Information provision device, information provision system, information provision method, and program

Abstract

An information providing device according to the present invention comprises: an acquisition unit that acquires sensor data including an acceleration and an angular velocity measured by a measurement device mounted on the footwear of a subject for whom credit information is to be created in order to provide credit-related information according to the mental and physical state of the subject; a risk estimation unit that uses the acquired sensor data to estimate a disease risk for each disease related to the subject; a credit-related information generation unit that generates credit-related information according to the estimated disease risk related to the subject and the status information of the subject registered in advance; and an output unit that outputs the generated credit-related information.

Description

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

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

[0002] Banks and other financial institutions review loan applications based on the credit information of their customers (target individuals). When reviewing loan applications, factors such as the customer's creditworthiness, income, and repayment ability are emphasized. These factors may be affected by the customer's physical and mental state. If the customer's physical and mental state can be estimated based on their daily behavior, the customer's credit information can be revised in accordance with the estimated physical and mental state.

[0003] Patent Document 1 discloses a device for calculating a pre-credit limit. The device in Patent Document 1 performs detailed analysis of loan default risk and loan demand based on the customer's personality and behavioral characteristics estimated through years of data analysis. The device in Patent Document 1 calculates a pre-credit limit and a recommended credit limit based on the analysis results.

[0004] JP 2017-054495 A

[0005] The method of Patent Document 1 calculates a preliminary credit limit and a recommended credit limit based on a customer's personality and behavioral characteristics. The method of Patent Document 1 estimates a customer's personality and behavioral characteristics based on information such as ATM (Automatic Teller Machine) usage, card loan amounts, repayment methods, and smartphone usage. Information such as ATM usage, card loan amounts, repayment methods, and smartphone usage reveals only a one-sided character. Therefore, the method of Patent Document 1 cannot accurately estimate a customer's physical and mental state, including their personality.

[0006] An object of the present disclosure is to provide an information provision device, an information provision system, an information provision method, and a recording medium that can provide credit-related information according to the physical and mental state of a subject.

[0007] An information providing device of one embodiment of the present disclosure includes an acquisition unit that acquires sensor data including acceleration and angular velocity measured by a measuring device mounted on the footwear of a subject for whom credit information is to be created, a risk estimation unit that uses the acquired sensor data to estimate a disease risk for each disease related to the subject, a credit-related information generation unit that generates credit-related information based on the estimated disease risk related to the subject and pre-registered status information of the subject, and an output unit that outputs the generated credit-related information.

[0008] In one aspect of the information provision method of the present disclosure, sensor data including acceleration and angular velocity measured by a measuring device mounted on the footwear of a subject for whom credit information is to be created is acquired, the acquired sensor data is used to estimate the disease risk for each disease related to the subject, credit-related information is generated based on the estimated disease risk related to the subject and pre-registered status information of the subject, and the generated credit-related information is output.

[0009] A program in one aspect of the present disclosure causes a computer to perform the following processes: acquiring sensor data including acceleration and angular velocity measured by a measuring device mounted on the footwear of a subject for whom credit information is to be created; using the acquired sensor data to estimate a disease risk for each disease related to the subject; generating credit-related information based on the estimated disease risk related to the subject and pre-registered status information of the subject; and outputting the generated credit-related information.

[0010] According to the present disclosure, it is possible to provide an information provision device, an information provision system, an information provision method, and a recording medium that can provide credit-related information according to the subject's physical and mental state.

[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 according to the present disclosure. FIG. 2 is a conceptual diagram showing an example of an arrangement of a measurement device according to the present disclosure. FIG. 3 is a conceptual diagram showing an example of a coordinate system set in a measurement device according to the present disclosure. FIG. 4 is a conceptual diagram showing an example of a human body surface according to the present disclosure. FIG. 5 is a block diagram showing an example of a configuration of an information providing device according to the present disclosure. FIG. 6 is a conceptual diagram showing an example of a gait cycle according to the present disclosure. FIG. 7 is a conceptual diagram showing an example of a physical ability estimation model according to the present disclosure. FIG. 8 is a conceptual diagram showing an example of an estimation of a disease risk score by a disease risk estimation model according to the present disclosure. FIG. 9 is a conceptual diagram showing an example of an estimation of a credit score by a credit score estimation model according to the present disclosure. FIG. 10 is a flowchart showing an example of operation of an information providing device according to the present disclosure. FIG. 11 is a flowchart showing an example of gait index calculation processing by an information providing device according to the present disclosure. FIG. 12 is a conceptual diagram showing a correlation diagram in an application example of the present disclosure. FIG. 13 is a conceptual diagram showing an example of a display of credit-related information in an application example of the present disclosure. FIG. 14 is a block diagram showing an example of a configuration of an information providing system according to the present disclosure. FIG. 15 is a block diagram showing an example of a configuration of an information providing device according to the present disclosure. FIG. 1 is a conceptual diagram for explaining an example of estimation of personality information by a personality model in the present disclosure. FIG. 2 is a flowchart for explaining an example of operation of an information providing device in the present disclosure. FIG. 3 is a conceptual diagram showing a correlation diagram in an application example in the present disclosure. FIG. 4 is a conceptual diagram showing an example of display of credit-related information in an application example in the present disclosure. FIG. 5 is a block diagram showing an example of the configuration of an information providing device in the present disclosure. FIG. 6 is a flowchart for explaining an example of operation of an information providing device in the present disclosure. FIG. 7 is a block diagram showing an example of a hardware configuration in the present disclosure.

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

[0013] First Embodiment First, an example of an information provision system according to this embodiment will be described with reference to the drawings. The information provision system according to this embodiment acquires sensor data related to foot movements measured during walking of a customer (subject) for whom credit information is to be created to be used by a financial institution when considering loans, etc. The information provision system according to this embodiment uses the acquired sensor data to estimate credit-related information that will be referenced by the financial institution when reviewing a loan for the subject.

[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 provision device 12. For example, the measurement device 10 is installed in the footwear of a customer (subject) of a financial institution. For example, the functions of the information provision device 12 are implemented on a server or cloud. The server or cloud is connected via a network to a mobile device carried by the subject or a repeater installed inside a building where the subject is staying. For example, the functions of the information provision device 12 may be implemented on a mobile device carried by the subject. Below, the configurations of the measurement device 10 and the information provision 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. The acceleration in three axial directions is also called spatial acceleration. The acceleration sensor 111 measures acceleration as a physical quantity related to foot movement. The acceleration sensor 111 outputs the measured acceleration to the control unit 113. There are no limitations on the sensor used as the acceleration sensor 111 as long as it can measure acceleration. For example, the acceleration sensor 111 may be a piezoelectric, piezo-resistive, or capacitive sensor.

[0017] The angular velocity sensor 112 is a sensor that measures angular velocity around three axes. The angular velocity around three axes is also called spatial angular velocity. The angular velocity sensor 112 measures 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. There are no limitations on the sensor used as the angular velocity sensor 112 as long as it can measure angular velocity. For example, a vibration type, a capacitance type, or other type of sensor can be used as the angular velocity sensor 112.

[0018] The sensor 110 is realized, for example, by an inertial measurement unit (IMU) that measures acceleration and angular velocity. An example of an IMU is an inertial measurement unit (IMU). The IMU includes an acceleration sensor that measures acceleration in three axes and an angular velocity sensor that measures angular velocity around three axes. The sensor 110 may be realized by an inertial measurement unit such as a vertical gyro (VG) or an attitude heading reference system (AHRS). The sensor 110 may also be realized by a global positioning system (GPS) / inertial navigation system (INS). 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. For example, the sensor 110 may include a pressure sensor that measures pressure applied by the sole of the foot.

[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 subject or in an accessory such as an anklet worn by the subject. 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 backside of the arch of the foot, and a world coordinate system (x-axis, y-axis, z-axis) set relative to the ground. FIG. 4 shows an example in which different coordinate systems are set for the left and right feet. In the world coordinate system (x-axis, y-axis, z-axis), when a subject is standing upright facing the direction of travel, the x-axis corresponds to the subject's lateral direction, the y-axis corresponds to the subject's front-to-back direction, and the z-axis 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 subject's walking.

[0022] FIG. 5 is a conceptual diagram illustrating planes (also called human body planes) set for the human body. The sagittal plane is a plane that divides the body into left and right halves. The coronal plane is a plane that divides the body into front and back halves. The horizontal plane is a plane that divides the body horizontally. Note that, as shown in FIG. 5 , when the user is standing upright with the center lines 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 the same coordinate system is set for the left and right feet. In this embodiment, rotation in the sagittal plane around the X-axis (x-axis) as the axis of rotation is defined as roll, rotation in the coronal plane around the Y-axis (y-axis) as the axis of rotation is defined as pitch, and rotation in the horizontal plane around the Z-axis (z-axis) as the axis of rotation is defined as yaw. Furthermore, the rotation angle in the sagittal plane around the X-axis (x-axis) as the axis of rotation is defined as roll angle, the rotation angle in the coronal plane around the Y-axis (y-axis) as the axis of rotation is defined as pitch angle, and the rotation angle in the horizontal plane around 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 providing 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 subject 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 moving in the direction of travel after both feet have remained at the same vertical height for a predetermined period of time. Alternatively, the control unit 113 may cause the acceleration sensor 111 and the angular velocity sensor 112 to start measurement at a predetermined timing.

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

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

[0026] For example, the control unit 113 is realized by a microcomputer or microcontroller that performs overall control of the measurement device 10 and performs data processing. For example, the control unit 113 includes a CPU (Central Processing Unit), RAM (Random Access Memory), ROM (Read Only Memory), flash memory, etc. 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 providing 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 providing device 12.

[0027] 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 providing device 12. The timing of transmitting the sensor data is not particularly limited. For example, the communication unit 115 transmits the sensor data at a preset transmission timing. For example, the communication unit 115 transmits the sensor data in real time in response to measurement of the sensor data. For example, the communication unit 115 may store sensor data measured over a predetermined period and transmit the stored sensor data all at once at a preset timing. For example, the communication unit 115 may be configured to receive a measurement start signal from the information providing device 12. In this case, the communication unit 115 outputs the received measurement start signal to the control unit 113.

[0028] For example, the communication unit 115 transmits the sensor data to the information providing device 12 via wireless communication. For example, the communication unit 115 transmits the sensor data to the information providing 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 be conforming to standards other than Bluetooth (registered trademark) or Wi-Fi (registered trademark). The communication unit 115 may transmit the sensor data to the information providing device 12 via a wired connection such as a cable.

[0029] 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.

[0030] [Information Providing Device] Fig. 6 is a block diagram showing an example of the configuration of the information providing device 12. The information providing 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 credit-related information generating unit 127, and an output unit 129. The waveform processing unit 122, the gait index calculating unit 123, the physical ability estimating unit 125, and the disease risk estimating unit 126 constitute the risk estimating unit 15. The waveform processing unit 122 and the gait index calculating unit 123 constitute the calculating unit 13. The physical ability estimating unit 125 and the disease risk estimating unit 126 constitute the estimating unit 14.

