Information generating device, information providing system, information providing method, and program

Abstract

The present invention provides, in order to provide a health measure according to a disease risk of a management target person engaged in daily work, an information generating device comprising: an acquisition unit that acquires sensor data including an acceleration and an angular velocity measured by a measurement device installed in a footwear of at least one management target person; a risk estimation unit that uses the acquired sensor data to estimate a disease risk for each disease concerning the at least one management target person; a proposal information generation unit that generates proposal information including a health measure according to the disease risk concerning the at least one management target person; and an output unit that outputs the generated proposal information.

Description

Information generating device, information providing system, information providing method, and recording medium

[0001] The present disclosure relates to an information generating device, an information providing system, an information providing method, and a recording medium.

[0002] With growing interest in healthcare, services that provide information based on gait patterns are gaining attention. For example, technology has been developed to analyze gait patterns using sensor data measured by sensors mounted on footwear such as shoes. Time-series data from sensor data reveals characteristics associated with walking events related to physical conditions. If a subject's disease risk can be estimated based on the characteristics associated with walking events, it will be possible to provide information based on disease risk to companies with many employees.

[0003] Patent Document 1 discloses a member health status management system that manages the health status of members, such as employees, belonging to a company. The system of Patent Document 1 acquires primary analysis data based on health status analyzed by a health service provider. The system of Patent Document 1 generates evaluation criteria based on the acquired primary analysis data. The system of Patent Document 1 generates secondary analysis data for a member based on the evaluation criteria and health status information for the member regarding the same items as the health status information related to the primary analysis data used to generate the evaluation criteria. The system of Patent Document 1 notifies the member of the generated secondary analysis data.

[0004] Japanese Patent Application Laid-Open No. 2020-013230

[0005] The method of Patent Document 1 required obtaining health status information of members from multiple health service providers. Therefore, the method of Patent Document 1 could not manage the health status of members unless the health status information of members was obtained from multiple health service providers. In other words, the method of Patent Document 1 could not provide health measures according to the disease risk of members engaged in daily work.

[0006] An object of the present disclosure is to provide an information generating device, an information providing system, an information providing method, and a recording medium that can provide health measures according to the disease risk of managed persons engaged in daily work.

[0007] An information generation device of one embodiment of the present disclosure includes an acquisition unit that acquires sensor data including acceleration and angular velocity measured by a measuring device mounted on the footwear of at least one managed person, a risk estimation unit that uses the acquired sensor data to estimate a disease risk for each disease for the at least one managed person, a proposed information generation unit that generates proposed information including health measures according to the disease risk for the at least one managed person, and an output unit that outputs the generated proposed information.

[0008] In one aspect of the information generation method of the present disclosure, sensor data including acceleration and angular velocity measured by a measuring device mounted on the footwear of at least one managed person is acquired, the acquired sensor data is used to estimate the disease risk for each disease for the at least one managed person, proposed information including health measures according to the disease risk for the at least one managed person is generated, and the generated proposed information is output.

[0009] A program of one embodiment of the present disclosure causes a computer to perform the following processes: acquiring sensor data including acceleration and angular velocity measured by a measuring device mounted on the footwear of at least one managed person; using the acquired sensor data to estimate a disease risk for each disease for at least one managed person; generating suggested information including health measures according to the disease risk for at least one managed person; and outputting the generated suggested information.

[0010] According to the present disclosure, it is possible to provide an information generating device, an information providing system, an information providing method, and a recording medium that can provide health measures according to the disease risk of managed persons engaged in daily work.

[0011] 1 is a block diagram illustrating an example of a configuration of an information providing system according to the present disclosure. FIG. 1 is a block diagram illustrating an example of a configuration of a measurement device provided in the information providing system according to the present disclosure. FIG. 2 is a conceptual diagram illustrating an example of the arrangement of a measurement device provided in the information providing system according to the present disclosure. FIG. 3 is a conceptual diagram illustrating a coordinate system set in a measurement device provided in the information providing system according to the present disclosure. FIG. 4 is a conceptual diagram illustrating a human body plane used in the description of the present disclosure. FIG. 5 is a block diagram illustrating an example of a configuration of an information generating device provided in the information providing system according to the present disclosure. FIG. 6 is a conceptual diagram illustrating a gait cycle used in the description of the present disclosure. FIG. 7 is a conceptual diagram illustrating a physical ability estimation model used by the information generating device provided in the information providing system according to the present disclosure. FIG. 8 is a conceptual diagram illustrating an example of an estimation of a disease risk by the information providing system according to the present disclosure. FIG. 9 is a conceptual diagram illustrating an example of an estimation of a disease risk by the information providing system according to the present disclosure. FIG. 10 is a flowchart illustrating an example of an operation of the information generating device provided in the information providing system according to the present disclosure. FIG. 11 is a flowchart illustrating an example of a gait index calculation process by the information generating device provided in the information providing system according to the present disclosure. FIG. 12 is a conceptual diagram illustrating a service using the information providing system according to the present disclosure. FIG. 13 is a conceptual diagram illustrating an example of a display of suggested information including a health measure provided from the information providing system according to the present disclosure. FIG. 1 is a conceptual diagram for explaining a service using an information providing system in the present disclosure. FIG. 2 is a conceptual diagram showing a display example of suggested information including health measures provided from the information providing system in the present disclosure. FIG. 3 is a conceptual diagram for explaining a service using the information providing system in the present disclosure. FIG. 4 is a conceptual diagram showing a display example of suggested information including health measures provided from the information providing system in the present disclosure. FIG. 5 is a block diagram showing an example of the configuration of an information generating device in the present disclosure. FIG. 6 is a flowchart for explaining an example of the operation of the information generating device in the present disclosure. FIG. 7 is a conceptual diagram showing an example of a hardware configuration in the present disclosure.

[0012] Hereinafter, embodiments for carrying out the present disclosure will be described with reference to the drawings. In this disclosure, the drawings used in describing each embodiment relate to one or more embodiments. Furthermore, elements included in each drawing may apply to one or more embodiments. The embodiments described below are limited in a manner that is technically preferable for carrying out the present disclosure, but the scope of the disclosure is not limited to the following. In all drawings used in describing the following embodiments, similar parts are designated by the same reference numerals unless otherwise specified. In the following embodiments, repeated description of similar configurations and operations may be omitted.

[0013] First Embodiment First, an example of an information provision system according to this embodiment will be described with reference to the drawings. The information provision system according to this embodiment uses sensor data related to foot movements measured during walking of employees (subjects to management) belonging to the company to estimate health measures customized for each company. The method according to this embodiment can be applied not only to companies but also to any organization with multiple members (subjects to management). For example, the method according to this embodiment can also be applied to organizations such as local governments.

[0014] (Configuration) FIG. 1 is a block diagram showing an example of the configuration of an information provision system 1 according to the present disclosure. The information provision system 1 includes a measurement device 10 and an information generating device 12. For example, the measurement device 10 is attached to the footwear of an employee (person to be managed) of a company. For example, the functions of the information generating device 12 are implemented in a server or cloud connected via a network to a mobile device carried by the person to be managed or to a repeater installed inside the building where the person to be managed works. For example, the functions of the information generating device 12 may be implemented in a mobile device carried by the person to be managed. Below, the configurations of the measurement device 10 and the information generating device 12 will be described individually.

[0015] [Measurement Device] Fig. 2 is a block diagram showing an example of the configuration of the measurement device 10. The measurement device 10 has a sensor 110, a control unit 113, a communication unit 115, and a power supply 117. The sensor 110 has an acceleration sensor 111 and an angular velocity sensor 112. The sensor 110 may include sensors other than the acceleration sensor 111 and the angular velocity sensor 112. Description of sensors other than the acceleration sensor 111 and the angular velocity sensor 112 that may be included in the sensor 110 will be omitted.

[0016] The acceleration sensor 111 is a sensor that measures acceleration in three axial directions (also called spatial acceleration). The acceleration sensor 111 measures acceleration (also called spatial acceleration) as a physical quantity related to foot movement. The acceleration sensor 111 outputs the measured acceleration to the control unit 113. For example, a piezoelectric, piezo-resistive, or capacitance type sensor can be used as the acceleration sensor 111. There are no limitations on the sensor used as the acceleration sensor 111 as long as it can measure acceleration.

[0017] The angular velocity sensor 112 is a sensor that measures angular velocity (also called spatial angular velocity) around three axes. The angular velocity sensor 112 measures angular velocity (also called spatial angular velocity) as a physical quantity related to foot movement. The angular velocity sensor 112 outputs the measured angular velocity to the control unit 113. For example, a vibration type or capacitance type sensor can be used as the angular velocity sensor 112. There are no limitations on the sensor used as the angular velocity sensor 112 as long as it can measure angular velocity.

[0018] The sensor 110 is realized by, for example, an inertial measurement unit (IMU) that measures acceleration and angular velocity. An example of an inertial measurement unit is an IMU (Inertial Measurement Unit). The IMU includes an acceleration sensor 111 that measures acceleration in three axial directions and an angular velocity sensor 112 that measures angular velocity around three axes. The sensor 110 may be realized by an inertial measurement unit such as a VG (Vertical Gyro) or an AHRS (Attitude Heading Reference System). The sensor 110 may also be realized by a GPS / INS (Global Positioning System / Inertial Navigation System). The sensor 110 may be realized by a device other than an inertial measurement unit as long as it can measure physical quantities related to foot movement.

[0019] FIG. 3 is a conceptual diagram showing an example in which the measurement device 10 is placed inside the shoes 100 of both feet. In the example of FIG. 3, the measurement device 10 is placed at a position corresponding to the back of the arch of the foot. For example, the measurement device 10 is placed in an insole inserted into the shoe 100. For example, the measurement device 10 may be placed on the bottom of the shoe 100. For example, the measurement device 10 may be embedded in the body of the shoe 100. The measurement device 10 may be detachable from the shoe 100 or may not be detachable from the shoe 100. The measurement device 10 may be placed at a position other than the back of the arch of the foot as long as it can measure sensor data related to foot movement. The measurement device 10 may also be placed in socks worn by the person being managed or in an accessory such as an anklet worn by the person being managed. The measurement device 10 may also be attached directly to the foot or embedded in the foot. The measurement device 10 may also be placed inside one of the shoes 100 as long as it can measure data that can be used to estimate disease risk.

[0020] In the example of FIG. 3 , a local coordinate system is set with the measurement device 10 (sensor 110) as the reference, and includes an x-axis in the left-right direction, a y-axis in the front-back direction, and a z-axis in the up-down direction. FIG. 3 shows an example in which the same coordinate system is set for the left foot and the right foot. For example, if sensors 110 manufactured to the same specifications are placed in left and right shoes 100, the up-down orientation (Z-axis orientation) of the sensors 110 placed in the left and right shoes 100 is the same. In this case, the three axes of the local coordinate system set for the sensor data derived from the left foot and the three axes of the local coordinate system set for the sensor data derived from the right foot are the same for the left and right. In the present disclosure, the x-axis is positive to the left, the y-axis is positive to the rear, and the z-axis is positive to the up.

[0021] FIG. 4 is a conceptual diagram illustrating a local coordinate system (x-axis, y-axis, z-axis) set in the measurement device 10 (sensor 110) installed on the back of the arch of the foot, and a world coordinate system (x-axis, y-axis, z-axis) set relative to the ground. FIG. 4 shows an example in which different coordinate systems are set for the left and right feet. In the world coordinate system (x-axis, y-axis, z-axis), when the managed person is standing upright facing the direction of travel, the x-axis direction is the lateral direction of the managed person, the y-axis direction is the back direction of the managed person, and the z-axis direction is the direction of gravity. Note that the example in FIG. 4 conceptually illustrates the relationship between the local coordinate system (x-axis, y-axis, z-axis) and the world coordinate system (x-axis, y-axis, z-axis), and does not accurately illustrate the relationship between the local coordinate system and the world coordinate system, which changes depending on the walking direction of the managed person.

[0022] FIG. 5 is a conceptual diagram illustrating planes (also referred to as human body planes) set for the human body. In this embodiment, a sagittal plane that divides the body into left and right, a coronal plane that divides the body into front and back, and a horizontal plane that divides the body horizontally are defined. Note that, as shown in FIG. 5 , when the user is standing upright with the centerline of the feet pointing in the direction of travel, the world coordinate system and the local coordinate system are assumed to coincide. FIG. 5 shows an example in which different coordinate systems are set for the left and right feet. In this embodiment, a rotation in the sagittal plane about the X-axis (x-axis) as the axis of rotation is defined as roll, a rotation in the coronal plane about the Y-axis (y-axis) as the axis of rotation is defined as pitch, and a rotation in the horizontal plane about the Z-axis (z-axis) as the axis of rotation is defined as yaw. Furthermore, a rotation angle in the sagittal plane about the X-axis (x-axis) as the axis of rotation is defined as roll angle, a rotation angle in the coronal plane about the Y-axis (y-axis) as the axis of rotation is defined as pitch angle, and a rotation angle in the horizontal plane about the Z-axis (z-axis) as the axis of rotation is defined as yaw angle.

