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

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

Body status display device, body status display method, control program, body status transmission device, and body status display system

[0001] The present disclosure relates to a building frame status display device, a building frame status display method, a control program, a building frame status transmission device, and a building frame status display system.

[0002] Conventionally, a system has been developed that displays changes in the physical condition of a user over time when the user takes supplements.

[0003] Patent Document 1 discloses an exercise posture derivation device that is worn on the user's body and derives walking or running posture.

[0004] Japanese Patent Application Laid-Open No. 2021-98027

[0005] In a system that displays the changes in a user's physical condition over time, it is required to display the changes in the physical condition over time according to the user's condition.

[0006] The purpose of the building frame condition display device, building frame condition display method, control program, building frame condition transmission device, and building frame condition display system is to make it possible to present changes in building frame condition over time according to the user's condition.

[0007] The body condition display device according to the embodiment includes a memory 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 user's walking posture state 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.

[0008] In the body condition display device according to the embodiment, it is preferable that the memory unit stores third aging data indicating changes in a person's body condition over time 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.

[0009] 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 according 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.

[0010] 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 taken per unit time, or walking distance that can be taken per unit time.

[0011] 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.

[0012] In the body state display device according to the embodiment, it is preferable that the body state display device is wearable by a user and further includes a sensor that measures data obtained when the user walks.

[0013] In the body state display device according to the embodiment, it is preferable that the memory 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.

[0014] 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.

[0015] The body condition display method according to the embodiment stores first aging data in a memory unit that indicates changes in a user's body condition over time when the user takes a specified supplement, acquires the user's walking posture state 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 user's walking posture state, and displays the second aging data on a display unit.

[0016] 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 body condition of a user 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.

[0017] The body condition transmission device according to the embodiment includes a memory unit that stores first aging data indicating changes in the body condition of a user over time when the user takes a specified supplement; an estimation unit that estimates 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 of the user; and a transmission unit that transmits the second aging data.

[0018] A body condition display system according to an embodiment is a body condition display system including a body condition transmission device and a body condition display device, wherein the body condition transmission device has a memory unit that stores first aging data indicating changes in a user's body condition over time when the user takes a specified supplement and a trained model that can output a walking posture state based on data obtained when the user walks, an estimation unit that estimates the user's walking posture state 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 user's walking posture state, and a transmission unit that transmits the second aging data, and the body condition display device has a display unit that displays the second aging data.

[0019] The building frame condition display device, building frame condition display method, control program, building frame condition transmission device, and building frame condition display system can present changes in the building frame condition over time according to the user's condition.

[0020] 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 and are not restrictive of the invention as claimed.

[0021] 1 is a diagram showing a schematic configuration of a body condition display system 1 according to an embodiment. FIG. 2 is a diagram showing a schematic configuration of a first wearable device 100. FIG. 3 is a diagram showing a schematic configuration of a second wearable device 200. FIG. 4 is a schematic diagram showing an example of the data structure of a data table 112. FIG. 5 is a sequence showing an example of the operation of a display process. FIG. 6 is a graph showing changes over time in the body condition of a specific user. FIG. 7 is a diagram showing a schematic configuration of another information processing device 300. FIG. 8 is a sequence showing an example of the operation of another display process.

[0022] Hereinafter, a structure status display device, a structure status display method, a control program, a structure status transmission device, and a structure status display system according to one aspect of an embodiment will be described with reference to the drawings. However, please note that the technical scope of the present invention is not limited to the embodiment, but extends to the inventions set forth in the claims and their equivalents.

[0023] FIG. 1 is a diagram showing a schematic configuration of a building structure condition display system 1 according to an embodiment.

[0024] 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. The information processing device 300 is used by an administrator or the like who manages the user's health. The server device 400 is, for example, a server or the like located on a cloud network. The server device 400 is an example of an external device.

[0025] 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 local area network (LAN). 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 also be a wireless network such as a wireless local area network (LAN). The second wearable devices 200, the information processing device 300, and the server device 400 may also be connected to each other so as to be able to communicate with each other via the network N.

[0026] FIG. 2 is a diagram showing a schematic configuration of the first wearable device 100.

[0027] 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 includes 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. 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.

[0028] 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 input operations by the user.

