０８）、一連のステップを終了する。これにより、ユーザは、自分の躯体状態の経年変化を認識することができる。 Next, the display control unit 123 outputs the unvaccinated time aging data 113, the vaccinated time aging data 114, the corrected unvaccinated time aging data, and the corrected vaccinated time aging data by displaying them on the first display device 102, notifying the user (step S108), and ends the series of steps. This allows the user to recognize changes in his or her physical condition over time.

　なお、ステップＳ１０１～Ｓ１０３及びＳ１０４のうちの一方は省略され、第１取得部１２１は、第１データ及び第２データのうちの一方のみに基づいて歩行姿勢状態を推定してもよい。第１ウェアラブル装置１００は、第１データに基づいて歩行姿勢状態を推定することにより、第２ウェアラブル装置２００を利用することなく、低コスト且つ低負荷に歩行姿勢状態を推定することができる。一方、第１ウェアラブル装置１００は、第２データに基づいて歩行姿勢状態を推定することにより、第２データを取得する体の部位の自由度を高めることができ、歩行姿勢状態を高精度に推定することができる。また、ステップＳ１０６及びＳ１０７のうちの一方は省略され、第１生成部１２２は、修正未接種時経年データ及び修正接種時経年データのうちの一方のみを生成してもよい。その場合、ステップＳ１０８において、表示制御部１２３は、修正未接種時経年データ及び修正接種時経年データのうちの一方のみを表示する。また、ステップＳ１０８において、表示制御部１２３は、未接種時経年データ１１３及び／又は接種時経年データ１１４を表示しなくてもよい。 Note that one of steps S101 to S103 and S104 may be omitted, and the first acquisition unit 121 may estimate the walking posture state based on only one of the first data and the second data. The first wearable device 100 can estimate the walking posture state at low cost and with low load without using the second wearable device 200 by estimating the walking posture state based on the first data. On the other hand, the first wearable device 100 can increase the degree of freedom of the body part from which the second data is acquired by estimating the walking posture state based on the second data, and can estimate the walking posture state with high accuracy. Also, one of steps S106 and S107 may be omitted, and the first generation unit 122 may generate only one of the modified unvaccinated time aging data and the modified vaccinated time aging data. In that case, in step S108, the display control unit 123 displays only one of the modified unvaccinated time aging data and the modified vaccinated time aging data. Also, in step S108, the display control unit 123 does not have to display the non-vaccination time-varying data 113 and/or the vaccination time-varying data 114.

　また、ステップＳ１０１の処理及びステップＳ１０４の処理が実行されるタイミングは任意でよく、同時でもよい。例えば、第１ウェアラブル装置１００の第１取得部１２１は、第１入力装置１０１を用いて利用者から測定開始の指示を受信した場合、測定開始を要求する測定要求信号を、第１インタフェース装置１０４を介して第２ウェアラブル装置２００に送信するとともに、第１データを取得する。一方、第２ウェアラブル装置２００の第２取得部２２１は、第２インタフェース装置２０４を介して第１ウェアラブル装置１００から測定要求信号を受信した場合、第２データを取得し、第２インタフェース装置２０４を介して第１ウェアラブル装置１００に送信する。第１取得部１２１は、第１入力装置１０１を用いて利用者から測定終了の指示を受信するまで、上記の処理を繰り返した後に、ステップＳ１０５の処理を実行する。 The timing of the processing of step S101 and the processing of step S104 may be arbitrary, and may be simultaneous. For example, when the first acquisition unit 121 of the first wearable device 100 receives an instruction to start measurement from the user using the first input device 101, the first acquisition unit 121 transmits a measurement request signal requesting the start of measurement to the second wearable device 200 via the first interface device 104, and acquires the first data. On the other hand, when the second acquisition unit 221 of the second wearable device 200 receives a measurement request signal from the first wearable device 100 via the second interface device 204, the second acquisition unit 221 acquires the second data and transmits it to the first wearable device 100 via the second interface device 204. The first acquisition unit 121 repeats the above processing until it receives an instruction to end measurement from the user using the first input device 101, and then executes the processing of step S105.

　また、第１学習済みモデル１１１は、第１記憶装置１１０でなく、サーバ装置４００に記憶されてもよい。その場合、ステップＳ１０５において、第１取得部１２１は、サーバ装置４００にアクセスすることによって、第１学習済みモデル１１１によって推定された歩行姿勢状態を取得する。第１取得部１２１は、ユーザの歩行姿勢状態の送信を要求する要求信号を、第１通信装置１０３を介してサーバ装置４００に送信する。要求信号には、第１学習済みモデル１１１に対応する形式に変換された第１データ及び／又は第２データが含まれる。サーバ装置４００は、第１ウェアラブル装置１００から要求信号を受信する。サーバ装置４００は、受信した要求信号に含まれる第１データ及び／又は第２データを第１学習済みモデル１１１に入力し、第１学習済みモデル１１１から出力された出力情報を取得する。サーバ装置４００は、取得した出力情報を第１ウェアラブル装置１００に送信する。第１取得部１２１は、第１通信装置１０３を介してサーバ装置４００から出力情報を受信することにより取得し、取得した出力情報に基づいて、第１ウェアラブル装置１００及び第２ウェアラブル装置２００を装着したユーザの歩行姿勢状態を推定する。 Also, the first learned model 111 may be stored in the server device 400 instead of the first storage device 110. In that case, in step S105, the first acquisition unit 121 acquires the walking posture state estimated by the first learned model 111 by accessing the server device 400. The first acquisition unit 121 transmits a request signal requesting transmission of the user's walking posture state to the server device 400 via the first communication device 103. The request signal includes the first data and/or the second data converted into a format corresponding to the first learned model 111. The server device 400 receives the request signal from the first wearable device 100. The server device 400 inputs the first data and/or the second data included in the received request signal to the first learned model 111 and acquires output information output from the first learned model 111. The server device 400 transmits the acquired output information to the first wearable device 100. The first acquisition unit 121 acquires output information by receiving it from the server device 400 via the first communication device 103, and estimates the walking posture state of the user wearing the first wearable device 100 and the second wearable device 200 based on the acquired output information.