[0023] The control unit 113 (control means) causes the acceleration sensor 111 and the angular velocity sensor 112 to measure sensor data. For example, the control unit 113 causes the acceleration sensor 111 and the angular velocity sensor 112 to start measurement in response to a measurement start signal transmitted from the information generating device 12. For example, the control unit 113 may cause the acceleration sensor 111 and the angular velocity sensor 112 to start measurement in response to detection of the walking of the managed person. For example, the control unit 113 starts measuring the step width starting from the point in time when it is detected that either the left or right foot has started moving in the direction of travel after both feet have remained at the same vertical height for a predetermined period of time. The control unit 113 may also be configured to start measuring the step width at a predetermined timing.

[0024] The control unit 113 acquires acceleration in three axial directions from the acceleration sensor 111. The control unit 113 also acquires angular velocities around three axes from the angular velocity sensor 112. For example, the control unit 113 performs analog-to-digital (AD) conversion on the acquired physical quantities (analog data) such as angular velocity and acceleration. The physical quantities (analog data) measured by the acceleration sensor 111 and the angular velocity sensor 112 may be converted to digital data by each of the acceleration sensor 111 and the angular velocity sensor 112. For example, an AD conversion circuit that AD converts the physical quantities (analog data) such as angular velocity and acceleration may be provided. The control unit 113 outputs the converted digital data (also referred to as sensor data) to the communication unit 115. For example, the control unit 113 may temporarily store the sensor data in a storage unit (not shown).

[0025] The sensor data includes at least acceleration data converted into digital data and angular velocity data converted into digital data. The acceleration data includes acceleration vectors in three axial directions. The angular velocity data includes angular velocity vectors around three axes. The acceleration data and angular velocity data are associated with the time at which they were acquired. The control unit 113 may also apply corrections, such as corrections for mounting errors, temperature corrections, and linearity corrections, to the acceleration data and angular velocity data.

[0026] For example, the control unit 113 may calculate at least one of the gait indices described below. In this case, the measurement device 10 outputs the calculated gait indices to the information generating device 12. For example, the control unit 113 may calculate feature amounts used to estimate physical abilities described below. In this case, the measurement device 10 outputs the calculated feature amounts to the information generating device 12.

[0027] For example, the control unit 113 is realized by a microcomputer or microcontroller that performs overall control and data processing of the measuring device 10. For example, the control unit 113 includes a CPU (Central Processing Unit), RAM (Random Access Memory), ROM (Read Only Memory), flash memory, etc.

[0028] The communication unit 115 (communication means) acquires sensor data from the control unit 113. The communication unit 115 transmits the acquired sensor data to the information generating device 12. The timing of transmitting the sensor data is not particularly limited. For example, the communication unit 115 transmits the sensor data at a preset transmission timing. For example, the communication unit 115 transmits the sensor data in real time in response to measurement of the sensor data. For example, the communication unit 115 may store sensor data measured over a predetermined period and transmit the stored sensor data all at once at a preset timing. For example, the communication unit 115 may be configured to receive a measurement start signal from the information generating device 12. In this case, the communication unit 115 outputs the received measurement start signal to the control unit 113.

[0029] For example, the communication unit 115 transmits the sensor data to the information generating device 12 via wireless communication. For example, the communication unit 115 transmits the sensor data to the information generating device 12 via a wireless communication function (not shown) conforming to standards such as Bluetooth (registered trademark) or Wi-Fi (registered trademark). The communication function of the communication unit 115 may conform to standards other than Bluetooth (registered trademark) or Wi-Fi (registered trademark). The communication unit 115 may transmit the sensor data to the information generating device 12 via a wired connection such as a cable.

[0030] The power supply 117 is a battery that supplies power for operating the measuring device 10. For example, the power supply 117 may be a thin battery, such as a coin or button type. For example, the power supply 117 may be a primary battery, such as a lithium primary battery, a silver oxide battery, an alkaline button battery, or a zinc-air battery. When the power supply 117 is a primary battery, it is preferable that the power supply 117 be a long-life battery. The power supply 117 may also be a rechargeable secondary battery. When the power supply 117 is a secondary battery, the power supply 117 may be a battery that can be charged via a wired connection or a battery that can be powered wirelessly. If the power supply 117 is capable of wireless power supply, a wireless power supply device may be placed in a location where footwear is kept, such as an entrance or a shoe locker. By placing footwear equipped with the measuring device 10 on the wireless power supply device, the measuring device 10 can be charged appropriately when not in use.

[0031] [Information Generating Device] Fig. 6 is a block diagram showing an example of the configuration of the information generating device 12. The information generating device 12 has an acquiring unit 121, a waveform processing unit 122, a gait index calculating unit 123, a storage unit 124, a physical ability estimating unit 125, a disease risk estimating unit 126, a proposed information generating unit 127, and an output unit 129. The waveform processing unit 122, the gait index calculating unit 123, the physical ability estimating unit 125, and the disease risk estimating unit 126 constitute the risk estimating unit 15. The waveform processing unit 122 and the gait index calculating unit 123 constitute the calculating unit 13. The physical ability estimating unit 125 and the disease risk estimating unit 126 constitute the estimating unit 14.

[0032] The acquisition unit 121 (acquisition means) acquires sensor data from the measurement device 10 mounted on the footwear of the person to be managed. The acquisition unit 121 receives the sensor data from the measurement device 10 via wireless communication. For example, the sensor data may include location information of the person to be managed's mobile terminal (not shown), which is the source of the sensor data. For example, the location information is measured using a global positioning system (GPS) function mounted on the mobile terminal and added to the sensor data. For example, the acquisition unit 121 receives the sensor data from the measurement device 10 via a wireless communication function (not shown) conforming to standards such as Bluetooth (registered trademark) or Wi-Fi (registered trademark). Note that the communication function of the acquisition unit 121 may conform to standards other than Bluetooth (registered trademark) or Wi-Fi (registered trademark) as long as it can communicate with the measurement device 10. The acquisition unit 121 may also receive the sensor data from the measurement device 10 via a wired connection such as a cable. For example, the acquisition unit 121 may acquire gait indices and feature amounts calculated by the measurement device 10 .

[0033] The acquisition unit 121 also acquires attribute data of the managed person. The attribute data includes gender, date of birth, height, and weight. The date of birth is converted to age. The gender, date of birth (age), height, and weight included in the attribute data are also called physical information. For example, the attribute data is input via an input device (not shown). For example, the attribute data is input via a terminal device used by the administrator. For example, the attribute data is input via a mobile terminal used by the managed person. For example, the attribute data may be stored in advance in the storage unit 124. The attribute data may be updated at any timing in response to input by the managed person or the administrator.

[0034] The waveform processing unit 122 (waveform processing means) acquires sensor data from the acquisition unit 121. The waveform processing unit 122 extracts time series data for one walking cycle from the time series data of acceleration in three axial directions and angular velocity around three axes included in the sensor data. The time series data for one walking cycle is also called walking waveform data. The waveform processing unit 122 extracts walking waveform data based on the timing of walking events detected from the time series data of the sensor data. For example, the waveform processing unit 122 extracts walking waveform data that starts at the timing of a heel strike and ends at the timing of the next heel strike.

[0035] FIG. 7 is a conceptual diagram illustrating a step cycle based on the right foot. The step cycle based on the left foot is similar to that of the right foot. The horizontal axis of FIG. 7 represents one step cycle of the right foot, starting from the point when the heel of the right foot hits the ground and ending from the point when the heel of the right foot hits the ground again. The horizontal axis of FIG. 7 is normalized with the step cycle set to 100%. Normalizing one step cycle to 100% is called first normalization. One step cycle of one foot is broadly divided into a stance phase, in which at least a portion of the sole of the foot is in contact with the ground, and a swing phase, in which the sole of the foot is off the ground. The stance phase is the period in which at least a portion of the sole of the foot is in contact with the ground. The stance phase is further divided into an early stance phase T1, a mid-stance phase T2, a final stance phase T3, and an early swing phase T4. The swing phase is the period in which the sole of the foot is off the ground. The swing phase is further divided into an early swing phase T5, a mid-swing phase T6, and a final swing phase T7. The horizontal axis in Figure 7 is normalized so that the stance phase is 60% and the swing phase is 40%. Normalizing gait waveform data so that the stance phase is 60% and the swing phase is 40% is called second normalization. Note that the periods shown in Figure 7 are merely examples and do not limit the periods that make up a gait cycle or the names of those periods.

[0036] As shown in Figure 7, multiple events occur during walking. These events are also referred to as walking events. P1 represents an event in which the heel of the right foot touches the ground (heel strike) (HS). P2 represents an event in which the toe of the left foot leaves the ground (opposite toe off) while the sole of the right foot remains on the ground (oto). P3 represents an event in which the heel of the right foot rises (heel rise) while the sole of the right foot remains on the ground (HR). P4 represents an event in which the heel of the left foot touches the ground (opposite heel strike) (OHS). P5 represents an event in which the toe of the right foot leaves the ground (toe off) while the sole of the left foot remains on the ground (TO). P6 represents an event in which the left and right feet cross (foot crossing) with the sole of the left foot touching the ground (FA: Foot Adjacent). P7 represents an event in which the tibia of the right foot is approximately perpendicular to the ground (TV: Tibia Vertical) with the sole of the left foot touching the ground. P8 represents an event in which the heel of the right foot touches the ground (heel strike) (HS: Heel Strike). P8 corresponds to the end point of the walking cycle that begins with P1 and the start point of the next walking cycle. Note that the walking events shown in Figure 7 are merely examples and do not limit the events that occur during walking or the names of these events.

[0037] The timing of heel strike is the timing of the minimum peak immediately after the maximum peak that appears in the time series data of forward acceleration (Y-direction acceleration). The maximum peak that marks the heel strike timing corresponds to the maximum peak of the walking waveform data for one step cycle. The section between consecutive heel strikes corresponds to one step cycle. The timing of toe lift is the timing of the rise of the maximum peak that appears after the stance phase period in which no fluctuations appear in the time series data of forward acceleration (Y-direction acceleration). The timing midway between the timing of the minimum roll angle and the timing of the maximum roll angle corresponds to the mid-stance phase.

[0038] The waveform processing unit 122 normalizes (first normalization) the time of the extracted walking waveform data for one step cycle to a walking cycle of 0 to 100% (percent). The timing of 1%, 10%, or the like included in the 0 to 100% walking cycle is also referred to as a walking phase. Furthermore, the waveform processing unit 122 normalizes (second normalization) the first-normalized walking waveform data for one step cycle so that the stance phase is 60% and the swing phase is 40%. Second-normalizing the walking waveform data can reduce discrepancies in the walking phases from which feature values ​​are extracted. The waveform processing unit 122 outputs the normalized walking waveform data to the gait index calculation unit 123.

[0039] For example, the waveform processing unit 122 extracts and normalizes gait waveform data for one walking cycle using the forward acceleration (Y-direction acceleration). With regard to accelerations / angular velocities other than the forward acceleration (Y-direction acceleration), the waveform processing unit 122 extracts and normalizes gait waveform data for one walking cycle in accordance with the gait cycle of the forward acceleration (Y-direction acceleration). The waveform processing unit 122 may also generate time series data of angles around three axes by integrating time series data of angular velocities around three axes. In this case, the waveform processing unit 122 extracts and normalizes gait waveform data for one walking cycle for angles around three axes in accordance with the gait cycle of the forward acceleration (Y-direction acceleration).

[0040] The waveform processor 122 may extract / normalize gait waveform data for one step gait cycle using acceleration / angular velocity other than forward acceleration (Y-direction acceleration). For example, the waveform processor 122 may detect heel strike and toe lift from time series data of vertical acceleration (Z-direction acceleration) (not shown). The timing of heel strike is the timing of a steep minimum peak that appears in the time series data of vertical acceleration (Z-direction acceleration). At the timing of the steep minimum peak, the value of vertical acceleration (Z-direction acceleration) becomes approximately zero. The minimum peak that marks the timing of heel strike corresponds to the minimum peak of the gait waveform data for one step gait cycle. The interval between consecutive heel strikes is a gait cycle. The timing of toe lift is the timing of an inflection point in the time series data of vertical acceleration (Z-direction acceleration) during a gradual increase after a period of small fluctuation following a maximum peak immediately after heel strike. The waveform processing unit 122 may also extract / normalize gait waveform data for one walking cycle using both forward acceleration (Y-direction acceleration) and vertical acceleration (Z-direction acceleration).The waveform processing unit 122 may also extract / normalize gait waveform data for one walking cycle using acceleration, angular velocity, angle, etc. other than forward acceleration (Y-direction acceleration) and vertical acceleration (Z-direction acceleration).