[0031] The acquisition unit 121 (acquisition means) acquires sensor data from the measurement device 10 mounted on the footwear of the subject. The acquisition unit 121 receives the sensor data from the measurement device 10 via wireless communication. For example, the sensor data may include location information of a mobile device (not shown) that is a source of the sensor data. For example, the location information is measured using a global positioning system (GPS) function mounted on the mobile device and added to the sensor data. For example, the acquisition unit 121 receives the sensor data from the measurement device 10 via a wireless communication function (not shown) conforming to standards such as Bluetooth (registered trademark) or Wi-Fi (registered trademark). Note that the communication function of the acquisition unit 121 may conform to standards other than Bluetooth (registered trademark) or Wi-Fi (registered trademark) as long as it can communicate with the measurement device 10. The acquisition unit 121 may receive the sensor data from the measurement device 10 via a wired connection such as a cable. For example, the acquisition unit 121 may acquire gait indices and feature amounts calculated by the measurement device 10.

[0032] 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 attribute data may also include age. The gender, date of birth (age), height, and weight included in the attribute data are also referred to as physical information. For example, the attribute data is input via an input device (not shown). For example, the attribute data is input via a terminal device used by the administrator. For example, the attribute data is input via a mobile terminal used by the 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 or the administrator.

[0033] The acquisition unit 121 also acquires the subject's status information. The status information is information related to the subject's social attributes. For example, the status information includes identification information, credit history information, contract performance information, interpersonal relationship information, and behavioral characteristic information. The identification information includes information that ensures that the subject's name is real and information indicating the subject's stability of status. The credit history information includes card repayment history information, loan repayment history information, tax history information, insurance premium payment history information, pension payment history information, and fine payment history information. The contract performance information includes fixed asset information, deposit and savings information, and income information. The interpersonal relationship information includes personal connection information and social influence information. The behavioral characteristic information includes consumption behavior information, travel behavior information, and health behavior information. Note that the information included in the status information is not limited to the information listed here. Of the status information listed here, the credit history information, contract performance information, interpersonal relationship information, and behavioral characteristic information are affected by the subject's disease risk. The sum of the scores related to the identification information, credit history information, contract fulfilment ability information, interpersonal relationship information, and behavioral characteristic information corresponds to the credit score described below. As described below, the credit history information, contract fulfilment ability information, interpersonal relationship information, and behavioral characteristic information are multiplied by a penalty coefficient according to the subject's status information and disease risk. For example, the status information is input via an input device (not shown). For example, the status information is input via a terminal device used by the administrator. For example, the status information is input via a mobile terminal used by the subject. For example, the status information may be registered in advance in the storage unit 124. The attribute data may be updated at any time according to input by the subject or the administrator.

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

[0035] Here, a gait cycle will be described with reference to the drawings. FIG. 7 is a conceptual diagram for explaining a step cycle based on the right foot. A step cycle based on the left foot is similar to that of the right foot. The horizontal axis of FIG. 7 indicates 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 gait cycle to 100% is called first normalization. One gait cycle of one leg 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 during 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 during 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 FIG. 7 , multiple events occur during walking. Multiple events that occur during walking are also called walking events. P1 represents heel strike (HS). Heel strike is an event in which the heel of the right foot touches the ground. P2 represents opposite toe off (OTO). Opposite toe off is an event in which the toe of the left foot leaves the ground while the sole of the right foot is in contact with the ground. P3 represents heel rise (HR). Heel rise is an event in which the heel of the right foot lifts while the sole of the right foot is in contact with the ground. P4 represents opposite heel strike (OHS). Opposite heel strike is an event in which the heel of the left foot touches the ground. P5 represents toe off (TO). Toe-off is an event in which the toe of the right foot leaves the ground while the sole of the left foot is in contact with the ground. P6 represents foot adjacent (FA). Foot crossing is an event in which the left and right feet cross while the sole of the left foot is in contact with the ground. P7 represents tibia vertical (TV). Tibia vertical is an event in which the tibia of the right foot becomes approximately perpendicular to the ground while the sole of the left foot is in contact with the ground. P8 represents heel strike. P8 corresponds to the end point of the gait cycle that begins with P1 and the start point of the next gait cycle. Note that the gait events shown in FIG. 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%, and so on included in the 0 to 100% walking cycle is also called 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%. By subjecting the walking waveform data to second normalization, it is possible to 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 gait waveform data for one walking cycle using the forward acceleration (Y-direction acceleration). With respect to accelerations / angular velocities other than the forward acceleration (Y-direction acceleration), the waveform processing unit 122 extracts 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 normalizes the extracted gait waveform data for one walking cycle. 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 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 gait waveform data for one step gait cycle using acceleration / angular velocity other than forward acceleration (Y-direction acceleration). For example, the waveform processor 122 may detect heel strike and toe lift from time series data of vertical acceleration (Z-direction acceleration) (not shown). The timing of heel strike is the timing of a steep minimum peak that appears in the time series data of vertical acceleration (Z-direction acceleration). At the timing of the steep minimum peak, the value of vertical acceleration (Z-direction acceleration) becomes approximately zero. The minimum peak that marks the timing of heel strike corresponds to the minimum peak of the gait waveform data for one step gait cycle. The section between consecutive heel strikes constitutes a gait cycle. The timing of toe lift is the timing of an inflection point in the time series data of vertical acceleration (Z-direction acceleration) that gradually increases after passing through a section of small fluctuation following the maximum peak immediately after heel strike. The waveform processing unit 122 may extract gait waveform data for one step cycle using both forward acceleration (Y-direction acceleration) and vertical acceleration (Z-direction acceleration).The waveform processing unit 122 may also extract gait waveform data for one step cycle using acceleration, angular velocity, angle, etc. other than forward acceleration (Y-direction acceleration) and vertical acceleration (Z-direction acceleration).

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

[0042] The gait index calculation unit 123 (gait index calculation means) acquires normalized gait waveform data from the waveform processing unit 122. The gait index calculation unit 123 uses the normalized gait waveform data to calculate gait indices used to estimate physical ability. There are no particular limitations on the gait indices to be calculated. For example, the gait index calculation unit 123 calculates gait indices related to distance, height, angle, speed, time, CPEI (Center of Pressure Exclusion Index), frailty level, 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 center of pressure exclusion index (CPEI) as a gait index. The CPEI indicates an estimated rate of expansion of the center of foot pressure on the ground during the stance phase.

[0048] For example, the gait index calculation unit 123 calculates a frailty level as the 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 indicating a determination result regarding frailty as the frailty level. If there is no possibility of frailty, the gait index calculation unit 123 estimates an index indicating that the subject is not frail. If there is a possibility of frailty, the gait index calculation unit 123 estimates an index indicating that the subject is likely to be frail. Furthermore, if there is a high possibility of frailty, the gait index calculation unit 123 estimates an index indicating that there is a high possibility of frailty.

[0049] The memory unit 124 (storage means) stores a physical ability estimation model (described below) that estimates physical ability using physical ability feature quantities extracted from the walking waveform data. For example, the physical ability is at least one of grip strength, dynamic balance, lower limb muscle strength, mobility, and static balance. The physical ability may include items other than grip strength, dynamic balance, lower limb muscle strength, mobility, and static balance. The memory unit 124 stores physical ability estimation models trained for multiple subjects. For example, the physical ability estimation model outputs an index of physical ability (physical ability score) in response to input of physical ability feature quantities extracted from the walking waveform data. If physical ability is not used in estimating disease risk, the physical ability estimation model can be omitted.

[0050] The memory unit 124 also stores a disease risk estimation model (described below). The disease risk estimation model estimates disease risk using attribute data, gait indices, and physical ability scores. The disease risk indicates the risk of contracting 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 of the knee, 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 an index related to disease risk (disease risk score) in response to input of attribute data, gait indices, and physical ability scores. For example, the disease risk estimation model may output a disease risk score in response to input of gait indices and attribute data without using a physical ability score. In this case, the physical ability estimation model is omitted.

[0051] The memory unit 124 may also store a credit score estimation model (described below). The credit score estimation model estimates a credit score using status information and a disease risk score. The memory unit 124 stores a credit score estimation model trained on multiple subjects. For example, the credit score estimation model outputs a credit-related indicator (credit score) in response to input of status information and a disease risk score.

[0052] The storage unit 124 stores the physical ability estimation model and the disease risk estimation model trained for a plurality of subjects. 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 a factory. The physical ability estimation model and the disease risk estimation model may also be stored in the storage unit 124 when the information providing device 12 is calibrated. 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 accessible via an interface (not shown) connected to the storage device.

[0053] The memory unit 124 also stores attribute data and status information of the subject. The attribute data includes gender, date of birth (age), height, and weight. The status information includes identification information, credit history information, contract enforcement ability information, interpersonal relationship information, and behavioral characteristic information. The attribute data and status information may be updated at any time. Furthermore, the memory unit 124 may store health checkup data of the subject. The health checkup data can be a factor in improving the accuracy of estimating disease risk scores and credit-related information. For example, the subject's health checkup data includes diagnostic results for statutory items in the initial employment health checkup and periodic health checkup. The subject's health checkup data may also include diagnostic results for items other than statutory items in the initial employment health checkup and periodic health checkup.

[0054] The physical ability estimation unit 125 (physical ability estimation means) acquires physical ability feature amounts extracted from the walking waveform data from the waveform processing unit 122. The physical ability estimation unit 125 also acquires attribute data stored in the memory unit 124. The physical ability estimation unit 125 estimates a physical ability score using the physical ability feature amounts and the attribute data. The physical ability estimation unit 125 inputs the subject's physical ability feature amounts and the attribute data 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.

[0055] 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.

[0056] <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.

[0057] 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.

[0058] 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.

[0059] 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.

[0060] <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.

[0061] 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.

[0062] 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.

[0063] <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.

[0064] 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.

[0065] 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.

[0066] <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.

[0067] The mobility index is the time required for TUG. For example, an estimated value of the TUG time is the mobility index. For example, a score (also referred to as a mobility score) according to the estimated value of the TUG time is the mobility index. The mobility score is a value obtained by scoring the TUG time, which is an index of mobility, based on a preset standard. Mobility is affected by attributes such as age. Therefore, the mobility score may be scored based on a standard for each attribute. Note that the mobility index is not limited to the TUG time as long as it can score mobility. The TUG time is correlated with the quadriceps, gluteus medius, and tibialis anterior. Therefore, feature quantities extracted from walking phases in which these features appear are used to estimate the TUG time.

[0068] 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.