　同様に、第２学習済みモデル１１５及び／又は第３学習済みモデル１１６は、第１記憶装置１１０でなく、サーバ装置４００に記憶されてもよい。その場合、ステップＳ１０６及び／又はＳ１０７において、第１生成部１２２は、サーバ装置４００にアクセスすることによって、第２学習済みモデル１１５及び／又は第３学習済みモデル１１６によって推定された修正未接種時経年データ及び／又は修正接種時経年データを取得する。第１生成部１２２は、修正未接種時経年データ及び／又は修正接種時経年データの送信を要求する要求信号を、第１通信装置１０３を介してサーバ装置４００に送信する。要求信号には、未接種時経年データ１１３及び／又は接種時経年データ１１４と、歩行姿勢状態とが含まれる。サーバ装置４００は、第１ウェアラブル装置１００から要求信号を受信する。サーバ装置４００は、受信した要求信号に含まれる未接種時経年データ１１３又は接種時経年データ１１４と、歩行姿勢状態とを第２学習済みモデル１１５又は第３学習済みモデル１１６に入力し、第２学習済みモデル１１５又は第３学習済みモデル１１６から出力された出力情報を取得する。サーバ装置４００は、取得した出力情報を第１ウェアラブル装置１００に送信する。第１取得部１２１は、第１通信装置１０３を介してサーバ装置４００から出力情報を受信することにより取得し、取得した出力情報に基づいて、修正未接種時経年データ及び／又は修正接種時経年データを推定する。 Similarly, the second trained model 115 and/or the third trained model 116 may be stored in the server device 400 instead of the first storage device 110. In that case, in steps S106 and/or S107, the first generation unit 122 accesses the server device 400 to obtain the modified unvaccinated time aging data and/or the modified vaccinated time aging data estimated by the second trained model 115 and/or the third trained model 116. The first generation unit 122 transmits a request signal to the server device 400 via the first communication device 103, requesting the transmission of the modified unvaccinated time aging data and/or the modified vaccinated time aging data. The request signal includes the unvaccinated time aging data 113 and/or the vaccinated time aging data 114 and the walking posture state. The server device 400 receives the request signal from the first wearable device 100. The server device 400 inputs the unvaccinated time aging data 113 or the vaccinated time aging data 114 contained in the received request signal and the walking posture state into the second trained model 115 or the third trained model 116, and acquires output information output from the second trained model 115 or the third trained model 116. The server device 400 transmits the acquired output information to the first wearable device 100. The first acquisition unit 121 acquires the output information by receiving it from the server device 400 via the first communication device 103, and estimates the modified unvaccinated time aging data and/or the modified vaccinated time aging data based on the acquired output information.

　躯体状態表示システム１は、サーバ装置４００に記憶されている学習済みモデルを利用することにより、サーバ装置４００によって更新されている最新の学習済みモデルを用いて歩行姿勢状態又は経年データを推定することができる。また、躯体状態表示システム１は、サーバ装置４００に記憶されている学習済みモデルを利用することにより、第１ウェアラブル装置１００の記憶容量の低減を図ることできる。一方、第１ウェアラブル装置１００は、自装置に記憶された学習済みモデルを利用することにより、サーバ装置４００との通信接続が切断されている状態でも歩行姿勢状態又は経年データを推定することができる。また、第１ウェアラブル装置１００は、自装置に記憶された学習済みモデルを利用することにより、第１ウェアラブル装置１００とサーバ装置４００の間の通信量の低減を図ることできる。 The body condition display system 1 can estimate the walking posture state or aging data by using the latest learned model updated by the server device 400, by utilizing the trained model stored in the server device 400. Furthermore, the body condition display system 1 can reduce the storage capacity of the first wearable device 100 by utilizing the trained model stored in the server device 400. On the other hand, the first wearable device 100 can estimate the walking posture state or aging data even when the communication connection with the server device 400 is disconnected, by utilizing the trained model stored in the first wearable device 100. Furthermore, the first wearable device 100 can reduce the amount of communication between the first wearable device 100 and the server device 400 by utilizing the trained model stored in the first wearable device 100.

　図６は、特定のユーザの躯体状態の経年変化を示すグラフである。 Figure 6 is a graph showing the changes in the physical condition of a specific user over time.

　図６において、横軸はユーザの年齢を示し、縦軸はユーザの筋肉量を示す。グラフ６０１、６０２は、それぞれ一般的な人物の未接種時経年データ、接種時経年データを示し、グラフ６０３、６０４は、それぞれ特定の歩行姿勢状態を有するユーザの修正未接種時経年データ、修正接種時経年データを示す。図６に示されるように、人物の筋肉量は、年齢とともに低下していく。特に、図６に例示されるユーザの歩行姿勢状態は良好でなく、このユーザの筋肉量の低下度合いは、一般的な人物の筋肉量の低下度合いより大きい。なお、エネルギー消費量、体重減少量、脳内流量、歩行可能速度、歩行可能歩数、及び、歩行可能距離の経年変化も、筋肉量の経年変化と同様の特性を有している。 In FIG. 6, the horizontal axis indicates the user's age, and the vertical axis indicates the user's muscle mass. Graphs 601 and 602 respectively show unvaccinated and vaccinated data over time for a typical person, while graphs 603 and 604 respectively show corrected unvaccinated and corrected vaccinated data over time for a user with a specific walking posture. As shown in FIG. 6, a person's muscle mass decreases with age. In particular, the walking posture of the user illustrated in FIG. 6 is not good, and the degree of decrease in the user's muscle mass is greater than that of a typical person. The changes over time in energy consumption, weight loss, cerebral flow, walking speed, number of steps, and walking distance also have similar characteristics to the changes over time in muscle mass.

　ユーザは、グラフ６０３を参照することにより、今後、所定のサプリメントを摂取せず且つ歩行姿勢状態を改善しない場合の躯体状態の経年変化を認識することができる。特に、ユーザは、グラフ６０１とグラフ６０３を比較することにより、所定のサプリメントを摂取しない状態で、歩行姿勢状態を改善する場合としない場合の躯体状態の経年変化の差を明確に認識することができる。また、ユーザは、グラフ６０４を参照することにより、今後、所定のサプリメントを摂取しつつ歩行姿勢状態を改善しない場合の躯体状態の経年変化を認識できる。特に、ユーザは、グラフ６０２とグラフ６０４を比較することにより、所定のサプリメントを摂取しつつ、歩行姿勢状態を改善する場合としない場合の躯体状態の経年変化の差を明確に認識することができる。また、ユーザは、グラフ６０１とグラフ６０４を比較することにより、所定のサプリメントを摂取し且つ歩行姿勢状態を改善する場合と、所定のサプリメントを摂取せず且つ歩行姿勢状態を改善しない場合との躯体状態の経年変化の差を明確に認識することができる。これらにより、躯体状態表示システム１は、ユーザに対して、所定のサプリメントの摂取及び歩行姿勢状態の改善に対するモチベーションを向上させることができる。 By referring to graph 603, the user can recognize the change over time in the body condition when the specified supplement is not taken and the walking posture is not improved in the future. In particular, by comparing graph 601 with graph 603, the user can clearly recognize the difference in the change over time in the body condition when the walking posture is improved and not improved in the state of not taking the specified supplement. In addition, by referring to graph 604, the user can recognize the change over time in the body condition when the specified supplement is taken but the walking posture is not improved in the future. In particular, by comparing graph 602 with graph 604, the user can clearly recognize the difference in the change over time in the body condition when the specified supplement is taken and the walking posture is improved and not improved. In addition, by comparing graph 601 with graph 604, the user can clearly recognize the difference in the change over time in the body condition when the specified supplement is taken and the walking posture is improved and not improved. As a result, the body condition display system 1 can increase the user's motivation to take prescribed supplements and improve their walking posture.