[0041] The waveform processing unit 122 extracts feature quantities (physical ability feature quantities) used to estimate physical ability from the walking waveform data. The waveform processing unit 122 extracts physical ability feature quantities used to estimate at least one physical ability. For example, the waveform processing unit 122 extracts physical ability feature quantities used to estimate at least one of physical abilities such as grip strength (total muscle strength of the entire body), dynamic balance, lower limb muscle strength, mobility, and static balance. For example, the waveform processing unit 122 extracts physical ability feature quantities for each walking phase cluster according to preset conditions. A walking phase cluster is a cluster that integrates temporally consecutive walking phases. A walking phase cluster includes at least one walking phase. A walking phase cluster may also include a single walking phase. The waveform processing unit 122 outputs the extracted physical ability feature quantities to the physical ability estimation unit 125. If physical ability is not used in estimating disease risk, extraction of physical ability feature quantities can be omitted.

[0042] The gait index calculation unit 123 (gait index calculation means) acquires normalized gait waveform data from the waveform processing unit 122. The gait index calculation unit 123 calculates gait indices used to estimate physical ability using the normalized gait waveform data. There are no particular limitations on the gait indices to be calculated, as long as they can be calculated using normalized gait waveform data. For example, the gait index calculation unit 123 calculates gait indices related to distance, height, angle, speed, time, frailty level, CPEI (Center of Pressure Exclusion Index), etc. Representative gait indices are listed below. Specific methods for calculating the following gait indices will not be described.

[0043] For example, the gait index calculation unit 123 calculates indices related to distance and height as gait indices. For example, the gait index calculation unit 123 calculates a stride length, a turning distance, a foot lift height, FTC (Foot Clearance), and MTC (Minimum Toe Clearance). The stride length indicates the distance between the front foot and the rear foot while walking. The turning distance indicates the maximum distance that the foot is separated outward in the direction of travel during the swing phase. The foot lift height indicates the maximum distance between the measurement device 10 (sensor 110) and the ground during the swing phase. The FTC indicates the maximum distance between the heel and the ground during the swing phase. The MTC indicates the minimum distance between the toe and the ground during the swing phase.

[0044] For example, the gait index calculation unit 123 calculates angle-related indices as gait indices. For example, the gait index calculation unit 123 calculates the contact angle, the takeoff angle, the toe direction, the heel-strike roll angle, the toe-off roll angle, the swing leg peak angular velocity, and the hallux angle. The contact angle indicates the maximum value of the angle between the sole of the foot and the ground at heel-strike. The takeoff angle indicates the angle between the sole of the foot and the ground during the swing phase. The toe direction indicates the average value of the orientation of the toe relative to the direction of forward motion during the swing phase. The heel-strike roll angle is the angle between the ankle and the ground at heel-strike, as viewed from a rear perspective. The toe-off roll angle is the angle between the ankle and the ground at push-off, as viewed from a rear perspective. The swing leg peak angular velocity is the angular velocity in the ankle dorsiflexion direction during the period from immediately after push-off until the toe comes closest to the ground. The hallux angle indicates the angle at which the big toe is tilted toward the index toe. Specifically, the hallux angle is the angle between the center line of the first metatarsal and the center line of the first proximal phalanx.

[0045] For example, the gait index calculation unit 123 calculates an index related to speed as a gait index. For example, the gait index calculation unit 123 calculates walking speed, cadence, and maximum swing speed. Walking speed indicates the walking speed. Cadence indicates the number of steps per minute. Maximum swing speed indicates the speed at which the leg is swung out during the swing phase.

[0046] For example, the gait index calculation unit 123 calculates time-related indices as gait indices. For example, the gait index calculation unit 123 calculates stance time, load time, sole contact time, push-off time, swing time, and DST (Double Support Time). Stance time indicates the time during which the foot is in contact with the ground during walking. Stance time is the sum of load time, sole contact time, and push-off time. Load time is the time during the stance phase from when the heel contacts the ground to when the toe contacts the ground. Sole contact time is the time during the stance phase when the entire sole of the foot is in contact with the ground and is horizontal to the ground. Push-off time is the time during the stance phase from when the sole is in contact with the ground to when the toe pushes off the ground. Swing time indicates the time during which the foot is off the ground during walking. DST is divided into DST1 and DST2. DST1 indicates the time during which the foot equipped with the measuring device 10 (sensor 110) is in front of the other foot during a period when both feet are in contact with the ground at the same time, and DST2 indicates the time during which the foot equipped with the measuring device 10 (sensor 110) is behind the other foot during a period when both feet are in contact with the ground at the same time.

[0047] For example, the gait index calculation unit 123 calculates a frailty level and a center of pressure exclusion index (CPEI) as gait indices. The frailty level is an estimated value of the frailty state according to the walking state. For example, the gait index calculation unit 123 estimates an index such as a judgment result R1 indicating health, a judgment result R2 indicating the possibility of frailty, or a judgment result R3 indicating a high possibility of frailty as the frailty level. The CPEI indicates an estimated value of the rate of expansion of the movement of the center of foot pressure acting on the ground during the stance phase.

[0048] The memory unit 124 (storage means) stores a physical ability estimation model (described below) that estimates physical ability using physical ability feature quantities extracted from the walking waveform data. For example, the physical ability is at least one of grip strength, dynamic balance, lower limb muscle strength, mobility, and static balance. The physical ability may include other features besides grip strength, dynamic balance, lower limb muscle strength, mobility, and static balance. The memory unit 124 stores physical ability estimation models trained for multiple subjects. For example, the physical ability estimation model outputs an index of physical ability (physical ability score) in response to input of physical ability feature quantities extracted from the walking waveform data. If physical ability is not used in estimating disease risk, the physical ability estimation model can be omitted.

[0049] The storage unit 124 also stores a disease risk estimation model (described below) that estimates disease risk using attribute data, gait indices, and physical ability scores. The disease risk indicates the risk of developing a specific disease. For example, specific diseases include gout, diabetes, hypertension, nephrolithiasis, liver cirrhosis, arteriosclerosis, thromboembolism, dyslipidemia, hypercholesterolemia, and hyperlipidemia. For example, specific diseases include lower back pain, sleep apnea syndrome, insomnia, depression, osteoarthritis, and Parkinson's syndrome. The specific diseases may also include diseases other than those listed above. The storage unit 124 stores disease risk estimation models trained on multiple subjects. For example, the disease risk estimation model outputs an index related to disease risk (disease risk score) in response to input of attribute data, gait indices, and physical ability scores. For example, the disease risk estimation model may output a disease risk score in response to input of gait indices and attribute data, without using a physical ability score. In this case, the physical ability estimation model may not be used.

[0050] The storage unit 124 also stores a health measure estimation model that outputs health measures for a managed individual in response to input of the individual's disease risk. For example, the health measure estimation model is a model trained using a dataset of disease risk scores and health measures as training data. For example, the health measure estimation model is a model trained to output information including advice and comments from experts such as industrial physicians, public health nurses, physical therapists, doctors, and nurses in response to input of disease risk scores. The health measures may be health measures targeted at individual disease risks, or health measures targeted at the disease risks of multiple managed individuals. For example, the health measure estimation model may be a model that outputs corporate health measures for multiple managed individuals in response to input of disease risks for those individuals. For example, the health measure estimation model may be a model customized for each company. For example, the corporate health measures may include health-related measures tailored to the company's work style. For example, the health measure estimation model may be a model customized to the company's work style. For example, the health measure estimation model may include a large-scale language model that outputs sentences including corporate health measures in response to input of disease risks for multiple managed individuals.

[0051] The storage unit 124 stores the physical ability estimation model, disease risk estimation model, and health measure estimation model trained for multiple subjects. For example, the physical ability estimation model, disease risk estimation model, and health measure estimation model may be stored in the storage unit 124 at the time of product shipment from a factory. The physical ability estimation model, disease risk estimation model, and health measure estimation model may also be stored in the storage unit 124 when the information generating device 12 is calibrated. For example, the physical ability estimation model, disease risk estimation model, and health measure estimation model stored in a storage device (not shown) such as an external server may be used. In this case, the physical ability estimation model, disease risk estimation model, and health measure estimation model may be accessible via an interface (not shown) connected to the storage device.

[0052] The storage unit 124 also stores attributes of the managed person. The attribute data includes gender, date of birth (age), height, and weight. The attribute data may be updated at any time. Furthermore, the storage unit 124 may store health checkup data of the managed person. The health checkup data can be a factor in improving the accuracy of estimating disease risk scores and health measures. For example, the health checkup data of the managed person includes diagnostic results for statutory items in the health checkup at the time of employment and periodic health checkups. The health checkup data of the managed person may also include diagnostic results for items other than statutory items in the health checkup at the time of employment and periodic health checkups.

[0053] The physical ability estimation unit 125 (physical ability estimation means) acquires physical ability feature quantities extracted from the walking waveform data from the waveform processing unit 122. The physical ability estimation unit 125 also acquires attributes stored in the memory unit 124. The physical ability estimation unit 125 estimates a physical ability score using the physical ability feature quantities and attributes. The physical ability estimation unit 125 inputs the physical ability feature quantities and attributes of the managed individual into a physical ability estimation model stored in the memory unit 124. For example, the physical ability estimation unit 125 estimates a physical ability score related to at least one of the physical abilities of grip strength (total muscle strength of the entire body), dynamic balance, lower limb muscle strength, mobility, and static balance. The estimation of the physical ability score by the physical ability estimation unit 125 will be described later. The physical ability estimation unit 125 outputs the physical ability score output from the physical ability estimation model to the disease risk estimation unit 126.

[0054] Next, an example of a physical ability score estimated by the physical ability estimation unit 125 will be described. Here, an example of feature quantities used to estimate grip strength (total muscle strength of the entire body), dynamic balance, lower limb muscle strength, mobility, and static balance will be described. Note that the following examples do not limit the physical abilities estimated by the physical ability estimation unit 125. The physical abilities estimated by the physical ability estimation unit 125 may be appropriately selected depending on the disease for which the disease risk is to be estimated. Note that the disease risk estimation unit 126 may be configured to estimate disease risk using gait indicators and attribute data without using a physical ability score. In this case, the physical ability estimation unit 125 may be omitted from the estimation unit 14.

[0055] <Grip strength (total muscle strength of the whole body)> Grip strength, which is one of the physical abilities, is correlated with total muscle strength of the whole body. Grip strength is also correlated with knee extension strength. For example, an estimated value of grip strength is an index of total muscle strength. For example, a score based on the estimated value of grip strength (also called a total muscle strength score) is an index of total muscle strength. The total muscle strength score is a value obtained by scoring grip strength, which is an index of total muscle strength, according to a preset standard. Grip strength is affected by attributes such as gender, age, and height. Therefore, the total muscle strength score may be scored according to a standard for each attribute. In particular, grip strength is affected by gender. Therefore, the total muscle strength score may be scored according to different standards depending on gender. Note that the index of total muscle strength is not limited to grip strength as long as it is possible to score total muscle strength.

[0056] The gait phases from which the features used to estimate grip strength are extracted differ depending on gender. For men, there is a correlation between quadriceps activity and grip strength. Therefore, to estimate men's grip strength, features extracted from gait phases that reveal the characteristics of quadriceps activity are used. For women, there is a correlation between grip strength and the activities of the vastus lateralis, vastus intermedius, and vastus medialis quadriceps. Therefore, to estimate women's grip strength, features extracted from gait phases that reveal the characteristics of vastus lateralis, vastus intermedius, and vastus medialis quadriceps activity are used.

[0057] Feature quantities AM1, AM2, AM3, and AM4 are used to estimate the male's grip strength. Feature quantity AM1 is extracted from the 3% section of the walking phase of gait waveform data related to time-series data of forward acceleration (Y-direction acceleration). The 3% walking phase is included in the initial stance phase T1. Feature quantity AM1 mainly includes features related to the movement of the vastus lateralis, vastus intermedius, and vastus medialis, which are quadriceps muscles. Feature quantity AM2 is extracted from the 59% to 62% section of the walking phase of gait waveform data related to time-series data of forward acceleration (Y-direction acceleration). The 59% to 62% walking phase is included in the early swing phase T4. Feature quantity AM2 mainly includes features related to the movement of the rectus femoris, which is one of the quadriceps muscles. Feature quantity AM3 is extracted from the 59% to 62% section of the walking phase of gait waveform data related to time-series data of vertical acceleration (Z-direction acceleration). The early swing phase T4 comprises 59 to 62% of the walking phase. Feature AM3 mainly includes features relating to the movement of the rectus femoris, one of the quadriceps muscles. Feature AM4 is the proportion of the period from heel-strike to toe-off of the opposite foot (DST1) during the period when both feet are simultaneously in contact with the ground. DST1 is the proportion of the period from heel-strike to toe-off of the opposite foot during a stride cycle. Feature AM4 mainly includes features attributable to the quadriceps muscles.

