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

Abstract

To provide an advertisement according to a disease risk of a subject leading a daily life, provided is an information providing device comprising: an acquisition unit that acquires time-series data of sensor data measured by a measurement device mounted on footwear of a subject and an activity time period of the subject; a risk estimation unit that estimates a disease risk of the subject by using the acquired sensor data; an advertisement-related information generation unit that selects advertisement information including an advertisement according to the subject by using the estimated disease risk of the subject and pre-registered attribute data of the subject, and generates advertisement-related information in which the selected advertisement information and the activity time period of the subject are associated with each other; and an output unit that outputs the generated advertisement-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] With growing interest in healthcare, services that provide information tailored to a user's health condition are attracting attention. If advertisements tailored to a user's health condition could be provided, the user could be given the opportunity to access products and services that are beneficial to the user. Patent Literature 1 discloses a life support device that displays advertisements tailored to the user's status, such as their behavior, stress level, and fatigue level. The device in Patent Literature 1 searches for advertisements suited to the user's status based on user status information. The device in Patent Literature 1 delivers the searched advertisements to the user.

[0003] Japanese Patent Application Laid-Open No. 2001-344352

[0004] The method of Patent Document 1 requires the user to wear multiple devices, such as a pulse wave sensor, a body temperature sensor, a GSR (Galvanic Skin Reflex) electrode, and an acceleration sensor, in order to recognize the user's condition. It is difficult for the user to go about their daily life while wearing such devices. Therefore, the method of Patent Document 1 cannot provide advertisements that correspond to the user's disease risk in their daily life.

[0005] An object of the present disclosure is to provide an information providing device, an information providing system, an information providing method, and a recording medium that can provide advertisements according to the disease risk of subjects living their daily lives.

[0006] An information providing device of one embodiment of the present disclosure includes an acquisition unit that acquires time series data of sensor data measured by a measuring device mounted on the footwear of a subject and the subject's activity time periods; a risk estimation unit that estimates the subject's disease risk using the acquired sensor data; an advertisement-related information generation unit that selects advertising information including advertisements appropriate to the subject using the estimated disease risk of the subject and pre-registered attribute data of the subject, and generates advertisement-related information in which the selected advertising information is associated with the subject's activity time periods; and an output unit that outputs the generated advertisement-related information.

[0007] In one aspect of the information provision method of the present disclosure, time series data of sensor data measured by a measuring device mounted on the subject's footwear and the subject's activity time periods are acquired, the acquired sensor data is used to estimate the subject's disease risk, the estimated subject's disease risk and pre-registered attribute data of the subject are used to select advertising information including advertisements appropriate to the subject, advertising-related information is generated in which the selected advertising information is associated with the subject's activity time periods, and the generated advertising-related information is output.

[0008] A program according to one aspect of the present disclosure causes a computer to perform the following processes: calculating a gait index using sensor data; inputting data including the gait index calculated using the sensor data into a disease risk estimation model that outputs a disease risk score indicating the degree of disease risk for each disease in response to input of data including the gait index; and estimating disease risk information according to the disease risk score output from the disease risk estimation model.

[0009] According to the present disclosure, it is possible to provide an information providing device, an information providing system, an information providing method, and a recording medium that can provide advertisements according to the disease risk of subjects living their daily lives.

[0010] 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 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 estimation of a disease risk score by a disease risk estimation model according to the present disclosure. FIG. 10 is a flowchart for explaining an example of operation of an information providing device according to the present disclosure. FIG. 11 is a flowchart for explaining an example of gait index calculation processing according to the present disclosure. FIG. 12 is a conceptual diagram showing a correlation diagram according to an application example of the present disclosure. FIG. 13 is a conceptual diagram showing an example of displaying advertisement-related information according to an application example of the present disclosure. FIG. 14 is a conceptual diagram showing an example of providing advertisements according to an application example of 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. 1 is a flowchart illustrating an example of an operation of an information providing device according to the present disclosure;

[0011] 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.

[0012] First Embodiment First, 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 relating to foot movements measured as the user (subject) to whom an advertisement is to be provided walks. The information provision system according to this embodiment estimates the subject's disease risk using the acquired sensor data. The information provision system according to this embodiment provides advertisement providers with information relating to products and services corresponding to the estimated disease risk.

[0013] (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 subject to whom an advertisement is to be provided. For example, the functions of the information provision device 12 are implemented in 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 in 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.

[0014] [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.

[0015] 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.

[0016] 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.

[0017] 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.

[0018] 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.

[0019] 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.

[0020] 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.

[0021] 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.

[0022] 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.

[0023] 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 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 in 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). The sensor data includes at least acceleration data converted to digital data and angular velocity data converted to digital data. The acceleration data includes acceleration vectors in the 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 to the acceleration data and angular velocity data, such as correction for mounting errors, temperature correction, and linearity correction.

[0024] 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.

[0025] 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.

[0026] 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.

[0027] 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.

[0028] [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 acquisition unit 121, a waveform processing unit 122, a gait index calculation unit 123, a storage unit 124, a physical ability estimation unit 125, a disease risk estimation unit 126, an advertisement information generation unit 127, and an output unit 129. The waveform processing unit 122, the gait index calculation unit 123, the physical ability estimation unit 125, and the disease risk estimation unit 126 constitute the risk estimation unit 15. The waveform processing unit 122 and the gait index calculation unit 123 constitute the calculation unit 13. The physical ability estimation unit 125 and the disease risk estimation unit 126 constitute the estimation unit 14. For example, the information providing device 12 is built on a server or cloud used by a business operator. The business operator is an entity that provides advertising-related information of a target person to an advertising company in accordance with the terms of a pre-established contract.

[0029] 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.

[0030] 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.

