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

Abstract

The present invention provides an information generation device in order to generate a risk map in which the location of a person at risk of a target disease is made visible, said information generation device comprising an acquisition unit that acquires sensor data measured by a measurement device mounted on footwear of at least one subject, a risk estimation unit that estimates the disease risk for each disease relating to at least one subject using the acquired sensor data, a map generation unit that generates a risk map in which a display corresponding to the disease risk for a target disease relating to at least one subject is overlaid on a map of a target district, and an output unit that outputs risk information including the generated risk map.

Description

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

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

[0002] In areas far from cities, the increase in the elderly population is increasing the public burden on local governments. In such areas, transportation is often inconvenient. Improving the convenience of activities related to the lives of the elderly, such as shopping, medical visits, nursing care, and home visits, is determined by urban development policies. If urban development can be achieved that improves the convenience of these activities, it can be expected that the health of local residents and the attractiveness of the area will improve. However, in reality, it is difficult to implement such policies because it is difficult to fully grasp the lifestyles and health status of local residents.

[0003] Patent Document 1 discloses a health information management server that provides analysis results of health information related to health for each area smaller than the entire analysis target area. The server in Patent Document 1 acquires health information related to the health of residents from a resident terminal. The server in Patent Document 1 stores the acquired health information in a memory unit in association with the resident related to the health information. The server in Patent Document 1 analyzes the health information corresponding to each resident and calculates the resident's personal health risk. The server in Patent Document 1 calculates an area health risk for each of a plurality of pre-set areas based on the calculated personal health risk and the resident's residential location. Patent Document 1 also discloses overlaying the level of area health risk calculated for each area on the corresponding area on a map and displaying it on a management terminal.

[0004] JP 2019-185408 A

[0005] The method of Patent Document 1 allows the degree of area health risk calculated for each area to be overlaid on a map. Therefore, according to the method of Patent Document 1, the degree of area health risk is overlaid on a map, making it easier to understand the health status of local residents. However, while the method of Patent Document 1 can verify the indicator of individual health risk, it cannot visualize the locations of people at high risk of contracting a specific disease.

[0006] An object of the present disclosure is to provide an information generation device, an information provision system, an information generation method, and a recording medium that can generate a risk map that visualizes the locations of people at risk of contracting a target disease.

[0007] An information generating device of one embodiment of the present disclosure includes an acquisition unit that acquires sensor data measured by a measuring device mounted on the footwear of at least one subject, a risk estimation unit that uses the acquired sensor data to estimate a disease risk for each disease for at least one subject, a map generation unit that generates a risk map in which a display corresponding to the disease risk of a target disease for at least one subject is superimposed on a map of a target area, and an output unit that outputs risk information including the generated risk map.

[0008] In one embodiment of the information generating device of the present disclosure, sensor data measured by a measuring device mounted on the footwear of at least one subject is acquired, the acquired sensor data is used to estimate the disease risk for each disease for the at least one subject, a risk map is generated in which a display corresponding to the disease risk of the target disease for the at least one subject is superimposed on a map of the target area, and risk information including the generated risk map is output.

[0009] A program of one embodiment of the present disclosure causes a computer to perform the following processes: acquiring sensor data measured by a measuring device mounted on the footwear of at least one subject; using the acquired sensor data to estimate a disease risk for each disease for at least one subject; generating a risk map in which an indication corresponding to the disease risk of the target disease for at least one subject is superimposed on a map of a target area; and outputting risk information including the generated risk map.

[0010] According to the present disclosure, it is possible to provide an information generation device, an information provision system, an information generation method, and a recording medium that can generate a risk map that visualizes the locations of people at risk of contracting a target disease.

[0011] 1 is a block diagram showing an example of a configuration of an information providing system according to the present disclosure. FIG. 1 is a block diagram showing 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 showing 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 for explaining 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 for explaining a human body plane used in explaining the present disclosure. FIG. 5 is a block diagram showing 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 for explaining a gait cycle used in explaining the present disclosure. FIG. 7 is a conceptual diagram for explaining 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 for explaining an example of an estimation of disease risk by the information providing system according to the present disclosure. FIG. 9 is a conceptual diagram for explaining an example of an estimation of disease risk by the information providing system according to the present disclosure. FIG. 10 is a conceptual diagram showing an example of a map of a target district used in generating a risk map by the information generating device provided in the information providing system according to the present disclosure. FIG. 11 is a conceptual diagram showing an example of a risk map generated by the information generating device provided in the information providing system according to the present disclosure. FIG. 12 is a conceptual diagram showing an example of a risk map generated by the information generating device provided in the information providing system according to the present disclosure. FIG. 1 is a conceptual diagram showing an example of a risk map generated by an information generating device included in the information providing system in the present disclosure. FIG. 2 is a conceptual diagram showing an example of a risk map generated by an information generating device included in the information providing system in the present disclosure. FIG. 3 is a flowchart for describing an example of the operation of the information generating device included in the information providing system in the present disclosure. FIG. 4 is a flowchart for describing an example of gait index calculation processing by the information generating device included in the information providing system in the present disclosure. FIG. 5 is a flowchart for describing an example of risk map generation processing by the information generating device included in the information providing system in the present disclosure. FIG. 6 is a conceptual diagram for describing a service that uses the information providing system in the present disclosure.FIG. 1 is a conceptual diagram showing an example display of a risk map provided from an information providing system in the present disclosure. FIG. 2 is a block diagram showing an example configuration of an information providing system in the present disclosure. FIG. 3 is a conceptual diagram showing an example configuration of an information generating device included in the information providing system in the present disclosure. FIG. 4 is a conceptual diagram for explaining an example of estimation of measures by the information providing system in the present disclosure. FIG. 5 is a flowchart for explaining an example operation of an information generating device included in the information providing system in the present disclosure. FIG. 6 is a conceptual diagram showing an example display of a measure proposal provided from the information providing system in the present disclosure. FIG. 7 is a block diagram showing an example configuration of an information generating device in the present disclosure. FIG. 8 is a flowchart for explaining an example operation of the information generating device in the present disclosure. FIG. 9 is a conceptual diagram showing an example 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 this does not limit the scope of the disclosure 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. The direction of arrows in the drawings is an example and does not limit the direction of data, signals, etc.

[0013] First Embodiment First, an example of an information provision system according to the present disclosure will be described with reference to the drawings. The information provision system according to this embodiment estimates the risk of contracting a specific disease (also referred to as disease risk) using sensor data related to foot movements according to the walking of subjects located in a target area for which a risk map is generated. The information provision system according to this embodiment generates a risk map that visualizes the distribution and locations of people at high disease risk. In this embodiment, an example is given in which a risk map that visualizes the disease risk for each disease is generated.

[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 generation device 12. For example, the measurement device 10 is attached to the footwear of a subject whose disease risk is to be estimated. For example, the functions of the information generation device 12 are installed in a mobile device carried by the subject. For example, the functions of the information generation device 12 are installed in a server or cloud connected via a network to the mobile device carried by the subject. Below, the configurations of the measurement device 10 and the information generation 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 user or in an accessory such as an anklet worn by the user. The measurement device 10 may also be attached directly to the foot or embedded in the foot. The measurement device 10 may also be placed 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 foot arch, 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 a user is standing upright facing the direction of travel, the x-axis direction corresponds to the user's side, the y-axis direction corresponds to the user's back, and the z-axis direction corresponds to 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 user's walking.

[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 user walking. For example, the control unit 113 starts measuring the step width starting from the point in time when it is detected that either the left or right foot has started to move in the 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 conversion (AD) 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 sensor data transmitted from the communication unit 115 is received by 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 predetermined 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 predetermined timing. For example, the communication unit 115 (communication means) 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] 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 map 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 footwear of a subject using the information provision system 1. The acquisition unit 121 receives the sensor data from the measurement device 10 via wireless communication. The sensor data includes location information of the subject'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. 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 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 quantities calculated by the measurement device 10.