[0029] The first display device 102 is an example of a display unit. The first display device 102 has a display such as a liquid crystal display, an organic electroluminescence (EL) display, or the like, and an interface circuit that outputs image data to the display, and displays the image data on the display.

[0030] 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, the server device 400, or the like 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, the server device 400, or the like via the network N.

[0031] The first interface device 104 has an antenna for transmitting and receiving wireless signals and a wireless communication interface circuit conforming to 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.

[0032] The first sensor 105 includes an acceleration sensor that measures acceleration in three axial directions applied to the first wearable device 100, and a gyro sensor that measures angular velocity in three axial directions applied to the first wearable device 100. The acceleration sensor and the gyro sensor output measurement signals indicating the measured acceleration and angular velocity to the first processing device 120.

[0033] The first sensor 105 may include a geomagnetic sensor that detects the geomagnetism 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 a barometric pressure sensor that detects the atmospheric 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 Global Positioning System (GPS) 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 geomagnetism, barometric pressure, or position to the first processing device 120.

[0034] The first storage device 110 is an example of a storage unit. The first storage device 110 includes a memory device such as a random access memory (RAM) or a read-only memory (ROM), a fixed disk device such as a hard disk, or a portable storage device such as a flexible disk or optical disk. The first storage device 110 also stores computer programs, databases, tables, and the like used for various processes of the first wearable device 100. The computer programs may be installed into the first storage device 110 from a computer-readable portable recording medium using a known setup program or the like. The portable recording medium is, for example, a CD-ROM (compact disc read-only memory) or a DVD-ROM (digital versatile disc read-only memory). The computer programs may be stored in a recording medium owned by a predetermined server and installed via a network N.

[0035] 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.

[0036] The unvaccinated longitudinal data 113 is an example of third longitudinal data. The unvaccinated longitudinal data 113 shows changes in a person's physical condition over time when the person does not take a specified supplement. The specified supplement is a supplement containing ingredients such as DHA, EPA, vitamin D, vitamin E, Oryza Plus, and / or DPA. The physical condition includes muscle mass, energy consumption, weight loss, brain flow, walking speed, number of steps that can be walked per unit time (e.g., per day), or distance that can be walked per unit time (e.g., per day). The longitudinal data shows the physical condition at predetermined intervals (e.g., monthly or yearly). The unvaccinated longitudinal data 113 is preset based on changes in the physical condition over time measured for a typical person who does not take a specified supplement.

[0037] The vaccination time aging data 114 is an example of first aging data. The vaccination time aging data 114 indicates changes in a person's physical condition over time when the person takes a predetermined supplement. The vaccination time aging data 114 is set in advance based on changes in a physical condition over time measured for a typical person who takes a predetermined supplement.

[0038] The second trained model 115 is a model for estimating, from the unvaccinated time-lapse data 113, aging data corresponding to the walking posture of a user wearing the first wearable device 100 and the second wearable device 200. The third trained model 116 is a model for estimating, from the vaccinated time-lapse data 114, aging data corresponding to the walking posture of a user wearing the first wearable device 100 and the second wearable device 200.

[0039] The first processing device 120 operates based on a program stored in advance in the first storage device 110. The first processing device 120 is, for example, a CPU. The first processing device 120 may be a digital signal processor (DSP), a large scale integration (LSI), an application specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or the like. 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, and the like, and controls each device.

[0040] The first processing device 120 reads the computer program stored in the first storage device 110 and operates in accordance with 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.

[0041] FIG. 3 is a diagram showing a schematic configuration of the second wearable device 200.

[0042] 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. Alternatively, the second wearable device 200 is an earphone-type wearable computer worn on the user's ear. Alternatively, the second wearable device 200 is an insole-type wearable computer worn inside the user's shoe. The second wearable device 200 includes 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. 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.

[0043] 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 in response to an input operation by the user. The second input device 201 may be omitted.

[0044] The second display device 202 is an example of an output unit. The second display device 202 has a display such as a liquid crystal display, an organic electroluminescence display, or the like, 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.

[0045] 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 a wireless LAN. The second communication device 203 is communicatively connected to the network N in accordance with a communication standard such as a wireless LAN. The second communication device 203 sends data received from the information processing device 300, the server device 400, or the like 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, the server device 400, or the like via the network N.

[0046] The second interface device 204 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 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.