[0031] The acquisition unit 121 also acquires the subject's activity time period. The activity time period includes the time period during which the subject uses a mobile device (not shown). The acquisition unit 121 acquires the activity time period in response to the subject's operation of the mobile device. The acquisition unit 121 acquires the activity time period in response to the use of application software (hereinafter referred to as "app") linked to the information providing device 12. For example, the activity time period is set to the time period during which the subject actually operates the mobile device. For example, the activity time period is set to the time period during which the subject uses the mobile device that is most frequently used each day. For example, the activity time period may be set in accordance with the definition of the Japan Meteorological Agency. The Japan Meteorological Agency's definition includes time periods such as pre-dawn (12:00-3:00 AM), dawn (3:00-6:00 AM), morning (6:00-9:00 AM), early afternoon (9:00-12:00 PM), early afternoon (12:00-3:00 PM), evening (3:00-6:00 PM), early evening (6:00-9:00 PM), and late evening (9:00-12:00 AM). The activity time period may also be set in accordance with standards other than the definition of the Japan Meteorological Agency.

[0032] The acquisition unit 121 may be configured to acquire the status of access to advertisements by the target person. For example, the acquisition unit 121 acquires the status of access for each category (also referred to as an advertising category) of products or services advertised in advertisements frequently accessed by the target person. For example, the acquisition unit 121 acquires the status of access for each advertising category accessed by the target person more than a predetermined number of times within a predetermined period. For example, the acquisition unit 121 may acquire a purchase history for each advertising category frequently accessed by the target person. The status of access to advertisements by the target person is used to narrow down the advertising categories of advertisements to be provided to the target person.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] 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.

[0037] 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.

[0038] 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).

[0039] 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).

[0040] 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.

[0041] 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.

[0042] 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.

[0043] 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.

[0044] 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.

[0045] 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.

[0046] 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.

[0047] 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.

[0048] The memory unit 124 (storage means) stores a physical ability estimation model (described below). The physical ability estimation model estimates physical ability using physical ability feature values ​​extracted from gait waveform data. For example, physical ability includes at least one of grip strength, dynamic balance, lower limb muscle strength, mobility, and static balance. Physical ability may also 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 values ​​extracted from gait waveform data. If physical ability is not used in estimating disease risk, the physical ability estimation model can be omitted.

[0049] 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.

[0050] The memory unit 124 also stores an advertising information estimation model (described later). The advertising information estimation model uses the subject's attribute data and disease risk score to select advertising information regarding advertisements to be provided to the subject. The advertising information is associated with the user's attribute data and activity time period. For example, the advertising information is information that prompts the subject to make a decision regarding disease risk. For example, the advertising information is information that supports the subject in making a decision regarding disease risk. For example, the advertising information is information optimized for the subject's attributes. For example, the advertising information estimation model outputs advertising information in response to input of attribute data and disease risk score. Details of the advertising information estimation model will be described later.

[0051] The storage unit 124 stores the physical ability estimation model, disease risk estimation model, and advertising information estimation model trained for multiple subjects. Hereinafter, when there is no need to distinguish between the physical ability estimation model, disease risk estimation model, and advertising information estimation model, they will be referred to as estimation models. For example, the estimation models may be stored in the storage unit 124 when the product is shipped from the factory. The estimation models may also be stored in the storage unit 124 when the information providing device 12 is calibrated. For example, estimation models stored in a storage device (not shown) such as an external server may be used. In this case, the estimation models may be accessible via an interface (not shown) connected to the storage device.

[0052] The memory unit 124 also stores the subject's attribute data and activity time periods. The attribute data includes gender, date of birth (age), height, and weight. The attribute data may be updated at any time. The activity time periods include time periods when the subject used a mobile device. Furthermore, the memory unit 124 may store the subject's health checkup data. The health checkup data can be a factor in improving the accuracy of estimating disease risk scores and advertising 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.

[0053] 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.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0073] The dynamic balance estimation model 152 outputs a dynamic balance score S2 related to dynamic balance in response to input of the feature quantities B1 to B5. For example, in addition to the feature quantities B1 to B5, attribute data such as height is input to the dynamic balance estimation model 152. 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 physical ability feature quantities for estimating dynamic balance. For example, the dynamic balance estimation model 152 may be a model that outputs FR distance in response to input of the feature quantities B1 to B5.

[0074] The lower limb muscle strength estimation model 153 outputs a lower limb muscle strength score S3 related to lower limb muscle strength in response to input of the feature amounts C1 to C4. For example, in addition to the feature amounts C1 to C4, attribute data such as age is input to the lower limb muscle strength estimation model 153. 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 physical ability feature amounts 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 amounts C1 to C4.

[0075] The mobility estimation model 154 outputs a mobility score S4 related to mobility in response to input of the feature quantities D1 to D6. For example, in addition to the feature quantities D1 to D6, attribute data such as age is input to the mobility estimation model 154. 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 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.

[0076] The static balance estimation model 155 outputs a static balance score S5 related to static balance in response to input of the feature quantities E1 to E7. For example, in addition to the feature quantities E1 to E7, attribute data such as age and height are input to the static balance estimation model 155. 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 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.

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

[0078] For example, the physical ability estimation model 150 may be generated by learning using a linear regression algorithm. For example, the physical ability estimation model 150 may be generated by learning using a support vector machine (SVM) algorithm. For example, the physical ability estimation model 150 may be generated by learning using a Gaussian process regression (GPR) algorithm. For example, the physical ability estimation model 150 may be generated by learning using a random forest (RF) algorithm. For example, the physical ability estimation model 150 may be generated by unsupervised learning that classifies the physical 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.

[0079] 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).

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

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

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

[0083] The disease risk estimation model 160 may be stored in an external storage device (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.

[0084] For example, the disease risk estimation model 160 is generated by learning using a linear regression algorithm. For example, the disease risk estimation model 160 is generated by learning using a support vector machine (SVM) algorithm. For example, the disease risk estimation model 160 is generated by learning using a Gaussian process regression (GPR) algorithm. For example, the disease risk estimation model 160 is generated by learning using a random forest (RF) algorithm. For example, the disease risk estimation model 160 may be generated by unsupervised learning that classifies the disease risk of 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.

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

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

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

[0088] In the first method, the disease risk estimation unit 126 calculates the disease risk score by calculating the ratio of the average annual number of medical receipts issued for a 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.