[0033] The acquisition unit 121 also acquires attribute data of the subject. 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 referred to as physical information. The attribute data also includes the subject's residential address (location information). The subject's residential address (location information) is used to generate a risk map of the target area. Typically, the subject's residential address (location information) is not used to estimate physical ability or disease risk. For example, the attribute data is input via an input device (not shown). For example, the attribute data is input via a mobile terminal used by the subject. For example, the attribute data may be stored in advance in the storage unit 124. The attribute data may be updated at any time in response to input by the subject.

[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 the gait waveform data for one step gait cycle using acceleration / angular velocity other than the 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 the 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) that gradually increases after passing through a period of small fluctuation following the 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 abilities 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.

[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 or a center of pressure exclusion index (CPEI) as a gait index. The frailty level is an estimated value of a frailty state according to a walking state. For example, the gait index calculation unit 123 estimates an index such as a judgment result indicating health, a judgment result indicating the possibility of frailty, or a judgment result indicating a high possibility of frailty as a 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 amounts 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 amounts extracted from the walking waveform data.

[0049] The memory 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 acquiring 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 memory unit 124 stores disease risk estimation models trained on multiple subjects. For example, the disease risk estimation model outputs a disease risk index (disease risk score) in response to input attribute data, gait indices, and physical ability scores. Typically, the subject's residential address (location information) included in the attribute data is not used to estimate physical ability or disease risk. For example, the disease risk estimation model may be a model that outputs 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 does not need to be used.

[0050] For example, the physical ability estimation model and the disease risk estimation model may be stored in the storage unit 124 when the product is shipped from the factory. The physical ability estimation model and the disease risk estimation model may also be stored in the storage unit 124 at the time of calibration before the subject uses the information generating device 12. For example, the physical ability estimation model and the disease risk 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 and the disease risk estimation model may be accessed via an interface (not shown) connected to the storage device.

[0051] The storage unit 124 also stores the attributes of the subject. The attribute data includes gender, date of birth (age), height, and weight. The attribute data also includes the subject's residential address (location information). Typically, the subject's residential address (location information) is not used to estimate physical ability or disease risk. The attribute data may be updated at any time.

[0052] Furthermore, a map of a target area for which a risk map is to be generated is stored in the storage unit 124. The map of the target area may be stored in advance in the storage unit 124. For example, the map of the target area may not be stored in the storage unit 124, but may be acquired by the acquisition unit 121 from an external database.

[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 the subject's attributes 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 time required for TUG is the mobility index. For example, a score (also referred to as a mobility score) according to the estimated value of the time required for TUG is the mobility index. The mobility score is a value obtained by scoring the time required for TUG, 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 time required for TUG, as long as it can score mobility. The time required for TUG 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 time required for TUG.

[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 (also referred to as single-leg standing time) that a subject maintains with one leg raised 5 cm (centimeters) from the ground with their eyes closed. 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 the phase switches 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 motion and the foot (circumflexion amount) at the timing when the central axis of the foot is farthest from the axis of forward motion during the swing phase. The feature value E7 is the circular movement amount normalized by the subject's height. The feature value E7 mainly includes features related to the movement of the abductor and adductor muscles.

[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 subject 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 subject from whom the physical ability feature amount was generated in accordance with the input of the physical ability feature amount. 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) acquires the estimation result of the physical ability (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 the subject's attributes from the storage unit 124. The disease risk estimation unit 126 estimates the disease risk for each disease using the physical ability score, the gait index, and the attributes. 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 subject and stores it in the storage unit 124. The estimated disease risk for each disease may be accumulated in a dedicated database (not shown) for generating a risk map.

[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 into 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 the 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. 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.

[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. For example, the disease risk estimation model 160 may be configured to estimate a disease risk score including test item data from a health checkup.

[0082] 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 attributes, gait indices, and physical abilities of multiple subjects are used as explanatory variables and a disease risk score for 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.

[0083] 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 subjects who generated the feature data according to the feature data. There are no particular limitations on the algorithm used to train the disease risk estimation model 160.

[0084] 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 a subject even if there are some missing data in the attribute data, gait index, physical ability score, etc.

[0085] 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.

[0086] 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 a person whose disease risk is to be estimated.

[0087] 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 standard person μ to the average annual number of medical receipts issued μ estimated for the subject. The disease risk estimation unit 126 calculates the disease risk score RS using the following equation 1.

[0088] 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 standard person to the probability mass function P(X=k) of the average annual number of medical receipts issued estimated for the subject (k is a natural number). The disease risk estimation unit 126 calculates the disease risk score RS using the following equation 2:

[0089] 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.

[0090] 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.

[0091] The map generation unit 127 acquires, from the storage unit 124, the disease risk of the disease for which the risk map is to be generated for the subject associated with the target district. For example, the location of the subject is identified by the subject's residential address. For example, the location of the subject may be identified by location information acquired from a mobile device that is the source of sensor data measured according to the subject's foot movements. The map generation unit 127 also acquires a map of the target district.

[0092] The map generation unit 127 sets display conditions for an image showing the distribution of the acquired disease risk. The map generation unit 127 generates an image (heat map) in which the display state of the disease risk distribution, such as color coding or shading, is set in association with the positions of areas, residences, etc. included in the target district. The map generation unit 127 sets the display state of an indicator according to the degree of disease risk in association with the positions of areas, residences, etc. included in the target district. For example, the map generation unit 127 sets a display state indicator, such as hue, brightness, saturation, etc., according to the degree of disease risk. The display state of the indicator according to the degree of disease risk may be set based on criteria other than hue, brightness, and saturation. For example, the map generation unit 127 may set the display state, such as shape, size, color, etc., for each disease.

[0093] The map generation unit 127 sets the display state of an indicator indicating the degree of disease risk in association with an area included in the target district. For example, the map generation unit 127 calculates statistical values ​​of disease risk scores for multiple subjects associated with an area included in the target district, and sets display conditions for the indicator according to the calculated statistical values ​​for that area. For example, the statistical values ​​include the total value or average value of the disease risk score. There are no particular limitations on the division of the target district into areas. For example, the map generation unit 127 divides the map of the target district into equal intervals. For example, the map generation unit 127 divides the map of the target district into a grid pattern. For example, the map generation unit 127 sets areas on the map of the target district by city, town, or village. For example, the map generation unit 127 sets areas on the map of the target district by house number units (such as chome and nochi) divided by a block system or a road system.

[0094] The map generation unit 127 may set the display state of an indicator indicating the degree of disease risk in association with a residence included in the target area. For example, the map generation unit 127 sets the display state for a subject associated with a residence included in the target area. For example, the map generation unit 127 calculates disease risk score statistics for multiple subjects associated with residences included in the target area, and sets display conditions for the indicator corresponding to the calculated statistics for the residence. For example, the statistics include the total value or average value of the disease risk score.

[0095] The map generation unit 127 may set the display state of an indicator indicating the degree of disease risk in association with the location of a subject staying in the target area. For example, the map generation unit 127 sets the display state of an indicator indicating the degree of disease risk in association with a location identified by the location information of a subject staying in the target area. For example, the map generation unit 127 updates the display state of the indicator indicating the degree of disease risk in association with a change in the location identified by the location information of the subject. In this case, the risk map can be updated in accordance with the movement of the subject.

[0096] The map generator 127 generates a risk map by overlaying the generated image (heat map) on a map of the target area according to the set display conditions. The map of the target area is preferably visible through the overlaid image (heat map). As long as it is known that the indicators correspond to areas, residences, and subjects included in the target area, the indicators indicating the degree of disease risk may be offset from the locations of the identified areas, residences, and subjects.