[0047] The second sensor 205 includes an acceleration sensor that measures acceleration in three axial directions applied to the second wearable device 200 and a gyro sensor that measures angular velocity in three axial directions applied to the second wearable device 200. The acceleration sensor and 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 resistance-change or capacitance-change pressure sensors arranged one-dimensionally or two-dimensionally, generates sole data in which the magnitude of pressure measured by each pressure sensor is used as the grayscale 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 used as the grayscale value of each pixel. The sole data may also be, for example, a sole vector whose elements are the pressures measured by each pressure sensor.

[0048] Second storage device 210 includes 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. Second storage device 210 also stores computer programs, databases, tables, and the like used for various processes of second wearable device 200. The computer programs may be installed into second storage device 210 from a computer-readable portable recording medium using a known setup program or the like. Portable recording media include, for example, CD-ROMs and DVD-ROMs. The computer programs may be stored in a recording medium owned by a predetermined server and installed via network N.

[0049] The second processing device 220 operates based on a program stored in advance 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.

[0050] The second processing device 220 reads the computer program stored in the second storage device 210 and operates in accordance with the read computer program. As a result, the second processing device 220 functions as a second acquisition unit 221 and a second transmission unit 222.

[0051] FIG. 4 is a schematic diagram showing an example of the data structure of the data table 112. As shown in FIG.

[0052] The data table 112 stores angular velocity and acceleration, geomagnetism, atmospheric pressure (not shown), position (not shown) or sole of foot data obtained from the first wearable device 100 or the second wearable device 200, in association with the identification information (device ID) of each device and the time when each data was obtained.

[0053] FIG. 5 is a sequence showing an example of the operation of the display processing in the building structure status display system 1.

[0054] An example of the operation of the display processing will be described below with reference to the flowchart shown in Fig. 5. The operation flow described below is executed mainly by each processing device of each device in cooperation with each element of each device, based on a program stored in advance in each storage device of each device included in the structure status display system 1.

[0055] 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 axes directions acting on the second wearable device 200 as indicated in the measurement signal, or sole 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.

[0056] Next, 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 at which 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 also transmit multiple pieces of second data acquired by the second acquisition unit 221 together to the first wearable device 100 at any timing.

[0057] 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).

[0058] 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, geomagnetism, atmospheric pressure, or position applied to 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 associates the acquired first data and second data with the corresponding device ID and acquisition time, and stores them in the data table 112.

[0059] 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.

[0060] 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 or a support vector machine. The first trained model 111 may also be trained in advance by a random forest, or the like. The first trained model 111 is trained so that, when input data is input, it outputs the walking posture state of a user wearing the wearable device that generated the input data.

[0061] The input data is first data acquired by the first wearable device 100 and / or 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 element is arranged one-dimensionally or two-dimensionally) 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 the multiple pieces of continuously measured first data and second data to detect maximum and minimum values ​​in the first data and second data arranged in time series. The first acquisition unit 121 detects vibration periods 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 determines the leg swing interval of the user wearing the first wearable device 100 and the second wearable device 200 based on the detected vibration periods. The first acquisition unit 121 then 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, median, maximum, or minimum value of the first data and / or the second data for the predetermined number of consecutive steps or for a predetermined period. The input data may also be a quaternion calculated from angular velocity, acceleration, or geomagnetism included in the first data and / or the second data.

[0062] Furthermore, the input data may be predetermined components (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 may be 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 status display system 1 to further improve the accuracy of the first trained model 111.

[0063] Furthermore, the input data may be a spectral image obtained by transforming each of the angular velocities and / or accelerations in the three-axis directions included in the first data and / or second data using a short-time Fourier transform or a wavelet transform. The spectral image obtained by transforming each of the angular velocities and / or accelerations in the three-axis directions included in the first data using a short-time Fourier transform or a wavelet transform is an example of first image data and image data. The spectral image obtained by transforming each of the angular velocities and / or accelerations in the three-axis directions included in the second data using a short-time Fourier transform or a wavelet transform is an example of 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 status display system 1 to further improve the accuracy of the first trained model 111.

[0064] The input data may also be a feature vector having elements of the gradation value (pressure) of each pixel of the sole image or the sole vector, and elements of angular velocity, acceleration, geomagnetism, atmospheric pressure and / or position.