[0069] <Static Balance> Static balance, which is one of the physical abilities, can be evaluated by the performance of a single-leg standing test. In the present disclosure, the performance of the single-leg standing test is evaluated based on the time spent with one leg raised 5 centimeters (cm) from the ground with the eyes closed (also referred to as single-leg standing time). The single-leg standing time is a static balance performance value. The longer the single-leg standing time, the higher the static balance performance. Static balance may also be evaluated by performance other than the eyes-closed single-leg standing test. For example, static balance may be evaluated by a single-leg standing test with the eyes open (eyes-open single-leg standing test) or other variations of the single-leg standing test.

[0070] 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.

[0071] 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.

[0072] 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.

[0073] 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.

[0074] 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.

[0075] 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.

[0076] 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.

[0077] 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.

[0078] 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.

[0079] For example, the physical ability estimation model 150 may be generated by learning using a linear regression algorithm. For example, the physical ability estimation model 150 may be generated by learning using a support vector machine (SVM) algorithm. For example, the physical ability estimation model 150 may be generated by learning using a Gaussian process regression (GPR) algorithm. For example, the physical ability estimation model 150 may be generated by learning using a random forest (RF) algorithm. For example, the physical ability estimation model 150 may be generated by unsupervised learning that classifies the physical ability of a subject according to physical ability feature amounts. There are no particular limitations on the algorithm used to train the physical ability estimation model 150.

[0080] The disease risk estimation unit 126 (disease risk estimation means) 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 attribute data of the subject 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 attribute data. The disease risk estimation unit 126 may be configured to estimate the disease risk for each disease, including health checkup data. For example, the disease risk estimation unit 126 may be configured to estimate the disease risk for each disease using at least the gait index. The disease risk estimation unit 126 associates the estimated disease risk for each disease with the subject and stores it in the storage unit 124. The disease risk for each disease of the subject may be accumulated in a dedicated database (not shown).

[0081] FIG. 9 is a conceptual diagram showing an example of disease risk estimation by the disease risk estimation unit 126. The disease risk estimation unit 126 inputs attribute data, gait index, and physical ability score used to estimate the disease risk for a specific disease to the disease risk estimation model 160. The attribute data, gait index, and physical ability score used to estimate the disease risk for a specific disease are input to the disease risk estimation model 160. In response to the input of the attribute data, gait index, and physical ability score, the disease risk estimation model 160 outputs a disease risk score for a specific disease. In the example of FIG. 9, a disease risk score is estimated for each of multiple diseases. The disease risk estimation model 160 may be configured as a model for each disease or as a single model. When a physical ability score is not used, the disease risk estimation model 160 may be configured to output a disease risk score for a specific disease in response to the input of the attribute data and gait index.

[0082] 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.

[0083] As the amount of data used for estimation increases, the accuracy of the disease risk score estimation by the disease risk estimation model 160 improves. For example, the disease risk estimation model 160 is configured to output a disease risk score for a specific disease in response to input of health checkup data, attribute data, gait index, and physical ability score. When attribute data is included in the health checkup data items, the disease risk estimation model 160 may be configured to output a disease risk score for a specific disease in response to input of the health checkup data, gait index, and physical ability score.

[0084] The disease risk estimation model 160 may be stored in an external storage device (not shown) constructed on a cloud, a server, or the like. In this case, the disease risk estimation unit 126 uses the disease risk estimation model 160 via an interface (not shown) connected to the storage device. The disease risk estimation model 160 is a machine learning model. For example, the disease risk estimation model 160 is a model trained using training data in which attribute data, gait indices, and physical ability scores for 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.

[0085] For example, the disease risk estimation model 160 is generated by learning using a linear regression algorithm. For example, the disease risk estimation model 160 is generated by learning using a support vector machine (SVM) algorithm. For example, the disease risk estimation model 160 is generated by learning using a Gaussian process regression (GPR) algorithm. For example, the disease risk estimation model 160 is generated by learning using a random forest (RF) algorithm. For example, the disease risk estimation model 160 may be generated by unsupervised learning that classifies the disease risk of a subject according to attribute data, gait index, and physical ability score. There are no particular limitations on the algorithm used to train the disease risk estimation model 160.

[0086] 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.

[0087] 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.

[0088] Here, an example will be described in which the disease risk estimation unit 126 calculates a disease risk score using the average annual number of medical receipts issued. Three calculation examples will be given below. It is assumed that the average annual number of medical receipts issued μ for a typical person has been obtained in advance. The disease risk estimation model 165 outputs the average annual number of medical receipts μ for a specific disease in response to input attribute data, gait index, and physical ability score for the subject.

[0089] 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.

[0090] 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:

[0091] 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.

[0092] 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.

[0093] The credit-related information generation unit 127 acquires the subject's disease risk score and status information. The credit-related information generation unit 127 adjusts the penalty coefficients of the credit score for the identification information, credit history information, contract fulfillment information, interpersonal relationship information, and behavioral characteristic information included in the status information according to the subject's disease risk score and status information. The credit-related information generation unit 127 adjusts the penalty coefficients of the credit score for each of the identification information, credit history information, contract fulfillment information, interpersonal relationship information, and behavioral characteristic information according to preset standards. The penalty coefficients are adjusted within a range of 0 to 1. A weight is set for each of the identification information, credit history information, contract fulfillment information, interpersonal relationship information, and behavioral characteristic information. The sum of the weights for each of the identification information, credit history information, contract fulfillment information, interpersonal relationship information, and behavioral characteristic information is set to 1. The credit-related information generating unit 127 calculates the credit score as the sum of the products of the penalty coefficients and weights for each of the identification information, credit history information, contract fulfilment ability information, human relationship information, and behavioral characteristic information.

[0094] FIG. 11 is a conceptual diagram illustrating an example of calculation of the credit score CS by the credit-related information generation unit 127. The status information IS includes identification information IS1, credit history information IS2, contract performance information IS3, human relationship information IS4, and behavioral characteristic information IS5. The identification information IS1 includes information ensuring that the subject's name is real and information indicating the subject's social stability. The credit history information IS2 includes card repayment history information, loan repayment history information, tax history information, insurance premium payment history information, pension payment history information, and fine payment history information. The contract performance information IS3 includes fixed asset information, deposit and savings information, and income information. The human relationship information IS4 includes personal network information and social influence information. The behavioral characteristic information IS5 includes consumption behavior information, travel behavior information, and health behavior information. The credit score of the identification information IS1 is cs1. The credit score of the credit history information IS2 is cs2. The credit score of the contract performance information IS3 is cs3. The credit score of the human relationship information IS4 is cs4. The credit score of the behavioral characteristic information IS5 is cs5.

[0095] In the example of Figure 11, the weight of the identification information IS1 is 0.15. The weight of the credit history information IS2 is 0.35. The weight of the contract fulfillment ability information IS3 is 0.20. The weight of the human relationship information IS4 is 0.05. The weight of the behavioral characteristic information IS5 is 0.25. Note that the weights of the identification information IS1, credit history information IS2, contract fulfillment ability information IS3, human relationship information IS4, and behavioral characteristic information IS5 are merely examples and are not intended to limit the values.

[0096] Furthermore, a penalty coefficient p is set for each of the identification information IS1, credit history information IS2, contract fulfilment ability information IS3, human relationship information IS4, and behavioral characteristic information IS5 according to the status information IS and disease risk score RS. The penalty coefficient for the identification information IS1 is p1. The penalty coefficient for the credit history information IS2 is p2. The penalty coefficient for the contract fulfilment ability information IS3 is p3. The penalty coefficient for the human relationship information IS4 is p4. The penalty coefficient for the behavioral characteristic information IS5 is p5.

[0097] The sum of the credit scores related to the identification information IS1, the credit history information IS2, the contract fulfilment ability information IS3, the human relationship information IS4, and the behavioral characteristic information IS5 corresponds to the credit score CS. For example, the credit-related information generation unit 127 calculates the credit score CS using the following formula 4.

[0098] The credit-related information generation unit 127 may estimate the credit score of the subject using a credit score estimation model. The credit score estimation model outputs a credit score in response to input of a disease risk score and status information. The credit score estimation model is a model trained using data of multiple subjects. For example, the credit score estimation model is stored in the memory unit 124.

[0099] FIG. 12 is a conceptual diagram showing an example of a credit score estimation model (credit score estimation model 170). FIG. 12 shows an example in which the credit score estimation model 170 includes a neural network. The credit score estimation model 170 includes an input layer, a first hidden layer, a second hidden layer, and an output layer. The input layer includes a plurality of nodes I. The first hidden layer includes a plurality of nodes H1. The second hidden layer includes a plurality of nodes H2. The output layer includes a plurality of nodes O. The credit score estimation model 170 may include hidden layers other than the first and second hidden layers.

[0100] The input layer receives the status information IS and the disease risk score RS. The input layer includes nodes I1, I2, I3, I4, I5, and I6. RS The node I1 receives the identification information IS1. The information included in the identification information IS1 is transmitted to a plurality of nodes (H 11 , ..., H 1a ) (a is a natural number). The credit history information IS2 is input to the node I2. The information contained in the credit history information IS2 is distributed to multiple nodes (H 21 , ..., H 2b ) (b is a natural number). Contract performance information IS3 is input to node I3. The information included in the contract performance information IS3 is distributed to multiple nodes (H 31 , ..., H 3c ) (c is a natural number). Human relationship information IS4 is input to node I4. The information contained in the human relationship information IS4 is distributed to multiple nodes (H 41 , ..., H 4d ) (d is a natural number). The behavioral feature information IS5 is input to the node I5. The information included in the behavioral feature information IS5 is distributed to multiple nodes (H 51 , ..., H 5e ) (e is a natural number).

[0101] Also, node I RSThe disease risk score RS is input to node I. RS In this embodiment, the status information IS, including the credit history information IS2, the contract performance information IS3, the human relationship information IS4, and the behavioral characteristic information IS5, is affected by the subject's disease risk. Therefore, the disease risk score RS is distributed to nodes H2, H3, H4, and H5, to which the credit history information IS2, the contract performance information IS3, the human relationship information IS4, and the behavioral characteristic information IS5 are respectively input.

[0102] Node H included in the first hidden layer 11 ~ Node H 1a The information contained in the identification information IS1 is distributed to each of the nodes H 11 ~ Node H 1a Each of these is a penalty coefficient p 11 ~ penalty coefficient p 1a Each of the penalty coefficients p 11 ~ penalty coefficient p 1a The sum of the penalty coefficients p is between 0 and 1. 11 ~ penalty coefficient p 1a is a node H included in the second hidden layer 21 is input to node H 21 is the input penalty coefficient p 11 ~ penalty coefficient p 1a Calculate the sum of the nodes H 21 is the calculated penalty coefficient p 11 ~ penalty coefficient p 1a The total of these is multiplied by a weight of 0.15 to calculate the credit score cs1. 21 outputs the calculated credit score cs1 to node O1 included in the output layer. Node O1 outputs the credit score cs1.