　なお、発明者は、歩行姿勢状態の第１要素から第５要素の全てについて、第１学習済みモデル１１１の評価を行い、第１学習済みモデル１１１から出力される情報の正解率が９３％以上であり、精度が十分に高いことを確認した。即ち、躯体状態表示システム１が、第１学習済みモデル１１１を用いることにより、ユーザの歩行姿勢状態をより精度良く推定できることが確認され、躯体状態の経年変化をより精度良く推定できることが確認された。 The inventors have evaluated the first trained model 111 for all of the first to fifth elements of the walking posture state, and have confirmed that the accuracy rate of the information output from the first trained model 111 is 93% or higher, indicating that the accuracy is sufficiently high. In other words, it has been confirmed that the structure condition display system 1 can use the first trained model 111 to more accurately estimate the user's walking posture state, and more accurately estimate the changes in the structure condition over time.

　また、躯体状態表示システム１は、学習済みモデルを用いることによって、様々な種類のパラメータの様々な組み合わせに対する歩行姿勢状態又は躯体状態の経年変化を高精度に且つ効率よく算出することができる。また、躯体状態表示システム１は、学習済みモデルを用いることによって、様々な種類のパラメータの様々な組み合わせについて対応する歩行姿勢状態又は躯体状態の経年変化を記憶しておくことなく、歩行姿勢状態又は躯体状態の経年変化を特定できるため、記憶装置の記憶容量を低減させることができる。また、躯体状態表示システム１は、学習済みモデルを用いることによって、様々な種類のパラメータの様々な組み合わせに応じた複雑な判定を実行することなく、歩行姿勢状態又は躯体状態の経年変化を特定できるため、表示処理の処理時間の低減及び処理負荷の低減を図ることができる。 Furthermore, by using the trained model, the structural body condition display system 1 can accurately and efficiently calculate the change over time in the walking posture state or structural body state for various combinations of various types of parameters. Furthermore, by using the trained model, the structural body condition display system 1 can identify the change over time in the walking posture state or structural body state without storing the change over time in the walking posture state or structural body state corresponding to various combinations of various types of parameters, thereby reducing the storage capacity of the storage device. Furthermore, by using the trained model, the structural body condition display system 1 can identify the change over time in the walking posture state or structural body state without performing complex judgments according to various combinations of various types of parameters, thereby reducing the processing time and processing load of the display process.

　以下、歩行姿勢状態が、第１要素から第５要素までを含むことの技術的意義について説明する。 The technical significance of the walking posture state including elements 1 to 5 will be explained below.

　歩く動作の主な動力源は、太ももの引き上げであり、走る動作の主な動力源は、つま先の蹴りである。したがって、歩く動作と、走る動作、即ち極限までゆっくり走る動作とは、根本的に異なる動作であり、歩く動作と走る動作とで明確に動きを変えることが、ユーザの健康に繋がる。つま先で蹴るためには、母指球を床に付けるために、足が内巻きになり、土踏まずがなくなってしまう。これにより、足の骨は全体的に緩み、足への衝撃が膝又は腰に伝わりやすくなる。走る動作は、足への負担を伴うため、歩いている際に、足への負担がかかる走る動作を行うことは望ましくない。 The main source of power for walking is pulling up the thighs, and the main source of power for running is kicking with the toes. Therefore, walking and running, i.e. running as slowly as possible, are fundamentally different movements, and clearly changing the movements between walking and running leads to the health of the user. In order to kick with the toes, the ball of the foot must be placed on the floor, causing the foot to roll inward and eliminating the arch. This loosens the bones of the foot overall, making it easier for impacts on the foot to be transmitted to the knee or hip. Running puts a strain on the feet, so it is not advisable to perform running, which puts a strain on the feet, while walking.

　躯体状態表示システム１は、ユーザの歩行姿勢状態として、第１要素から第５要素までの各要素を推定することにより、ユーザが、走る動作を行わずに適切に歩く動作を行っているか否かを識別することができる。特に、第１要素から第５要素までの各要素は、相互に関連しており、第１要素から第５要素の順に、ユーザの姿勢が改善されることにより、ユーザは、歩行姿勢を容易に改善することができる。 The body state display system 1 estimates each of the first to fifth elements as the walking posture state of the user, and can identify whether the user is walking appropriately without running. In particular, each of the first to fifth elements is interrelated, and as the user's posture improves in the order of the first to fifth elements, the user can easily improve their walking posture.

　まず、歩幅が短くなりすぎて歩行速度が遅くなる、ということはあってはならず、歩幅は一定以上担保されている必要がある。歩行姿勢状態が、前提として、ユーザの歩幅が十分であるか不十分であるかを示す第１要素を含むことにより、躯体状態表示システム１は、ユーザの歩幅を一定以上担保することができる。 First, the stride length must not be too short, which would slow down the walking speed, and a certain level of stride length must be guaranteed. The walking posture state includes, as a premise, a first element that indicates whether the user's stride length is sufficient or insufficient, so that the body state display system 1 can guarantee that the user's stride length is at least a certain level.

　次に、ユーザの姿勢がまっすぐでない場合、ユーザは、走る動作を行わざるを得なくなる。ユーザは、かかとを重心にしてまっすぐ立てていると、太ももを使って足を上げることができるが、前傾姿勢になると、上半身がかぶさって、太ももを使って足を上げることが困難になり、つま先で蹴らざるを得なくなる。歩行姿勢状態が、前後方向の軸に関する要素として、ユーザの重心が正常であるか前傾であるか後傾であるかを示す第２要素を含むことにより、躯体状態表示システム１は、ユーザの姿勢がまっすぐに保たれているか否かを確認することができる。 Next, if the user's posture is not straight, the user will be forced to perform a running motion. If the user stands straight with their heels as their center of gravity, they can use their thighs to lift their feet, but if they lean forward, their upper body will overlap, making it difficult to lift their feet using their thighs, and they will be forced to kick with their toes. By including a second element indicating whether the user's center of gravity is normal, leaning forward, or leaning backward as an element related to the front-to-back axis, the walking posture state can allow the body state display system 1 to check whether the user's posture is being kept straight.