[0065] 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 outgoing 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-to-back direction. The fourth and fifth elements are elements related to the axis of rotation (left-to-right direction). The walking posture state does not have to include all of the first to fifth elements, but must include at least one element.

[0066] 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 also be a predetermined feature value related to each of the first to fifth elements.

[0067] 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.

[0068] 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 second wearable device 200.

[0069] As a result, the body state display system 1 can estimate the walking posture state for each part of the body where the wearable device is attached, and can comprehensively estimate the user's walking posture state based on the walking posture state estimated for each part.

[0070] 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 body condition display system 1 to flexibly change the number of processing layers (number of processing steps) in each trained model, thereby suppressing increases in processing time and processing load.

[0071] Furthermore, the first acquisition unit 121 may 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 may 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 among the intermediate layers or the output layers. This second layer is a layer different from the first layer and is a specific layer among the intermediate layers or the output layers. This allows the structure condition display system 1 to flexibly change the number of processing layers (number of processing steps) in each trained model, thereby suppressing increases in processing time and processing load.

[0072] Note that 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.

[0073] 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.

[0074] In these cases, the first trained model 111 is trained to output a walking posture state when a vector whose elements are first data and second data obtained from multiple wearable devices at corresponding times (approximately the same times) is input.

[0075] This allows the body state display system 1 to estimate the walking posture state in a composite manner based on data acquired from multiple wearable devices.

[0076] Furthermore, the first trained model 111 includes a trained model 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 about each of the first to fifth elements. The first trained model 111 may also include a single trained model capable of collectively outputting information about each of the first to fifth elements.

[0077] 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.

[0078] 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. Alternatively, the third upstream trained model is trained to output a predetermined feature amount when a spectral image transformed by short-time Fourier transform or wavelet transform on 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 enables the structure condition display system 1 to further improve the accuracy of the first trained model 111.

[0079] 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 amount when sole data is input. The second upstream trained model is trained to output a predetermined feature amount 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 allows the body state display system 1 to further improve the accuracy of the first trained model 111.

[0080] In this way, the first trained model 111 is configured to be able to output the estimated walking posture state of the user wearing the first wearable device 100 and the second wearable device 200.

[0081] The training data for the first trained model 111 includes a combination of input data generated in advance and the walking posture state of a user wearing the wearable device that generated the input data. Each training data is created by an expert who views video images 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 the wearable device.

[0082] Each training data set may be 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, combined with a walking posture state. In this case, each training data set may be generated so that a portion of the first data or second data included in each training data set overlaps. That is, the first training data set may include data for a predetermined number of steps or a predetermined period of time from a first time, and the second training data set 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 body condition display system 1 to efficiently generate the first trained model 111 from a small amount of sample data, thereby reducing the time and effort required to generate the first trained model 111.

[0083] When the first trained model 111 is pre-trained using a random forest or the like, each training data is composed of, for example, a combination of a vector in which first data or 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 angular velocities in three axial directions of the first data or second data. The body status display system 1 can improve the accuracy of the first trained model 111 by generating the first trained model 111 based on both angular velocity and acceleration. On the other hand, the body 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 angular velocity. In these cases, each training data may be generated so that part of the first data or second data included in each training data overlaps.

[0084] The first acquisition unit 121 converts the first data and second data acquired in steps S103 and S104 into a format compatible with 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.

[0085] When the information output from the first trained model 111 is a feature, the first wearable device 100 stores in advance a table or formula indicating the relationship between the feature and the walking posture state in the first storage device 110. 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 corresponding to the feature output from the first trained model 111.

[0086] 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 most trained models among the walking posture states output from each trained model is the walking posture state of the user.

[0087] 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 by using the first trained model 111. That is, the first acquisition unit 121 uses the first trained model 111 to acquire the walking posture state estimated based on the acquired first data and second data.

[0088] Next, 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 fourth aging data. The modified unvaccinated aging data indicates, for example, changes in the body condition over time 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.

[0089] 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, the server device 400, or the like. The second trained model 115 is pre-trained by supervised learning such as deep learning or a support vector machine. When the unvaccinated aging data 113 and a walking posture state are input, the second trained model 115 is trained to output estimated aging data for a user having that walking posture state. The second trained model 115 is pre-trained using the unvaccinated aging data 113, multiple sets of walking posture states measured for each of multiple users, and aging data measured for each user.