[0103] Node H included in the first hidden layer 21 ~ Node H 2b The information included in the credit history information IS2 is distributed to each of the nodes H 21 ~ Node H 2bA disease risk score RS is input to each of the nodes H. The disease risk score RS may be input only to nodes that are affected by disease risk. 21 ~ Node H 2b Each of these is calculated based on the penalty coefficient p 21 ~ penalty coefficient p 2b Each of the penalty coefficients p 21 ~ penalty coefficient p 2b The sum of the penalty coefficients p is between 0 and 1. 21 ~ penalty coefficient p 2b is a node H included in the second hidden layer 22 is input to node H 22 is the input penalty coefficient p 21 ~ penalty coefficient p 2b Calculate the sum of the nodes H 22 is the calculated penalty coefficient p 21 ~ penalty coefficient p 2b The total sum of these is multiplied by a weight of 0.35 to calculate the credit score cs2. 22 outputs the calculated credit score cs2 to node O2 included in the output layer. Node O2 outputs the credit score cs2.

[0104] Node H included in the first hidden layer 31 ~ Node H 3c The information included in the contract enforcement ability information IS3 is distributed to each of the nodes H 31 ~ Node H 3c A disease risk score RS is input to each of the nodes H. The disease risk score RS may be input only to nodes that are affected by disease risk. 31 ~ Node H 3c Each of these is calculated based on the penalty coefficient p 31 ~ penalty coefficient p 3c Each of the penalty coefficients p 31 ~ penalty coefficient p 3c The sum of the penalty coefficients p is between 0 and 1. 31 ~ penalty coefficient p 3cis a node H included in the second hidden layer 23 is input to node H 23 is the input penalty coefficient p 31 ~ penalty coefficient p 3c Calculate the sum of the nodes H 23 is the calculated penalty coefficient p 31 ~ penalty coefficient p 3c The total of these is multiplied by a weight of 0.20 to calculate the credit score cs3. 23 outputs the calculated credit score cs3 to node O3 included in the output layer. Node O3 outputs the credit score cs3.

[0105] Node H included in the first hidden layer 41 ~ Node H 4d The information included in the human relationship information IS4 is distributed to each of the nodes H 41 ~ Node H 4d A disease risk score RS is input to each of the nodes H. The disease risk score RS may be input only to nodes that are affected by disease risk. 41 ~ Node H 4d Each of these is calculated based on the penalty coefficient p 41 ~ penalty coefficient p 4d Each of the penalty coefficients p 41 ~ penalty coefficient p 4d The sum of the penalty coefficients p is between 0 and 1. 41 ~ penalty coefficient p 4d is a node H included in the second hidden layer 24 is input to node H 24 is the input penalty coefficient p 41 ~ penalty coefficient p 4d Calculate the sum of the nodes H 24 is the calculated penalty coefficient p 41 ~ penalty coefficient p 4d The total of these is multiplied by a weight of 0.05 to calculate the credit score cs4. 24 is the calculated credit score cs ４ The node O5 outputs the credit score cs４ Output.

[0106] Node H included in the first hidden layer 51 ~ Node H 5e The information included in the behavioral feature information IS5 is distributed to each of the nodes H 51 ~ Node H 5e A disease risk score RS is input to each of the nodes H. The disease risk score RS may be input only to nodes that are affected by disease risk. 51 ~ Node H 5e Each of these is calculated based on the penalty coefficient p 51 ~ penalty coefficient p 5e Each of the penalty coefficients p 51 ~ penalty coefficient p 5e The sum of the penalty coefficients p is between 0 and 1. 51 ~ penalty coefficient p 5e is a node H included in the second hidden layer 25 is input to node H 25 is the input penalty coefficient p 51 ~ penalty coefficient p 5e Calculate the sum of the nodes H 25 is the calculated penalty coefficient p 51 ~ penalty coefficient p 5e The total sum of these is multiplied by a weight of 0.25 to calculate the credit score cs5. 25 outputs the calculated credit score cs to node O included in the output layer. Node O outputs the credit score cs.

[0107] In the example of Figure 12, the credit-related information generation unit 127 calculates the sum of all credit scores (cs1, cs2, cs3, cs4, cs5) output from the credit score estimation model 170. The sum of credit score cs1, credit score cs2, credit score cs3, credit score cs4, and credit score cs5 corresponds to the credit score CS. The credit score CS is a numerical value between 0 and 1. The higher the creditworthiness of the subject, the higher the credit score CS. The lower the creditworthiness of the subject, the lower the credit score CS. The credit-related information generation unit 127 generates credit-related information including the value of the credit score CS.

[0108] The credit score estimation model 170 may be stored in an external storage device (not shown) constructed on a cloud, a server, or the like. In this case, the credit-related information generation unit 127 uses the credit score estimation model 170 via an interface (not shown) connected to the storage device. The credit score estimation model 170 is a machine learning model. For example, the credit score estimation model 170 is a model trained using a dataset that uses status information and disease risk scores for multiple subjects as explanatory variables and credit scores as a target variable as training data.

[0109] For example, the credit score estimation model 170 is generated by learning using a linear regression algorithm. For example, the credit score estimation model 170 is generated by learning using a support vector machine (SVM) algorithm. For example, the credit score estimation model 170 is generated by learning using a Gaussian process regression (GPR) algorithm. For example, the credit score estimation model 170 is generated by learning using a random forest (RF) algorithm. For example, the credit score estimation model 170 may be generated by unsupervised learning that classifies the credit-related tendencies of a subject depending on input status information and a disease risk score. There are no particular limitations on the algorithm used to train the credit score estimation model 170.

[0110] For example, the credit score estimation model 170 may be a machine learning model such as an incomplete heterogeneous variational autoencoder or a random forest. The incomplete heterogeneous variational autoencoder can estimate the credit score of a subject even if there are some missing data in the status information, gait index, physical ability score, etc.

[0111] For example, the credit-related information generating unit 127 generates credit-related information including a determination result indicating whether or not a loan can be provided to the subject based on the value of the credit score CS. In this case, if the credit score CS exceeds a preset threshold, the credit-related information generating unit 127 determines that a loan can be provided to the subject. On the other hand, if the credit score CS is below the preset threshold, the credit-related information generating unit 127 determines that a loan cannot be provided to the subject.

[0112] For example, the credit-related information generation unit 127 calculates preferential treatment for the loan to the subject based on the difference between the credit score CS and a preset threshold. In this case, the more the credit score CS exceeds the preset threshold and the greater the difference between the credit score CS and the threshold, the greater the degree of preferential treatment the credit-related information generation unit 127 will provide to the subject. On the other hand, if the credit score CS exceeds the preset threshold but the difference between the credit score CS and the threshold becomes smaller, the credit-related information generation unit 127 will reduce the degree of preferential treatment the subject will provide.

[0113] For example, the credit-related information generation unit 127 calculates preferential treatment for a loan to a target person according to changes in the credit score over time. In this case, if the credit score CS exceeds a preset threshold and the credit score CS tends to increase over time, the credit-related information generation unit 127 increases the degree of preferential treatment for the target person. On the other hand, if the credit score CS exceeds the preset threshold but tends to decrease over time, the credit-related information generation unit 127 decreases the degree of preferential treatment for the target person.

[0114] The output unit 129 (output means) outputs credit-related information including the credit score estimated by the credit-related information generation unit 127. For example, the output unit 129 outputs the credit-related information to a terminal device or server managed by a financial institution with which the subject is about to enter into a contract. For example, the output unit 129 outputs the credit-related information to a terminal device or server managed by a credit investigation company commissioned by the financial institution with which the subject is about to enter into a contract. For example, the output unit 129 may display the credit-related information on the screen of the subject's mobile terminal. For example, the output unit 129 may output the credit-related information to an external system or the like that uses the credit-related information. Because the credit-related information includes personal information, the output destinations are limited.

[0115] For example, the information providing device 12 is connected to an external system, such as a cloud or a server, via a mobile terminal (not shown) carried by the subject. The mobile terminal is a portable communication device. For example, the mobile terminal is a portable communication device with a communication function, such as a smartphone, a smart watch, or a mobile phone. For example, the information providing device 12 is connected to the mobile terminal via wireless communication. For example, the information providing 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 providing device 12 may conform to standards other than Bluetooth (registered trademark) or Wi-Fi (registered trademark). For example, the information providing device 12 may be connected to the mobile terminal via a wired connection, such as a cable. The credit-related information may be used by an application installed on the mobile terminal. For example, the mobile terminal executes processing using the credit-related information using application software, etc., installed on the mobile terminal.

[0116] (Operation) Next, the operation of the information providing system 1 will be described with reference to the drawings. The operation of the information providing device 12 included in the information providing system 1 will be described below. FIG. 13 is a flowchart for explaining an example of the operation of the information providing device 12. In the description of the processing according to the flowchart of FIG. 13, the components of the information providing device 12 will be described as the subject of the operations. The subject of the processing according to the flowchart of FIG. 13 may be the information providing device 12.

[0117] 13 , first, the acquiring unit 121 acquires time-series data of sensor data measured by the measurement device 10 mounted on the footwear of the subject (step S11). The sensor data includes accelerations in three axial directions and angular velocities around three axes.

[0118] 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. 14 ).

[0119] 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.

[0120] Next, the disease risk estimation unit 126 estimates the disease risk of the subject using the attribute data, gait index, and physical ability (step S14). When the disease risk is estimated without using the physical ability, the disease risk estimation unit 126 estimates the disease risk of the subject using the attribute data and gait index. The disease risk estimation unit 126 estimates a disease risk score of the subject. 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.

[0121] Next, the credit-related information generating unit 127 calculates a credit score for the subject using the estimated disease risk (step S15). The credit-related information generating unit 127 generates credit-related information including the calculated credit score.

[0122] Next, the output unit 129 outputs the credit-related information including the generated credit score (step S16). For example, the output unit 129 outputs the credit-related information to a terminal device or a server managed by a financial institution with which the subject is going to enter into a contract. For example, the output unit 129 outputs the credit-related information to an external system that uses the credit-related information. For example, the output unit 129 may display the credit-related information on the screen of the subject's mobile terminal.

[0123] [Gait Index Calculation Process] Next, the gait index calculation process (step S12 in FIG. 13 ) by the calculation unit 13 of the information provision device 12 will be described with reference to the drawings. FIG. 14 is a flowchart for explaining an example of the operation of the calculation unit 13. In describing the process according to the flowchart in FIG. 14 , the components of the calculation unit 13 will be described as the subject of operations. The subject of operations in the process according to the flowchart in FIG. 14 may be the information provision device 12 or the calculation unit 13.

[0124] 14, 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.

[0125] 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%.

[0126] 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.