　次に、ユーザの重心がまっすぐであっても、太ももの引き上げが正しく行われていない場合、ユーザは、走る動作を行わざるを得ない。ユーザの重心がまっすぐであり、つま先で蹴らなくても足が上がるにもかかわらず、つま先で蹴ってしまうと上下動が激しくなる。また、ユーザの重心がまっすぐであるにもかかわらず、太ももで足を上げる癖がついていないため、ユーザは足を引きずってしまう場合がある。歩行姿勢状態が、前後方向の軸に関する要素として、ユーザが出す足を蹴っていないか引きずっているか蹴っているかを示す第３要素を含むことにより、躯体状態表示システム１は、ユーザの太ももの引き上げが正しく行われているか否かを確認することができる。 Next, even if the user's center of gravity is straight, if the user is not pulling up the thighs correctly, the user will be forced to perform a running motion. Even if the user's center of gravity is straight and the foot can be raised without kicking with the toes, kicking with the toes will result in violent up and down movements. Also, even if the user's center of gravity is straight, the user may not have the habit of lifting the foot with the thighs, and may end up dragging their foot. By including a third element indicating whether the user is not kicking, dragging, or kicking the leg as an element related to the front-to-back axis in the walking posture state, the body state display system 1 can check whether the user is pulling up the thighs correctly.

　次に、前後方向の軸が正しくても、回転（左右方向の）軸が正しくない場合、適切に歩く動作が行われない。つま先に対して膝が内側に入っていると、膝が邪魔になって反対側の足が正しく出ない。即ち、軸足が突っ張っている場合、又は、股関節が開いていない場合、出したい足が正しく上がらない。一方、ユーザが股関節を回すと、反対側の足が出るスペースが確保され、足が上がりやすくなり、太ももの引き上げが正しく行われる。歩行姿勢状態が、回転（左右方向の）軸に関する要素として、ユーザの軸足が突っ張っているか突っ張っていないかを示す第４要素と、ユーザの股関節が開いているか開いていないかを示す第５要素とを含むことにより、躯体状態表示システム１は、回転軸が正しいか否かを確認することができる。 Next, even if the axis in the front-to-back direction is correct, if the axis of rotation (left-to-right direction) is incorrect, the walking motion will not be performed properly. If the knee is turned inward relative to the toes, the knee will get in the way and the opposite leg will not come out correctly. In other words, if the supporting leg is stiff or the hip joint is not open, the leg that is to be put out will not rise correctly. On the other hand, if the user rotates the hip joint, space is secured for the opposite leg to come out, making it easier to lift the leg and correctly pulling up the thigh. By including a fourth element indicating whether the user's supporting leg is stiff or not as elements related to the axis of rotation (left-to-right direction), and a fifth element indicating whether the user's hip joint is open or not, the body state display system 1 can confirm whether the axis of rotation is correct or not.

　以上詳述したように、躯体状態表示システム１は、ユーザが歩行した際に得られるデータに基づいてユーザの歩行姿勢状態を取得し、取得した歩行姿勢状態に応じたユーザの躯体状態の経年変化を推定して表示する。これにより、躯体状態表示システム１は、ユーザの状態に応じた躯体状態の経年変化を提示することが可能となる。 As described above in detail, the structural body condition display system 1 acquires the walking posture state of the user based on data obtained when the user walks, and estimates and displays the change over time in the structural body condition of the user according to the acquired walking posture state. This enables the structural body condition display system 1 to present the change over time in the structural body condition according to the user's condition.

　図７は、他の実施形態に係る躯体状態表示システム１における情報処理装置３００の概略構成を示す図である。 FIG. 7 is a diagram showing the schematic configuration of an information processing device 300 in a structure condition display system 1 according to another embodiment.

　本実施形態に係る情報処理装置３００は、躯体状態送信装置の一例である。情報処理装置３００は、第３通信装置３０１、第３記憶装置３１０及び第３処理装置３２０等を有する。第３通信装置３０１、第３記憶装置３１０及び第３処理装置３２０は、ＣＰＵバス等を介して相互に接続される。 The information processing device 300 according to this embodiment is an example of a body state transmission device. The information processing device 300 has a third communication device 301, a third storage device 310, a third processing device 320, etc. The third communication device 301, the third storage device 310, and the third processing device 320 are connected to each other via a CPU bus or the like.

　第３通信装置３０１は、送信部の一例である。第３通信装置３０１は、ＴＣＰ／ＩＰ等の通信プロトコルに従った有線通信インタフェース回路を有する。第３通信装置３０１は、イーサネット（登録商標）等の通信規格に従って、ネットワークＮと通信接続する。第３通信装置３０１は、ネットワークＮを介して第１ウェアラブル装置１００、第２ウェアラブル装置２００又はサーバ装置４００等から受信したデータを第３処理装置３２０に送る。第３通信装置３０１は、第３処理装置３２０から受け取ったデータを、ネットワークＮを介して第１ウェアラブル装置１００、第２ウェアラブル装置２００又はサーバ装置４００等に送信する。なお、第３通信装置３０１は、無線信号を送受信するアンテナと、無線ＬＡＮ等の通信プロトコルに従った無線通信インタフェース回路とを有し、無線ＬＡＮ等の通信規格に従って、ネットワークＮと通信接続してもよい。 The third communication device 301 is an example of a transmitter. The third communication device 301 has a wired communication interface circuit that complies with a communication protocol such as TCP/IP. The third communication device 301 is communicatively connected to the network N in accordance with a communication standard such as Ethernet (registered trademark). The third communication device 301 sends data received from the first wearable device 100, the second wearable device 200, the server device 400, etc. via the network N to the third processing device 320. The third communication device 301 transmits data received from the third processing device 320 to the first wearable device 100, the second wearable device 200, the server device 400, etc. via the network N. The third communication device 301 may have an antenna for transmitting and receiving wireless signals and a wireless communication interface circuit that complies with a communication protocol such as wireless LAN, and may be communicatively connected to the network N in accordance with a communication standard such as wireless LAN.

　第３記憶装置３１０は、ＲＡＭ、ＲＯＭ等のメモリ装置、ハードディスク等の固定ディスク装置、又はフレキシブルディスク、光ディスク等の可搬用の記憶装置等を有する。また、第３記憶装置３１０には、情報処理装置３００の各種処理に用いられるコンピュータプログラム、データベース、テーブル等が格納される。コンピュータプログラムは、例えばＣＤ－ＲＯＭ、ＤＶＤ－ＲＯＭ等のコンピュータ読み取り可能な可搬型記録媒体から、公知のセットアッププログラム等を用いて第３記憶装置３１０にインストールされてもよい。コンピュータプログラムは、所定のサーバが有する記録媒体に記憶され、ネットワークＮを介してインストールされてもよい。 The third storage device 310 has a memory device such as RAM or ROM, a fixed disk device such as a hard disk, or a portable storage device such as a flexible disk or optical disk. The third storage device 310 also stores computer programs, databases, tables, etc. used for various processes of the information processing device 300. The computer programs may be installed in the third storage device 310 from a computer-readable portable recording medium such as a CD-ROM or DVD-ROM using a known setup program, etc. The computer programs may be stored in a recording medium owned by a specific server and installed via the network N.