[0090] The second trained model 115 includes, for example, 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.) The second trained model 115 may also include trained models corresponding to each element included in the walking posture state.

[0091] The first generation unit 122 inputs the unvaccinated aging data 113 and the walking posture state into the second trained model 115, and acquires the aging data output from the second trained model 115 as corrected unvaccinated aging data. In this way, the first generation unit 122 generates (estimates) corrected unvaccinated aging data.

[0092] The first generation 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 users who do not take a predetermined supplement and the degree of change (difference, percentage, etc.) in the aging data, based on past measurement results for multiple users. The first generation unit 122 refers to the table to identify the degree corresponding to each user's walking posture state, corrects the unvaccinated aging data 113 based on the identified degree, and generates the corrected unvaccinated aging data.

[0093] Next, the first generation unit 122 modifies the vaccination time aging data 114 based on the walking posture state acquired by the first acquisition 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, changes 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 acquisition unit 121. The first generation unit 122 estimates the modified vaccination time aging data using, for example, the third trained model 116.

[0094] 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, the server device 400, or the like. The third trained model 116 is pre-trained by supervised learning such as deep learning or a support vector machine. When the vaccination time-lapse data 114 and a walking posture state are input, the third trained model 116 is trained to output estimated aging data for a user having that walking posture state. The third trained model 116 is pre-trained using the vaccination time-lapse data 114, multiple sets of walking posture states measured for each of multiple users, and aging data measured for each user.

[0095] The third trained model 116 includes, for example, 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.) The third trained model 116 may also include trained models corresponding to each element included in the walking posture state.

[0096] 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 acquires 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.

[0097] The first generation unit 122 may generate the modified vaccination time-over-time 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 predetermined supplement and the degree of change (difference, percentage, etc.) in the aging data based on past measurement results of multiple users. The first generation unit 122 refers to the table to identify the degree corresponding to each user's walking posture state, and corrects the vaccination time-over-time data 114 based on the identified degree to generate the modified vaccination time-over-time data.

[0098] Next, the display control unit 123 outputs the unvaccinated age data 113, the vaccinated age data 114, the corrected unvaccinated age data, and the corrected vaccinated age data by displaying them on the first display device 102 to notify the user (step S108), thereby completing the series of steps. This allows the user to recognize changes in their physical condition over time.

[0099] Alternatively, 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. By estimating the walking posture state based on the first 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. On the other hand, by estimating the walking posture state based on the second data, the first wearable device 100 can increase the degree of freedom of the body part from which the second data is acquired, and can estimate the walking posture state with high accuracy. Alternatively, one of steps S106 and S107 may be omitted, and the first generation unit 122 may generate only one of the modified unvaccinated aging data and the modified vaccinated aging data. In this case, in step S108, the display control unit 123 displays only one of the modified unvaccinated aging data and the modified vaccinated aging data. In addition, in step S108, the display control unit 123 may not display the non-vaccination time aging data 113 and / or the vaccination time aging data 114.

[0100] Furthermore, the timing of execution of the process of step S101 and the process 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, it 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 the measurement request signal from the first wearable device 100 via the second interface device 204, it 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 process until it receives an instruction to end measurement from the user using the first input device 101, and then executes the process of step S105.

[0101] Furthermore, the first trained model 111 may be stored in the server device 400 instead of the first storage device 110. In this case, in step S105, the first acquisition unit 121 accesses the server device 400 to acquire the walking posture state estimated by the first trained model 111. 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 first data and / or second data converted into a format compatible with the first trained 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 second data included in the received request signal into the first trained model 111 and acquires output information output from the first trained 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.

[0102] 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 aging data and / or the modified vaccinated 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 transmission of the modified unvaccinated aging data and / or the modified vaccinated aging data. The request signal includes the unvaccinated aging data 113 and / or the vaccinated 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-lapse data 113 or the vaccinated time-lapse data 114 and the walking posture state included in the received request signal 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-lapse data and / or the modified vaccinated time-lapse data based on the acquired output information.