[0058] Feature AF1, feature AF2, and feature AF3 are used to estimate the grip strength of women. Feature AF1 is extracted from a 13% section of the walking phase of gait waveform data related to time-series data of lateral acceleration (X-direction acceleration). The 13% walking phase is included in the mid-stance phase T2. Feature AF1 mainly includes features related to the movement of the vastus lateralis, vastus intermedius, and vastus medialis, which are quadriceps muscles. Feature AF2 is extracted from a 7% to 10% section of the walking phase of gait waveform data related to time-series data of angular velocity (pitch angular velocity) in the coronal plane (around the Y-axis). The 7% to 10% walking phase is included in the initial stance phase T1. Feature AF2 mainly includes features related to the movement of the vastus lateralis, vastus intermedius, and vastus medialis. Feature AF3 is the proportion of the period from heel-contact to toe-off of the opposite foot to the period during which both feet are simultaneously on the ground (DST2). DST2 is the proportion of the period from heel contact to toe-off of the opposite foot in a gait cycle. The sum of DST1 and DST2 corresponds to the period in a gait cycle during which both feet are simultaneously in contact with the ground. Feature AF3 mainly includes features related to the movements of the vastus lateralis, vastus intermedius, and vastus medialis.

[0059] <Dynamic Balance> Dynamic balance, which is one of the physical abilities, can be evaluated by the performance of the Functional Reach Test (FRT). In the present disclosure, the performance of the FRT is evaluated based on the distance between the fingertips (also referred to as the functional reach distance) when the subject stands with both hands raised 90 degrees relative to the horizontal and then moves the upper limbs as far forward as possible. The functional reach distance (hereinafter referred to as the FR distance) is the performance value of the FRT. The larger the FR distance, the higher the performance of the FRT. Dynamic balance may also be evaluated by a method other than the FRT performed with both hands. For example, dynamic balance may be evaluated based on the performance of the FRT performed with one hand or other variations of the FRT.

[0060] The dynamic balance index is the FR distance. For example, an estimated value of the FR distance is the dynamic balance index. For example, a score corresponding to the estimated value of the FR distance (also referred to as the dynamic balance score) is the dynamic balance index. The dynamic balance score is a value obtained by scoring the FR distance, which is an index of dynamic balance, based on a preset criterion. Dynamic balance is affected by attributes such as height. Therefore, the dynamic balance score may be scored based on a criterion for each attribute. Note that the dynamic balance index is not limited to the FR distance as long as it can score dynamic balance. The FR distance is correlated with the activity of the gluteus medius, iliacus, hamstrings (long head of biceps femoris), tibialis anterior, etc., and the magnitude of the compensatory movement of turning the toes outward. Therefore, feature quantities extracted from walking phases in which these features appear are used to estimate the FR distance.

[0061] Feature B1, feature B2, feature B3, feature B4, and feature B5 are used to estimate the FR distance. Feature B1 is extracted from the 75-79% gait phase section of gait waveform data related to time-series data of forward acceleration (Y-direction acceleration). The 75-79% gait phase is included in the mid-swing phase T6. Feature B1 mainly includes features related to the movement of the tibialis anterior and the short head of the biceps femoris. Feature B2 is extracted from the 62% gait phase section of gait waveform data related to time-series data of vertical acceleration (Z-direction acceleration). The 62% gait phase is included in the early swing phase T5. Feature B2 mainly includes features related to the movement of the iliacus muscle. Feature B3 is extracted from the 7-8% gait phase section of gait waveform data related to time-series data of angular velocity in the coronal plane (around the Y-axis). The 7-8% gait phase is included in the early stance phase T1. Feature B3 mainly includes features related to the movement of the gluteus medius muscle. Feature B4 is extracted from the section of the walking phase 57-58% of the gait waveform data related to time-series data of angles (postural angles) in the horizontal plane (around the Z-axis). The walking phase 57-58% is included in the early swing phase T4. Feature B4 mainly includes features related to compensatory movements. Compensatory movements are movements that change the foot angle to achieve stability in order to compensate for the decline in balance ability and muscle function that occurs with aging. Feature B5 is the average value of the foot angle in the horizontal plane during the swing phase. For example, feature B5 is the average value of the gait waveform data during the swing phase. In other words, feature B5 is the integrated value of the gait waveform data related to time-series data of angular velocity in the horizontal plane (around the Z-axis). Feature B5 mainly includes features related to compensatory movements.

[0062] <Lower limb muscle strength> Lower limb muscle strength, which is one of the physical abilities, can be evaluated by the results of a chair stand test. In the present disclosure, the results of the 5-chair stand test, in which a subject stands up and sits down from a chair five times, are evaluated. The 5-chair stand test is also called the SS-5 (Sit to Stand-5) test. The results of the 5-chair stand test are evaluated based on the time it takes to stand up and sit down from a chair five times (also called the sit-to-stand time). The sit-to-stand time is the score value of the SS-5 test. The shorter the sit-to-stand time, the higher the score of the SS-5 test. Lower limb muscle strength may also be evaluated based on the results of a 30-second chair stand (CS-30) test, which measures the number of times the subject stands up and sits down from a chair in 30 seconds.

[0063] An indicator of lower limb muscle strength is the stand-sit time. For example, an estimated value of the stand-sit time five times is an indicator of lower limb muscle strength. For example, a score corresponding to the estimated stand-sit time (also referred to as a lower limb muscle strength score) is an indicator of lower limb muscle strength. The lower limb muscle strength score is a value obtained by scoring the stand-sit time, which is an indicator of lower limb muscle strength, based on a preset standard. Lower limb muscle strength is affected by attributes such as age. Therefore, the lower limb muscle strength score may be scored based on a standard for each attribute. Note that the indicator of lower limb muscle strength is not limited to the stand-sit time, as long as the lower limb muscle strength can be scored. The stand-sit time is correlated with the quadriceps, hamstrings, tibialis anterior, and gastrocnemius. Therefore, feature quantities extracted from walking phases in which these features are present are used to estimate the stand-sit time.

[0064] The estimation of lower limb muscle strength includes feature values ​​C1, C2, C3, and C4. Feature value C1 is extracted from the gait phase 42-54% section of gait waveform data related to time series data of angular velocity in the sagittal plane (around the X-axis). The gait phase 42-54% corresponds to the section from the end of stance phase T3 to the early swing phase T4. Feature value C1 mainly includes features related to the movement of the gastrocnemius muscle. Feature value C2 is extracted from the gait phase 99-100% section of gait waveform data related to time series data of angular velocity in the coronal plane (around the Y-axis). The gait phase 99-100% corresponds to the end of the end of swing phase T7. Feature value C2 mainly includes features related to the movement of the quadriceps, hamstrings, and tibialis anterior. Feature C3 is extracted from the 10% to 12% walking phase section of gait waveform data related to time series data of angular velocity in the coronal plane (around the Y-axis). The 10% to 12% walking phase corresponds to the beginning of mid-stance phase T2. Feature C3 mainly includes features related to the movements of the quadriceps, hamstrings, and gastrocnemius. Feature C4 is extracted from the 99% walking phase section of gait waveform data related to time series data of angles (postural angles) in the horizontal plane (around the Z-axis). The 99% walking phase corresponds to the end of end-swing phase T7. Feature C4 mainly includes features related to the movements of the quadriceps, hamstrings, and tibialis anterior.

[0065] <Mobility> Mobility, which is one of physical abilities, can be evaluated by the results of a TUG (Time Up and Go) test. In the present disclosure, the results of the TUG test are evaluated based on the time it takes to stand up from a chair, walk to a landmark 3 meters away, change direction, and sit back down in the chair (also referred to as the TUG time). The TUG time is the score value of the TUG test. The shorter the TUG time, the higher the score on the TUG test. Mobility may also be evaluated by the results of a mobility test other than the TUG test.

[0066] The mobility index is the time required for TUG. For example, an estimated value of the TUG time is the mobility index. For example, a score (also referred to as a mobility score) according to the estimated value of the TUG time is the mobility index. The mobility score is a value obtained by scoring the TUG time, which is an index of mobility, based on a preset standard. Mobility is affected by attributes such as age. Therefore, the mobility score may be scored based on a standard for each attribute. Note that the mobility index is not limited to the TUG time as long as it can score mobility. The TUG time is correlated with the quadriceps, gluteus medius, and tibialis anterior. Therefore, feature quantities extracted from walking phases in which these features appear are used to estimate the TUG time.

[0067] Feature values ​​D1, D2, D3, D4, D5, and D6 are used to estimate mobility. Feature value D1 is extracted from the 64-65% walking phase section of gait waveform data related to time-series data of lateral acceleration (X-direction acceleration). The 64-65% walking phase is included in the initial swing phase T5. Feature value D1 mainly includes features related to the movement of the quadriceps during standing-to-sitting movements. Feature value D2 is extracted from the 57-58% walking phase section of gait waveform data related to time-series data of angular velocity in the sagittal plane (around the X-axis). The 57-58% walking phase is included in the early swing phase T4. Feature value D2 mainly includes features related to the movement of the quadriceps related to foot kick-off velocity. Feature value D3 is extracted from the 19-20% walking phase section of gait waveform data related to time-series data of angular velocity in the coronal plane (around the Y-axis). The gait phase 19-20% is included in the mid-stance phase T2. The feature D3 mainly includes features related to the movement of the gluteus medius muscle during changes of direction. The feature D4 is extracted from the section of the gait phase 12-13% of the gait waveform data related to the time series data of angular velocity in the horizontal plane (around the Z-axis). The gait phase 12-13% corresponds to the beginning of the mid-stance phase T2. The feature D4 mainly includes features related to the movement of the gluteus medius muscle during changes of direction. The feature D5 is extracted from the section of the gait phase 74-75% of the gait waveform data related to the time series data of angular velocity in the horizontal plane (around the Z-axis). The gait phase 74-75% corresponds to the beginning of the mid-swing phase T6. The feature D5 mainly includes features related to the movement of the tibialis anterior muscle during standing up and sitting down and changes of direction. Feature D6 is extracted from the section of the walking phase 76-80% of the walking waveform data related to the time-series data of angles (postural angles) in the coronal plane (around the Y-axis). The walking phase 76-80% is included in the mid-swing phase T6. Feature D6 mainly includes features related to the movement of the tibialis anterior muscle when standing up and sitting down and changing direction.

[0068] <Static Balance> Static balance, which is one of the physical abilities, can be evaluated by the performance of a single-leg standing test. In the present disclosure, the performance of the single-leg standing test is evaluated based on the time spent with one leg raised 5 centimeters (cm) from the ground with the eyes closed (also referred to as single-leg standing time). The single-leg standing time is a static balance performance value. The longer the single-leg standing time, the higher the static balance performance. Static balance may also be evaluated by performance other than the eyes-closed single-leg standing test. For example, static balance may be evaluated by a single-leg standing test with the eyes open (eyes-open single-leg standing test) or other variations of the single-leg standing test.

[0069] An index of static balance is the single-leg standing time. For example, an estimated value of the single-leg standing time is an index of static balance. For example, a score (also called a static balance score) corresponding to the estimated value of the single-leg standing time is an index of static balance. The static balance score is a value obtained by scoring the single-leg standing time, which is an index of static balance, based on a preset criterion. Static balance is affected by attributes such as age and height. Therefore, the static balance score may be scored based on a criterion for each attribute. Note that the index of static balance is not limited to the single-leg standing time as long as it can score static balance. The single-leg standing time is correlated with the gluteus medius, adductor longus, sartorius, and abductor / adductor muscle groups. Therefore, feature quantities extracted from gait phases in which these features appear are used to estimate the single-leg standing time.

[0070] Feature values ​​E1, E2, E3, E4, E5, E6, and E7 are used to estimate static balance. Feature value E1 is extracted from the 13-19% gait phase section of gait waveform data related to time series data of lateral acceleration (X-direction acceleration). Gait phase 13-19% is included in mid-stance phase T2. Feature value E1 mainly includes features related to the movement of the gluteus medius muscle. Feature value E2 is extracted from the 95% gait phase section of gait waveform data related to time series data of vertical acceleration (Z-direction acceleration). Gait phase 95% is the final stage of end-swing phase T7. Feature value E2 mainly includes features related to the movement of the gluteus medius muscle. Feature value E3 is extracted from the 64-65% gait phase section of gait waveform data related to time series data of angular velocity in the coronal plane (around the Y-axis). The 64-65% gait phase is included in the early swing phase T5. Feature E3 mainly includes features related to the movement of the adductor longus and sartorius muscles. Feature E4 is extracted from the 11-16% gait phase section of gait waveform data related to time series data of angular velocity in the horizontal plane (around the Z-axis). The 11-16% gait phase section is included in mid-stance phase T2. Feature E4 mainly includes features related to the movement of the gluteus medius muscles. Feature E5 is extracted from the 57-58% gait phase section of gait waveform data related to time series data of angular velocity in the horizontal plane (around the Z-axis). The 57-58% gait phase section is included in early swing phase T4. Feature E5 mainly includes features related to the movement of the adductor longus and sartorius muscles. Feature E6 is extracted from the 100% gait phase section of gait waveform data related to time series data of angles (postural angles) in the horizontal plane (around the Z-axis). The 100% walking phase corresponds to the timing of heel contact when switching from the final swing phase T7 to the initial stance phase T1. The feature value of the walking waveform data at the 100% walking phase corresponds to the foot angle when the sole of the foot is in contact with the ground. The feature value E6 mainly includes features related to the movement of the gluteus medius. The feature value E7 is the distance between the axis of forward movement and the foot (circumflexion amount) at the timing when the central axis of the foot is farthest from the axis of forward movement during the swing phase. The feature value E7 is the circular movement amount normalized by the height of the person to be managed. The feature value E7 mainly includes features related to the movement of the abductor and adductor muscle groups.