[0089] In the second method, the disease risk estimation unit 126 calculates the disease risk score under the assumption that the average annual number of medical receipts issued for a specific disease follows a Poisson distribution. In the second method, the disease risk estimation unit 126 calculates the disease risk score as the ratio of the probability mass function P(X=k) of the average annual number of medical receipts issued for a 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:

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

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

[0092] The advertisement information generation unit 127 acquires the subject's disease risk score, attribute data, and activity time zone. The advertisement information generation unit 127 uses the disease risk score, attribute data, and activity time zone to select advertisement information indicating advertisements tailored to the subject's user segment. The advertisement information generation unit 127 generates advertisement-related information in which the selected advertisement information is associated with the subject's activity time zone. For example, the advertisement information generation unit 127 selects at least one piece of advertisement information output from a model (advertisement information estimation model) that outputs advertisement information tailored to the subject's user segment in response to input of the disease risk score and attribute data. For example, the advertisement information generation unit 127 may be configured to select at least one piece of advertisement information from a table in which the disease risk score, attribute data, and advertisement information are associated. In selecting the advertisement information, the advertisement information generation unit 127 may use a gait index, physical ability, and activity time zone in addition to the disease risk score and attribute data activity.

[0093] FIG. 11 is a conceptual diagram showing an example of estimating advertising information using the advertising information estimation model 170. At least attribute data and a disease risk score are used to estimate advertising information. The advertising information generation unit 127 inputs attribute data and a disease risk score related to the subject to the advertising information estimation model 170. The advertising information estimation model 170 outputs advertising information according to the attribute data and disease risk score related to the subject. The advertising information includes information related to the advertisement of at least one product or service. For example, the advertising information includes information related to the field of at least one product or service. For example, the advertising information includes information related to the advertisement provider of the advertisement related to at least one product or service. The advertising information estimation model 170 outputs advertising information according to the attribute data, disease risk score, and activity time period related to the subject.

[0094] For example, a gait index and physical ability may be used to estimate the advertising information. In this case, the advertising information generation unit 127 inputs the gait index and physical ability to the advertising information estimation model 170 in addition to the attribute data and disease risk score related to the subject. The advertising information estimation model 170 outputs advertising information according to the attribute data, disease risk score, gait index, and physical ability related to the subject. For example, an activity time zone may be used to estimate the advertising information. In this case, the advertising information generation unit 127 inputs the activity time zone to the advertising information estimation model 170 in addition to the attribute data and disease risk score related to the subject.

[0095] For example, the advertising information estimation model 170 outputs advertising information tailored to a subject at risk of a specific disease according to the disease risk score. In such a case, the advertising information estimation model 170 outputs advertising information related to products and services such as medicines, equipment, and gyms that can treat or alleviate the specific disease. The advertising information generation unit 127 selects at least one of the advertising information output from the advertising information estimation model 170. The selected advertising information is information that prompts the subject to make a decision regarding disease risk. A subject at risk of a specific disease can refer to the selected advertising information to find products and services that can treat or alleviate the specific disease.

[0096] For example, the advertising information estimation model 170 outputs advertising information according to the stage of progression of a specific disease, depending on the disease risk score. In such a case, the advertising information estimation model 170 outputs advertising information about products and services such as medicines, equipment, and gyms according to the stage of progression of the specific disease. The advertising information generation unit 127 selects at least one of the advertising information output from the advertising information estimation model 170. The selected advertising information is information that prompts the subject to make a decision according to the stage of progression of the specific disease. A subject at risk of a specific disease can refer to the selected advertising information to find products and services according to the stage of progression of their specific disease.

[0097] For example, the advertising information estimation model 170 outputs advertising information according to the type of specific disease according to the disease risk score. In such a case, the advertising information estimation model 170 outputs advertising information related to products and services such as medicines, equipment, gyms, and distribution information according to the type of specific disease. The advertising information generation unit 127 selects at least one of the advertising information output from the advertising information estimation model 170. The selected advertising information is information that prompts the subject to make a decision according to the type of specific disease. For example, if a specific disease indicating a stress-related abnormality is predicted for the subject, advertising information including distribution information related to dramas and movies that are expected to have a stress-relieving effect is output. A subject at risk of a specific disease can refer to the selected advertising information to find products and services according to the type of their specific disease.

[0098] For example, the advertising information estimation model 170 outputs advertising information appropriate for a healthy subject who is not at risk of a specific disease, according to the disease risk score. In such a case, the advertising information estimation model 170 outputs advertising information related to products and services that contribute to health promotion, such as health foods, daily necessities, cosmetics, and training gyms. The advertising information generation unit 127 selects at least one of the advertising information output from the advertising information estimation model 170. The selected advertising information is information that encourages the subject to make decisions that contribute to health promotion. A healthy subject who is not at risk of a specific disease can refer to products and services that contribute to health promotion by referring to the selected advertising information.

[0099] For example, the advertising information estimation model 170 outputs advertising information optimized according to the attributes of the target person based on the attribute data. In such a case, the advertising information estimation model 170 outputs advertising information related to products and services optimized according to the target person's age, sex, height, and weight. The advertising information generation unit 127 selects at least one of the advertising information output from the advertising information estimation model 170. The selected advertising information is information optimized for the target person's attributes. By referring to the selected advertising information, the target person can refer to products and services optimized according to their own attributes.

[0100] The advertising information 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 advertising information generation unit 127 uses the advertising information estimation model 170 via an interface (not shown) such as an API (Application Programming Interface) connected to the storage device. The advertising information estimation model 170 is a machine learning model. For example, the advertising information estimation model 170 is a model trained using a dataset that uses disease risk scores and attribute data for multiple subjects as explanatory variables and advertising information as a target variable as training data.

[0101] For example, the advertising information estimation model 170 is generated by learning using a linear regression algorithm. For example, the advertising information estimation model 170 is generated by learning using a support vector machine (SVM) algorithm. For example, the advertising information estimation model 170 is generated by learning using a Gaussian process regression (GPR) algorithm. For example, the advertising information estimation model 170 is generated by learning using a random forest (RF) algorithm. For example, the advertising information estimation model 170 may be generated by unsupervised learning that classifies a subject's credit tendency according to input status information and a disease risk score. There are no particular limitations on the algorithm used to train the advertising information estimation model 170.

[0102] For example, the advertisement information 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 select advertisement information for a target person even if there are some missing data in the status information, gait index, physical ability score, etc.