[0097] 11 to 16 are conceptual diagrams for explaining risk maps generated by the map generation unit 127. FIG. 11 is a conceptual diagram showing an example of a map (Map M) of a target area. Map M in FIG. 11 shows the locations of facilities (Facility A, Facility B, Facility C, Facility D) in the target area. As shown in FIG. 11, even if a facility is in the target area, it may be difficult to access depending on the positional relationship between the subject's residence and the facility. For example, a facility located on the other side of a railroad track or a river from the subject's location may be difficult to access, even though it is close in distance.

[0098] FIG. 12 is an example of a risk map (risk map RM1) on which an indicator showing the degree of diabetes disease risk is displayed. In the risk map RM1, the indicator showing the degree of diabetes disease risk is displayed as a circle (dashed line). The indicator showing the degree of diabetes disease risk may not be a closed figure such as a circle, but may be expressed as a continuous change across the entire risk map RM1. In the risk map RM1, the degree of diabetes disease risk is shown in shades of gray. For example, the higher the degree of diabetes disease risk, the darker the indicator is set to be displayed. For example, the higher the degree of diabetes disease risk, the larger the indicator is set to be displayed.

[0099] In the example of FIG. 12 , a diabetes specialist is stationed at facility D. Across the railroad tracks from facility D is region R1, which has a large indicator area. It is highly likely that an increasing number of people living in region R1 will receive diabetes treatment in the future. Therefore, if an overpass is installed over the railroad tracks between facility D and region R1, even if the number of people receiving diabetes treatment increases, it will be easier for people to visit facility D from region R1. For example, by referring to risk map RM1, city hall staff in the target area can develop a plan to install an overpass over the railroad tracks between facility D and region R1. Also, in the example of FIG. 12 , if a diabetes specialist is stationed at facility B near region R1, it will be easier for people living in region R1 to visit facility B for diabetes treatment. For example, city hall staff in the target area can develop a plan to attract a diabetes specialist to facility B by referring to risk map RM1. In other words, risk map RM1 can provide information to support decision-making regarding plans for the target area.

[0100] FIG. 13 is an example of a risk map (risk map RM2) on which an indicator showing the degree of disease risk of lower back pain is displayed. In risk map RM2, the indicator showing the degree of disease risk of lower back pain is displayed as a hexagon (dash-dotted line). The indicator showing the degree of disease risk of lower back pain may be expressed as a continuous change across the entire risk map RM2, rather than as a closed figure such as a hexagon. In risk map RM2, the degree of disease risk of lower back pain is shown in shades of gray. For example, the greater the degree of disease risk of lower back pain, the darker the indicator is set to be. For example, the higher the degree of disease risk of lower back pain, the larger the indicator is set to be.

[0101] In the example of FIG. 13 , facility C is a hospital specializing in lower back pain. Facility C is located far from a station and is currently far from a bus route. For example, to get to facility C from area R1, a taxi or other means of transportation is used. To get to facility C from other areas, a taxi or other means of transportation is used. For example, by referring to the risk map RM2, city hall staff in the target area can get an opportunity to plan a bus route near facility C. Also, in the example of FIG. 13 , if there is a bus that travels within the target area and heads to facility C, it will be easier to visit facility C. For example, by referring to the risk map RM2, city hall staff in the target area can get an opportunity to plan a bus route that travels within the target area. In other words, the risk map RM2 can provide information to support decision-making regarding plans for the target area.

[0102] FIG. 14 shows an example of a risk map (risk map RM3) on which an indicator indicating the degree of disease risk for knee osteoarthritis is displayed. In the risk map RM3, the indicator indicating the degree of disease risk for knee osteoarthritis is displayed as a star polygon (two-dot chain line). The indicator indicating the degree of disease risk for knee osteoarthritis may be expressed as a continuous change across the entire risk map RM3, rather than as a closed figure such as a star polygon. In the risk map RM3, the degree of disease risk for knee osteoarthritis is indicated by a darker display state. For example, the higher the degree of disease risk for knee osteoarthritis, the darker the indicator is set to. For example, the higher the degree of disease risk for knee osteoarthritis, the larger the indicator is set to be displayed. For example, the higher the degree of disease risk for knee osteoarthritis, the larger the number of points of the star polygon is set to be displayed.

[0103] In the example of FIG. 14 , facility A is a rehabilitation facility for knee osteoarthritis. Facility A is close to a train station, making it easy for residents living in the area surrounding the station to commute to the facility. However, to get to facility A from area R3, people use transportation such as taxis. To get to facility A from other areas, people also use transportation such as taxis. For example, by referring to the risk map RM3, city hall staff in the target area can develop a plan to attract a rehabilitation facility for knee osteoarthritis near an area with a high indicator. Also, in the example of FIG. 14 , if there is a bus that travels within the target area and heads to facility A, it will be easier for residents to visit facility A. For example, by referring to the risk map RM3, city hall staff in the target area can develop a plan to establish a bus route that travels within the target area. In other words, the risk map RM3 can provide information to support decision-making regarding plans for the target area.

[0104] FIG. 15 is an example of a risk map (risk map RM4) displaying indicators indicating the degree of disease risk for multiple diseases. The risk map RM4 displays indicators indicating the degree of disease risk for diabetes, lower back pain, and osteoarthritis, as illustrated in FIGS. 12 to 14. For example, if the indicators indicating the disease risk for diabetes, lower back pain, and osteoarthritis are displayed in different shapes and colors, the distribution of these diseases can be distinguished. For example, if diabetes, lower back pain, and osteoarthritis are not distinguished from each other, the indicators indicating the disease risk for these diseases may be displayed in the same shape and color. The risk map RM3 displays the degree of disease risk using shading. For example, the higher the degree of disease risk, the darker the indicator. For example, the higher the degree of disease risk, the larger the indicator. For example, by referring to the risk map RM4, city hall staff in the target area can be motivated to consider measures that can comprehensively address multiple diseases. In other words, the risk map RM4 can provide information to support decision-making regarding measures for the target area.

[0105] FIG. 16 shows an example of a risk map (risk map RM5) in which an indicator indicating the degree of disease risk is displayed in association with the location of a subject in a target area. The subject's location is identified using location information from a mobile device (not shown) carried by the subject. An indicator indicating the subject's disease risk is displayed at the subject's location. For example, the greater the disease risk, the darker the indicator may be displayed. For example, the higher the disease risk, the larger the indicator may be displayed. For example, by referring to the risk map RM5, city hall staff in the target area can grasp the disease risk of subjects in the target area in real time. For example, city hall staff in the target area can be prompted to consider measures based on the movement patterns of subjects at risk of disease. For example, city hall staff in the target area can be prompted to consider certain measures by comparing trends in daytime and nighttime locations. For example, city hall staff in the target area can be prompted to consider measures such as delivery and crime prevention by examining the proportion of time spent at home and out. In other words, the risk map RM5 can provide information to support decision-making regarding measures for the target area.

[0106] The output unit 129 (output means) outputs risk information including the risk map estimated by the map generation unit 127. For example, the output unit 129 outputs the risk information to a terminal device or server managed by the town office of the target district. For example, the output unit 129 may display the risk information on the screen of the target person's mobile terminal. For example, the output unit 129 may output the risk information to an external system that uses the risk information. There are no particular limitations on the use of the output risk information. For example, the risk information may be used for statistical analysis, research on disease prevention, etc.

[0107] For example, the information generating device 12 is connected to an external system, such as a cloud or server, via a mobile terminal (not shown) carried by the subject. The mobile terminal (not shown) is a portable communication device. For example, the mobile terminal is a portable communication device with a communication function, such as a smartphone, a smartwatch, 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 disease risk information may be used by an application installed on the mobile terminal. In this case, the mobile terminal performs processing using the risk information using application software, etc. installed on the mobile terminal.

[0108] (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. 17 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. 17, 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. 17 may be the information generating device 12.

[0109] 17, first, the acquisition unit 121 acquires time-series data of sensor data measured by the measurement device 10 mounted on the footwear (step S11). The sensor data includes accelerations in three axial directions and angular velocities around three axes.

[0110] 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. 18 ).

[0111] 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.