[0071] FIG. 8 is a conceptual diagram illustrating an example of a physical ability estimation model 150 that estimates physical ability. Feature quantities extracted from gait waveform data are input to the physical ability estimation model 150 that estimates physical ability. In addition to the feature quantity data extracted from the gait waveform data, attributes of the managed individual are also input. In FIG. 8 , the attributes input to the physical ability estimation model 150 are omitted. In response to the input of the physical ability feature quantities extracted from the gait waveform data, the physical ability estimation model 150 outputs a physical ability score related to the physical ability. In the example of FIG. 8 , the physical ability estimation model 150 includes a grip strength estimation model 151, a dynamic balance estimation model 152, a lower limb strength estimation model 153, a mobility estimation model 154, and a static balance estimation model 155. Each of the grip strength estimation model 151, the dynamic balance estimation model 152, the lower limb strength estimation model 153, the mobility estimation model 154, and the static balance estimation model 155 outputs a score for each estimation target of the model. The physical ability estimation model 150 may be configured by a single model rather than by a model for each physical ability. Furthermore, the physical ability estimation model 150 may be configured by a physical ability value such as grip strength, FR distance, stand-up / sit-down time, TUG time, or one-leg standing time instead of a physical ability score.

[0072] The grip strength estimation model 151 outputs a grip strength score S1 related to grip strength (total muscle strength of the entire body) in response to input of the feature amounts AM1 to AM4 or the feature amounts AF1 to AF3. For example, the grip strength estimation model 151 may be a model that outputs grip strength in response to input of the feature amounts AM1 to AM4 or the feature amounts AF1 to AF3. For example, the grip strength estimation model 151 may be a model that outputs grip strength in response to input of the feature amounts AM1 to AM4 or the feature amounts AF1 to AF3. For example, the grip strength estimation model 151 may be a model that outputs grip strength in response to input of the feature amounts AM1 to AM4 or the feature amounts AF1 to AF3. For example, the grip strength estimation model 151 may be a model that estimates grip strength using attribute data such as age and height in addition to the feature amounts AM1 to AM4 or the feature amounts AF1 to AF3.

[0073] The dynamic balance estimation model 152 outputs a dynamic balance score S2 related to dynamic balance in response to input of the feature quantities B1 to B5. There are no limitations on the estimation results of the dynamic balance estimation model 152 as long as an estimation result related to a dynamic balance index is output in response to input of the physical ability feature quantities for estimating dynamic balance. For example, the dynamic balance estimation model 152 may be a model that outputs an FR distance in response to input of the feature quantities B1 to B5. For example, the dynamic balance estimation model 152 may be a model that estimates dynamic balance using attribute data such as height in addition to the feature quantities B1 to B5.

[0074] The lower limb muscle strength estimation model 153 outputs a lower limb muscle strength score S3 related to lower limb muscle strength in response to input of the feature quantities C1 to C4. There are no limitations on the estimation results of the lower limb muscle strength estimation model 153, as long as an estimation result related to a lower limb muscle strength index is output in response to input of the physical ability feature quantities for estimating lower limb muscle strength. For example, the lower limb muscle strength estimation model 153 may be a model that outputs a lower limb muscle strength score S3 related to lower limb muscle strength in response to input of the feature quantities C1 to C4. For example, the lower limb muscle strength estimation model 153 may be a model that estimates dynamic balance using attribute data such as age in addition to the feature quantities C1 to C4.

[0075] The mobility estimation model 154 outputs a mobility score S4 related to mobility in response to input of the feature quantities D1 to D6. There are no limitations on the estimation results of the mobility estimation model 154, as long as an estimation result related to a mobility index is output in response to input of the physical ability feature quantities for estimating mobility. For example, the mobility estimation model 154 may be a model that outputs a TUG required time in response to input of the feature quantities D1 to D6. For example, the mobility estimation model 154 may be a model that estimates mobility using attribute data such as age in addition to the feature quantities D1 to D6.

[0076] The static balance estimation model 155 outputs a static balance score S5 related to static balance in response to input of the feature quantities E1 to E7. There are no limitations on the estimation results of the static balance estimation model 155 as long as an estimation result related to a static balance index is output in response to input of the physical ability feature quantities for estimating static balance. For example, the static balance estimation model 155 may be a model that outputs a one-leg standing time in response to input of the feature quantities E1 to E7. For example, the static balance estimation model 155 may be a model that estimates static balance using attribute data such as age and height in addition to the feature quantities E1 to E7.

[0077] The physical ability estimation model 150 may be stored in an external storage device constructed on a cloud, a server, or the like. In this case, the physical ability estimation unit 125 uses the physical ability estimation model 150 via an interface (not shown) connected to the storage device. The physical ability estimation model 150 is a machine learning model. For example, the physical ability estimation model 150 is a model trained using a dataset in which attributes and gait indices of multiple subjects are used as explanatory variables and physical ability scores are used as objective variables. The physical ability estimation model 150 may also be a model trained using a dataset in which attributes and gait waveform data of multiple subjects are used as explanatory variables and physical ability scores are used as objective variables. For example, the physical ability estimation model 150 may be a model trained using training data in which gait waveform data of acceleration in three axial directions, angular velocity around three axes, and angles around three axes (posture angles) are used as explanatory variables.

[0078] For example, the physical ability estimation model 150 may be generated by learning using a linear regression algorithm. For example, the physical ability estimation model 150 may be generated by learning using a support vector machine (SVM) algorithm. For example, the physical ability estimation model 150 may be generated by learning using a Gaussian process regression (GPR) algorithm. For example, the physical ability estimation model 150 may be generated by learning using a random forest (RF) algorithm. For example, the physical ability estimation model 150 may be generated by unsupervised learning that classifies the physical abilities of the managed individual according to physical ability feature values. There are no particular limitations on the algorithm used to train the physical ability estimation model 150.

[0079] The disease risk estimation unit 126 (disease risk estimation means) acquires the physical ability estimation result (physical ability score) estimated by the physical ability estimation unit 125. The disease risk estimation unit 126 also acquires a gait index from the gait index calculation unit 123. Furthermore, the disease risk estimation unit 126 acquires attribute data of the managed individual from the storage unit 124. The disease risk estimation unit 126 estimates the disease risk for each disease using the physical ability score, gait index, and attribute data. The disease risk estimation unit 126 may be configured to estimate the disease risk for each disease, including health checkup data. For example, the disease risk estimation unit 126 may be configured to estimate the disease risk for each disease using at least the gait index. The disease risk estimation unit 126 associates the estimated disease risk for each disease with the managed individual and stores it in the storage unit 124. The disease risk for each disease of the managed individual may be accumulated in a dedicated database (not shown).

[0080] FIG. 9 is a conceptual diagram showing an example of disease risk estimation by the disease risk estimation unit 126. The disease risk estimation unit 126 inputs attribute data, gait index, and physical ability score used to estimate the disease risk for a specific disease to the disease risk estimation model 160. The attribute data, gait index, and physical ability score used to estimate the disease risk for a specific disease are input to the disease risk estimation model 160. In response to the input of the attribute data, gait index, and physical ability score, the disease risk estimation model 160 outputs a disease risk score for a specific disease. In the example of FIG. 9, a disease risk score is estimated for each of multiple diseases. The disease risk estimation model 160 may be configured as a model for each disease or as a single model. When a physical ability score is not used, the disease risk estimation model 160 may be configured to output a disease risk score for a specific disease in response to the input of the attribute data and gait index.

[0081] For example, the disease risk estimation model 160 outputs a disease risk score for a specific disease such as a lifestyle-related disease. For example, the disease risk estimation model 160 outputs a disease risk score for a specific disease such as gout, diabetes, hypertension, nephrolithiasis, liver cirrhosis, arteriosclerosis, thromboembolism, dyslipidemia, hypercholesterolemia, and hyperlipidemia. For example, the disease risk estimation model 160 includes lower back pain, sleep apnea syndrome, insomnia, depression, osteoarthritis of the knee, Parkinson's syndrome, and the like. Note that the disease risk estimation model 160 may be configured to output a disease risk score for a disease other than those described above.

[0082] As the amount of data used for estimation increases, the accuracy of the disease risk score estimation by the disease risk estimation model 160 improves. For example, the disease risk estimation model 160 is configured to output a disease risk score for a specific disease in response to input of health checkup data, attribute data, gait index, and physical ability score. When attribute data is included in the health checkup data items, the disease risk estimation model 160 may be configured to output a disease risk score for a specific disease in response to input of the health checkup data, gait index, and physical ability score.

[0083] The disease risk estimation model 160 may be stored in an external storage device constructed on a cloud, a server, or the like. In this case, the disease risk estimation unit 126 uses the disease risk estimation model 160 via an interface (not shown) connected to the storage device. The disease risk estimation model 160 is a machine learning model. For example, the disease risk estimation model 160 is a model trained using training data in which attribute data, gait indices, and physical ability scores related to multiple managed individuals are used as explanatory variables and a disease risk score related to a specific disease is used as a target variable. For example, the disease risk estimation model 160 may be a model trained using training data in which gait waveform data of acceleration in three axial directions, angular velocity around three axes, and angles around three axes (posture angles) are used as explanatory variables.

[0084] For example, the disease risk estimation model 160 is generated by learning using a linear regression algorithm. For example, the disease risk estimation model 160 is generated by learning using a support vector machine (SVM) algorithm. For example, the disease risk estimation model 160 is generated by learning using a Gaussian process regression (GPR) algorithm. For example, the disease risk estimation model 160 is generated by learning using a random forest (RF) algorithm. For example, the disease risk estimation model 160 may be generated by unsupervised learning that classifies the disease risk of the managed individual according to attribute data, gait index, and physical ability score. There are no particular limitations on the algorithm used to train the disease risk estimation model 160.

[0085] For example, the disease risk estimation model 160 may be a machine learning model such as an incomplete heterogeneous variational autoencoder or a random forest. The incomplete heterogeneous variational autoencoder can estimate the disease risk of the managed individual even if there are some missing data in the attribute data, gait index, physical ability score, etc.

[0086] FIG. 10 is a conceptual diagram showing an example of a disease risk estimation model 165 that estimates the average annual number of medical receipts issued. The disease risk estimation unit 126 inputs attribute data, a gait index, and a physical ability score into the disease risk estimation model 165. The disease risk estimation model 165 receives input of attribute data, a gait index, and a physical ability score used to estimate the disease risk for a specific disease. In response to the input of the attribute data, the gait index, and the physical ability score, the disease risk estimation model 165 outputs the average annual number of medical receipts issued for the specific disease. In the example of FIG. 10, the average annual number of medical receipts issued is estimated for each of a plurality of diseases. The disease risk estimation unit 126 calculates a disease risk score using the average annual number of medical receipts issued output from the disease risk estimation model 165. Note that the average annual number of medical receipts issued may also be used as the disease risk score.

[0087] Here, an example will be described in which the disease risk estimation unit 126 calculates a disease risk score using the average annual number of medical receipts issued. Three calculation examples will be given below. It is assumed that the average annual number of medical receipts issued μ for a typical person has been obtained in advance. The disease risk estimation model 165 outputs the average annual number of medical receipts μ for a specific disease in response to input attribute data, gait index, and physical ability score for the person under management.

[0088] In the first method, the disease risk estimation unit 126 calculates the disease risk score by calculating the ratio of the average annual number of medical receipts issued for a typical person μ to the average annual number of medical receipts issued μ estimated for the person under management. The disease risk estimation unit 126 calculates the disease risk score RS using the following formula 1:

[0089] In the second method, the disease risk estimation unit 126 calculates the disease risk score under the assumption that the average annual number of medical receipts issued for a specific disease follows a Poisson distribution. In the second method, the disease risk estimation unit 126 calculates the disease risk score as the ratio of the probability mass function P(X=k) of the average annual number of medical receipts issued for a typical person to the probability mass function P(X=k) of the average annual number of medical receipts issued estimated for the managed person (k is a natural number). The disease risk estimation unit 126 calculates the disease risk score RS using the following equation 2:

[0090] In the third method, the disease risk estimation unit 126 calculates the odds ratio of the average annual number of medical receipts issued for a specific disease. The disease risk estimation unit 126 calculates the disease risk score RS3 using the following Equation 3.