[0103] The advertising information generation unit 127 may narrow down the advertising field of the advertisements provided to the target person according to the target person's access status to the advertisements. For example, if the advertising information selected by the advertising information estimation model 170 includes advertisements in multiple advertising fields, the advertising information generation unit 127 narrows down the advertising field according to the target person's access status to the advertisements. For example, the advertising information generation unit 127 narrows down the advertisements to advertisements in advertising fields that the target person frequently accesses. In this way, advertisements for products and services in fields that are estimated to be of interest to the target person can be provided to the target person. If advertisements for products and services that the target person is interested in can be provided, the target person is more likely to purchase the advertised products and services.

[0104] For example, the advertisement information generation unit 127 narrows down the advertisements to those in an advertising field that the target has accessed more than a predetermined number of times within a predetermined period. For example, the advertisement information generation unit 127 narrows down the advertisements to those in an advertising field that the target has accessed more than a predetermined number of times within a predetermined period and that is for products or services that the target has not purchased. In this way, advertisements for products or services that the target is interested in but has not yet purchased can be provided to the target. If advertisements for products or services that the target is interested in but has not yet purchased can be provided, the target is more likely to purchase the advertised products or services.

[0105] The advertisement information generation unit 127 may add improvement advice to the advertisement-related information. The improvement advice is advice according to the level of disease risk. For example, the improvement advice includes advice such as "walk more" or "exercise" depending on the disease risk. The improvement advice may also include more specific advice on flexibility exercises, gymnastics, etc. The advertisement-related information may also include advertisements for goods to be used in activities according to the improvement advice. For example, the advertisement information generation unit 127 may select advertisement information according to the improvement advice.

[0106] The output unit 129 (output means) outputs the advertisement-related information estimated by the advertisement information generation unit 127. For example, the output unit 129 outputs the advertisement-related information to a terminal device or server used by a company that provides advertisements. The company that provides advertisements is an entity that receives advertisement-related information about the target person from a business operator in accordance with the terms of a pre-established contract. For example, the output unit 129 may output the advertisement-related information to a mobile terminal (not shown) of the target person. For example, the output unit 129 may output the advertisement-related information to an external system that uses the advertisement-related information. However, because the advertisement-related information includes personal information, care must be taken in handling the information to prevent it from being provided to unspecified output destinations.

[0107] For example, the information providing device 12 is constructed in a cloud or a server connected to a mobile terminal (not shown) carried by the subject via a communication network. The mobile terminal is a portable communication device. For example, the mobile terminal is a portable communication device with communication functions, 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 device (not shown) conforming to standards such as Bluetooth (registered trademark) or Wi-Fi (registered trademark). Note that the wireless communication device may conform to standards other than Bluetooth (registered trademark) or Wi-Fi (registered trademark). The advertisement-related information may be used by an application installed on the mobile terminal. For example, the mobile terminal executes a process using the advertisement-related information using an app or the like installed on the mobile terminal.

[0108] (Operation) Next, the operation of the information providing system 1 will be described with reference to the drawings. The operation of the information providing device 12 included in the information providing system 1 will be described below. FIG. 12 is a flowchart for explaining an example of the operation of the information providing device 12. In the description of the processing according to the flowchart of FIG. 12, 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. 12 may be the information providing device 12.

[0109] 12 , first, the acquiring unit 121 acquires time-series data of sensor data measured by the measuring device 10 mounted on the subject's footwear (step S11). The sensor data includes acceleration in three axial directions and angular velocity around three axes. The acquiring unit 121 also acquires activity periods including periods when the subject uses a mobile device.

[0110] Next, the calculation unit 13 executes a gait index calculation process using the acquired sensor data (step S12). In the gait index calculation process, the calculation unit 13 calculates a gait index used to estimate physical ability. Details of the gait index calculation process in step S12 will be described later ( FIG. 13 ).

[0111] Next, the physical ability estimation unit 125 estimates physical ability using the attribute data and gait indices (step S13). Here, it is assumed that the attribute data is pre-registered in the storage unit 124. For example, the physical ability estimation unit 125 estimates physical ability scores such as grip strength (total muscle strength of the entire body), dynamic balance, lower limb muscle strength, mobility, and static balance. If disease risk is estimated without using physical ability, step S13 can be omitted.

[0112] Next, the disease risk estimation unit 126 estimates the disease risk 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.

[0113] Next, the advertisement information generation unit 127 uses the estimated disease risk and attribute data to select advertisement information including advertisements to be provided to the subject (step S15).

[0114] Next, the advertisement information generating unit 127 generates advertisement-related information in which the selected advertisement information is associated with the target person's activity time period (step S16).

[0115] Next, the output unit 129 outputs the generated advertisement-related information (step S17). For example, the output unit 129 outputs the advertisement-related information to a terminal device or server used by the advertisement providing company. The advertisement providing company is an entity that receives the advertisement-related information of the target person from the business operator in accordance with the terms of a pre-established contract. For example, the output unit 129 may output the advertisement-related information to the target person's mobile terminal (not shown). For example, the output unit 129 may output the advertisement-related information to an external system or the like that uses the advertisement-related information.

[0116] [Gait Index Calculation Process] Next, the gait index calculation process (step S12 in FIG. 12 ) by the calculation unit 13 of the information provision device 12 will be described with reference to the drawings. FIG. 13 is a flowchart for describing an example of the gait index calculation process in the present disclosure. In describing the process according to the flowchart in FIG. 13 , 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. 13 may be the information provision device 12 or the calculation unit 13.

[0117] 13, first, the waveform processing unit 122 extracts walking waveform data from the time-series data of the sensor data (step S121). The walking waveform data corresponds to the time-series data of the sensor data for one walking cycle.

[0118] 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%.

[0119] 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.

[0120] (Application Example) Next, an application example according to the present embodiment will be described with reference to the drawings. In this application example, the relationship between a business, an advertisement provider, and a target person is shown. Also, in this application example, the relationship including the SNS (Social Networking Service) used by the target person is shown. For example, the target person who is the advertisement recipient views the delivered advertisement and purchases a product or service. The target person posts information such as their experience using the purchased product or service on the SNS. For example, the advertisement provider provides feedback to the target person who posted the information on the SNS in response to the response to the post.