[0112] Next, the disease risk estimation unit 126 estimates the disease risk for each disease 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 for each disease using the attribute data and gait index. The disease risk estimation unit 126 estimates a disease risk score for each disease. 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.

[0113] Next, the storage unit 124 stores the estimated disease risks (step S15). The disease risks stored in the storage unit 124 are used to generate a risk map.

[0114] Next, the map generator 127 executes a risk map generation process using the disease risks stored in the storage unit 124 (step S16). In the risk map generation process, the map generator 127 generates a risk map of the target area. Details of the risk map generation process of step S16 will be described later ( FIG. 19 ).

[0115] Next, the output unit 129 outputs the risk information including the generated risk map (step S17). For example, the output unit 129 outputs the risk information to a terminal device or a server managed by the town office of the target district. For example, the output unit 129 outputs the risk information to an external system that uses the risk information. For example, the output unit 129 may display the risk information on the screen of the target person's mobile terminal.

[0116] It should be noted that it is not necessary to acquire attribute data of the subject. If attribute data is not acquired from the subject, a model for estimating disease risk without using attribute data can be used. Furthermore, the subject may be asked to consent to acquiring attribute data in advance. At that time, the subject may be informed of the benefits to be obtained by acquiring attribute data, and may be encouraged to consent to the acquisition of attribute data. Here, the benefits may include, for example, obtaining more accurate risk estimation results.

[0117] [Gait Index Calculation Process] Next, the gait index calculation process (step S12 in FIG. 17 ) by the calculation unit 13 of the information generating device 12 will be described with reference to the drawings. FIG. 18 is a flowchart for explaining an example of the operation of the calculation unit 13. In the description of the process according to the flowchart in FIG. 18 , the components of the calculation unit 13 will be described as the main actors in the operation. The main actors in the process according to the flowchart in FIG. 18 may be the information generating device 12 or the calculation unit 13.

[0118] 18, 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.

[0119] 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%.

[0120] 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.

[0121] [Risk Map Generation Process] Next, the risk map generation process (step S16 in FIG. 17 ) by the map generation unit 127 of the information generation device 12 will be described with reference to the drawings. FIG. 19 is a flowchart for describing an example of the operation of the map generation unit 127. In describing the process according to the flowchart in FIG. 19 , the map generation unit 127 will be described as the subject of operations. The subject of operations in the process according to the flowchart in FIG. 19 may be the information generation device 12.

[0122] In FIG. 19, first, the map generating unit 127 identifies the position of the subject (step S161).

[0123] Next, the map generating unit 127 calculates the distribution of disease risk of the target disease for each area included in the target district according to the location of the identified subject (step S162).

[0124] Next, the map generating unit 127 sets the display conditions for the indicator that indicates the degree of disease risk of the target disease (step S163).

[0125] If a risk map of the target area has not been generated (No in step S164), the map generator 127 acquires a map of the target area (step S165). If a risk map of the target area has been generated (Yes in step S164), the process proceeds to step S166.

[0126] After step S165, or if the answer is Yes in step S164, the map generator 127 superimposes an indicator indicating the degree of disease risk of the target disease on the map of the target district in accordance with the set display conditions (step S166). The map of the target district on which the indicator indicating the degree of disease risk of the target disease is superimposed is the risk map.

[0127] Next, application examples according to the present embodiment will be described with reference to the drawings. The application examples below illustrate the relationship between a business that provides a service using the information provision system 1, a local government that uses the service, and residents (target individuals) living in an area managed by the local government.

[0128] Figure 20 is a correlation diagram showing the relationship between businesses, local governments, and residents (subjects). Businesses provide services to local governments using the information provision system 1. Based on a contract concluded with the local government, businesses provide risk information, including risk maps, to the local government. The local government pays the business a usage fee for services using the information provision system 1. If resident health checkup data is used to generate the risk map, the local government provides the resident health checkup data to the business. The contract between the business and the local government clarifies rules regarding the handling of personal information and appropriate data management. The business clearly explains that the risk information is for reference only and does not guarantee medical accuracy or completeness.

[0129] Municipalities will fully explain to residents the details of their personal information protection policies and data management practices, and obtain their consent. If there are any changes to their personal information protection policies or data management practices, the municipality will explain the changes to residents and obtain their consent. For example, consent from residents will be obtained electronically. Municipalities will implement measures for the areas where residents live, depending on the risk information provided by businesses.

[0130] Residents are the entities that pay taxes to local governments. Residents are loaned or provided with special insoles equipped with the measuring device 10 by a business operator that has a contract with the local government. Residents wear shoes equipped with the special insoles and walk around carrying a mobile device that can communicate with the measuring device 10. The mobile device uploads sensor data measured by the measuring device 10 to the business operator's cloud server.

[0131] Terminal devices used by local governments download risk information for the target area from the operator's cloud server. The local government refers to the risk information. The local government refers to the risk map included in the risk information and considers measures for residents. The local government regularly refers to the risk map for the target area and considers measures in response to changes in the risk map. For example, the local government holds consultation sessions and events to incorporate residents' opinions and requests regarding measures in response to changes in the risk map.

[0132] 21 shows an example of a risk map displayed on the screen of a terminal device 180 used by a local government. Risk information including a risk map generated for a target area managed by the local government is displayed on the screen of the terminal device 180, optimized for each local government. After checking the risk information including the risk map displayed on the screen of the terminal device 180, staff can consider measures for the target area.

[0133] As described above, the information provision system of this embodiment includes a measuring device and an information generating device. The measuring device is attached to the footwear of at least one of the subjects. The measuring device measures spatial acceleration and spatial angular velocity. The measuring device generates sensor data using the measured spatial acceleration and spatial 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 map generating unit, and an output unit. The acquiring unit acquires sensor data measured by the measuring device attached to 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 the at least one subject. The map generating unit generates a risk map in which an indication corresponding to the disease risk of the target disease for the at least one subject is superimposed on a map of the target area. The output unit outputs risk information including the generated risk map.

[0134] The information generating device of this embodiment estimates the disease risk of a target disease using sensor data measured by a measuring device mounted on the footwear of a subject. The information generating device of this embodiment generates a risk map in which a display corresponding to the estimated disease risk of the target disease is superimposed on a map of a target area. Therefore, according to this embodiment, a risk map can be generated in which the locations of people at risk of contracting the target disease are visualized.

[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, and estimates disease risk information corresponding to a disease risk score output from the disease risk estimation model. The disease risk estimation model outputs a disease risk score indicating the degree of disease risk for each disease in response to input of data including the gait index. According to this aspect, by inputting data including the gait index calculated using sensor data to the disease risk estimation model, disease risk information corresponding to the disease risk score can be estimated.

[0136] In one aspect of this embodiment, the map generation unit identifies the location of at least one subject. The map generation unit calculates the distribution of disease risk for the target disease for each area included in the target district according to the location of the identified at least one subject. The map generation unit sets display conditions for an indicator indicating the degree of disease risk for the target disease for each area included in the target district. The map generation unit superimposes an indicator indicating the degree of disease risk for the target disease on a map of the target district in accordance with the set display conditions. According to this aspect, a risk map can be generated on which an indicator indicating the degree of disease risk for the target disease is superimposed.

[0137] In one aspect of this embodiment, the map generation unit sets display conditions for indicators indicating the degree of disease risk for multiple target diseases for each area included in the target district. The map generation unit superimposes the indicators indicating the degree of disease risk for the multiple target diseases on a map of the target district in accordance with the set display conditions. According to this aspect, it is possible to generate a risk map displaying indicators for multiple target diseases.

[0138] In one aspect of this embodiment, the map generation unit calculates disease risk score statistics for at least one subject associated with a position within an area included in the target district. The map generation unit sets display conditions for an indicator corresponding to the calculated disease risk score statistics for the area. According to this aspect, a risk map can be generated in which indicators corresponding to the disease risk score statistics are displayed.