[0091] The above three calculation examples are merely examples and do not limit the method of calculating the disease risk score using the average annual number of medical receipts issued. The disease risk estimation unit 126 may be configured to calculate the disease risk score using an index other than the average annual number of medical receipts issued.

[0092] The proposal information generation unit 127 acquires disease risk scores of the managed persons. For example, the proposal information generation unit 127 acquires disease risk scores of specific diseases for multiple managed persons. For example, the proposal information generation unit 127 acquires disease risk scores of multiple specific diseases for multiple managed persons. The proposal information generation unit 127 inputs the acquired disease risk scores of the multiple managed persons into a health measure estimation model. The proposal information generation unit 127 generates proposal information according to health measures output from the health measure estimation model in response to the input of disease risk scores for the multiple managed persons.

[0093] The health measure estimation model 170 may be stored in an external storage device constructed on a cloud, a server, or the like. In this case, the proposal information generator 127 uses the health measure estimation model 170 via an interface (not shown) connected to the storage device. The health measure estimation model 170 is a machine learning model. For example, the health measure estimation model 170 is a model trained using a dataset that uses disease risk scores for multiple subjects as explanatory variables and health measures as objective variables as training data.

[0094] For example, the health measure estimation model 170 is generated by learning using a linear regression algorithm. For example, the health measure estimation model 170 is generated by learning using a support vector machine (SVM) algorithm. For example, the health measure estimation model 170 is generated by learning using a Gaussian process regression (GPR) algorithm. For example, the health measure estimation model 170 is generated by learning using a random forest (RF) algorithm. For example, the health measure estimation model 170 may be generated by unsupervised learning that classifies the health measures of the managed individuals according to their disease risk scores. There are no particular limitations on the algorithm used to train the health measure estimation model 170.

[0095] For example, the health measure estimation model 170 may be a machine learning model such as an incomplete heterogeneous variational autoencoder or a random forest. The incomplete heterogeneous variational autoencoder can estimate the health measure of the managed person even if there are some missing disease risk scores.

[0096] FIG. 11 is a conceptual diagram showing an example of health measures estimated using the health measure estimation model 170. The proposal information generator 127 inputs disease risk scores for multiple managed persons 1 to M into the health measure estimation model 170 (M is a natural number). In response to the input of disease risk scores for multiple managed persons 1 to M, the health measure estimation model 170 outputs at least one health measure 1 to N (N is a natural number). In the example of FIG. 11 , the health measure estimation model 170 outputs at least one health measure 1 to N in response to the input of a disease risk score for a specific disease. The health measure estimation model 170 may be configured to output at least one health measure 1 to N in response to the input of disease risk scores for multiple specific diseases. Furthermore, the health measure estimation model 170 may be configured to output at least one health measure 1 to N in response to the input of a disease risk score for at least one specific disease for a managed person.

[0097] For example, the proposed information generation unit 127 generates proposed information including at least one health measure for the company to which the managed person belongs. For example, the proposed information generation unit 127 generates the proposed information by applying the measure to a predetermined document format. For example, the proposed information generation unit 127 may generate the proposed information using a large-scale language model. Upon obtaining the proposed information, the company's health management officer can take action according to the proposed information.

[0098] For example, the proposal information generation unit 127 generates proposal information including a health measure such as periodically broadcasting music for exercises or broadcasting a voice message encouraging employees to go home when work hours end. For example, the proposal information generation unit 127 generates proposal information including a health measure such as turning off the lights on the work floor when work hours end. For example, the proposal information generation unit 127 generates proposal information including a proposal to a company to reduce the salt content of food in the convenience store. For example, the proposal information generation unit 127 generates proposal information including health measures related to cafeteria menus and smoking area opening and closing times. For example, the proposal information generation unit 127 generates proposal information including a proposal to employees who are subject to management to eat less instant noodles and more salads. For example, the proposal information generation unit 127 generates proposal information including a proposal to limit elevator use to encourage employees who are subject to management to exercise. For example, the proposal information generation unit 127 may generate health measures at a level related to corporate management. For example, the proposal information generation unit 127 may generate information that visualizes the company's financial indicators in accordance with changes in the disease risk of employees who are subject to management.

[0099] The output unit 129 (output means) outputs proposed information including the health measures estimated by the proposed information generation unit 127. For example, the output unit 129 outputs the proposed information to a terminal device or a server managed by the company to which the managed person belongs. For example, the output unit 129 may display the proposed information on the screen of the managed person's mobile terminal. For example, the output unit 129 may output the proposed information to an external system that uses the proposed information. There are no particular limitations on the use of the output proposed information. For example, the proposed information may be used for statistical analysis, research on disease prevention, etc.

[0100] For example, the information generating device 12 is connected to an external system, such as a cloud or a server, via a mobile terminal (not shown) carried by the managed user. The mobile terminal is a portable communication device. For example, the mobile terminal is a portable communication device having a communication function, such as a smartphone, a smart watch, or a mobile phone. For example, the information generating device 12 is connected to the mobile terminal via wireless communication. For example, the information generating device 12 is connected to the mobile terminal via a wireless communication function (not shown) conforming to standards such as Bluetooth (registered trademark) or Wi-Fi (registered trademark). Note that the communication function of the information generating device 12 may conform to standards other than Bluetooth (registered trademark) or Wi-Fi (registered trademark). For example, the information generating device 12 may be connected to the mobile terminal via a wired connection, such as a cable. The proposed information may be used by an application installed on the mobile terminal. In this case, the mobile terminal executes a process using the proposed information using application software, etc., installed on the mobile terminal.

[0101] (Operation) Next, the operation of the information providing system 1 will be described with reference to the drawings. The operation of the information generating device 12 included in the information providing system 1 will be described below. FIG. 12 is a flowchart for explaining an example of the operation of the information generating device 12. In the description of the processing according to the flowchart of FIG. 12, the components of the information generating device 12 will be described as the subject of the operations. The subject of the processing according to the flowchart of FIG. 12 may be the information generating device 12.

[0102] 12, first, the acquiring unit 121 acquires time-series data of sensor data measured by the measuring device 10 mounted on the footwear of the person to be managed (step S11). The sensor data includes acceleration in three axial directions and angular velocity around three axes.

[0103] Next, the calculation unit 13 executes a gait index calculation process using the acquired sensor data (step S12). In the gait index calculation process, the calculation unit 13 calculates a gait index used to estimate physical ability. Details of the gait index calculation process in step S12 will be described later ( FIG. 13 ).

[0104] Next, the physical ability estimation unit 125 estimates physical ability using the attribute data and gait index (step S13). For example, the physical ability estimation unit 125 estimates physical ability scores such as grip strength (total muscle strength of the entire body), dynamic balance, lower limb muscle strength, mobility, and static balance. If disease risk is estimated without using physical ability, step S13 can be omitted.

[0105] Next, the disease risk estimation unit 126 estimates the disease risk of the managed person using the attribute data, gait index, and physical ability (step S14). When the disease risk is estimated without using the physical ability, the disease risk estimation unit 126 estimates the disease risk of the managed person using the attribute data and gait index. The disease risk estimation unit 126 estimates a disease risk score of the managed person. For example, the disease risk estimation unit 126 estimates a disease risk score for each disease, such as gout, diabetes, hypertension, nephrolithiasis, liver cirrhosis, arteriosclerosis, thromboembolism, dyslipidemia, hypercholesterolemia, and hyperlipidemia. For example, the disease risk estimation unit 126 estimates a disease risk score for each disease, such as lower back pain, sleep apnea syndrome, insomnia, depression, osteoarthritis, and Parkinson's syndrome.

[0106] Next, the storage unit 124 stores the disease risk estimated for the managed individual (step S15). The disease risk stored in the storage unit 124 is used to estimate health measures. The disease risk may be stored in a database (not shown) connected to the information generating device 12.

[0107] Next, the proposed information generator 127 estimates health measures for at least one of the managed individuals using the disease risks stored in the storage unit 124 (step S16). The proposed information generator 127 generates proposed information including the estimated health measures.

[0108] Next, the output unit 129 outputs the proposed information including the generated health measure (step S17). For example, the output unit 129 outputs the proposed information to a terminal device or a server managed by the company to which the managed person belongs. For example, the output unit 129 outputs the proposed information to an external system that uses the proposed information. For example, the output unit 129 may display the proposed information on the screen of a mobile terminal of the managed person.

[0109] It should be noted that it is not necessary to acquire attribute data of the managed person. If attribute data is not acquired from the managed person, a model for estimating disease risk without using attribute data can be used. Furthermore, the managed person may be asked to consent to acquiring attribute data in advance. At that time, the benefits of acquiring attribute data may be communicated to the managed person, encouraging them to consent to acquiring the attribute data. Here, the benefits may include, for example, obtaining more accurate risk estimation results.

[0110] [Gait Index Calculation Process] Next, the gait index calculation process (step S12 in FIG. 12 ) by the calculation unit 13 of the information generating device 12 will be described with reference to the drawings. FIG. 13 is a flowchart for explaining an example of the operation of the calculation unit 13. In describing the process according to the flowchart in FIG. 13 , the components of the calculation unit 13 will be described as the actors performing the operations. The actors performing the process according to the flowchart in FIG. 13 may be the information generating device 12 or the calculation unit 13.

[0111] 13, first, the waveform processing unit 122 extracts walking waveform data from the time-series data of the sensor data (step S121). The walking waveform data corresponds to the time-series data of the sensor data for one walking cycle.

[0112] Next, the waveform processing unit 122 normalizes the extracted walking waveform data (step S122). The waveform processing unit 122 performs first normalization on the walking waveform data so that the step period is 100%. The waveform processing unit 122 also performs second normalization on the walking waveform data so that the stance phase is 60% and the swing phase is 40%.

[0113] Next, the gait index calculation unit 123 calculates gait indices used to estimate physical ability using the normalized walking waveform data (step S123). For example, the gait index calculation unit 123 calculates gait indices related to distance, height, angle, speed, time, frailty level, CPEI, etc.

[0114] (Application Examples) Next, application examples according to the present embodiment will be described with reference to the drawings. The following application examples illustrate the relationship between a business providing a service using information provision system 1, a company using the service, and employees who are subject to management by the company. Application examples 1 to 3 relating to three types of business formats will be described below. The following business formats are merely examples and do not limit the scope to which information provision system 1 can be applied. In addition, the following examples illustrate the use of a health measure estimation model customized to the work style of employees who are subject to management. The health measure estimation model may also be customized to the industry of the company (organization) to which the employees who are subject to management belong. The proposed information generated by information generation device 12 is not limited to the following examples, as long as it includes health measures for employees who are subject to management.

[0115] The business provides a company with a service using the information provision system 1. Based on a contract concluded with the company, the business provides the company with proposed information including estimated health measures according to the employee's disease risk. The company pays the business a usage fee for the service using the information provision system 1. If employee health checkup data is used to estimate health measures, the company provides the business with the employee's health checkup data. The contract between the business and the company clarifies rules regarding the handling of personal information and appropriate data management. The business clearly explains that the proposed information is for reference only and does not guarantee medical accuracy or completeness.

[0116] Companies will fully explain to employees the details of their personal information protection policy and data management, and obtain consent from employees regarding the use of personal information and data. Furthermore, if there are any changes to the details of their personal information protection policy or data management, companies will explain the changes to employees and obtain consent from them. For example, consent from employees will be obtained electronically. Companies will consider what health measures to offer to employees based on the content of the proposed information provided by the business operator, and provide appropriate health measures to employees.

[0117] An employee is a person employed by a company. The employee is loaned or provided with a dedicated insole equipped with the measuring device 10 by a business operator that has a contract with the company. The employee performs their work while wearing shoes equipped with the dedicated insole and carrying a mobile terminal (not shown) that can communicate with the measuring device 10. The mobile terminal uploads sensor data measured by the measuring device 10 to the business operator's cloud server. The sensor data uploaded to the cloud server is used to estimate disease risks and health measures.

[0118] A terminal device (not shown) used by a company downloads proposal information including health measures from the business operator's cloud server. A company manager refers to the health measures included in the proposal information and considers health measures for employees. For example, the company manager periodically refers to the health measures included in the proposal information and considers countermeasures in response to changes in the health measures. For example, the company holds health consultation sessions and events and incorporates employee opinions and requests regarding countermeasures in response to changes in the health measures.