　第３記憶装置３１０には、データとして、第１学習済みモデル３１１、データテーブル３１２、未接種時経年データ３１３、接種時経年データ３１４、第２学習済みモデル３１５及び第３学習済みモデル３１６等が記憶される。第１学習済みモデル３１１、データテーブル３１２、未接種時経年データ３１３、接種時経年データ３１４、第２学習済みモデル３１５、第３学習済みモデル３１６は、それぞれ第１学習済みモデル１１１、データテーブル１１２、未接種時経年データ１１３、接種時経年データ１１４、第２学習済みモデル１１５及び第３学習済みモデル１１６と同様のモデル、テーブル又はデータである。 The third storage device 310 stores data such as a first trained model 311, a data table 312, unvaccinated time aging data 313, vaccination time aging data 314, a second trained model 315, and a third trained model 316. The first trained model 311, the data table 312, unvaccinated time aging data 313, vaccination time aging data 314, the second trained model 315, and the third trained model 316 are models, tables, or data similar to the first trained model 111, the data table 112, unvaccinated time aging data 113, vaccination time aging data 114, the second trained model 115, and the third trained model 116, respectively.

　第３処理装置３２０は、予め第３記憶装置３１０に記憶されているプログラムに基づいて動作する。第３処理装置３２０は、例えばＣＰＵである。第３処理装置３２０として、ＤＳＰ、ＬＳＩ、ＡＳＩＣ、ＦＰＧＡ等が用いられてもよい。第３処理装置３２０は、第３通信装置３０１及び第３記憶装置３１０等と接続され、各装置を制御する。 The third processing device 320 operates based on a program that is prestored in the third storage device 310. The third processing device 320 is, for example, a CPU. A DSP, an LSI, an ASIC, an FPGA, etc. may also be used as the third processing device 320. The third processing device 320 is connected to the third communication device 301, the third storage device 310, etc., and controls each device.

　第３処理装置３２０は、第３記憶装置３１０に記憶されたコンピュータプログラムを読み取り、読み取ったコンピュータプログラムに従って動作する。これにより、第３処理装置３２０は、第３取得部３２１、推定部３２２、第３生成部３２３及び送信制御部３２４として機能する。 The third processing device 320 reads the computer program stored in the third storage device 310 and operates according to the read computer program. As a result, the third processing device 320 functions as a third acquisition unit 321, an estimation unit 322, a third generation unit 323, and a transmission control unit 324.

　図８は、本実施形態に係る躯体状態表示システム１における表示処理の動作の例を示すシーケンスである。 FIG. 8 is a sequence showing an example of the operation of the display process in the structure condition display system 1 according to this embodiment.

　以下、図８示したフローチャートを参照しつつ、本実施形態に係る表示処理の動作の例を説明する。なお、以下に説明する動作のフローは、予め躯体状態表示システム１が有する各装置の各記憶装置に記憶されているプログラムに基づき主に各装置の各処理装置により各装置の各要素と協働して実行される。図８のステップＳ２０１～Ｓ２０４、Ｓ２１２の処理は、図５のステップＳ１０１～Ｓ１０４、Ｓ１０８の処理と同様であるため説明を省略し、以下では、ステップＳ２０５～Ｓ２１１についてのみ説明する。 Below, an example of the operation of the display processing according to this embodiment will be described with reference to the flowchart shown in Figure 8. Note that the flow of the operation described below is executed mainly by each processing device of each device in cooperation with each element of each device, based on a program previously stored in each storage device of each device of the structure condition display system 1. The processing of steps S201 to S204 and S212 in Figure 8 is similar to the processing of steps S101 to S104 and S108 in Figure 5, so the description will be omitted, and only steps S205 to S211 will be described below.

　ステップＳ２０５において、第１ウェアラブル装置１００の第１取得部１２１は、取得した第１データ及び第２データを、各データに対応する装置ＩＤ及び取得時刻とともに、第１通信装置１０３を介して情報処理装置３００に送信する（ステップＳ２０５）。第１取得部１２１は、例えばユーザの所定歩数分ずつの第１データ及び第２データを情報処理装置３００に送信する。なお、第１取得部１２１は、第１データ又は第２データを取得するたびに、第１データ又は第２データを情報処理装置３００に送信してもよい。また、第１取得部１２１は、取得した複数の第１データ及び第２データをまとめて任意のタイミングに情報処理装置３００に送信してもよい。 In step S205, the first acquisition unit 121 of the first wearable device 100 transmits the acquired first data and second data together with the device ID and acquisition time corresponding to each data to the information processing device 300 via the first communication device 103 (step S205). The first acquisition unit 121 transmits the first data and the second data to the information processing device 300, for example, for a predetermined number of steps of the user. Note that the first acquisition unit 121 may transmit the first data or the second data to the information processing device 300 each time it acquires the first data or the second data. The first acquisition unit 121 may also transmit the acquired multiple pieces of first data and second data to the information processing device 300 at any timing.

　次に、情報処理装置３００の第３取得部３２１は、第３通信装置３０１を介して第１ウェアラブル装置１００から第１データ、第２データ、装置ＩＤ及び取得時刻を受信することにより取得する（ステップＳ２０６）。第３取得部３２１は、取得した第１データ及び第２データを対応する装置ＩＤ及び取得時刻と関連付けてデータテーブル３１２に記憶する。 Next, the third acquisition unit 321 of the information processing device 300 acquires the first data, the second data, the device ID, and the acquisition time from the first wearable device 100 by receiving them via the third communication device 301 (step S206). The third acquisition unit 321 stores the acquired first data and second data in the data table 312 in association with the corresponding device ID and acquisition time.

　次に、推定部３２２は、図６のステップＳ１０５の処理と同様にして、第３取得部３２１が取得した第１データ及び第２データに基づいて、第１ウェアラブル装置１００及び第２ウェアラブル装置２００を装着したユーザの歩行姿勢状態を推定する（ステップＳ２０７）。第３取得部３２１は、第１学習済みモデル３１１を用いて、ユーザの歩行姿勢状態を推定する。 Next, the estimation unit 322 estimates the walking posture state of the user wearing the first wearable device 100 and the second wearable device 200 based on the first data and the second data acquired by the third acquisition unit 321, similar to the processing of step S105 in FIG. 6 (step S207). The third acquisition unit 321 estimates the walking posture state of the user using the first trained model 311.