[0121] FIG. 14 is a correlation diagram showing the relationship between businesses, advertisement providers, targets (advertising destinations), and SNSs in the present disclosure. Businesses are business entities that provide services using the information provision system 1. Advertisement providers are entities that provide advertisements, products, and services to targets according to contracts. Advertisement providers provide advertisements to targets using services using the information provision system 1. Targets are entities that receive advertisements from advertisement providers. In this application example, an example is given in which the target is an individual. The target may also be a corporation. SNSs are services used by targets.

[0122] The following describes an example in which the advertisement-related information estimation model is used to estimate advertisement-related information. The advertisement-related information is used as reference information for advertisements provided by advertisement providers. For example, the advertisement-related information estimation model is optimized according to the type of product or service handled by the advertisement provider company. For example, advertisement providers include industries such as advertising agencies. For example, advertisement providers include industries that provide products or services according to advertisements. The advertisement-related information generated by the information providing device 12 is not particularly limited as long as it includes advertising information related to advertisements provided to targets.

[0123] The business provides the advertisement provider with a service using the information provision system 1. Based on a contract concluded with the advertisement provider, the business provides the advertisement provider with advertisement-related information corresponding to the subject's disease risk. The advertisement-related information includes advertisement information selected according to the subject's disease risk. The business may also provide the advertisement provider with information optimized for each contract. For example, the information optimized for each contract is optional information such as "how much fee will be charged for what information." The advertisement provider pays the business a usage fee for the service using the information provision system 1. The contract between the advertisement provider and the business clarifies rules regarding the handling of personal information and appropriate data management. The business clearly explains that the advertisement-related information is for reference only and does not guarantee medical accuracy or completeness.

[0124] The advertisement provider is an entity that provides advertisements to the target person. The advertisement provider fully explains the details of the personal information protection policy and data management to the target person. The advertisement provider obtains consent from the target person regarding the use of personal information and data. Furthermore, if there are any changes to the personal information protection policy or the details of data management, the advertisement provider explains the changes to the target person and obtains consent from the target person. For example, consent from the target person is obtained electronically. The advertisement provider enters into a contract with a business operator regarding the use of services using the information provision system 1. The advertisement provider pays the business operator a usage fee for using the information provision system 1. The advertisement provider receives advertisement-related information about the target person from the business operator. The advertisement provider selects an advertisement to be provided to the target person according to the advertisement-related information provided by the business operator. The advertisement provider delivers the selected advertisement to the target person's mobile device (not shown). For example, the advertisement provider delivers the selected advertisement to the target person's mobile device during the target person's active time period included in the advertisement-related information. For example, an advertisement delivered to a target person is displayed on the screen of the target person's mobile terminal during the target person's active time period included in the advertisement-related information.

[0125] The subject is an entity that receives advertisements from an advertisement provider. The subject uses dedicated insoles equipped with the measuring device 10. For example, the subject purchases the dedicated insoles equipped with the measuring device 10 for purposes such as health management and collecting information related to their health condition. For example, the subject may be loaned or provided with the dedicated insoles equipped with the measuring device 10 by a business that has a contract with the advertisement provider. The subject may also purchase the dedicated insoles equipped with the measuring device 10. The subject wears shoes equipped with the dedicated insoles and carries a mobile terminal (not shown) that can communicate with the measuring device 10 while on the move. The mobile terminal uploads sensor data measured by the measuring device 10 to the business's cloud server. The sensor data uploaded to the cloud server is used to generate advertisement-related information. Advertisements for products and services corresponding to the advertisement information included in the advertisement-related information are delivered to the subject's mobile device during the subject's active time period. The subject can view the advertisements delivered to the mobile device during the active time period.

[0126] A target person who purchases a product or service in response to an advertisement posts information about the purchased product or service on an SNS. The information posted on the SNS is viewed by users of the SNS. For example, the target person receives feedback from the advertisement provider in response to the response to the post on the SNS.

[0127] A terminal device (not shown) used by the advertisement provider downloads advertisement-related information, including advertisement information, from the cloud server of the business operator. A person in charge of the advertisement provider refers to the advertisement-related information and selects advertisements to be delivered to the target users. The advertisements to be delivered to the target users may be selected according to preset selection conditions.

[0128] 15 and 16 are conceptual diagrams for explaining application example 1 of the present disclosure. Fig. 15 shows an example in which advertisement information is displayed on a terminal device 180S of an advertisement provider S. Fig. 16 shows an example in which an advertisement corresponding to the advertisement information is displayed on the screen of a mobile terminal 190A carried by a target person (person A) who is the advertisement recipient. Person A has a knee problem.

[0129] 15 shows an example in which advertisement-related information about person A, generated by the information providing device 12, is displayed on the screen of a terminal device 180S used by an advertisement provider S that distributes advertisements. The screen of the terminal device 180S displays advertisement-related information including advertisement information optimized for the advertisement provider S. In the example of FIG. 15, advertisement information including advertisements for a plurality of products and services is displayed on the screen of the terminal device 180S.

[0130] Advertisement-related information is displayed on the screen of terminal device 180S. The advertisement-related information includes notification destinations, attributes, and activity time periods related to target person A. The notification destinations are, for example, apps installed on a mobile device owned by A, and correspond to the destination of the advertisement. The attributes are information such as gender and age. In this application example, A is male and 70 years old. The activity time periods indicate the time periods during which A frequently uses the mobile device. In this application example, A's activity time periods are from 8:00 to 11:00. Furthermore, the screen of terminal device 180S displays information such as "Your knee is not feeling well" according to A's disease risk.

[0131] Furthermore, advertising information is displayed on the screen of the terminal device 180S. Information about a product called "Chondroitin XXX" is displayed on the screen of the terminal device 180S as an advertisement recommended to Person A. Information about a service called "Gym XYY for Seniors" is also displayed on the screen of the terminal device 180S. A person in charge of the advertisement provider S can consider an advertisement to be sent to Person A by referring to the advertisement-related information (advertising information) displayed on the screen of the terminal device 180S. If an advertisement included in the advertising information is to be sent to Person A, the person in charge of the advertisement provider S selects a "YES" button displayed in the lower right corner of the screen of the terminal device 180S. In response to the selection of the "YES" button, the advertisement included in the advertising information is sent to Person A's notification destination (mobile terminal). The advertisement sent to Person A's mobile terminal is displayed on the screen of the mobile terminal during Person A's active time period. If an advertisement included in the advertising information is not to be sent to Person A, the person in charge of the advertisement provider S selects a "NO" button displayed in the lower right corner of the screen of the terminal device 180S. For example, in response to the selection of the "NO" button, a notification indicating that the advertisement information was not selected is transmitted to the terminal device of the business operator. For example, the business operator may retrain the advertisement information estimation model using the advertisement information that was not selected.