[0119] [Application Example 1] Figure 14 is a correlation diagram showing the relationship between a business operator, Company A, and employees (persons to be managed) according to the present disclosure. Company A is in an industry where many employees engage in desk work. In industries where desk work is common, employees often sit in the same position for long periods of time, putting them at risk of developing back pain. In addition, in industries where desk work is common, opportunities to walk are reduced, which weakens leg muscles, putting employees at risk of developing lifestyle-related diseases such as obesity, diabetes, and high blood pressure.

[0120] 15 shows an example in which proposed information generated by information generating device 12 is displayed on the screen of terminal device 180A used by a manager who manages the health conditions of employees. Proposed information including health measures optimized for company A is displayed on the screen of terminal device 180A. In the example of FIG. 15, proposed information including multiple health measures is displayed on the screen.

[0121] The first health measure is a suggestion that "There is a department with many employees who are at high risk of developing lower back pain. We suggest that you regularly do health exercises." For Company A, where a lot of employees do desk work, the risk of lower back pain tends to increase due to sitting for long periods of time. For example, for Company A, health measures including the keyword "health exercises," which involves standing up and moving the body regularly, are estimated. For example, words other than the keyword "health exercises" are generated using templates or large-scale language models. The screen of terminal device 180A displays the address of a link related to "health exercises." The administrator can refer to the information at the link and consider whether to adopt "health exercises" as a health measure.

[0122] The second health measure is a proposal that reads, "There is a department with many employees who are at high risk of lifestyle-related diseases. We suggest increasing the number of walking events." For Company A, where much of the work is desk-based, there is a tendency for the risk of lifestyle-related diseases to increase due to weakened leg muscles. For example, for Company A, health measures that include the keyword "walking," which refers to walking long distances, are estimated. For example, phrases other than the keyword "walking" are generated using templates or large-scale language models. The screen of terminal device 180A displays the address of a link related to "walking." The administrator can refer to the information at the link and consider whether to adopt "walking" as a health measure.

[0123] [Application Example 2] FIG. 16 is a correlation diagram showing the relationship between a business operator, Company B, and employees (persons to be managed) according to the present disclosure. Company B is a delivery company. Employees of the delivery company often perform physical labor, such as transporting packages stored in warehouses and packages loaded onto freight vehicles. For example, employees may be at risk of developing back pain due to repeated awkward postures while lifting heavy packages. Furthermore, delivery company employees may repeatedly engage in binge eating and drinking to satisfy hunger after work. Repeated binge eating and drinking increases the risk of developing various diseases, such as diabetes, high blood pressure, nephrolithiasis, liver cirrhosis, arteriosclerosis, thromboembolism, dyslipidemia, hypercholesterolemia, and hyperlipidemia.

[0124] 17 shows an example in which proposed information generated by information generating device 12 is displayed on the screen of terminal device 180B used by a manager who manages the health conditions of employees. Proposed information including health measures optimized for company B is displayed on the screen of terminal device 180B. In the example of FIG. 17, proposed information including multiple health measures optimized for company B is displayed on the screen.

[0125] The first health measure is a proposal that reads, "There is a department with many employees at high risk of lower back pain. We suggest hiring a masseuse." For Company B, which has a lot of physical labor, the risk of lower back pain tends to increase due to repeated awkward postures when lifting heavy loads. For example, for Company B, health measures including the keyword "massage therapist," who provides timely physical care for employees, are estimated. For example, phrases other than the keyword "massage therapist" are generated using templates and large-scale language models. The screen of terminal device 180B displays the address of a link related to "massage therapist." The administrator can refer to the information at the link and consider whether to hire a "massage therapist" as a health measure.

[0126] The second health measure is a proposal that reads, "There is a department with many employees who are at high risk for various diseases. We suggest that you hold regular interviews with a registered dietitian." For Company B, which has a lot of physical labor, the risk of various diseases tends to increase due to repeated overeating to satisfy hunger after work. For example, for Company B, health measures including the keyword "registered dietitian," who cares for employees' eating habits, are estimated. For example, phrases other than the keyword "registered dietitian" are generated using templates and large-scale language models. The screen of terminal device 180B displays the address of a link related to "registered dietitian." The administrator can refer to the information at the link and consider whether to hire a "registered dietitian" as part of the health measure.

[0127] [Application Example 3] FIG. 18 is a correlation diagram showing the relationship between a business operator, company C, and employees (persons to be managed) according to the present disclosure. Company C is a retailer such as a supermarket. Employees working as cashiers at supermarkets often work for long periods of time while standing. This posture places strain on the knees, posing a risk of developing osteoarthritis in the future. Furthermore, cashiers must continually deal with a variety of customers, which can lead to an accumulation of mental stress and a risk of developing mental illnesses such as insomnia and depression.

[0128] 19 shows an example in which proposed information generated by information generating device 12 is displayed on the screen of terminal device 180C used by a manager who manages the health conditions of employees. Proposed information including health measures optimized for company C is displayed on the screen of terminal device 180C. In the example of FIG. 19, proposed information including multiple health measures optimized for company C is displayed on the screen.

[0129] The first health measure is a proposal that reads, "There is a department with many employees who are at high risk of knee osteoarthritis. We suggest installing a training machine in the break room." For Company C, the risk of knee osteoarthritis tends to increase due to employees working long periods of time while standing. For example, for Company C, health measures including the keyword "training machine," which allows employees to train their legs during breaks, are estimated. For example, phrases other than the keyword "training machine" are generated using templates and large-scale language models. The screen of terminal device 180C displays the address of a link related to "training machine." The administrator can refer to the information at the link and consider whether to adopt "training machine" as a health measure.

[0130] The second health measure is a proposal that reads, "There is a department with many employees who are at high risk of mental illness. We suggest that you hold regular interviews with a counselor." For Company C, the risk of mental illness tends to increase due to the need to continually deal with a variety of customers. For example, for Company C, health measures including the keyword "counselor," who cares for the mental health of employees, are estimated. For example, phrases other than the keyword "counselor" are generated using templates or large-scale language models. The screen of terminal device 180C displays the address of a link related to "counselor." The administrator can refer to the information at the link and consider whether to adopt "counselor" as a health measure.

[0131] As described above, the information provision system of this embodiment includes a measuring device and an information generating device. The measuring device is installed in the footwear of at least one of the subjects. The measuring device measures acceleration and angular velocity. The measuring device generates sensor data using the measured acceleration and angular velocity. The measuring device transmits the generated sensor data to the information generating device. The information generating device includes an acquiring unit, a risk estimation unit, a proposed information generating unit, and an output unit. The acquiring unit acquires sensor data including acceleration and angular velocity measured by a measuring device installed in the footwear of at least one of the subjects. The risk estimation unit uses the acquired sensor data to estimate a disease risk for each disease for at least one of the subjects. The proposed information generating unit generates proposed information including health measures corresponding to the disease risk for at least one of the subjects. The output unit outputs the generated proposed information.

[0132] The information generating device of this embodiment estimates disease risk using sensor data including acceleration and angular velocity measured by a measuring device mounted on the footwear of the managed person. The information generating device of this embodiment generates suggested information including health measures corresponding to the estimated disease risk. Therefore, according to this embodiment, health measures corresponding to the disease risk of the managed person engaged in daily work can be provided.

[0133] To maintain the health of employees under management in organizations such as companies, it is desirable to continuously implement health measures to improve lifestyle-related issues such as diet, exercise, and work habits. To achieve this, it is important for employees to feel the effects of taking actions in accordance with the health measures and to have external motivation to continue the actions. In recent years, as part of health management, companies are being asked to implement health measures that encourage employees to improve and maintain their lifestyle habits. However, it is difficult for a company to continuously implement measures related to employees' lifestyle habits on its own.

[0134] According to the method of this embodiment, it is possible to promote health management to improve the health awareness and lifestyle habits of the people who are the subject of management in an organization and reduce the risk of diseases such as diabetes, high blood pressure, etc. For example, according to the method of this embodiment, it is possible to reduce the effort required for efforts to improve lifestyle habits in an organization by automatically suggesting and motivating actions that are tailored to various factors such as the attributes, industry, and working hours of the people who are the subject of management in multiple organizations.

[0135] In one aspect of this embodiment, the risk estimation unit has a calculation unit and an estimation unit. The calculation unit calculates a gait index using sensor data. The estimation unit inputs data including the gait index calculated using the sensor data to a disease risk estimation model that outputs a disease risk score indicating the degree of disease risk for each disease in response to input of data including the gait index. The estimation unit estimates disease risk information corresponding to the disease risk score output from the disease risk estimation model. According to this aspect, disease risk information corresponding to the disease risk score can be estimated by inputting data including the gait index calculated using the sensor data into the disease risk estimation model.

[0136] In one aspect of the present embodiment, the proposal information generator estimates a health measure corresponding to the disease risk score of at least one person to be managed, using a health measure estimation model that outputs a health measure in response to an input of a disease risk score. According to this aspect, by inputting the disease risk score into the health measure estimation model, it is possible to estimate a health measure corresponding to the disease risk score of the person to be managed.

[0137] In one aspect of the present embodiment, the proposal information generator estimates health measures according to the disease risk score of at least one of the managed persons using a health measure estimation model customized to the working style of the managed persons. According to this aspect, by using the health measure estimation model customized to the working style of the managed persons, it is possible to estimate health measures optimized for the working style of the managed persons.

[0138] In one aspect of the present embodiment, the proposal information generator estimates health measures based on the disease risk score of at least one of the managed persons using a health measure estimation model customized to the industry of the organization to which the managed persons belong. According to this aspect, by using a health measure estimation model customized to the industry of the organization to which the managed persons belong, it is possible to estimate health measures optimized for the industry of the organization.

[0139] In one aspect of the present embodiment, the health measure estimation model and the disease risk estimation model are models trained using machine learning techniques. The disease risk estimation model includes an incomplete heterogeneous variational autoencoder. According to this aspect, even if there is some loss of data such as gait indicators, it is possible to estimate a health measure according to the disease risk of the person to be managed.

[0140] In one aspect of this embodiment, the information generating device displays, on a screen of a terminal device used in an organization to which the managed person belongs, proposed information including health measures optimized for the organization. According to this aspect, proposed information including health measures estimated according to the disease risk of the managed person can be provided, optimized for the organization to which the managed person belongs.

[0141] Second Embodiment Next, an information generating device according to a second embodiment will be described with reference to the drawings. The information generating device according to this embodiment has a simplified configuration of the information generating device included in the information providing system according to the first embodiment.

[0142] 20 is a block diagram showing an example of the configuration of the information generating device 20 in the present disclosure. The information generating device 20 includes an acquiring unit 21, a risk estimating unit 25, a proposed information generating unit 27, and an output unit 29.

[0143] The acquisition unit 21 acquires sensor data including acceleration and angular velocity measured by a measuring device mounted on the footwear of at least one of the managed individuals. The risk estimation unit 25 uses the acquired sensor data to estimate a disease risk for each disease for at least one of the managed individuals. The proposed information generation unit 27 generates proposed information including health measures corresponding to the disease risk for at least one of the managed individuals. The output unit 29 outputs the generated proposed information.

[0144] (Operation) Next, the operation of the information generating device 20 will be described with reference to the drawings. Fig. 21 is a flowchart for explaining an example of the operation of the information generating device 20. In the description of the processing according to the flowchart of Fig. 21, the components of the information generating device 20 will be described as the subject of the operations. The subject of the processing according to the flowchart of Fig. 21 may be the information generating device 20.

[0145] In FIG. 21, first, the acquisition unit 21 acquires sensor data including acceleration and angular velocity measured by a measurement device mounted on the footwear of at least one person to be managed (step S21).

[0146] Next, the risk estimation unit 25 estimates the disease risk of at least one of the managed individuals using the acquired sensor data (step S22).

[0147] Next, the proposal information generating unit 27 generates proposal information including health measures according to the disease risk of at least one of the managed individuals (step S23).

[0148] Next, the output unit 29 outputs the generated proposal information (step S24).

[0149] As described above, the information generating device of this embodiment estimates disease risk using sensor data including acceleration and angular velocity measured by a measuring device mounted on the footwear of a managed person. The information generating device of this embodiment generates suggested information including health measures corresponding to the estimated disease risk. Therefore, according to this embodiment, health measures corresponding to the disease risk of a managed person engaged in daily work can be provided.

[0150] (Hardware) Next, a hardware configuration for executing control and processing according to each embodiment of the present disclosure will be described with reference to the drawings. Here, an information processing device 90 (computer) shown in FIG. 22 is given as an example of such a hardware configuration. The information processing device 90 in FIG. 22 is an example configuration for executing control and processing according to each embodiment, and does not limit the scope of the present disclosure.