　次に、第３生成部３２３は、図６のステップＳ１０６の処理と同様にして、推定部３２２が推定した歩行姿勢状態に基づいて、未接種時経年データ３１３を修正し、ユーザの歩行姿勢状態に応じた修正未接種時経年データを生成する（ステップＳ２０８）。第３生成部３２３は、例えば第２学習済みモデル３１５を用いて、修正未接種時経年データを推定する。 Next, the third generation unit 323 corrects the unvaccinated time aging data 313 based on the walking posture state estimated by the estimation unit 322, in a manner similar to the processing of step S106 in FIG. 6, and generates corrected unvaccinated time aging data corresponding to the user's walking posture state (step S208). The third generation unit 323 estimates the corrected unvaccinated time aging data using, for example, the second trained model 315.

　次に、第３生成部３２３は、図６のステップＳ１０６の処理と同様にして、推定部３２２が推定した歩行姿勢状態に基づいて、接種時経年データ３１４を修正し、ユーザの歩行姿勢状態に応じた修正接種時経年データを生成する（ステップＳ２０９）。第３生成部３２３は、例えば第３学習済みモデル３１６を用いて、修正接種時経年データを推定する。 Next, the third generation unit 323 corrects the vaccination time aging data 314 based on the walking posture state estimated by the estimation unit 322, in a manner similar to the processing of step S106 in FIG. 6, and generates corrected vaccination time aging data according to the user's walking posture state (step S209). The third generation unit 323 estimates the corrected vaccination time aging data using, for example, the third trained model 316.

　次に、送信制御部３２４は、未接種時経年データ３１３、接種時経年データ３１４、修正未接種時経年データ及び修正接種時経年データを、第３通信装置３０１を介して第１ウェアラブル装置１００に送信することにより、出力する（ステップＳ２１０）。 Next, the transmission control unit 324 outputs the unvaccinated time aging data 313, the vaccinated time aging data 314, the corrected unvaccinated time aging data, and the corrected vaccinated time aging data by transmitting them to the first wearable device 100 via the third communication device 301 (step S210).

　次に、第１ウェアラブル装置１００の第１取得部１２１は、第１通信装置１０３を介して情報処理装置３００から、未接種時経年データ３１３、接種時経年データ３１４、修正未接種時経年データ及び修正接種時経年データを受信することにより取得する（ステップＳ２１１）。 Next, the first acquisition unit 121 of the first wearable device 100 acquires the unvaccinated time aging data 313, the vaccinated time aging data 314, the corrected unvaccinated time aging data, and the corrected vaccinated time aging data by receiving them from the information processing device 300 via the first communication device 103 (step S211).

　なお、ステップＳ２０１～Ｓ２０３及びＳ２０４のうちの一方は省略され、推定部３２２は、第１データ及び第２データのうちの一方のみに基づいて歩行姿勢状態を推定してもよい。情報処理装置３００は、第１データに基づいて歩行姿勢状態を推定することにより、第２ウェアラブル装置２００を利用することなく、低コスト且つ低負荷に歩行姿勢状態を推定することができる。一方、情報処理装置３００は、第２データに基づいて歩行姿勢状態を推定することにより、第２データを取得する体の部位の自由度を高めることができ、歩行姿勢状態を高精度に推定することができる。また、ステップＳ２０８及びＳ２０９のうちの一方は省略され、第３生成部３２３は、修正未接種時経年データ及び修正接種時経年データのうちの一方のみを生成してもよい。その場合、ステップＳ２１２において、表示制御部１２３は、修正未接種時経年データ及び修正接種時経年データのうちの一方のみを表示する。また、ステップＳ２１２において、表示制御部１２３は、未接種時経年データ３１３及び／又は接種時経年データ３１４を表示しなくてもよい。 Note that one of steps S201 to S203 and S204 may be omitted, and the estimation unit 322 may estimate the walking posture state based on only one of the first data and the second data. By estimating the walking posture state based on the first data, the information processing device 300 can estimate the walking posture state at low cost and with low load without using the second wearable device 200. On the other hand, by estimating the walking posture state based on the second data, the information processing device 300 can increase the degree of freedom of the body part from which the second data is obtained, and can estimate the walking posture state with high accuracy. Also, one of steps S208 and S209 may be omitted, and the third generation unit 323 may generate only one of the modified unvaccinated time aging data and the modified vaccinated time aging data. In that case, in step S212, the display control unit 123 displays only one of the modified unvaccinated time aging data and the modified vaccinated time aging data. Also, in step S212, the display control unit 123 does not have to display the non-vaccination time chronological data 313 and/or the vaccination time chronological data 314.

　また、ステップＳ２１０において、送信制御部３２４は、各経年データを、ユーザの健康を管理する管理者が所有する管理者装置へ送信してもよい。その場合、管理者装置は、受信した経年データを表示することにより管理者に通知する。管理者は、ユーザの躯体状態の経年変化を認識し、ユーザに歩行姿勢状態の改善を提案することができる。 In addition, in step S210, the transmission control unit 324 may transmit each piece of aging data to an administrator device owned by an administrator who manages the user's health. In this case, the administrator device notifies the administrator by displaying the received aging data. The administrator can recognize changes in the user's physical condition over time and suggest improvements to the user's walking posture.

　また、情報処理装置３００が歩行姿勢状態を推定する場合も、第１学習済みモデル３１１は、第３記憶装置３１０でなく、サーバ装置４００に記憶されてもよい。その場合、ステップＳ２０７において、推定部３２２は、サーバ装置４００にアクセスすることによって、第１学習済みモデル３１１によって推定された歩行姿勢状態を取得する。また、情報処理装置３００が各経年データを推定する場合も、第２学習済みモデル３１５及び／又は第３学習済みモデル３１６は、第３記憶装置３１０でなく、サーバ装置４００に記憶されてもよい。その場合、ステップＳ２０８及び／又はＳ２０９において、第３生成部３２３は、サーバ装置４００にアクセスすることによって、第２学習済みモデル３１５及び／又は第３学習済みモデル３１６によって推定された各経年データを取得する。 Also, when the information processing device 300 estimates the walking posture state, the first learned model 311 may be stored in the server device 400, not in the third storage device 310. In that case, in step S207, the estimation unit 322 obtains the walking posture state estimated by the first learned model 311 by accessing the server device 400. Also, when the information processing device 300 estimates each aging data, the second learned model 315 and/or the third learned model 316 may be stored in the server device 400, not in the third storage device 310. In that case, in steps S208 and/or S209, the third generation unit 323 obtains each aging data estimated by the second learned model 315 and/or the third learned model 316 by accessing the server device 400.