[0127] (Application Example) Next, an application example according to this embodiment will be described with reference to the drawings. In this application example, the relationship between a business operator, a financial institution, a credit investigation company, a financial institution, and a target person is shown. FIG. 15 is a correlation diagram showing the relationship between a business operator, a financial institution, a credit investigation company, and a target person in this disclosure. A business operator is a business entity that provides services using the information provision system 1. A financial institution is an institution that provides financial services according to a contract. A financial institution uses a service using the information provision system 1. A credit investigation company is an organization that creates credit information for a target person under commission from a financial institution. In this application example, the credit investigation company also uses a service using the information provision system 1. A target person is an entity that is about to enter into a contract with a financial institution. In this application example, an example is given in which the target person is an individual. The target person may also be a corporation.

[0128] Below, an example will be given in which the credit-related information estimation model is used to estimate credit-related information. The credit-related information is used to create credit information used by financial institutions. For example, the credit-related information estimation model is optimized according to the type of business of the financial institution. For example, financial institutions include types such as banks, securities companies, insurance companies, and credit card companies. The credit-related information generated by the information providing device 12 is not limited to the following example, as long as it includes the credit-related information of the subject.

[0129] An operator provides a service to a financial institution or a credit bureau using the information provision system 1. Based on a contract concluded with the financial institution or credit bureau, the operator provides the financial institution or credit bureau with credit-related information corresponding to the subject's disease risk. The credit-related information includes a credit score estimated according to the subject's disease risk. The financial institution pays the operator a fee for using the service using the information provision system 1. If the subject's health checkup data is used to estimate the credit score, the financial institution obtains the health checkup data from the subject. The financial institution provides the operator with the obtained health checkup data of the subject. The contract between the financial institution or credit bureau and the operator clarifies rules regarding the handling of personal information and appropriate data management. The operator clearly explains that the credit-related information is for reference only and does not guarantee medical accuracy or completeness.

[0130] The financial institution is an institution with which the subject intends to enter into a contract. The financial institution will fully explain to the subject the details of its personal information protection policy and data management, and then obtain the subject's consent regarding the use of personal information and data. Furthermore, if there are any changes to the details of the personal information protection policy or data management, the financial institution will explain these to the subject and obtain the subject's consent. For example, the subject's consent may be obtained electronically. The financial institution enters into a contract with a business operator regarding the use of services using the information provision system 1. The financial institution pays the business operator a fee for using the information provision system 1. The financial institution receives credit-related information about the subject from the business operator. The financial institution creates credit information about the subject based on the credit-related information provided by the business operator. The financial institution refers to the content of the created credit information when considering whether to provide a loan to the subject, etc. If the financial institution commissions a credit investigation company to create the credit information, the financial institution does not need to receive credit-related information.

[0131] A credit investigation company is commissioned to create credit information and provides a service of conducting a credit investigation of a subject. A credit investigation company handles personal information and data related to a subject in response to a commission from a financial institution. The credit investigation company obtains consent from the subject regarding the use of the personal information and data through the financial institution. The credit investigation company may obtain consent directly from the subject regarding the use of the personal information and data. For example, consent from the subject is obtained electronically. The credit investigation company enters into a contract with a business operator regarding the use of services using the information provision system 1. The credit investigation company pays the business operator a fee for using the information provision system 1. The credit investigation company is provided with credit-related information of the subject from the business operator. The credit investigation company may obtain credit-related information via a financial institution. The credit investigation company creates credit information of the subject by referring to the content of the credit-related information provided by the business operator. The credit investigation company provides the created credit information to the financial institution.

[0132] The subject is an entity that is about to enter into a contract with a financial institution. The subject is loaned or provided with special insoles equipped with the measuring device 10 by a business operator that has a contract with the financial institution. The subject wears shoes equipped with the special insoles and carries a mobile terminal (not shown) that can communicate with the measuring device 10 while performing work. The mobile terminal uploads sensor data measured by the measuring device 10 to the business operator's cloud server. The sensor data uploaded to the cloud server is used to estimate disease risk and credit score. The sensor data measured as the subject walks is used to estimate the subject's credit score. The subject is provided with financial products and receives preferential interest rate loans depending on their credit score.

[0133] Terminal devices (not shown) used by financial institutions and credit bureaus download credit-related information, including credit scores, from the business's cloud server. Administrators at the financial institutions and credit bureaus refer to the credit-related information to create credit information about the subject. Financial institutions refer to the created credit information to consider loans, etc., to the subject. For example, administrators at financial institutions may periodically refer to the credit-related information to consider countermeasures in response to changes in credit practice information.

[0134] 16 shows an example in which credit-related information for Person A generated by the information providing device 12 is displayed on the screen of a terminal device 180 used at a financial institution with which the subject is about to enter into a contract. Credit-related information including a credit score CS optimized for the financial institution is displayed on the screen of the terminal device 180. In the example of FIG. 16, credit-related information including a credit score for each status information is displayed on the screen of the terminal device 180.

[0135] The screen of terminal device 180 displays the value of the credit score CS. The value of the credit score CS displayed on the screen of terminal device 180 is 0.73. The screen of terminal device 180 also displays the credit score for each information status. The credit score cs1 of identification information IS1 is 0.15. The credit score cs2 of credit history information IS2 is 0.25. The credit score cs3 of contract performance information IS3 is 0.10. The credit score cs4 of human relationship information IS4 is 0.03. The credit score cs5 of behavioral characteristic information IS5 is 0.20. Furthermore, the screen of terminal device 180 displays credit-related information corresponding to the value of the credit score CS. The screen of terminal device 180 displays credit-related information indicating whether a loan is available depending on the credit score, such as "The credit score exceeds the loan eligibility threshold (0.6 or higher) so a loan is available." Furthermore, the screen of the terminal device 180 displays credit-related information regarding the loan amount according to the credit score, such as "The loan limit according to the credit score is XXX million yen." A loan officer at the financial institution can refer to the credit-related information displayed on the screen of the terminal device 180 and consider whether to lend to the target person.

[0136] As described above, the information provision system of this embodiment includes a measurement device and an information provision device. The measurement device is installed on the subject's footwear. The measurement device measures acceleration and angular velocity. The measurement device generates sensor data using the measured acceleration and angular velocity. The measurement device transmits the generated sensor data to the information provision device. The information provision device includes an acquisition unit, a risk estimation unit, a credit-related information generation unit, and an output unit. The acquisition unit acquires sensor data including acceleration and angular velocity measured by a measurement device mounted on the footwear of the subject for whom credit information is to be created. The risk estimation unit uses the acquired sensor data to estimate a disease risk for each disease related to the subject. The credit-related information generation unit generates credit-related information based on the estimated disease risk related to the subject and pre-registered status information of the subject. The output unit outputs the generated credit-related information.

[0137] The information providing device of this embodiment estimates disease risk using sensor data measured by a measuring device mounted on the footwear of a subject for whom credit information is to be created. The information providing device of this embodiment generates credit-related information according to the subject's disease risk and status information. Therefore, according to this embodiment, credit-related information according to the subject's physical and mental state can be provided.

[0138] In one aspect of this embodiment, the risk estimation unit has a calculation unit and an estimation unit. The calculation unit calculates a gait index using sensor data. The estimation unit inputs data including the gait index calculated using the sensor data to a disease risk estimation model that outputs a disease risk score indicating the degree of disease risk for each disease in response to input of data including the gait index. The estimation unit estimates disease risk information corresponding to the disease risk score output from the disease risk estimation model. According to this aspect, disease risk information corresponding to the disease risk score can be estimated by inputting data including the gait index calculated using the sensor data into the disease risk estimation model.

[0139] In one aspect of this embodiment, the credit-related information generation unit estimates a credit score for each of the identification, credit history, contract performance, interpersonal relationships, and behavioral characteristics included in the status information. The credit-related information generation unit generates credit-related information including a credit score for each of the estimated identification, credit history, contract performance, interpersonal relationships, and behavioral characteristics. According to this aspect, it is possible to provide credit-related information including a credit score for each of the identification, credit history, contract performance, interpersonal relationships, and behavioral characteristics included in the status information.

[0140] In one aspect of this embodiment, the credit-related information generation unit calculates the credit score for the subject as the sum of values ​​obtained by multiplying the weights for each of the identification, credit history, ability to fulfill contracts, interpersonal relationships, and behavioral characteristics by a penalty coefficient. The penalty coefficient is a coefficient corresponding to at least one of the status information and the disease risk score. According to this aspect, a credit score whose value is adjusted according to the status information or the disease risk score can be calculated.

[0141] In one aspect of this embodiment, the credit-related information generation unit uses a credit score estimation model to estimate credit scores for each of identification, credit history, contract enforcement ability, interpersonal relationships, and behavioral characteristics. The credit score estimation model outputs a credit score in response to input of a disease risk score and status information. According to this aspect, a credit score can be calculated in accordance with the status information or the disease risk score.

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

[0143] In one aspect of this embodiment, the information providing device displays credit-related information optimized for the financial institution on a screen of a terminal device used by the financial institution that reviews credit information about the subject. According to this aspect, credit-related information estimated according to the subject's disease risk can be provided in an optimized manner for the financial institution.

[0144] In this embodiment, an example is given in which credit-related information including a credit score calculated using a subject's status information and disease risk score is provided. The credit-related information may be updated according to changes in disease risk over time. For example, the credit-related information may be updated according to the trend or amount of change in disease risk over a specific period, such as one month, three months, six months, or one year. For example, if the disease risk for a specific disease over a specific period is on a downward trend, the subject's health condition is estimated to be improving. In such a case, information indicating the subject's health condition is improving may be added to the credit-related information. For example, if the disease risk for a specific disease over a specific period is on an upward trend, the subject's health condition is estimated to be deteriorating. In such a case, information indicating the subject's health condition is deteriorating may be added to the credit-related information. For example, by accumulating the timing of updates to credit-related information for multiple subjects and applying machine learning to the accumulated update timing, the timing of credit information review can be estimated. Financial institutions and credit investigation companies can review a subject's credit information in response to updates to the subject's credit-related information.

[0145] When assessing a loan, factors such as creditworthiness, income, and repayment ability are emphasized. For example, income is affected by health status. Even if your current income is sufficient, if you have health risks, your income may decrease in the future. Also, even if your current income is insufficient, if you have no health risks and good healthy habits, you may be able to earn a sufficient income in the future. Therefore, if you can estimate your future health risks, financial institutions can appropriately assess whether to provide you with a loan.

[0146] With growing interest in healthcare, services that provide information according to gait are attracting attention. As in the present embodiment, gait can be analyzed using sensor data measured by sensors mounted on footwear such as shoes. Time-series data of the sensor data contains features associated with walking events related to physical conditions. If a subject's future disease risk can be estimated based on the features associated with walking events, as in the present embodiment, useful information can be provided for loan screening.