[0103] By utilizing the trained model stored in the server device 400, the body condition display system 1 can estimate the walking posture state or aging data using the latest trained model updated by the server device 400. Furthermore, by utilizing the trained model stored in the server device 400, the body condition display system 1 can reduce the storage capacity of the first wearable device 100. Meanwhile, by utilizing the trained model stored in the first wearable device 100, 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. Furthermore, by utilizing the trained model stored in the first wearable device 100, the first wearable device 100 can reduce the amount of communication between the first wearable device 100 and the server device 400.

[0104] FIG. 6 is a graph showing the change over time in the physical condition of a specific user.

[0105] In Figure 6, the horizontal axis represents the user's age, and the vertical axis represents the user's muscle mass. Graphs 601 and 602 show the unvaccinated and vaccinated data over time for a typical person, respectively. Graphs 603 and 604 show the corrected unvaccinated and vaccinated data over time for a user with a specific walking posture, respectively. As shown in Figure 6, a person's muscle mass decreases with age. In particular, the walking posture of the user illustrated in Figure 6 is not good, and the degree of decline in this user's muscle mass is greater than that of a typical person. Note that the changes over time in energy consumption, weight loss, cerebral flow, maximum walking speed, maximum number of steps, and maximum walking distance also have similar characteristics to the changes over time in muscle mass.

[0106] By referring to graph 603, the user can recognize the change in body condition over time if the user does not take the specified supplement and does not improve their walking posture. In particular, by comparing graphs 601 and 603, the user can clearly recognize the difference in the change in body condition over time if the user does not take the specified supplement and improves their walking posture. Furthermore, by referring to graph 604, the user can recognize the change in body condition over time if the user takes the specified supplement but does not improve their walking posture. In particular, by comparing graphs 602 and 604, the user can clearly recognize the difference in the change in body condition over time if the user takes the specified supplement and improves their walking posture and if they do not. Furthermore, by comparing graphs 601 and 604, the user can clearly recognize the difference in the change in body condition over time if the user takes the specified supplement and improves their walking posture and if they do not take the specified supplement and do not improve their walking posture. As a result, the body condition display system 1 can increase the user's motivation to take prescribed supplements and improve their walking posture.

[0107] The inventors evaluated the first trained model 111 for all of the first to fifth elements of the walking posture state and confirmed that the accuracy rate of the information output from the first trained model 111 was 93% or higher, indicating sufficiently high accuracy. In other words, it was confirmed that the body state display system 1 can estimate the user's walking posture state with higher accuracy by using the first trained model 111, and that it can estimate changes in the body state over time with higher accuracy.

[0108] Furthermore, by using the trained model, the structural body condition display system 1 can accurately and efficiently calculate changes 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 changes over time in the walking posture state or structural body state without storing changes 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 changes over time in the walking posture state or structural body state without performing complex determinations according to various combinations of various types of parameters, thereby reducing the processing time and processing load of the display process.

[0109] The technical significance of the walking posture state including the first to fifth elements will be described below.

[0110] The main power source for walking is the lifting of the thighs, and the main power source for running is the kicking of the toes. Therefore, walking and running, i.e., running as slowly as possible, are fundamentally different actions, and clearly changing the movements between walking and running contributes to the user's health. In order to kick with the toes, the ball of the foot must be placed on the floor, causing the foot to curl inward and eliminating the arch. This loosens the bones of the foot overall, making it easier for the impact on the foot to be transmitted to the knee or hip. Running puts strain on the feet, so it is not advisable to perform running, which puts strain on the feet, while walking.

[0111] The body state display system 1 estimates each of the first to fifth elements as the user's walking posture state, thereby identifying whether the user is walking properly without running. In particular, the first to fifth elements are 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.

[0112] First, the stride length must not be too short, which would slow down the walking speed, and a certain stride length or more must be ensured. By including a first element indicating whether the user's stride length is sufficient or insufficient as a premise of the walking posture state, the body state display system 1 can ensure that the user's stride length is at least a certain length.

[0113] Next, if the user's posture is not straight, the user will be forced to perform a running motion. If the user stands upright with their heels as their center of gravity, they can lift their feet using their thighs, but if they lean forward, their upper body will bend over, 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 in the walking posture state, the body state display system 1 can confirm whether the user's posture is being maintained straight.

[0114] Next, even if the user's center of gravity is straight, if the user does not properly raise the thighs, 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 intense up-and-down movement. Furthermore, even if the user's center of gravity is straight, the user may drag their foot because they have not developed the habit of lifting their foot with their thighs. 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 confirm whether the user is properly raising the thighs.