　また、情報処理装置３００が歩行姿勢状態を推定し、第１ウェアラブル装置１００が各経年データを推定してもよい。その場合、ステップＳ２０７において、推定部３２２は、歩行姿勢状態を、第３通信装置３０１を介して第１ウェアラブル装置１００に送信する。第１ウェアラブル装置１００の第１取得部１２１は、第１通信装置１０３を介して情報処理装置３００から、歩行姿勢状態を受信することにより取得する。第１生成部１２２は、図５のステップＳ１０６又はＳ１０７の処理と同様にして、第１取得部１２１が取得した歩行姿勢状態に基づいて、各経年データを生成する。 Alternatively, the information processing device 300 may estimate the walking posture state, and the first wearable device 100 may estimate each piece of aging data. In this case, in step S207, the estimation unit 322 transmits the walking posture state to the first wearable device 100 via the third communication device 301. The first acquisition unit 121 of the first wearable device 100 acquires the walking posture state by receiving it from the information processing device 300 via the first communication device 103. The first generation unit 122 generates each piece of aging data based on the walking posture state acquired by the first acquisition unit 121, in a manner similar to the processing of step S106 or S107 in FIG. 5.

　また、第１ウェアラブル装置１００が歩行姿勢状態を推定し、情報処理装置３００が各経年データを推定してもよい。その場合、ステップＳ２０４において、第１取得部１２１は、ステップＳ１０５の処理と同様にして、歩行姿勢状態を推定することにより取得し、取得した歩行姿勢状態を、第１通信装置１０３を介して情報処理装置３００に送信する。情報処理装置３００の推定部３２２は、第３通信装置３０１を介して第１ウェアラブル装置１００から、歩行姿勢状態を受信することにより取得する。ステップＳ２０８又はＳ２０９において、第３生成部３２３は、推定部３２２が取得した歩行姿勢状態に基づいて、各経年データを生成する。 Alternatively, the first wearable device 100 may estimate the walking posture state, and the information processing device 300 may estimate each piece of aging data. In that case, in step S204, the first acquisition unit 121 acquires the walking posture state by estimating it in the same manner as in the processing of step S105, and transmits the acquired walking posture state to the information processing device 300 via the first communication device 103. The estimation unit 322 of the information processing device 300 acquires the walking posture state by receiving it from the first wearable device 100 via the third communication device 301. In step S208 or S209, the third generation unit 323 generates each piece of aging data based on the walking posture state acquired by the estimation unit 322.

　以上詳述したように、躯体状態表示システム１は、情報処理装置３００がユーザの歩行姿勢状態及び／又は各経年データを推定する場合も、ユーザの状態に応じた躯体状態の経年変化を提示することが可能となる。 As described above in detail, the structure condition display system 1 is capable of presenting changes in the structure condition over time according to the user's condition, even when the information processing device 300 estimates the user's walking posture state and/or each aging data.

　以上、好適な実施形態について説明してきたが、実施形態はこれらに限定されない。例えば、第２ウェアラブル装置２００は、第２データを、第１ウェアラブル装置１００を介して情報処理装置３００に送信するのでなく、情報処理装置３００に直接送信してもよい。これにより、躯体状態表示システム１は、第１ウェアラブル装置１００における処理負荷を軽減することができる。一方、第１ウェアラブル装置１００が第２ウェアラブル装置２００からの第２データを集約することにより、躯体状態表示システム１は、情報処理装置３００における処理負荷を軽減することができる。 Although preferred embodiments have been described above, the embodiments are not limited to these. For example, the second wearable device 200 may transmit the second data directly to the information processing device 300, rather than transmitting the second data to the information processing device 300 via the first wearable device 100. This allows the structural body status display system 1 to reduce the processing load on the first wearable device 100. On the other hand, the first wearable device 100 aggregates the second data from the second wearable device 200, allowing the structural body status display system 1 to reduce the processing load on the information processing device 300.

　また、躯体状態表示システム１において、複数の第１ウェアラブル装置１００及び／又は複数の情報処理装置３００が協働して、上記した各処理の各ステップを分担してもよい。 Furthermore, in the body condition display system 1, multiple first wearable devices 100 and/or multiple information processing devices 300 may cooperate to share each step of the above-mentioned processes.

　１　躯体状態表示システム、１００　第１ウェアラブル装置、１０２　第１表示装置、１０５　第１センサ、１１０　第１記憶装置、１１１　第１学習済みモデル、１２１　第１取得部、１２２　第１生成部、２００　第２ウェアラブル装置、３００　情報処理装置、３０１　第３通信装置、３１０　第３記憶装置、３１１　第１学習済みモデル、３２２　推定部、３２３　第３生成部 1 Body condition display system, 100 First wearable device, 102 First display device, 105 First sensor, 110 First storage device, 111 First trained model, 121 First acquisition unit, 122 First generation unit, 200 Second wearable device, 300 Information processing device, 301 Third communication device, 310 Third storage device, 311 First trained model, 322 Estimation unit, 323 Third generation unit

Claims (12)