[0147] In addition, even if a person currently has sufficient income, if they have health risks, their income may decrease in the future. On the other hand, even if their current income is insufficient, if they have no health risks and good healthy habits, they may be able to earn a sufficient income in the future. According to this embodiment, credit-related information that reflects future health risks is provided, allowing financial institutions to appropriately review loan applications.

[0148] Second Embodiment Next, an information provision system according to a second embodiment will be described with reference to the drawings. The information provision system according to this embodiment estimates the personality of a subject using a personality estimation model. Credit-related information including information on the estimated personality of the subject is provided to financial institutions and the like.

[0149] (Configuration) Fig. 17 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 provision device 22. For example, the measurement device 20 is installed on the footwear of a subject whose credit-related information 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 provision device 22 will be described. Note that the main configuration of the information provision device 22 is the same as the configuration of the information provision device 12 of the first embodiment, and therefore, description thereof may be omitted.

[0150] 18 is a block diagram showing an example of the configuration of the information providing device 22. The information providing device 22 has an acquisition unit 221, a calculation unit 23, an estimation unit 24, a storage unit 224, a credit-related information generation unit 227, and an output unit 229. The calculation unit 23 and the estimation unit 24 constitute a risk estimation unit 25.

[0151] 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.

[0152] 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.

[0153] The acquisition unit 221 also acquires the subject's status information. The status information includes identification information, credit history information, contract fulfilment ability information, interpersonal relationship information, and behavioral characteristic information. The identification information includes information verifying that the subject's name is real and information indicating the subject's stability of status. Note that the information included in the status information is not limited to the information listed here. Of the status information listed here, the credit history information, contract fulfilment ability information, interpersonal relationship information, and behavioral characteristic information are affected by the subject's disease risk. The sum of the scores related to the identification information, credit history information, contract fulfilment ability information, interpersonal relationship information, and behavioral characteristic information corresponds to the credit score described below. The credit history information, contract fulfilment ability information, interpersonal relationship information, and behavioral characteristic information are multiplied by a penalty coefficient corresponding to the subject's status information and disease risk. For example, the status information is input via an input device (not shown). For example, the status information is input via a terminal device used by the administrator. For example, the status information is input via a mobile terminal used by the subject. For example, the status information may be stored in advance in the storage unit 224. The attribute data may be updated at any time in response to input by the subject or the administrator.

[0154] 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.

[0155] 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).

[0156] 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.

[0157] 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).

[0158] The storage unit 224 (storage means) has the same configuration as the storage unit 124 of the first embodiment. The storage unit 224 stores a physical ability estimation model. The physical ability estimation model estimates physical ability using physical ability feature amounts extracted from the gait waveform data. For example, the physical ability estimation model outputs an index related to physical ability (physical ability score) in response to input of the physical ability feature amounts extracted from the gait waveform data.

[0159] The storage unit 224 also stores a disease risk estimation model. The disease risk estimation model estimates disease risk using attribute data, a gait index, and a physical ability score. For example, the disease risk estimation model outputs an index related to disease risk (disease risk score) in response to input of attribute data, a gait index, and a physical ability score. For example, the disease risk estimation model may be a model that outputs a disease risk score in response to input of a gait index and attribute data, without using a physical ability score. In this case, the physical ability estimation model does not need to be used.

[0160] Furthermore, the storage unit 224 stores a personality estimation model. The personality estimation model outputs personality information of the subject in response to input of attribute data, gait index, and disease risk score. The personality estimation model may be a model that estimates personality information of the subject using physical information and status information in addition to attribute data, gait index, and disease risk score. Details of the personality estimation model will be described later.

[0161] The storage unit 224 stores the physical ability estimation model, disease risk estimation model, and personality estimation model trained for multiple test subjects (subjects). For example, the physical ability estimation model, disease risk estimation model, and personality estimation model may be stored in the storage unit 224 at the time of product shipment from a factory. The physical ability estimation model, disease risk estimation model, and personality estimation model may also be stored in the storage unit 224 at a timing such as during calibration before the subject uses the information providing device 22. For example, the physical ability estimation model, disease risk estimation model, and personality 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 personality estimation model may be accessed via an interface (not shown) connected to the storage device.

[0162] The storage unit 224 also stores attribute data and status information of the subject. The attribute data includes gender, date of birth (age), height, and weight. The status information includes identification information, credit history information, contract enforcement ability information, interpersonal relationship information, and behavioral characteristic information. The attribute data and status information may be updated at any time. Furthermore, the storage unit 224 may store health checkup data of the subject.

[0163] 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.

[0164] The credit-related information generation unit 227 acquires attribute data, gait index, disease risk score, and status information related to the subject. Similar to the credit-related information generation unit 127 of the first embodiment, the credit-related information generation unit 227 calculates the subject's credit score using the subject's disease risk score and status information. Explanation of the calculation of the credit score by the credit-related information generation unit 227 will be omitted.

[0165] Furthermore, the credit-related information generation unit 227 estimates personality information of the subject using the attribute data, gait index, and disease risk score. The credit-related information generation unit 227 inputs the subject's attribute data, gait index, and disease risk score into a personality estimation model. The credit-related information generation unit 227 estimates the subject's personality using the personality score output from the personality estimation model in response to the input of the subject's attribute data, gait index, and disease risk score.

[0166] 19 is a conceptual diagram showing an example of estimation of personality information using the personality estimation model 275. The example in FIG. 19 shows five personality elements. The five personality elements include an openness element, a conscientiousness element, an extroversion element, a agreeableness element, and a neuroticism element. Note that the personality elements may include other elements in addition to the five elements shown in FIG. 19.

[0167] The openness factor indicates the tendency to be open to new experiences. O is a score indicating the degree of the openness element. O The larger the score, the more open-minded the person is. O The smaller the score, the more closed-minded the person is. O People with a high PER are curious, have a good eye for aesthetics, and are full of ideas.

[0168] The conscientiousness element indicates a tendency to be responsible and serious. C is a score indicating the degree of the integrity element. C The higher the score, the more responsible and serious the person tends to be. C The smaller the score, the less responsible and less likely one is to be serious. C People with a high level of confidence are self-disciplined, conscientious, and proceed carefully.

[0169] The extroversion element is an element that indicates that interests and concerns are directed towards the outside world. E is a score indicating the degree of extroversion. EThe higher the score, the more likely one is to have interests and concerns directed towards the outside world. E The smaller the score, the less interest or concern one has in the outside world. E People with a large heart rate are proactive, sociable, and cheerful.

[0170] The cooperative element indicates that the individual values ​​harmony with others and has a cooperative tendency. A is a score indicating the degree of cooperativeness. A The larger the score, the more likely one is to value harmony with others and be cooperative. A The smaller the score, the more likely one is to neglect harmony with others and to be confrontational. A People with large heart rates are caring, kind, and devoted.

[0171] Neuroticism is a factor that indicates the tendency to self-control emotions and feelings. Neuroticism score P N is a score indicating the degree of neurotic elements. N The higher the score, the more likely one is to be able to control their emotions and feelings. N The smaller the score, the harder it is to control your emotions and feelings. N People with a high BMI are more tolerant of stress, less prone to anxiety, and able to maintain a stable mental state.

[0172] The personality estimation model 275 may be stored in an external storage device (not shown) constructed on a cloud, a server, or the like. In this case, the credit-related information generation unit 227 uses the personality estimation model 275 via an interface (not shown) connected to the storage device. The personality estimation model 275 is a machine learning model. For example, the personality estimation model 275 is a model trained using a dataset as training data in which attribute data, gait indexes, and disease risk scores related to multiple subjects are used as explanatory variables and personality-related scores are used as objective variables. Personality-related scores include an openness score, a conscientiousness score, an extroversion score, an agreeableness score, and a neuroticism score. Explanatory variables may include a physical ability score and status information.

[0173] For example, the personality estimation model 275 is generated by learning using a linear regression algorithm. For example, the personality estimation model 275 is generated by learning using a support vector machine (SVM) algorithm. For example, the personality estimation model 275 is generated by learning using a Gaussian process regression (GPR) algorithm. For example, the personality estimation model 275 is generated by learning using a random forest (RF) algorithm. For example, the personality estimation model 275 may be generated by unsupervised learning that classifies personality tendencies of a subject based on input attribute data, gait indexes, and disease risk scores. The algorithm for training the personality estimation model 275 is not particularly limited.

[0174] For example, the personality estimation model 275 may be a machine learning model such as an incomplete heterogeneous variational autoencoder or a random forest, etc. The incomplete heterogeneous variational autoencoder can estimate personality information of a subject even if there are some missing attribute data, gait indices, disease risk scores, etc.

[0175] The credit-related information generating unit 227 generates credit-related information including a credit score and character information. The credit-related information does not have to include a credit score. For example, the credit-related information generating unit 227 generates the credit-related information including the openness score P O , Conscientiousness score P C , extraversion score P E , cooperativeness score P A , and neurotic score P N For example, the credit-related information generating unit 227 adds each of the openness scores P O , Conscientiousness score P C , extraversion score P E , cooperativeness score P A , and neurotic score P NThe sum of the above may be added to the credit-related information. For example, the credit-related information generation unit 227 generates the credit-related information by applying the personality information to a preset document format. For example, the credit-related information generation unit 227 may generate the credit-related information using a large-scale language model.

[0176] The output unit 229 (output means) has the same configuration as the output unit 129 of the first embodiment. The output unit 229 outputs credit-related information including personality information generated by the credit-related information generation unit 227. For example, the output unit 229 outputs the credit-related information including personality information to an external system that uses the personality information. For example, the output unit 229 outputs the credit-related information including personality information to a terminal device (not shown) used by a financial institution. There are no particular limitations on the use of the output credit-related information including personality information. For example, the credit-related information including personality information is used by a financial institution to consider financing, etc. for the subject. For example, the financial institution can select financial products tailored to the subject's personality information based on the credit-related information including personality information obtained. In other words, the credit-related information generation unit 227 generates credit-related information that supports the financial institution's decision-making.

[0177] (Operation) Next, the operation of the information providing system 2 will be described with reference to the drawings. The operation of the information providing device 22 included in the information providing system 2 will be described below. FIG. 20 is a flowchart for explaining an example of the operation of the information providing device 22. In describing the processing according to the flowchart of FIG. 20, the components of the information providing device 22 will be described as the subject of the operations. The subject of the processing according to the flowchart of FIG. 20 may be the information providing device 22.

[0178] 20 , 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.

[0179] 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 ( FIG. 13 ) of the first embodiment.

[0180] 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.

[0181] 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.

[0182] Next, the credit-related information generating unit 227 calculates a credit score for the subject using the estimated disease risk (step S25). The credit-related information generating unit 227 generates credit-related information including the calculated credit score.

[0183] Next, the estimation unit 24 estimates personality information of the subject using the attribute data, the gait index, and the disease risk (step S26). The estimation unit 24 estimates personality information of the subject according to the personality score output from the personality estimation model 275 in response to the input of the attribute data, the gait index, and the disease risk.