[0115] Next, even if the axis in the front-to-back direction is correct, if the rotational (left-to-right) axis 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 foot will not step out correctly. In other words, if the supporting foot is stiff or the hip joint is not open, the desired foot will not rise correctly. On the other hand, if the user rotates the hip joint, space for the opposite foot to step out is secured, making it easier to lift the foot and correctly lifting the thigh. By including a fourth element indicating whether the user's supporting foot is stiff or not and a fifth element indicating whether the user's hip joint is open or not as elements related to the rotational (left-to-right) axis, the body state display system 1 can confirm whether the rotational axis is correct.

[0116] As described above in detail, the structural body condition display system 1 acquires the walking posture of the user based on data obtained when the user walks, and estimates and displays changes over time in the structural body condition of the user according to the acquired walking posture. This enables the structural body condition display system 1 to present changes over time in the structural body condition according to the user's condition.

[0117] FIG. 7 is a diagram showing a schematic configuration of an information processing device 300 in a building structure status display system 1 according to another embodiment.

[0118] The information processing device 300 according to this embodiment is an example of a body state transmission device. The information processing device 300 includes a third communication device 301, a third storage device 310, and a third processing device 320. 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.

[0119] The third communication device 301 is an example of a transmitter. The third communication device 301 has a wired communication interface circuit conforming to 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 conforming to a communication protocol such as a wireless LAN, and may be communicatively connected to the network N in accordance with a communication standard such as a wireless LAN.

[0120] The third storage device 310 includes a memory device such as a RAM or a ROM, a fixed disk device such as a hard disk, or a portable storage device such as a flexible disk or an optical disk. The third storage device 310 also stores computer programs, databases, tables, and the like used for various processes of the information processing device 300. The computer programs may be installed into the third storage device 310 from a computer-readable portable recording medium such as a CD-ROM or a DVD-ROM using a known setup program or the like. The computer programs may be stored in a recording medium owned by a predetermined server and installed via the network N.

[0121] The third storage device 310 stores data such as a first trained model 311, a data table 312, unvaccinated time-varying data 313, vaccination time-varying data 314, a second trained model 315, and a third trained model 316. The first trained model 311, the data table 312, unvaccinated time-varying data 313, vaccination time-varying 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-varying data 113, vaccination time-varying data 114, the second trained model 115, and the third trained model 116, respectively.

[0122] The third processing device 320 operates based on a program stored in advance 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.

[0123] The third processing device 320 reads the computer program stored in the third storage device 310 and operates in accordance with 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.

[0124] FIG. 8 is a sequence showing an example of the operation of the display process in the building structure condition display system 1 according to this embodiment.

[0125] An example of the operation of the display processing according to this embodiment will be described below with reference to the flowchart shown in Figure 8. The operation flow described below is executed mainly by the processing devices of each device in cooperation with the elements of each device, based on programs previously stored in the storage devices of each device included in the building structure status 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 description thereof will be omitted, and only steps S205 to S211 will be described below.

[0126] 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 every time it acquires the first data or the second data. Alternatively, the first acquisition unit 121 may transmit the acquired multiple pieces of first data and second data to the information processing device 300 collectively at any timing.

[0127] 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 by receiving them from the first wearable device 100 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.

[0128] 6 , 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 (step S207). The third acquisition unit 321 estimates the walking posture state of the user using the first trained model 311.

[0129] 6, the third generation unit 323 corrects the unvaccinated aging data 313 based on the walking posture state estimated by the estimation unit 322, and generates corrected unvaccinated aging data corresponding to the user's walking posture state (step S208). The third generation unit 323 estimates the corrected unvaccinated aging data using, for example, the second trained model 315.

[0130] 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 the same manner as in step S106 of 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.

[0131] Next, the transmission control unit 324 outputs the unvaccinated age data 313, the vaccinated age data 314, the corrected unvaccinated age data, and the corrected vaccinated age data by transmitting them to the first wearable device 100 via the third communication device 301 (step S210).

[0132] Next, the first acquisition unit 121 of the first wearable device 100 acquires the unvaccinated age data 313, the vaccinated age data 314, the corrected unvaccinated age data, and the corrected vaccinated age data by receiving them from the information processing device 300 via the first communication device 103 (step S211).