[0139] In one aspect of the present embodiment, the map generator identifies the location of the subject based on the location of the subject's residence. According to this aspect, a risk map can be generated in which an indicator showing the degree of disease risk of the target disease is displayed for each area corresponding to the subject's residence.

[0140] In one aspect of the present embodiment, the map generator identifies the location of the subject based on location information of a mobile device carried by the subject. According to this aspect, a risk map can be generated in which an indicator showing the degree of disease risk of the target disease is displayed for each area corresponding to the location of the mobile device carried by the subject.

[0141] In one aspect of this embodiment, the map generation unit identifies the location of the subject based on location information of a mobile device carried by the subject. The map generation unit updates the risk map in response to changes in the location of the subject. According to this aspect, the risk map, which displays an indicator showing the degree of disease risk of the target disease, can be updated in response to changes in the location of the mobile device carried by the subject.

[0142] In one aspect of the present embodiment, the countermeasure estimation model and the disease risk estimation model are models trained using a machine learning technique. The disease risk estimation model includes an incomplete heterogeneous variational autoencoder. According to this aspect, even if there is some loss of data such as gait indicators, the disease risk of a subject can be estimated.

[0143] In one aspect of this embodiment, the information generating device displays risk information about the target area, optimized for each local government, on the screen of a terminal device used by the local government that manages the target area. According to this aspect, risk information including a risk map and policy proposals for the target area can be provided in an optimized manner for each local government that manages the target area.

[0144] 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 generates a policy proposal according to a risk map. The information generating device according to this embodiment outputs risk information including a policy proposal for a local government.

[0145] (Configuration) Fig. 22 is a block diagram showing an example of the configuration of the information provision system 2 in the present disclosure. The information provision system 2 includes a measurement device 20 and an information generation device 22. For example, the measurement device 20 is installed in the footwear of a subject whose disease risk is to be estimated. The measurement device 20 has the same configuration as the measurement device 10 of the first embodiment. In the following, a description of the measurement device 20 will be omitted, and only the information generation device 22 will be described. Note that the main configuration of the information generation device 22 is the same as the configuration of the information generation device 12 of the first embodiment, and therefore, description thereof may be omitted.

[0146] 23 is a block diagram showing an example of the configuration of the information generating device 22. The information generating device 22 has an acquiring unit 221, a calculating unit 23, an estimating unit 24, a memory unit 224, a map generating unit 227, a policy proposal generating unit 228, and an output unit 229. The calculating unit 23 and the estimating unit 24 constitute a risk estimating unit 25.

[0147] The acquisition unit 221 (acquisition means) has a configuration similar to that of the acquisition unit 121 in the first embodiment. The acquisition unit 221 acquires sensor data from a measurement device 20 mounted on footwear of a subject who uses the information provision system 2. The acquisition unit 221 receives the sensor data from the measurement device 20 via wireless communication. The sensor data includes location information of the subject's mobile terminal (not shown), which is the source of the sensor data. For example, the acquisition unit 221 receives the sensor data from the measurement device 20 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 221 may conform to standards other than Bluetooth (registered trademark) or Wi-Fi (registered trademark) as long as it can communicate with the measurement device 20. The acquisition unit 221 may receive the sensor data from the measurement device 20 via a wired connection such as a cable. For example, the acquisition unit 221 may acquire gait indices and feature amounts calculated by the measurement device 20.

[0148] The acquisition unit 221 also acquires attributes of the subject. The attribute data includes gender, date of birth, height, and weight. The date of birth is converted to age. The attribute data also includes the subject's residential address (location information). The subject's residential address (location information) is used to generate a risk map of the target area. Typically, the subject's residential address (location information) is not used to estimate physical ability or disease risk. For example, the attribute data is input via an input device (not shown). For example, the attribute data is input via a mobile terminal used by the subject. For example, the attribute data may be stored in advance in the storage unit 224. The attribute data may be updated at any time in accordance with input by the subject.

[0149] The calculation unit 23 (calculation means) has the same configuration as the calculation unit 13 in the first embodiment. The calculation unit 23 has the functions of the waveform processing unit 122 and the gait index calculation unit 123 in the first embodiment. The calculation unit 23 acquires sensor data from the acquisition unit 221. The calculation unit 23 extracts time-series data for one walking cycle (gait waveform data) from time-series data of acceleration in three axial directions and angular velocity around three axes included in the sensor data. The calculation unit 23 extracts the gait waveform data based on the timing of walking events detected from the time-series data of the sensor data. For example, the calculation unit 23 extracts gait waveform data that starts at the timing of a heel strike and ends at the timing of the next heel strike.

[0150] The calculation unit 23 normalizes the time of the extracted walking waveform data for one step cycle to a walking cycle of 0 to 100% (percent) (first normalization). Furthermore, the calculation unit 23 normalizes the first normalized walking waveform data for one step cycle so that the stance phase is 60% and the swing phase is 40% (second normalization).

[0151] The calculation unit 23 extracts, from the walking waveform data, feature quantities (physical ability feature quantities) used to estimate physical abilities. The calculation unit 23 extracts physical ability feature quantities used to estimate at least one physical ability. For example, the calculation unit 23 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 calculation unit 23 extracts physical ability feature quantities for each walking phase cluster according to preset conditions.

[0152] The calculation unit 23 uses the normalized walking waveform data to calculate gait indices used to estimate physical ability, such as distance, height, angle, speed, time, frailty level, and CPEI (Center of Pressure Exclusion Index).

[0153] The memory unit 224 (storage means) has a configuration similar to that of the memory unit 124 in the first embodiment. The memory unit 224 stores a physical ability estimation model that estimates physical ability using physical ability feature quantities extracted from gait waveform data. For example, the physical ability estimation model outputs a physical ability index (physical ability score) in response to input of the physical ability feature quantities extracted from the gait waveform data. The memory unit 224 also stores a disease risk estimation model that estimates disease risk using attribute data, a gait index, and a physical ability score. For example, the disease risk estimation model outputs a disease risk index (disease risk score) in response to input of the attribute data, the gait index, and the physical ability score. Typically, the subject's residential address (location information) included in the attribute data is not used to estimate physical ability or disease risk. For example, the disease risk estimation model may output a disease risk score in response to input of the gait index and attribute data, without using a physical ability score. In this case, the physical ability estimation model may not be used.

[0154] The storage unit 224 also stores a policy estimation model that outputs policies related to a target area in response to input of a risk map generated by the map generation unit 227. For example, a policy is information in which keywords related to the policy are linked to locations included in the target area. For example, a policy proposal is information in which keywords related to policies related to facilities included in the target area are linked. For example, the policy estimation model may include a large-scale language model that outputs sentences including policies in response to input of a risk map.

[0155] The storage unit 224 stores the physical ability estimation model, disease risk estimation model, and measure estimation model trained for multiple subjects. For example, the physical ability estimation model, disease risk estimation model, and measure estimation model may be stored in the storage unit 224 when the product is shipped from a factory. The physical ability estimation model, disease risk estimation model, and measure estimation model may also be stored in the storage unit 224 at a timing such as during calibration before the information generating device 22 is used by a subject. For example, the physical ability estimation model, disease risk estimation model, and 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 measure estimation model may be accessible via an interface (not shown) connected to the storage device.

[0156] The storage unit 224 also stores the attributes of the subject. The attribute data includes gender, date of birth (age), height, and weight. The attribute data also includes the subject's residential address (location information). Typically, the subject's residential address (location information) is not used to estimate physical ability or disease risk. The attribute data may be updated at any time.

[0157] Furthermore, a map of a target area for which a risk map is to be generated is stored in the storage unit 224. The map of the target area may be stored in advance in the storage unit 224. For example, the map of the target area may not be stored in the storage unit 224, but may be acquired by the acquisition unit 221 from an external database.