[0184] Next, the output unit 229 outputs the credit-related information including the generated credit score (step S27). For example, the output unit 229 outputs the credit-related information to a terminal device or server managed by a financial institution with which the subject is going to enter into a contract. For example, the output unit 229 outputs the credit-related information to an external system that uses the credit-related information. For example, the output unit 229 may display the credit-related information on the screen of the subject's mobile terminal.

[0185] (Application Example) Next, an application example according to this embodiment will be described with reference to the drawings. In this application example, the relationship between a business operator, a financial institution, a credit investigation company, a financial institution, and a target person is shown. FIG. 21 is a correlation diagram showing the relationship between a business operator, a financial institution, a credit investigation company, and a target person in the present disclosure. A business operator is a business entity that provides services using the information provision system 2. A financial institution is an institution that provides financial services according to a contract. A financial institution uses a service using the information provision system 2. A credit investigation company is an organization that creates credit information for a target person under commission from a financial institution. In this application example, the credit investigation company also uses a service using the information provision system 2. A target person is an entity that is about to enter into a contract with a financial institution. In this application example, an example is given in which the target person is an individual. The target person may also be a corporation.

[0186] Below, an example will be given in which a personality estimation model is used to estimate personality information. For example, personality information is used by a financial institution to select financial products to recommend to a subject. For example, the personality estimation model is optimized according to the type of financial product offered by the financial institution. For example, financial products include investment trusts, stocks, corporate bonds, government bonds, insurance, and other products. The credit-related information generated by the information providing device 22 is not limited to the following example, as long as it includes the subject's personality information.

[0187] The business provides a service to a financial institution or a credit bureau using the information provision system 2. Based on a contract concluded with the financial institution or credit bureau, the business provides the financial institution or credit bureau with credit-related information corresponding to the subject's disease risk. The credit-related information includes a credit score estimated according to the subject's disease risk. The credit-related information provided to the financial institution also includes personality information about the subject. The financial institution pays the business a usage fee for the service using the information provision system 2. If the subject's health checkup data is used to estimate the credit score, the financial institution obtains the health checkup data from the subject. The financial institution provides the business with the obtained health checkup data of the subject. The contract between the financial institution or credit bureau and the business clarifies rules regarding the handling of personal information and appropriate data management. The business clearly explains that the credit-related information is for reference only and does not guarantee medical accuracy or completeness.

[0188] The financial institution is an institution with which the subject intends to enter into a contract. The financial institution will fully explain to the subject the details of its personal information protection policy and data management, and then obtain the subject's consent regarding the use of personal information and data. Furthermore, if there are any changes to the details of the personal information protection policy or data management, the financial institution will explain these to the subject and obtain the subject's consent. For example, the subject's consent may be obtained electronically. The financial institution enters into a contract with the business operator regarding the use of services using the information provision system 2. The financial institution pays the business operator a fee for using the information provision system 2. The financial institution receives credit-related information about the subject from the business operator. The financial institution creates credit information about the subject based on the credit-related information provided by the business operator. The financial institution will consider loans, etc. to the subject by referring to the content of the created credit information. If the creation of credit information is entrusted to a credit investigation company, the financial institution does not need to receive credit-related information.

[0189] A credit investigation company is commissioned to create credit information and provides a service of conducting a credit investigation of a subject. A credit investigation company handles personal information and data related to a subject in response to a commission from a financial institution. The credit investigation company obtains consent from the subject regarding the use of personal information and data through the financial institution. The credit investigation company may obtain consent directly from the subject regarding the use of personal information and data. For example, consent from the subject is obtained electronically. The credit investigation company enters into a contract with a business operator regarding the use of services using the information provision system 2. The credit investigation company pays the business operator a fee for using the information provision system 2. The credit investigation company is provided with credit-related information of the subject from the business operator. The credit investigation company may obtain credit-related information via a financial institution. The credit investigation company creates credit information for the subject by referring to the content of the credit-related information provided by the business operator. The credit investigation company provides the created credit information to the financial institution.

[0190] The subject is an entity that is about to enter into a contract with a financial institution. The subject is loaned or provided with special insoles equipped with a measuring device 20 by a business operator that has a contract with the financial institution. The subject wears shoes equipped with the special insoles and carries a mobile terminal (not shown) that can communicate with the measuring device 20 while performing work. The mobile terminal uploads sensor data measured by the measuring device 20 to the business operator's cloud server. The sensor data uploaded to the cloud server is used to estimate disease risk and credit score. The sensor data measured as the subject walks is used to estimate the subject's credit score. The subject is provided with financial products and receives preferential interest rate loans depending on their credit score.

[0191] Terminal devices (not shown) used by financial institutions and credit bureaus download credit-related information, including credit scores, from the business's cloud server. Administrators at the financial institutions and credit bureaus refer to the credit-related information to create credit information about the subject. Financial institutions refer to the created credit information to consider loans, etc., to the subject. For example, administrators at financial institutions may periodically refer to the credit-related information to consider countermeasures in response to changes in credit practice information.

[0192] 22 shows an example in which credit-related information for person B generated by information providing device 22 is displayed on the screen of terminal device 280 used at a financial institution with which the subject is about to enter into a contract. Credit-related information including personality information optimized for the financial institution's selection of financial products is displayed on the screen of terminal device 280. In the example of FIG. 22, estimated personality information for person B is displayed on the screen of terminal device 280.

[0193] A radar chart of the five personality elements is displayed on the screen of the terminal device 280. In the radar chart, the openness score P O and extraversion score P E Therefore, the screen of the terminal device 280 displays the message "Openness score P O and extraversion score P E Information based on the personality score is displayed, such as "You have high openness." O People with a high extroversion score are expected to be more tolerant of the risks posed by financial products. E Since people with high personality traits tend to be sociable, it is expected that they will be able to get loans from acquaintances even if they lack funds, and that many people will act as guarantors. Therefore, the screen of the terminal device 280 displays information about financial products according to the personality information, such as "Financial product Z, which has higher risk, is recommended." A financial institution's financial product selection officer can refer to the information displayed on the screen of the terminal device 280 and consider which financial product to recommend to the target person.

[0194] As described above, the information provision system of this embodiment includes a measurement device and an information provision device. The measurement device is attached to the subject's footwear. The measurement device measures acceleration and angular velocity. The measurement device generates sensor data using the measured acceleration and angular velocity. The measurement device transmits the generated sensor data to the information provision device. The information provision device includes an acquisition unit, a risk estimation unit, a credit-related information generation unit, and an output unit. The acquisition unit acquires sensor data including acceleration and angular velocity measured by a measurement device attached to the footwear of the subject for whom credit information is to be created. The risk estimation unit estimates a disease risk for each disease of the subject using the acquired sensor data. The credit-related information generation unit generates credit-related information based on the estimated disease risk of the subject and pre-registered status information of the subject. The credit-related information generation unit also estimates personality information of the subject using a personality estimation model. The personality estimation model outputs a personality score based on input of a disease risk score, a gait index, and attribute data. The credit-related information generation unit adds the estimated personality information of the subject to the credit-related information. The output unit outputs the generated credit-related information.

[0195] The information providing device of this embodiment estimates disease risk using sensor data measured by a measuring device mounted on the footwear of a subject for whom credit information is to be created. The information providing device of this embodiment generates credit-related information according to the disease risk and status information of the subject. The information providing device of this embodiment also estimates personality information of the subject. Therefore, according to this embodiment, credit-related information according to the personality information of the subject can be provided.

[0196] In one aspect of the present embodiment, the personality estimation model is a model trained using a machine learning technique. The personality estimation model includes an incomplete heterogeneous variational autoencoder. According to this aspect, even if there is some loss of data such as gait indicators, it is possible to estimate credit-related information including personality information of the subject.

[0197] Third Embodiment Next, an information providing device according to a third embodiment will be described with reference to the drawings. The information providing device according to this embodiment has a simplified configuration of the information providing device included in the information providing systems according to the first and second embodiments.

[0198] 23 is a block diagram showing an example of the configuration of the information providing device 30 according to the present disclosure. The information providing device 30 includes an acquisition unit 31, a risk estimation unit 35, a credit-related information generation unit 37, and an output unit 39.

[0199] The acquisition unit 31 acquires sensor data including acceleration and angular velocity measured by a measuring device mounted on the footwear of a subject for whom credit information is to be created. The risk estimation unit 35 uses the acquired sensor data to estimate a disease risk for each disease related to the subject. The credit-related information generation unit 37 generates credit-related information based on the estimated disease risk related to the subject and pre-registered status information of the subject. The output unit 39 outputs the generated credit-related information.

[0200] (Operation) Next, the operation of the information providing device 30 will be described with reference to the drawings. Fig. 24 is a flowchart for explaining an example of the operation of the information providing device 30. In the description of the processing according to the flowchart of Fig. 24, the components of the information providing device 30 will be described as the subject of the operations. The subject of the processing according to the flowchart of Fig. 24 may be the information providing device 30.

[0201] In FIG. 24, first, the acquisition unit 31 acquires sensor data including acceleration and angular velocity measured by a measurement device mounted on the footwear of a subject for whom credit information is to be created (step S31).

[0202] Next, the risk estimation unit 35 estimates the disease risk of each disease for the subject using the acquired sensor data (step S32).

[0203] Next, the credit-related information generating unit 37 generates credit-related information according to the estimated disease risk of the subject and the subject's pre-registered status information (step S33).

[0204] Next, the output unit 39 outputs the generated credit-related information (step S34).

[0205] As described above, the information providing device of this embodiment estimates disease risk using sensor data measured by a measuring device mounted on the footwear of a subject for whom credit information is to be generated. The information providing device of this embodiment generates credit-related information according to the subject's disease risk and status information. Therefore, according to this embodiment, credit-related information according to the subject's physical and mental state can be provided.

[0206] (Hardware) Next, a hardware configuration for executing the control and processing in the present disclosure will be described with reference to the drawings. Here, an information processing device 90 (computer) in Fig. 25 is given as an example of such a hardware configuration. The information processing device 90 in Fig. 25 is an example configuration for executing the control and processing in the present disclosure and does not limit the scope of the present disclosure.

[0207] As shown in Fig. 25 , 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. 25 , 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.

[0208] 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 the present disclosure. 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 the present disclosure.

[0209] 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.

[0210] 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.

[0211] 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.

[0212] 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.

[0213] 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.

[0214] 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.

[0215] The above is an example of a hardware configuration for enabling the control and processing in the present disclosure. The hardware configuration in Figure 25 is an example of a hardware configuration for executing the control and processing in the present disclosure and does not limit the scope of the present disclosure. A program that causes a computer to execute the control and processing in the present disclosure is also included in the scope of the present invention.