[0133] Alternatively, 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 acquired, and estimate the walking posture state with high accuracy. Alternatively, one of steps S208 and S209 may be omitted, and the third generation unit 323 may generate only one of the modified unvaccinated aging data and the modified vaccinated aging data. In this case, in step S212, the display control unit 123 displays only one of the modified unvaccinated aging data and the modified vaccinated aging data. In addition, in step S212, the display control unit 123 does not need to display the non-vaccination time aging data 313 and / or the vaccination time aging data 314.

[0134] 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.

[0135] Furthermore, even when the information processing device 300 estimates the walking posture state, the first learned model 311 may be stored in the server device 400, rather than the third storage device 310. In this case, in step S207, the estimation unit 322 acquires the walking posture state estimated by the first learned model 311 by accessing the server device 400. Also, even when the information processing device 300 estimates each piece of aging data, the second learned model 315 and / or the third learned model 316 may be stored in the server device 400, rather than the third storage device 310. In this case, in steps S208 and / or S209, the third generation unit 323 acquires each piece of aging data estimated by the second learned model 315 and / or the third learned model 316 by accessing the server device 400.

[0136] 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, similar to the processing of step S106 or S107 in FIG. 5 .

[0137] 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 this case, in step S204, the first acquisition unit 121 acquires the walking posture state by estimating it, similar to the processing in 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.

[0138] As described above in detail, the body condition display system 1 is capable of presenting changes in the body condition over time according to the user's condition, even when the information processing device 300 estimates the user's walking posture and / or each aging data.

[0139] Although preferred embodiments have been described above, the embodiments are not limited thereto. 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 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 devices 200, allowing the body status display system 1 to reduce the processing load on the information processing device 300.

[0140] Furthermore, in the body state display system 1, a plurality of first wearable devices 100 and / or a plurality of information processing devices 300 may cooperate to share the respective steps of the above-described processes.

[0141] 1 Body state 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

1. A body condition display device comprising: a memory 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 condition of a user 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 walking posture condition; and a display unit that displays the second aging data.

2. The body condition display device of claim 1, wherein the memory unit stores third aging data indicating changes in a person's body condition over time 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.

3. A body condition display device as described in claim 2, wherein 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.

4. A body condition display device according to claim 1 or 2, wherein the body condition is muscle mass, energy consumption, weight loss, cerebral flow rate, possible walking speed, possible number of steps per unit time, or possible walkable distance per unit time.

5. A body condition display device as described in 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.

6. The body status display device according to claim 1 or 2, further comprising a sensor that is wearable by a user and that measures data obtained when the user walks.

7. A body state display device as described in claim 1 or 2, wherein the memory unit stores a trained model capable of outputting the walking posture state based on data obtained when a user walks, and the acquisition unit acquires the walking posture state estimated using the trained model.

8. A body state display device as described in 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 a user walks.

9. A method for displaying a physical condition, comprising: storing in a memory unit first aging data indicating changes in a user's physical condition over time when the user takes a specified supplement; acquiring the user's walking posture state based on data obtained when the user walks; modifying the first aging data based on the walking posture state, generating second aging data corresponding to the user's walking posture state; and displaying the second aging data on a display unit.

10. A control program for a body condition display device having a memory unit and a display unit, the control program causing the body condition display device to execute the following: storing in the memory unit first aging data indicating changes in a user's body condition over time when the user takes a specified supplement; acquiring the user's walking posture state based on data obtained when the user walks; modifying the first aging data based on the walking posture state, generating second aging data corresponding to the user's walking posture state; and displaying the second aging data on the display unit.

11. A body condition transmission device comprising: a memory unit for storing first aging data indicating changes in a user's body condition over time when the user takes a specified supplement; an estimation unit for estimating the user's walking posture condition based on data obtained when the user walks; a generation unit for modifying the first aging data based on the walking posture condition and generating second aging data corresponding to the user's walking posture condition; and a transmission unit for transmitting the second aging data.

12. A structural body condition display system including a structural body condition transmission device and a structural body condition display device, wherein the structural body condition transmission device has a memory unit that stores first aging data indicating changes in a user's structural 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 corrects 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, and the structural body condition display device has a display unit that displays the second aging data.

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