[0158] The estimation unit 24 (estimation means) has the same configuration as the estimation unit 14 in the first embodiment. The estimation unit 24 includes the functions of the physical ability estimation unit 125 and the disease risk estimation unit 126 in the first embodiment. The estimation unit 24 acquires physical ability feature amounts extracted from gait waveform data from the calculation unit 23. The estimation unit 24 also acquires attributes stored in the storage unit 224. The estimation unit 24 estimates a physical ability score using the physical ability feature amounts and the attributes. The estimation unit 24 inputs the physical ability feature amounts and the subject's attributes into a physical ability estimation model stored in the storage unit 224. For example, the estimation unit 24 estimates a physical ability score related to at least one of the physical abilities of grip strength (total muscle strength of the entire body), dynamic balance, lower limb muscle strength, mobility, and static balance. The estimation unit 24 estimates a disease risk score for each disease using the physical ability score, gait index, and attributes. The estimation unit 24 inputs the physical ability score, the gait index, and the attributes into a disease risk model to estimate a disease risk score, and outputs the estimated disease risk score.

[0159] The map generation unit 227 has the same configuration as the map generation unit 127 of the first embodiment. The map generation unit 227 acquires the disease risk of the disease for which the risk map is to be generated, for the subject associated with the target district, from the storage unit 224. The map generation unit 227 also acquires a map of the target district.

[0160] The map generation unit 227 sets display conditions for an image showing the distribution of the acquired disease risks. The map generation unit 227 generates an image (heat map) in which the distribution of disease risks is set in display states such as color coding and shading, in association with the positions of areas, residences, etc. included in the target district. The map generation unit 227 sets the display state of indicators according to the degree of disease risk in association with the positions of areas, residences, etc. included in the target district.

[0161] The map generation unit 227 sets the display state of an indicator indicating the degree of disease risk in association with an area included in the target district. The map generation unit 227 may set the display state of an indicator indicating the degree of disease risk in association with a residence included in the target district. The map generation unit 227 may set the display state of an indicator indicating the degree of disease risk in association with the location of a subject staying in the target district. The map generation unit 227 generates a risk map by superimposing the generated image (heat map) on a map of the target district in accordance with the set display conditions.

[0162] The measure proposal generation unit 228 acquires a risk map of the target area. The measure proposal generation unit 228 inputs the acquired risk map into a measure estimation model. The measure proposal generation unit 228 uses measures output from the measure estimation model in response to the input of the risk map to generate risk information including measure proposals related to the measures.

[0163] FIG. 24 is a conceptual diagram showing an example of a measure estimation using the measure estimation model 260. The measure proposal generator 228 inputs a risk map of a target area into the measure estimation model 260. The risk map of the target area is input to the measure estimation model 260. In response to the input risk map of the target area, the measure estimation model 260 outputs measures for the target area. In the example of FIG. 24 , multiple measures (measure 1, measure 2, ..., measure N) are estimated for the target area (N is a natural number). In addition to the risk map of the target area, a map of the target area may be input to the measure estimation model 260. In this case, the measure estimation model 260 can estimate measures after extracting the degree of disease risk corresponding to the indicator based on the difference between the risk map of the target area and the map.

[0164] For example, the policy proposal generation unit 228 generates risk information including policy proposals for a target area for the local government that manages the target area. For example, the policy proposal generation unit 228 generates policy proposals by applying policies to a predetermined document format. For example, the policy proposal generation unit 228 may generate policy proposals using a large-scale language model. Upon obtaining risk information including policy proposals, the local government can take action according to the policy proposals. In other words, the policy proposal generation unit 228 generates risk information that supports the local government's decision-making.

[0165] The output unit 229 (output means) has the same configuration as the output unit 129 of the first embodiment. The output unit 229 outputs risk information including the policy proposal generated by the policy proposal generation unit 228. The output unit 229 may also output risk information including a risk map. For example, the output unit 229 outputs the risk information including the policy proposal to an external system that uses the policy proposal. For example, the output unit 229 outputs the risk information including the policy proposal to a terminal device (not shown) used by the local government. There are no particular limitations on the use of the output risk information including the policy proposal. For example, the risk information including the policy proposal is used by the local government to consider policies to be implemented in the target area.

[0166] (Operation) Next, the operation of the information providing system 2 will be described with reference to the drawings. The operation of the information generating device 22 included in the information providing system 2 will be described below. FIG. 25 is a flowchart for explaining an example of the operation of the information generating device 22. In the description of the processing according to the flowchart of FIG. 25, the components of the information generating device 22 will be described as the subject of the operations. The subject of the processing according to the flowchart of FIG. 25 may be the information generating device 22.

[0167] 25 , first, the acquisition unit 221 acquires time-series data of sensor data measured by the measurement device 20 mounted on the footwear (step S21). The sensor data includes accelerations in three axial directions and angular velocities around three axes.

[0168] Next, the calculation unit 23 executes a gait index calculation process using the acquired sensor data (step S22). In the gait index calculation process, the calculation unit 23 calculates gait indexes used to estimate physical ability. The gait index calculation process in step S22 is similar to the gait index calculation process in the first embodiment ( FIG. 18 ).

[0169] Next, the estimation unit 24 estimates physical ability using the attribute data and gait index (step S23). For example, the estimation unit 24 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 S23 can be omitted.

[0170] Next, the estimation unit 24 estimates a disease risk for each disease using the attribute data, gait index, and physical ability (step S24). When a disease risk is estimated without using physical ability, the estimation unit 24 estimates the disease risk for each disease using the attribute data and gait index. The estimation unit 24 estimates a disease risk score for each disease. For example, the estimation unit 24 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 estimation unit 24 estimates a disease risk score for each disease, such as lower back pain, sleep apnea syndrome, insomnia, depression, osteoarthritis, and Parkinson's syndrome.

[0171] Next, the storage unit 224 stores the estimated disease risks (step S25). The disease risks stored in the storage unit 224 are used to generate a risk map.

[0172] Next, the map generation unit 227 executes a risk map generation process using the disease risks accumulated in the storage unit 224 (step S26). In the risk map generation process, the map generation unit 227 generates a risk map of the target area. The risk map generation process of step S26 is similar to the risk map generation process of the first embodiment ( FIG. 19 ).

[0173] Next, the measure proposal generating unit 228 generates a measure proposal according to the risk map (step S27). The measure proposal generating unit 228 uses the measures output from the measure estimation model in response to the input risk map to generate risk information including a measure proposal related to the measures.

[0174] Next, the output unit 229 outputs the risk information including the policy proposal (step S28). For example, the output unit 229 outputs the risk information to a terminal device or a server managed by the town office of the target district. For example, the output unit 229 outputs the risk information to an external system that uses the risk information. For example, the output unit 229 may display the risk information on the screen of the target person's mobile terminal.

[0175] (Application Example) Next, an application example according to this embodiment will be described with reference to the drawings. FIG. 26 illustrates an example in which a policy proposal generated by the information generating device 22 is displayed on the screen of a terminal device 280 used by a local government. The risk information, including proposal information generated for target areas managed by the local government, is optimized for each local government and displayed on the screen of the terminal device 280. In the example of FIG. 26, multiple policy proposals are displayed on the screen. The first policy proposal is, "We propose installing an overpass over the railway between Area S, where many residents have a high risk of knee osteoarthritis, and the hospital." The second policy proposal is, "We propose opening a day care facility in Area T, where many residents have a high risk of frailty." The third policy proposal is, "We propose attracting a sports gym to Area U, where many residents have a high risk of diabetes." After reviewing the risk information including the policy proposals displayed on the screen of the terminal device 280, an employee can consider policies for the target areas.

[0176] The policy proposals generated by the information generating device 22 are not limited to the above examples, as long as they include policies related to the target area. For example, the policy proposals include policies related to the opening or relocation of medical facilities such as new clinics and pharmacies. For example, the policy proposals include policies related to holding healthcare events and seminars that will lead to a reduction in the health risks of residents living in the target area. For example, the policy proposals include policies related to optimizing the placement of medical institutions. For example, the policy proposals include city design policies related to the number and geographical placement of commercial facilities, the installation of fitness equipment in parks, etc. For example, the policy proposals include policies related to improving public transportation such as buses and taxis to improve access to medical institutions. For example, the policy proposals include policies related to the opening and widening of roads.