[0132] FIG. 16 is a conceptual diagram showing an example of an advertisement displayed on the screen of mobile terminal 190A owned by person A. An advertisement for chondroitin XXX included in the advertising information is displayed on the screen of mobile terminal 190A. The advertisement included in the advertising information is displayed at 8:30, which is within person A's active time period. After viewing the advertisement displayed on the screen of mobile terminal 190A, person A can consider purchasing the product. To view detailed information about a product advertised in an advertisement displayed on the screen of mobile terminal 190A, person A selects a "YES" button displayed at the bottom of the screen of mobile terminal 190A. For example, in response to the selection of the "YES" button, detailed product information is displayed on the screen of mobile terminal 190A. In this way, person A can access products that correspond to his or her disease risk. To not view detailed information about a product advertised in an advertisement included in the advertising information, person A selects a "NO" button displayed at the bottom of the screen of mobile terminal 190A. For example, in response to the selection of the "NO" button, a notification indicating that the advertised product was not selected is sent to the business operator or the terminal device that sent the advertisement. For example, the business operator may retrain the advertising information estimation model, including information about products that were not selected.

[0133] 17 and 18 are conceptual diagrams for explaining application example 2 of the present disclosure. Fig. 17 shows an example in which advertisement information is displayed on a terminal device 180T of an advertisement provider T. Fig. 18 shows an example in which an advertisement corresponding to the advertisement information is displayed on the screen of a mobile terminal 190B carried by a target person (person B) who is the advertisement recipient. Person B is in perfect health.

[0134] 17 shows an example in which advertisement-related information about person B, generated by the information providing device 12, is displayed on the screen of a terminal device 180T used by an advertisement provider T that distributes advertisements. The screen of the terminal device 180T displays advertisement-related information including advertisement information optimized for the advertisement provider T. In the example of FIG. 17, advertisement information including advertisements for a plurality of products and services is displayed on the screen of the terminal device 180T.

[0135] Advertisement-related information is displayed on the screen of terminal device 180T. The advertisement-related information includes notification destinations, attributes, and activity time periods related to target person B. The notification destinations are, for example, apps installed on a mobile device owned by B, and correspond to the destination of advertisements. The attributes are information such as gender and age. In this application example, B is male and 25 years old. The activity time periods indicate the time periods during which B most frequently uses his mobile device. In this application example, B's activity time periods are from 9:00 PM to 11:00 PM. Furthermore, the screen of terminal device 180T displays information such as "He is in perfect health" according to B's disease risk.

[0136] Furthermore, advertising information is displayed on the screen of terminal device 180T. Information about a product called "Protein ZZZ" is displayed on the screen of terminal device 180T as an advertisement recommended to person B. Information about a service called "Training Gym YZZ" is also displayed on the screen of terminal device 180T. A person in charge of advertisement provider T can consider an advertisement to be sent to person B by referring to the advertisement-related information (advertising information) displayed on the screen of terminal device 180T. When an advertisement included in the advertising information is to be delivered to person B, the person in charge of advertisement provider T selects a "YES" button displayed in the lower right corner of the screen of terminal device 180T. In response to the selection of the "YES" button, the advertisement included in the advertising information is sent to person B's notification destination (mobile terminal). The advertisement sent to person B's mobile terminal is displayed on the screen of the mobile terminal during person B's active time period. When an advertisement included in the advertising information is not to be delivered to person B, the person in charge of advertisement provider T selects a "NO" button displayed in the lower right corner of the screen of terminal device 180T. For example, in response to the selection of the "NO" button, a notification indicating that the advertisement information was not selected is transmitted to the terminal device of the business operator. For example, the business operator may retrain the advertisement information estimation model using the advertisement information that was not selected.

[0137] FIG. 18 is a conceptual diagram showing an example of an advertisement displayed on the screen of mobile terminal 190B owned by person B. An advertisement for Protein ZZZ included in the advertising information is displayed on the screen of mobile terminal 190B. The advertisement included in the advertising information is displayed at 22:00, which is within person B's active time period. Person B, who sees the advertisement displayed on the screen of mobile terminal 190B, can consider purchasing the product. To view detailed information about a product advertised in an advertisement displayed on the screen of mobile terminal 190B, person B selects a "YES" button displayed at the bottom of the screen of mobile terminal 190B. For example, in response to selection of the "YES" button, detailed information about the product is displayed on the screen of mobile terminal 190B. In this way, person B can access products that suit his or her health condition. To not view detailed information about a product advertised in an advertisement included in the advertising information, person B selects a "NO" button displayed at the bottom of the screen of mobile terminal 190B. For example, in response to selection of the "NO" button, a notification indicating that the advertised product was not selected is sent to the business operator or the terminal device that sent the advertisement. For example, the business operator may retrain the advertising information estimation model, including information about products that were not selected.

[0138] There may also be situations in which a target person plays the role of an information influencer. For example, if a target person purchases a product or service in response to an advertisement they viewed, they post that information on SNS. For example, if a product is purchased as a result of the target person's post, the advertising source provides feedback to the target person. For example, the advertising source may provide feedback to the target person in the form of points or a discount service. Furthermore, if a product is purchased as a result of the target person's post, the advertising source may provide feedback to the business. In this way, if there is feedback in response to the spread of information through posts on SNS, an Nth-order diffusion effect can be expected for the advertisement of the product or service (N is a natural number).

[0139] The business operator may provide information to the subject according to the disease risk. For example, if the disease risk increases sharply during a specific period, the business operator may provide the subject with information to alert the subject. For example, alert information such as "Are you overeating or drinking too much?" may be displayed on the screen of the subject's mobile device via an app linked to the information provision system 1.