[0151] As shown in Fig. 22 , an information processing device 90 includes a processor 91, a main storage device 92, an auxiliary storage device 93, an input / output interface 95, and a communication interface 96. In Fig. 22 , interface is abbreviated as I / F (Interface). The processor 91, the main storage device 92, the auxiliary storage device 93, the input / output interface 95, and the communication interface 96 are connected to each other via a bus 98 so as to be able to communicate data with each other. The processor 91, the main storage device 92, the auxiliary storage device 93, and the input / output interface 95 are also connected to a network such as the Internet or an intranet via the communication interface 96.

[0152] The processor 91 loads a program (instructions) stored in the auxiliary storage device 93 or the like onto the main storage device 92. For example, the program is a software program for executing the control and processing of each embodiment. The processor 91 executes the program loaded onto the main storage device 92. The processor 91 executes the program to execute the control and processing of each embodiment.

[0153] The main memory device 92 has an area in which programs are loaded. The processor 91 loads programs stored in the auxiliary memory device 93 or the like into the main memory device 92. The main memory device 92 is realized by a volatile memory such as a dynamic random access memory (DRAM). Alternatively, a non-volatile memory such as a magneto-resistive random access memory (MRAM) may be configured / added to the main memory device 92.

[0154] The auxiliary storage device 93 stores various data such as programs. The auxiliary storage device 93 is realized by a local disk such as a hard disk or flash memory. Note that it is also possible to configure the main storage device 92 to store various data, thereby omitting the auxiliary storage device 93.

[0155] The input / output interface 95 is an interface for connecting the information processing device 90 to peripheral devices based on standards and specifications. The communication interface 96 is an interface for connecting to external systems and devices via a network such as the Internet or an intranet based on standards and specifications. The input / output interface 95 and the communication interface 96 may be a common interface for connecting to external devices.

[0156] Input devices such as a keyboard, mouse, and touch panel may be connected to the information processing device 90 as needed. These input devices are used to input information and settings. When a touch panel is used as the input device, a screen having the function of the touch panel serves as the interface. The processor 91 and the input devices are connected via an input / output interface 95.

[0157] The information processing device 90 may be equipped with a display device for displaying information. When the display device is equipped, the information processing device 90 is equipped with a display control device (not shown) for controlling the display of the display device. The information processing device 90 and the display device are connected via an input / output interface 95.

[0158] The information processing device 90 may be equipped with a drive device. The drive device acts as an intermediary between the processor 91 and a recording medium (program recording medium) to read data and programs stored on the recording medium and to write processing results of the information processing device 90 to the recording medium. The information processing device 90 and the drive device are connected via an input / output interface 95.

[0159] The above is an example of a hardware configuration for enabling control and processing according to each embodiment of the present invention. The hardware configuration in Fig. 22 is an example of a hardware configuration for executing control and processing according to each embodiment, and does not limit the scope of the present invention. A program that causes a computer to execute control and processing according to each embodiment is also included in the scope of the present invention.

[0160] The scope of the present invention also includes a program recording medium on which the program according to each embodiment is recorded. The recording medium can be realized, for example, as an optical recording medium such as a CD (Compact Disc) or a DVD (Digital Versatile Disc). The recording medium may also be realized as a semiconductor recording medium such as a USB (Universal Serial Bus) memory or an SD (Secure Digital) card. The recording medium may also be realized as a magnetic recording medium such as a flexible disk or other recording medium. When a program executed by a processor is recorded on a recording medium, the recording medium corresponds to a program recording medium.

[0161] The components of each embodiment may be combined in any manner, may be realized by software, or may be realized by a circuit.

[0162] Although the present disclosure has been described above with reference to the embodiments, the present disclosure is not limited to the above-described embodiments. Various modifications that can be understood by those skilled in the art can be made to the configuration and details of the present disclosure within the scope of the present disclosure. Furthermore, each embodiment can be combined with other embodiments as appropriate.

[0163] Some or all of the above embodiments can be described as, but are not limited to, the following supplementary notes. (Supplementary Note 1) An information generation device comprising: an acquisition unit that acquires sensor data including acceleration and angular velocity measured by a measuring device mounted on the footwear of at least one of the managed persons; a risk estimation unit that uses the acquired sensor data to estimate a disease risk for each disease for the at least one of the managed persons; a proposed information generation unit that generates proposed information including health measures according to the disease risk for the at least one of the managed persons; and an output unit that outputs the generated proposed information. (Supplementary Note 2) The information generation device according to Supplementary Note 1, wherein the risk estimation unit comprises: a calculation unit that calculates a gait index using the sensor data; and an estimation unit that inputs data including the gait index calculated using the sensor data into a disease risk estimation model that outputs a disease risk score indicating the degree of disease risk for each disease in response to input data including the gait index, and estimates disease risk information according to the disease risk score output from the disease risk estimation model. (Supplementary Note 3) The information generating device according to Supplementary Note 2, wherein the proposed information generating unit estimates the health measure according to the disease risk score of at least one of the managed persons using a health measure estimation model that outputs the health measure in response to input of the disease risk score. (Supplementary Note 4) The information generating device according to Supplementary Note 3, wherein the proposed information generating unit estimates the health measure according to the disease risk score of at least one of the managed persons using the health measure estimation model customized to suit a working style of the managed persons. (Supplementary Note 5) The information generating device according to Supplementary Note 3, wherein the proposed information generating unit estimates the health measure according to the disease risk score of at least one of the managed persons using the health measure estimation model customized to suit the industry of an organization to which the managed persons belong. (Supplementary Note 6) The information generating device according to Supplementary Note 3, wherein the health measure estimation model and the disease risk estimation model are models trained using a machine learning technique, and the disease risk estimation model includes an incomplete heterogeneous variational autoencoder.(Supplementary Note 7) An information provision system comprising: the information generation device according to any one of Supplements 1 to 6; and the measurement device, wherein the measurement device is attached to footwear of at least one of the managed persons, measures spatial acceleration and spatial angular velocity, generates the sensor data using the measured spatial acceleration and spatial angular velocity, and transmits the generated sensor data to the information generation device. (Supplementary Note 8) The information provision system according to Supplementary Note 7, wherein the information generation device displays the proposed information, including the health measures optimized for the organization, on a screen of a terminal device used in an organization to which the managed persons belong. (Supplementary Note 9) An information generation method, comprising: a computer acquiring sensor data including acceleration and angular velocity measured by a measurement device attached to the footwear of at least one of the managed persons, estimating a disease risk for each disease for at least one of the managed persons using the acquired sensor data, generating proposed information, including health measures corresponding to the disease risk for at least one of the managed persons, and outputting the generated proposed information. (Supplementary Note 10) The information generation method according to Supplementary Note 9, comprising: calculating a gait index using the sensor data; inputting data including the gait index calculated using the sensor data into a disease risk estimation model that outputs a disease risk score indicating the degree of disease risk for each disease in response to input of data including the gait index; and estimating disease risk information in response to the disease risk score output from the disease risk estimation model. (Supplementary Note 11) The information generation method according to Supplementary Note 10, estimating the health measure in response to the disease risk score of at least one of the managed persons using a health measure estimation model that outputs the health measure in response to input of the disease risk score. (Supplementary Note 12) The information generation method according to Supplementary Note 11, estimating the health measure in response to the disease risk score of at least one of the managed persons using the health measure estimation model customized to suit the working style of the managed persons. (Supplementary Note 13) The information generation method according to Supplementary Note 11, estimating the health measure in response to the disease risk score of at least one of the managed persons using the health measure estimation model customized to suit the industry of an organization to which the managed persons belong.(Supplementary Note 14) The information generation method according to Supplementary Note 11, wherein the health measure estimation model and the disease risk estimation model are models trained using a machine learning technique, and the disease risk estimation model includes an incomplete heterogeneous variational autoencoder. (Supplementary Note 15) A computer-readable non-transitory recording medium having recorded thereon a program causing a computer to execute the following processes: acquiring sensor data including acceleration and angular velocity measured by a measuring device mounted on the footwear of at least one of the managed persons; estimating a disease risk for each disease for the at least one of the managed persons using the acquired sensor data; generating suggested information including health measures according to the disease risk for the at least one of the managed persons; and outputting the generated suggested information. (Supplementary Note 16) The computer-readable non-transitory recording medium according to Supplementary Note 15, having recorded thereon a program causing a computer to execute the following processes: calculating a gait index using the sensor data; inputting data including the gait index calculated using the sensor data into a disease risk estimation model that outputs a disease risk score indicating the degree of disease risk for each disease in response to input of data including the gait index; and estimating disease risk information in response to the disease risk score output from the disease risk estimation model. (Supplementary Note 17) The computer-readable non-transitory recording medium according to Supplementary Note 16, having recorded thereon a program causing a computer to execute the following processes: calculating a gait index using the sensor data; inputting data including the gait index calculated using the sensor data into a disease risk estimation model that outputs a disease risk score indicating the degree of disease risk for each disease in response to input of data including the gait index; and estimating disease risk information in response to the disease risk score output from the disease risk estimation model. (Supplementary Note 18) The computer-readable non-transitory recording medium according to Supplementary Note 17, having recorded thereon a program causing a computer to execute the following processes: estimating the health measure in response to the disease risk score of at least one of the managed persons, using the health measure estimation model that outputs the health measure in response to input of the disease risk score.(Supplementary Note 19) The computer-readable non-transitory recording medium according to Supplementary Note 17, having recorded thereon a program that causes a computer to execute a process of estimating the health measure according to the disease risk score of at least one of the managed persons, using the health measure estimation model customized to the industry of an organization to which the managed persons belong. (Supplementary Note 20) The computer-readable non-transitory recording medium according to Supplementary Note 17, wherein the health measure estimation model and the disease risk estimation model are models trained using a machine learning technique, and the disease risk estimation model includes an incomplete heterogeneous variational autoencoder.

[0164] REFERENCE SIGNS LIST 1 Information provision system 10 Measurement device 12 Information generation device 13 Calculation unit 14 Estimation unit 15 Risk estimation unit 20 Information generation device 21 Acquisition unit 25 Risk estimation unit 27 Proposal information generation unit 29 Output unit 110 Sensor 111 Acceleration sensor 112 Angular velocity sensor 113 Control unit 115 Communication unit 117 Power supply 121 Acquisition unit 122 Waveform processing unit 123 Gait index calculation unit 124 Memory unit 125 Physical ability estimation unit 126 Disease risk estimation unit 127 Proposal information generation unit 129 Output unit

Claims

1. an acquisition unit that acquires sensor data including acceleration and angular velocity measured by a measurement device mounted on the footwear of at least one person to be managed; a risk estimation unit that estimates a disease risk for each disease of at least one of the managed individuals using the acquired sensor data; a proposal information generation unit that generates proposal information including health measures according to disease risks of at least one of the managed individuals; an output unit that outputs the generated proposal information.

2. The risk estimation unit a calculation unit that calculates a gait index using the sensor data; an estimation unit that inputs data including the gait index calculated using the sensor data into a disease risk estimation model that outputs a disease risk score indicating the degree of disease risk for each disease in response to input of data including the gait index, and estimates disease risk information according to the disease risk score output from the disease risk estimation model.

3. The proposal information generation unit The information generating device according to claim 2, wherein the health measure corresponding to the disease risk score of at least one of the managed persons is estimated using a health measure estimation model that outputs the health measure in response to input of the disease risk score.

4. The proposal information generation unit The information generation device according to claim 3, wherein the health measure estimation model is customized to suit the working style of the managed person, and the health measure is estimated according to the disease risk score of at least one of the managed persons.

5. The proposal information generation unit The information generation device according to claim 3, wherein the health measure estimation model is customized to suit the industry of the organization to which the managed persons belong, and the health measure is estimated according to the disease risk score of at least one of the managed persons.

6. the health measure estimation model and the disease risk estimation model are models trained using a machine learning technique, The disease risk estimation model is The information generating apparatus of claim 3 , including an incomplete heterogeneous variational autoencoder.

7. An information generating device according to any one of claims 1 to 6; The measuring device, The measuring device is An information provision system that is installed on the footwear of at least one of the managed persons, measures acceleration and angular velocity, generates the sensor data using the measured acceleration and angular velocity, and transmits the generated sensor data to the information generation device.

8. The information generating device 8. The information providing system according to claim 7, wherein the proposed information including the health measures optimized for the organization is displayed on a screen of a terminal device used in an organization to which the person to be managed belongs.

9. The computer Acquire sensor data including acceleration and angular velocity measured by a measuring device mounted on the footwear of at least one person to be managed; Using the acquired sensor data, estimate a disease risk for each disease for at least one of the managed individuals; generating proposal information including health measures according to disease risks for at least one of the managed persons; An information generating method for outputting the generated proposal information.

10. A process of acquiring sensor data including acceleration and angular velocity measured by a measurement device mounted on the footwear of at least one person to be managed; A process of estimating a disease risk for each disease related to at least one of the managed individuals using the acquired sensor data; A process of generating proposal information including health measures according to disease risks of at least one of the managed individuals; and outputting the generated proposal information.

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