[0177] As described above, the information provision system of this embodiment includes a measurement device and an information generation device. The measurement device is attached to the footwear of at least one of the subjects. The measurement device measures spatial acceleration and spatial angular velocity. The measurement device generates sensor data using the measured spatial acceleration and spatial angular velocity. The measurement device transmits the generated sensor data to the information generation device. The information generation device includes an acquisition unit, a risk estimation unit, a map generation unit, a policy proposal generation unit, and an output unit. The acquisition unit acquires sensor data measured by the measurement device attached to the footwear of at least one of the subjects. The risk estimation unit estimates a disease risk for each disease for the at least one subject using the acquired sensor data. The map generation unit generates a risk map in which an indication corresponding to the disease risk of the target disease for the at least one subject is superimposed on a map of the target area. The policy proposal generation unit generates policy proposals corresponding to the risk map of the target area using a policy estimation model that outputs policies for the target area in response to input of the risk map. The output unit outputs risk information including the generated policy proposals.

[0178] The information generating device of this embodiment estimates the disease risk of a target disease using sensor data measured by a measuring device mounted on the subject's footwear. The information generating device of this embodiment generates a risk map in which a display corresponding to the estimated disease risk of the target disease is superimposed on a map of the target area. The information generating device of this embodiment uses the generated risk map to generate policy proposals for the target area. Therefore, according to this embodiment, it is possible to generate policy proposals that reflect the disease risk of at least one subject located in the target area.

[0179] Third Embodiment Next, an information generating device according to a third 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 systems according to the first and second embodiments.

[0180] 27 is a block diagram showing an example of the configuration of the information generating device 30 in the present disclosure. The information generating device 30 includes an acquiring unit 31, a risk estimating unit 35, a map generating unit 37, and an output unit 39.

[0181] The acquisition unit 31 acquires sensor data measured by a measuring device mounted on the footwear of at least one subject. The risk estimation unit 35 uses the acquired sensor data to estimate a disease risk for each disease for at least one subject. The map generation unit 37 generates a risk map in which an indication corresponding to the disease risk of a target disease for at least one subject is superimposed on a map of a target area. The output unit 39 outputs risk information including the generated risk map.

[0182] (Operation) Next, the operation of the information generating device 30 will be described with reference to the drawings. Fig. 28 is a flowchart for explaining an example of the operation of the information generating device 30. In the description of the processing according to the flowchart of Fig. 28, the components of the information generating device 22 will be described as the subject of the operations. The subject of the processing according to the flowchart of Fig. 28 may be the information generating device 30.

[0183] In FIG. 28, first, the acquisition unit 31 acquires sensor data measured by a measurement device mounted on the footwear of at least one subject (step S31).

[0184] Next, the risk estimation unit 35 uses the acquired sensor data to estimate the disease risk for each disease for at least one subject (step S32).

[0185] Next, the map generating unit 37 generates a risk map in which a display according to the disease risk of the target disease for at least one subject is superimposed on a map of the target district (step S33).

[0186] Next, the output unit 39 outputs the risk information including the generated risk map (step S34).

[0187] As described above, the information generating device of this embodiment estimates the disease risk of a target disease using sensor data measured by a measuring device mounted on the subject's footwear. The information generating device of this embodiment generates a risk map in which a display corresponding to the estimated disease risk of the target disease is superimposed on a map of the target area. Therefore, according to this embodiment, it is possible to generate a risk map that visualizes the locations of people at risk of contracting the target disease.

[0188] (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. 29 is given as an example of such a hardware configuration. The information processing device 90 in FIG. 29 is an example configuration for executing control and processing according to each embodiment, and does not limit the scope of the present disclosure.

[0189] As shown in Fig. 29, 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. 29, 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] The above is an example of a hardware configuration for enabling the control and processing of the present disclosure. The hardware configuration of Fig. 29 is an example of a hardware configuration for executing the control and processing according to each embodiment, and does not limit the scope of the present disclosure. A program that causes a computer to execute the control and processing according to each embodiment is also included in the scope of the present disclosure.

[0198] A program recording medium on which a program according to each embodiment is recorded is also included within the scope of the present disclosure. The recording medium can be realized 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.

[0199] The components of each embodiment may be combined in any manner, may be realized by software, or may be realized by a circuit.

[0200] 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.