[0140] 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, an advertisement-related information generation unit, and an output unit. The acquisition unit acquires time-series data of sensor data measured by the measurement device attached to the subject's footwear and the subject's activity time period. The risk estimation unit estimates the subject's disease risk using the acquired sensor data. The advertisement-related information generation unit selects advertisement information including advertisements appropriate for the subject using the estimated disease risk of the subject and pre-registered attribute data of the subject. The advertisement-related information generation unit generates advertisement-related information in which the selected advertisement information is associated with the subject's activity time period. The output unit outputs the generated advertisement-related information.

[0141] 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 to whom advertisements are to be provided. The information providing device of this embodiment generates advertisement-related information including advertisements according to the disease risk and attribute data of the subject. Therefore, according to this embodiment, advertisement-related information can be provided that provides advertisements according to the disease risk of the subject in their daily lives.

[0142] 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.

[0143] In one aspect of the present embodiment, the advertisement-related information generation unit selects advertisement information according to the disease risk score of the subject using a health measure estimation model that outputs advertisement information in response to input of a disease risk score and attribute data. The advertisement information estimation model outputs advertisement information in response to input of a disease risk score and attribute data. According to this aspect, the health measure estimation model can be used to select advertisement information according to the disease risk score and attribute data.

[0144] In one aspect of the present embodiment, the advertisement-related information generation unit selects advertisement information including advertisements according to the disease state of the subject. According to this aspect, advertisements that lead to improvement of the disease state of the subject can be provided to the subject.

[0145] In one aspect of the present embodiment, the advertisement-related information generation unit selects advertisement information including advertisements tailored to the health condition of the subject. According to this aspect, advertisements that contribute to improving the health condition of the subject can be provided to the subject.

[0146] In one aspect of the present embodiment, the advertisement information is optimized for the attributes of the target person. According to this aspect, advertisements optimized for the attributes of the target person can be provided to the target person.

[0147] In one aspect of the present embodiment, the disease risk estimation model and the advertising information estimation model are models trained using machine learning techniques. The disease risk estimation model includes an incomplete heterogeneous variational autoencoder. According to this aspect, advertising-related information for a subject can be generated even if there is some loss of data such as gait indicators.

[0148] In one aspect of this embodiment, the advertisement information is information that prompts the subject to make a decision regarding the disease risk. According to this aspect, an advertisement that prompts the subject to make a decision regarding the disease risk can be provided to the subject.

[0149] In one aspect of the present embodiment, the information providing device displays advertisement-related information optimized for the advertisement provider on a screen of a terminal device used by the advertisement provider. According to this aspect, advertisement-related information estimated according to the disease risk of the subject can be provided in an optimized state for the advertisement provider.

[0150] In one aspect of this embodiment, a terminal device of an advertisement provider distributes advertisement-related information to a mobile terminal owned by a target person. The mobile terminal displays an advertisement included in the advertisement information on a screen during an active time period included in the advertisement-related information. According to this aspect, by displaying the advertisement during the target person's active time period, the target person is more likely to view the advertisement.

[0151] Second Embodiment Next, an information providing device according to a second 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 system according to the second embodiment.

[0152] 19 is a block diagram showing an example of the configuration of the information providing device 20 according to the present disclosure. The information providing device 20 includes an acquisition unit 21, a risk estimation unit 25, an advertisement-related information generation unit 27, and an output unit 29.

[0153] The acquisition unit 21 acquires time-series data of sensor data measured by a measuring device mounted on the subject's footwear and the subject's activity time periods. The risk estimation unit 25 estimates the subject's disease risk using the acquired sensor data. The advertisement-related information generation unit 27 selects advertisement information including advertisements appropriate to the subject using the estimated disease risk of the subject and pre-registered attribute data of the subject. The advertisement-related information generation unit 27 generates advertisement-related information in which the selected advertisement information is associated with the subject's activity time periods. The output unit 29 outputs the generated advertisement-related information.

[0154] (Operation) Next, the operation of the information providing device 20 will be described with reference to the drawings. Fig. 20 is a flowchart for explaining an example of the operation of the information providing device 20. In the description of the processing according to the flowchart of Fig. 20, the components of the information providing device 20 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 20.

[0155] In FIG. 20, first, the acquisition unit 21 acquires time-series data of sensor data measured by a measurement device mounted on the subject's footwear and the subject's activity time period (step S21).

[0156] Next, the risk estimation unit 25 estimates the disease risk of the subject using the acquired sensor data (step S22).

[0157] Next, the advertisement-related information generating unit 27 selects advertisement information including advertisements appropriate for the subject, using the estimated disease risk of the subject and pre-registered attribute data of the subject (step S23).

[0158] Next, the advertisement-related information generating unit 27 generates advertisement-related information in which the selected advertisement information is associated with the target person's activity time period (step S24).

[0159] Next, the output unit 29 outputs the generated advertisement-related information (step S25).

[0160] 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 to whom advertisements are to be provided. The information providing device of this embodiment generates advertisement-related information according to the disease risk and attribute data of the subject. Therefore, according to this embodiment, advertisement-related information can be provided that provides advertisements according to the disease risk of the subject in their daily lives.

[0161] (Hardware) Next, a hardware configuration for executing the processes of the present disclosure will be described with reference to the drawings. Here, an information processing device 90 (computer) shown in Fig. 21 is given as an example of such a hardware configuration. The information processing device 90 of Fig. 21 is an example configuration for executing the processes of the present disclosure and does not limit the scope of the present disclosure.

[0162] As shown in Fig. 21 , 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. 21 , 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.