　人物が所定のサプリメントを摂取した際における人物の躯体状態の経年変化を示す第１経年データを記憶する記憶部と、
　ユーザが歩行した際に得られるデータに基づいてユーザの歩行姿勢状態を取得する取得部と、
　前記歩行姿勢状態に基づいて、前記第１経年データを修正し、前記歩行姿勢状態に応じた第２経年データを生成する生成部と、
　前記第２経年データを表示する表示部と、
　を有することを特徴とする躯体状態表示装置。 A storage unit that stores first aging data indicating a change in a person's physical condition over time when the person takes a certain supplement;
An acquisition unit that acquires a walking posture state of a user based on data obtained when the user walks;
A generation unit that corrects the first aging data based on the walking posture state and generates second aging data corresponding to the walking posture state;
A display unit that displays the second aging data; and
A body condition display device comprising: 　前記記憶部は、人物が所定のサプリメントを摂取しなかった際における人物の躯体状態の経年変化を示す第３経年データを記憶し、
　前記表示部は、前記第１経年データ、前記第２経年データ、及び、前記第３経年データを表示する、請求項１に記載の躯体状態表示装置。 The storage unit stores third aging data indicating a change in the physical condition of the person over time when the person does not take a predetermined supplement,
The body condition display device according to claim 1 , wherein the display unit displays the first aging data, the second aging data, and the third aging data. 　前記生成部は、前記歩行姿勢状態に基づいて、前記第３経年データを修正し、前記歩行姿勢状態に応じた第４経年データを生成し、
　前記表示部は、前記第１経年データ、前記第２経年データ、前記第３経年データ、及び、前記第４経年データを表示する、請求項２に記載の躯体状態表示装置。 The generation unit modifies the third aging data based on the walking posture state to generate fourth aging data according to the walking posture state;
The body condition display device according to claim 2 , wherein the display unit displays the first aging data, the second aging data, the third aging data, and the fourth aging data. 　前記躯体状態は、筋肉量、エネルギー消費量、体重減少量、脳内流量、歩行可能速度、単位時間あたりの歩行可能歩数、又は、単位時間あたりの歩行可能距離である、請求項１または２に記載の躯体状態表示装置。 The body condition display device according to claim 1 or 2, wherein the body condition is muscle mass, energy consumption, weight loss, cerebral flow rate, walking speed, number of steps that can be walked per unit time, or walking distance that can be walked per unit time. 　前記取得部は、ユーザに装着可能で且つユーザが歩行した際に得られるデータを取得するウェアラブル装置からユーザが歩行した際に得られるデータを受信する、請求項１または２に記載の躯体状態表示装置。 The body condition display device according to claim 1 or 2, wherein the acquisition unit receives data obtained when the user walks from a wearable device that can be worn by the user and acquires data obtained when the user walks. 　前記躯体状態表示装置は、ユーザに装着可能であり、
　ユーザが歩行した際に得られるデータを計測するセンサをさらに有する、請求項１または２に記載の躯体状態表示装置。 The body state display device is wearable by a user,
3. The body state display device according to claim 1, further comprising a sensor for measuring data obtained when the user walks. 　前記記憶部は、ユーザが歩行した際に得られるデータに基づいて前記歩行姿勢状態を出力可能な学習済みモデルを記憶し、
　前記取得部は、前記学習済みモデルを用いて推定された前記歩行姿勢状態を取得する、請求項１または２に記載の躯体状態表示装置。 The storage unit stores a trained model capable of outputting the walking posture state based on data obtained when the user walks,
The body state display device according to claim 1 , wherein the acquisition unit acquires the walking posture state estimated using the trained model. 　前記取得部は、ユーザが歩行した際に得られるデータに基づいて前記歩行姿勢状態を出力可能な学習済みモデルを記憶する外部装置にアクセスすることによって、前記学習済みモデルによって推定された前記歩行姿勢状態を取得する、請求項１または２に記載の躯体状態表示装置。 The body state display device according to claim 1 or 2, wherein the acquisition unit acquires the walking posture state estimated by the trained model by accessing an external device that stores a trained model capable of outputting the walking posture state based on data obtained when the user walks. 　ユーザが所定のサプリメントを摂取した際におけるユーザの躯体状態の経年変化を示す第１経年データを記憶部に記憶し、
　ユーザが歩行した際に得られるデータに基づいてユーザの歩行姿勢状態を取得し、
　前記歩行姿勢状態に基づいて、前記第１経年データを修正し、ユーザの前記歩行姿勢状態に応じた第２経年データを生成し、
　前記第２経年データを表示部に表示する、
　ことを特徴とする躯体状態表示方法。 storing first aging data in a storage unit, the first aging data indicating a change in the user's physical condition over time when the user takes a predetermined supplement;
Acquire a walking posture state of the user based on data obtained when the user walks;
correcting the first aging data based on the walking posture state, and generating second aging data corresponding to the walking posture state of the user;
displaying the second aging data on a display unit;
A structure condition display method comprising the steps of: 　記憶部及び表示部を有する躯体状態表示装置の制御プログラムであって、
　ユーザが所定のサプリメントを摂取した際におけるユーザの躯体状態の経年変化を示す第１経年データを前記記憶部に記憶し、
　ユーザが歩行した際に得られるデータに基づいてユーザの歩行姿勢状態を取得し、
　前記歩行姿勢状態に基づいて、前記第１経年データを修正し、ユーザの前記歩行姿勢状態に応じた第２経年データを生成し、
　前記第２経年データを前記表示部に表示する、
　ことを前記躯体状態表示装置に実行させることを特徴とする制御プログラム。 A control program for a structure state display device having a storage unit and a display unit,
storing in the storage unit first aging data indicating a change in the user's physical condition over time when the user takes a predetermined supplement;
Acquire a walking posture state of the user based on data obtained when the user walks;
correcting the first aging data based on the walking posture state, and generating second aging data corresponding to the walking posture state of the user;
displaying the second aging data on the display unit;
A control program for causing the structure status display device to execute the above. 　ユーザが所定のサプリメントを摂取した際におけるユーザの躯体状態の経年変化を示す第１経年データを記憶する記憶部と、
　ユーザが歩行した際に得られるデータに基づいてユーザの歩行姿勢状態を推定する推定部と、
　前記歩行姿勢状態に基づいて、前記第１経年データを修正し、ユーザの前記歩行姿勢状態に応じた第２経年データを生成する生成部と、
　前記第２経年データを送信する送信部と、
　を有することを特徴とする躯体状態送信装置。 A storage unit that stores first aging data indicating a change in a user's physical condition over time when the user takes a certain supplement;
An estimation unit that estimates a walking posture state of a user based on data obtained when the user walks;
A generation unit that corrects the first aging data based on the walking posture state and generates second aging data corresponding to the walking posture state of the user;
A transmitter for transmitting the second aging data;
A body condition transmitting device comprising: 　躯体状態送信装置と、躯体状態表示装置と、を含む躯体状態表示システムであって、
　前記躯体状態送信装置は、
　　ユーザが所定のサプリメントを摂取した際におけるユーザの躯体状態の経年変化を示す第１経年データを記憶する記憶部と、
　　ユーザが歩行した際に得られるデータに基づいてユーザの歩行姿勢状態を推定する推定部と、
　　前記歩行姿勢状態に基づいて、前記第１経年データを修正し、ユーザの前記歩行姿勢状態に応じた第２経年データを生成する生成部と、
　　前記第２経年データを送信する送信部と、を有し、
　前記躯体状態表示装置は、前記第２経年データを表示する表示部を有する、
　ことを特徴とする躯体状態表示システム。 A structure status display system including a structure status transmission device and a structure status display device,
The body state transmission device includes:
A storage unit that stores first aging data indicating a change in a user's physical condition over time when the user takes a certain supplement;
An estimation unit that estimates a walking posture state of a user based on data obtained when the user walks;
A generation unit that corrects the first aging data based on the walking posture state and generates second aging data corresponding to the walking posture state of the user;
A transmission unit that transmits the second aging data,
The body condition display device has a display unit that displays the second aging data.
A structure status display system comprising:
PCT/JP2024/046314 2023-12-28 2024-12-27 Body condition display device, body condition display method, control program, body condition transmission device, and body condition display system Pending WO2025143185A1 (en)

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WO2020075842A1 (en) * 2018-10-12 2020-04-16 大日本住友製薬株式会社 Method, device, and program for assessing relevance of respective preventive interventional actions to health in health domain of interest
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