[0201] 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 measured by a measuring device mounted on the footwear of at least one subject; a risk estimation unit that uses the acquired sensor data to estimate a disease risk for each disease for the at least one subject; a map generation unit that generates a risk map in which an indication corresponding to the disease risk of a target disease for the at least one subject is superimposed on a map of a target area; and an output unit that outputs risk information including the generated risk map. (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 of data including the gait index, and estimates disease risk information corresponding 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 map generating unit: identifies a position of at least one of the subjects; calculates a distribution of disease risk of the target disease for each area included in the target district according to the position of the identified at least one subject; sets display conditions for an indicator indicating the degree of disease risk of the target disease for each of the areas included in the target district; and superimposes the indicator indicating the degree of disease risk of the target disease on the map of the target district in accordance with the set display conditions. (Supplementary Note 4) The information generating device according to Supplementary Note 3, wherein the map generating unit: sets display conditions for the indicator indicating the degree of disease risk of a plurality of the target diseases for each of the areas included in the target district; and superimposes the indicator indicating the degree of disease risk of the plurality of the target diseases on the map of the target district in accordance with the set display conditions.(Supplementary Note 5) The information generating device according to Supplementary Note 3, wherein the map generating unit calculates statistics of the disease risk score for at least one of the subjects associated with a position within the area included in the target district, and sets the display condition of the indicator according to the calculated statistics of the disease risk score for the area. (Supplementary Note 6) The information generating device according to Supplementary Note 3, wherein the map generating unit identifies the position of the subject based on a position of the subject's residence. (Supplementary Note 7) The information generating device according to Supplementary Note 3, wherein the map generating unit identifies the position of the subject based on position information of a mobile device carried by the subject. (Supplementary Note 8) The information generating device according to Supplementary Note 3, wherein the map generating unit identifies the position of the subject based on position information of a mobile device carried by the subject, and updates the risk map according to changes in the position of the subject. (Supplementary Note 9) The information generating device according to Supplementary Note 3, further comprising: a policy proposal generating unit that generates policy proposals according to the risk map of the target district using a policy estimation model that outputs policies for the target district according to input of the risk map, and the output unit outputs risk information including the generated policy proposals. (Supplementary Note 10) The information generation device according to Supplementary Note 9, wherein the policy 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 11) An information provision system comprising: the information generation device according to any one of Supplements 1 to 10; and a measurement device that is installed in footwear of at least one of the subjects, 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 12) The information provision system according to Supplementary Note 11, wherein the information generation device displays the risk information optimized for the target district on a screen of a terminal device used by a local government that manages the target district.(Supplementary Note 13) An information generation method in which a computer acquires sensor data measured by a measuring device mounted on the footwear of at least one subject, estimates a disease risk for each disease for the at least one subject using the acquired sensor data, generates a risk map in which an indication according to the disease risk of the target disease for the at least one subject is superimposed on a map of a target area, and outputs risk information including the generated risk map. (Supplementary Note 14) The information generation method according to Supplementary Note 13, in which the computer calculates a gait index using the sensor data, 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. (Supplementary Note 15) The information generating method according to Supplementary Note 14, wherein the computer: identifies a location of at least one of the subjects; calculates a distribution of disease risk for the target disease for each area included in the target district according to the location of the identified at least one subject; sets display conditions for an indicator indicating the degree of disease risk for the target disease for each of the areas included in the target district; and superimposes the indicator indicating the degree of disease risk for the target disease on the map of the target district in accordance with the set display conditions. (Supplementary Note 16) The information generating method according to Supplementary Note 15, wherein the computer: sets display conditions for indicators indicating the degree of disease risk for a plurality of the target diseases for each of the areas included in the target district; and superimposes the indicators indicating the degree of disease risk for the plurality of the target diseases on the map of the target district in accordance with the set display conditions. (Appendix 17) An information generation method as described in Appendix 15, in which the computer calculates a statistical value of a disease risk score indicating the disease risk for at least one of the subjects associated with a position within the area included in the target district, and sets the display conditions of the indicator in the area according to the calculated statistical value of the disease risk score.(Supplementary Note 18) The information generation method according to Supplementary Note 15, wherein the computer identifies the location of the subject based on the location of the subject's residence. (Supplementary Note 19) The information generation method according to Supplementary Note 15, wherein the computer identifies the location of the subject based on location information of a mobile device carried by the subject. (Supplementary Note 20) The information generation method according to Supplementary Note 15, wherein the computer identifies the location of the subject based on location information of a mobile device carried by the subject, and updates the risk map in accordance with changes in the subject's location. (Supplementary Note 21) The information generation method according to Supplementary Note 15, wherein the computer generates policy proposals in accordance with the risk map of the target district using a policy estimation model that outputs policies for the target district in accordance with input of the risk map, and outputs risk information including the generated policy proposals. (Supplementary Note 22) The information generation method according to Supplementary Note 21, wherein the policy 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 23) A non-transitory computer-readable recording medium having recorded thereon a program that causes a computer to execute the following processes: acquiring sensor data measured by a measuring device mounted on the footwear of at least one subject, estimating a disease risk for each disease for at least one of the subjects using the acquired sensor data, generating a risk map in which an indication according to the disease risk of the target disease for at least one of the subjects is superimposed on a map of a target district, and outputting risk information including the generated risk map. (Supplementary Note 24) A non-transitory computer-readable recording medium according to Supplementary Note 23, having recorded thereon a program that causes 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 according to the disease risk score output from the disease risk estimation model.(Supplementary Note 25) A non-transitory computer-readable recording medium according to Supplementary Note 24, having recorded thereon a program that causes a computer to execute the following processes: a process of identifying the location of at least one of the subjects; a process of calculating a distribution of disease risk of the target diseases for each area included in the target district according to the location of the identified at least one subject; a process of setting display conditions for an indicator indicating the degree of disease risk of the target diseases for each of the areas included in the target district; and a process of superimposing the indicator indicating the degree of disease risk of the target diseases on the map of the target district in accordance with the set display conditions. (Supplementary Note 26) A non-transitory computer-readable recording medium according to Supplementary Note 25, having recorded thereon a program that causes a computer to execute the following processes: a process of setting display conditions for an indicator indicating the degree of disease risk of a plurality of the target diseases for each of the areas included in the target district; and a process of superimposing the indicator indicating the degree of disease risk of the plurality of the target diseases on the map of the target district in accordance with the set display conditions. (Supplementary Note 27) A non-transitory computer-readable recording medium according to Supplementary Note 25, having recorded thereon a program that causes a computer to execute the following processes: calculating statistics of a disease risk score indicating the disease risk for at least one of the subjects associated with a position within the area included in the target district; and setting the display conditions of the indicator in the area according to the calculated statistics of the disease risk score. (Supplementary Note 28) A non-transitory computer-readable recording medium according to Supplementary Note 25, having recorded thereon a program that causes a computer to execute a process of identifying the position of the subject based on the position of the subject's residence. (Supplementary Note 29) A non-transitory computer-readable recording medium according to Supplementary Note 15, having recorded thereon a program that causes a computer to execute a process of identifying the position of the subject based on position information of a mobile device carried by the subject.(Supplementary Note 30) A non-transitory computer-readable recording medium according to Supplementary Note 25, having recorded thereon a program that causes a computer to execute the following processes: identifying the location of the subject based on location information of a mobile device carried by the subject; and updating the risk map in accordance with changes in the location of the subject. (Supplementary Note 31) A non-transitory computer-readable recording medium according to Supplementary Note 25, having recorded thereon a program that causes a computer to execute the following processes: generating policy proposals in accordance with the risk map of the target district using a policy estimation model that outputs policies for the target district in accordance with input of the risk map; and outputting risk information including the generated policy proposals. (Supplementary Note 32) The policy 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.

[0202] 1, 2 Information provision system 10, 20 Measurement device 12, 22 Information generation device 13, 23 Calculation unit 14, 24 Estimation unit 15, 25 Risk estimation unit 30 Information generation device 31 Acquisition unit 35 Risk estimation unit 37 Map generation unit 39 Output unit 110 Sensor 111 Acceleration sensor 112 Angular velocity sensor 113 Control unit 115 Communication unit 117 Power supply 121, 221 Acquisition unit 122 Waveform processing unit 123 Gait index calculation unit 124, 224 Memory unit 125 Physical ability estimation unit 126 Disease risk estimation unit 127, 227 Map generation unit 129, 229 Output unit 228 Policy proposal generation unit

Claims

1. an acquisition unit that acquires sensor data measured by a measurement device mounted on the footwear of at least one subject; a risk estimation unit that estimates a disease risk for each disease for at least one of the subjects using the acquired sensor data; a map generating unit that generates a risk map in which an indication according to the disease risk of the target disease for at least one of the subjects is superimposed on a map of the target area; an output unit that outputs risk information including the generated risk map.

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 a 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, The map generation unit locating at least one of said subjects; Calculating a distribution of disease risk of the target disease for each area included in the target district according to the location of at least one of the identified subjects; setting display conditions for an indicator showing the degree of disease risk of the target disease for each of the areas included in the target district; The information generating device according to claim 1 , wherein the indicator showing the degree of disease risk of the target disease is superimposed on the map of the target area in accordance with the set display conditions.

3. The map generation unit setting the display conditions of the indicators indicating the degree of disease risk of the plurality of target diseases for each of the areas included in the target district; The information generating device according to claim 2 , wherein the indicators showing the degree of disease risk of the plurality of target diseases are superimposed on the map of the target area in accordance with the set display conditions.

4. The map generation unit calculating a statistic of the disease risk score for at least one of the subjects associated with a location within the area included in the target district; The information generating device according to claim 2 , wherein the display condition of the indicator is set in the area according to the calculated statistical value of the disease risk score.

5. The map generation unit Identifying the location of the subject based on location information of a mobile device carried by the subject; The information generating device according to claim 2 , wherein the risk map is updated in response to a change in the position of the subject.

6. a policy proposal generation unit that generates policy proposals according to the risk map of the target area using a policy estimation model that outputs policies for the target area in response to an input of the risk map; The output unit The information generating device according to claim 2 , wherein risk information including the generated policy proposal is output.

7. the countermeasure estimation model and the disease risk estimation model are models trained using a machine learning technique, The disease risk estimation model is 7. The information generating apparatus of claim 6, comprising an incomplete heterogeneous variational autoencoder.

8. An information generating device according to any one of claims 1 to 7; an information provision system comprising: a measuring device that is installed in the footwear of at least one of the subjects, 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.

9. The computer acquiring sensor data measured by a measurement device mounted on the footwear of at least one subject; Using the acquired sensor data, estimate a disease risk for each disease for at least one of the subjects; generating a risk map in which an indication according to the disease risk of the target disease for at least one of the subjects is superimposed on a map of the target area; An information generation method for outputting risk information including the generated risk map.

10. acquiring sensor data measured by a measurement device mounted on the footwear of at least one subject; A process of estimating a disease risk for each disease for at least one of the subjects using the acquired sensor data; A process of generating a risk map in which an indication according to the disease risk of the target disease for at least one of the subjects is superimposed on a map of the target area; and outputting risk information including the generated risk map.

Images