[0216] A program recording medium on which a program for executing the processing of this embodiment is recorded is also included within the scope of the present invention. For example, the program recording medium is a computer-readable, non-transitory recording medium. The recording medium can be, for example, an optical recording medium such as a CD (Compact Disc) or a DVD (Digital Versatile Disc). The recording medium may also be a semiconductor recording medium such as a USB (Universal Serial Bus) memory or an SD (Secure Digital) card. The recording medium may also be a magnetic recording medium such as a flexible disk, or other recording medium.

[0217] The components in the present disclosure may be combined in any manner. The components in the present disclosure may be realized by software. The components in the present disclosure may be realized by circuits.

[0218] 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.

[0219] 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 providing device comprising: an acquisition unit that acquires sensor data including acceleration and angular velocity measured by a measuring device mounted on footwear of a subject for whom credit information is to be created; a risk estimation unit that uses the acquired sensor data to estimate a disease risk for each disease related to the subject; a credit-related information generation unit that generates credit-related information according to the estimated disease risk of the subject and pre-registered status information of the subject; and an output unit that outputs the generated credit-related information. (Supplementary Note 2) The information providing device according to Supplementary Note 1, wherein the risk estimation unit includes: a calculation unit that calculates a gait index using the sensor data; and an estimation unit that inputs data including the gait index calculated using the sensor data into a disease risk estimation model that outputs a disease risk score indicating the degree of disease risk for each disease in response to input data including the gait index, and estimates disease risk information according to the disease risk score output from the disease risk estimation model. (Supplementary Note 3) The information providing device according to Supplementary Note 2, wherein the credit-related information generation unit estimates a credit score for each of the identification, credit history, ability to fulfill a contract, personal relationships, and behavioral characteristics included in the status information, and generates the credit-related information including the credit scores for each of the estimated identification, credit history, ability to fulfill a contract, personal relationships, and behavioral characteristics. (Supplementary Note 4) The information providing device according to Supplementary Note 3, wherein the credit-related information generation unit calculates, as the credit score for the subject, a sum of values ​​obtained by multiplying a weight for each of the identification, credit history, ability to fulfill a contract, personal relationships, and behavioral characteristics by a penalty coefficient corresponding to at least one of the status information and the disease risk score. (Appendix 5) The credit-related information generation unit of the information providing device described in Appendix 3 estimates the credit scores for each of the identification, the credit history, the ability to fulfill contracts, the interpersonal relationships, and the behavioral characteristics using a credit score estimation model that outputs the credit score in response to input of the disease risk score and the status information.(Supplementary Note 6) The information provision device according to Supplementary Note 5, wherein the credit-related information generation unit estimates personality information of the subject using a personality estimation model that outputs a score related to personality in response to input of the disease risk score, the gait index, and attribute data, and generates the credit-related information including the estimated personality information of the subject. (Supplementary Note 7) The information provision device according to Supplementary Note 6, wherein the disease risk estimation model, credit score estimation model, and personality estimation model are models trained using a machine learning technique, and the disease risk estimation model and the personality estimation model include an incomplete heterogeneous variational autoencoder. (Supplementary Note 8) An information provision system comprising: the information provision device according to any one of Supplements 1 to 6; and the measurement device, wherein the measurement device is attached to footwear of the subject, measures acceleration and angular velocity, generates the sensor data using the measured acceleration and angular velocity, and transmits the generated sensor data to the information provision device, and the information provision device displays the credit-related information optimized for the financial institution on a screen of a terminal device used by the financial institution that reviews the credit information related to the subject. (Supplementary Note 9) An information provision method in which a computer acquires sensor data including acceleration and angular velocity measured by a measuring device mounted on the footwear of a subject for whom credit information is to be created, estimates a disease risk for each disease related to the subject using the acquired sensor data, generates credit-related information according to the estimated disease risk for the subject and pre-registered status information of the subject, and outputs the generated credit-related information. (Supplementary Note 10) An information provision method according to Supplementary Note 9, in which a 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 11) The information provision method of Supplementary Note 10, wherein a computer estimates a credit score for each of the identification, credit history, ability to fulfill contracts, interpersonal relationships, and behavioral characteristics included in the status information, and generates the credit-related information including the credit scores for each of the estimated identification, credit history, ability to fulfill contracts, interpersonal relationships, and behavioral characteristics. (Supplementary Note 12) The information provision method of Supplementary Note 11, wherein a computer calculates, as the credit score for the subject, the sum of values ​​obtained by multiplying weights for each of the identification, credit history, ability to fulfill contracts, interpersonal relationships, and behavioral characteristics by a penalty coefficient corresponding to at least one of the status information and the disease risk score. (Supplementary Note 13) The information provision method of Supplementary Note 11, wherein a computer estimates the credit score for each of the identification, credit history, ability to fulfill contracts, interpersonal relationships, and behavioral characteristics using a credit score estimation model that outputs the credit score in response to input of the disease risk score and the status information. (Supplementary Note 14) The information providing method according to Supplementary Note 10, wherein a computer estimates personality information of the subject using a personality estimation model that outputs a score related to personality in response to input of the disease risk score, the gait index, and attribute data, and generates the credit-related information including the estimated personality information of the subject. (Supplementary Note 15) A computer-readable non-transitory recording medium having recorded thereon a program that causes a computer to execute the following processes: acquiring sensor data including acceleration and angular velocity measured by a measuring device mounted on footwear of the subject for which credit information is to be created, estimating a disease risk for each disease of the subject using the acquired sensor data, generating credit-related information in accordance with the estimated disease risk of the subject and pre-registered status information of the subject, and outputting the generated credit-related information.(Supplementary Note 16) The computer-readable non-transitory recording medium according to Supplementary Note 15, having recorded thereon a program that causes a computer to execute the following processes: a process of calculating a gait index using the sensor data, a process of inputting data including the gait index 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 a process of estimating disease risk information according to the disease risk score output from the disease risk estimation model. (Supplementary Note 17) The computer-readable non-transitory recording medium according to Supplementary Note 16, having recorded thereon a program that causes a computer to execute the following processes: a process of estimating credit scores for each of the identification, credit history, ability to fulfill contracts, interpersonal relationships, and behavioral features included in the status information, and a process of generating the credit-related information including the credit scores for each of the estimated identification, credit history, ability to fulfill contracts, interpersonal relationships, and behavioral features. (Supplementary Note 18) The computer-readable non-transitory recording medium according to Supplementary Note 17, having recorded thereon a program that causes a computer to execute a process of calculating the credit score for the subject as the sum of values ​​obtained by multiplying weights for each of the identification, credit history, ability to fulfill contracts, human relationships, and behavioral characteristics by a penalty coefficient corresponding to at least one of the status information and the disease risk score. (Supplementary Note 19) The computer-readable non-transitory recording medium according to Supplementary Note 17, having recorded thereon a program that causes a computer to execute a process of estimating the credit score for each of the identification, credit history, ability to fulfill contracts, human relationships, and behavioral characteristics using a credit score estimation model that outputs the credit score in response to input of the disease risk score and the status information. (Appendix 20) A computer-readable non-transitory recording medium according to Appendix 16, having recorded thereon a program that causes a computer to execute the following processes: a process of estimating personality information of the subject using a personality estimation model that outputs a score related to personality in response to input of the disease risk score, the gait index, and attribute data; and a process of generating the credit-related information including the estimated personality information of the subject.

[0220] 1, 2 Information provision system 10, 20 Measurement device 12, 22 Information provision device 13, 23 Calculation unit 14, 24 Estimation unit 15, 25 Risk estimation unit 30 Information provision device 31 Acquisition unit 35 Risk estimation unit 37 Credit-related information 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 Credit-related information generation unit 129, 229 Output unit

Claims

1. An acquisition unit that acquires sensor data, including acceleration and angular velocity, measured by a measuring device mounted on the footwear of a person for whom credit information is to be created, A risk estimation unit that uses the acquired sensor data to estimate the disease risk for each disease related to the subject, A credit-related information generation unit generates credit-related information corresponding to the estimated disease risk of the subject and the status information of the subject that has been registered in advance. An information providing device comprising: an output unit that outputs the generated credit-related information.

2. The risk estimation unit, A calculation unit that calculates a gait index using the aforementioned sensor data, The information providing device according to claim 1, comprising: 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.

3. The aforementioned credit-related information generation unit, A credit score is estimated for each of the following included in the status information: identity verification, credit history, contract enforcement ability, interpersonal relationships, and behavioral characteristics. The information providing device according to claim 2, which generates the credit-related information including the estimated credit score relating to each of the following: the estimated identity, the credit history, the ability to execute contracts, the interpersonal relationships, and the behavioral characteristics.

4. The aforementioned credit-related information generation unit, The information providing device according to claim 3, which calculates the credit score for the subject as the sum of values ​​obtained by multiplying the weights for each of the aforementioned identification, credit history, contract execution ability, interpersonal relationships, and behavioral characteristics by a penalty coefficient corresponding to at least one of the status information and the disease risk score.

5. The aforementioned credit-related information generation unit, The information providing device according to claim 3, which estimates the credit score for each of the following: identity verification, credit history, contract execution ability, interpersonal relationships, and behavioral characteristics, using a credit score estimation model that outputs the credit score in response to input of the disease risk score and the status information.

6. The aforementioned credit-related information generation unit, Using a personality estimation model that outputs a personality score in response to the input of the disease risk score, the gait index, and attribute data, the personality information of the subject is estimated. The information providing device according to claim 5, which generates credit-related information including the estimated personality information of the subject.

7. The disease risk estimation model, the credit score estimation model, and the personality estimation model are This is a model trained using machine learning techniques. The disease risk estimation model, the credit score estimation model, and the personality estimation model are The information providing device according to claim 6, comprising an incomplete heterogeneous variational autoencoder.

8. An information providing device according to any one of claims 1 to 6, The device includes the aforementioned measuring device, The aforementioned measuring device is The sensor is installed on the footwear of the subject, measures acceleration and angular velocity, generates sensor data using the measured acceleration and angular velocity, and transmits the generated sensor data to the information providing device. The aforementioned information providing device is An information provision system that displays the credit-related information optimized for the financial institution on the screen of a terminal device used by a financial institution that examines the credit information concerning the aforementioned subject.

9. Computers Sensor data, including acceleration and angular velocity, is acquired by measuring devices installed in the footwear of the person for whom credit information is to be created. Using the acquired sensor data, the disease risk for each disease related to the subject is estimated. Credit-related information is generated based on the estimated disease risk of the subject and the status information of the subject that has been registered in advance. An information provision method for outputting the generated credit-related information.

10. A process to acquire sensor data, including acceleration and angular velocity, measured by a measuring device installed in the footwear of a person for whom credit information is to be created, Using the acquired sensor data, a process is performed to estimate the disease risk for each disease related to the subject, A process for generating credit-related information corresponding to the estimated disease risk of the subject and the status information of the subject that has been registered in advance, A program that causes a computer to perform a process to output the generated credit-related information.