[0163] 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 processes 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 processes of the present disclosure.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] The above is an example of a hardware configuration for enabling the processing in the present disclosure. The hardware configuration in Fig. 21 is an example of a hardware configuration for executing the processing in the present disclosure and does not limit the scope of the present disclosure. A program that causes a computer to execute the processing in the present disclosure is also included in the scope of the present disclosure.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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 time-series data of sensor data measured by a measuring device mounted on the footwear of a subject and the subject's activity time period; an acquisition unit that acquires time-series data of sensor data measured by a measuring device mounted on the subject's footwear and the subject's activity time period; an advertisement-related information generation unit that selects advertising information including advertisements appropriate to the subject using the estimated disease risk of the subject and pre-registered attribute data of the subject, and generates advertisement-related information in which the selected advertising information is associated with the subject's activity time period; and an output unit that outputs the generated advertisement-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 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 3) The information providing device according to Supplementary Note 2, wherein the advertisement-related information generation unit selects the advertisement information according to the disease risk score of the subject using a health measure estimation model that outputs the advertisement information in response to input of the disease risk score and the attribute data. (Supplementary Note 4) The information providing device according to Supplementary Note 3, wherein the health measure estimation model selects the advertisement information including advertisements according to a disease state of the subject. (Supplementary Note 5) The information providing device according to Supplementary Note 3, wherein the advertisement-related information generation unit selects the advertisement information including advertisements according to a health state of the subject. (Supplementary Note 6) The information providing device according to Supplementary Note 3, wherein the advertisement information is optimized to an attribute of the subject. (Supplementary Note 7) The information providing device according to Supplementary Note 3, wherein the disease risk estimation model and the advertisement information estimation model are models trained using a machine learning technique, and the disease risk estimation model includes an incomplete heterogeneous variational autoencoder. (Supplementary Note 8) The information providing device according to Supplementary Note 7, wherein the advertisement information is information that prompts the subject to make a decision regarding the disease risk.(Supplementary Note 9) An information provision system comprising: the information provision device according to any one of Supplements 1 to 8; 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 advertisement-related information optimized for the advertisement provider on a screen of a terminal device used by an advertisement provider. (Supplementary Note 10) The terminal device of the advertisement provider distributes the advertisement-related information to a mobile terminal owned by the subject, and the mobile terminal displays an advertisement included in the advertisement information on a screen during the activity time period included in the advertisement-related information. (Supplementary Note 11) An information provision method in which a computer acquires time-series data of sensor data measured by a measuring device mounted on the footwear of a subject and a time period during which the subject is active, estimates a disease risk of the subject using the acquired sensor data, selects advertising information including an advertisement appropriate to the subject using the estimated disease risk of the subject and pre-registered attribute data of the subject, generates advertising-related information in which the selected advertising information is associated with the time period during which the subject is active, and outputs the generated advertising-related information. (Supplementary Note 12) An information provision method according to Supplementary Note 11, 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 in response to the disease risk score output from the disease risk estimation model. (Supplementary Note 13) The information providing method according to Supplementary Note 12, wherein a computer selects the advertising information according to the disease risk score of the subject using a health measure estimation model that outputs the advertising information in response to input of the disease risk score and the attribute data. (Supplementary Note 14) The information providing method according to Supplementary Note 13, wherein a computer selects the advertising information including an advertisement according to the disease state of the subject.(Supplementary Note 15) The information provision method according to Supplementary Note 13, wherein a computer selects the advertising information including advertising appropriate to the health condition of the subject. (Supplementary Note 16) The information provision method according to Supplementary Note 12, wherein the disease risk estimation model and the advertising information estimation model are models trained using a machine learning technique, and the disease risk estimation model includes an incomplete heterogeneous variational autoencoder. (Supplementary Note 17) A computer-readable non-transitory recording medium having recorded thereon a program causing a computer to execute the following processes: acquiring time-series data of sensor data measured by a measuring device mounted on the subject's footwear and the subject's activity time zone; estimating the subject's disease risk using the acquired sensor data; selecting advertising information including advertising appropriate to the subject using the estimated disease risk of the subject and pre-registered attribute data of the subject; generating advertising-related information in which the selected advertising information is associated with the subject's activity time zone; and outputting the generated advertising-related information. (Supplementary Note 18) The computer-readable non-transitory recording medium according to Supplementary Note 17, having recorded thereon a program causing a computer to execute the following processes: calculating a gait index using the sensor data; inputting data including the gait index calculated using the sensor data into a disease risk estimation model that outputs a disease risk score indicating the degree of disease risk for each disease in response to input of data including the gait index; and estimating disease risk information in response to the disease risk score output from the disease risk estimation model. (Supplementary Note 19) The computer-readable non-transitory recording medium according to Supplementary Note 18, having recorded thereon a program causing a computer to execute the following processes: selecting the advertising information in response to the disease risk score of the subject, using a health measure estimation model that outputs the advertising information in response to input of the disease risk score and the attribute data. (Supplementary Note 20) The computer-readable non-transitory recording medium according to Supplementary Note 19, having recorded thereon a program causing a computer to execute the following processes: calculating a gait index using the sensor data; inputting data including the gait index calculated using the sensor data into a disease risk estimation model that outputs a disease risk score indicating the degree of disease risk for each disease in response to input of data including the gait index; and estimating disease risk information in response to the disease risk score output from the disease risk estimation model.(Supplementary Note 21) The computer-readable non-transitory recording medium according to Supplementary Note 19, having recorded thereon a program for causing a computer to execute a process of selecting the advertising information including advertisements according to the health condition of the subject. (Supplementary Note 22) The computer-readable non-transitory recording medium according to Supplementary Note 18, wherein the disease risk estimation model and the advertising information estimation model are models trained using a machine learning technique, and the disease risk estimation model includes an incomplete heterogeneous variational autoencoder.

[0175] REFERENCE SIGNS LIST 1 Information provision system 10 Measurement device 12 Information provision device 13 Calculation unit 14 Estimation unit 15 Risk estimation unit 21 Acquisition unit 25 Risk estimation unit 27 Advertisement-related information generation unit 29 Output unit 110 Sensor 111 Acceleration sensor 112 Angular velocity sensor 113 Control unit 115 Communication unit 117 Power supply 121 Acquisition unit 122 Waveform processing unit 123 Gait index calculation unit 124 Memory unit 125 Physical ability estimation unit 126 Disease risk estimation unit 127 Advertisement information generation unit 129 Output unit

Claims

1. An acquisition unit that acquires time-series data of sensor data measured by a measuring device mounted on the subject's footwear, and the subject's activity period, A risk estimation unit that estimates the disease risk of the subject using acquired sensor data, An advertising-related information generation unit selects advertising information, including advertisements, that are appropriate for the target person, using the estimated disease risk of the target person and the attribute data of the target person, and generates advertising-related information that associates the selected advertising information with the target person's activity time period. An information providing device comprising an output unit that outputs generated advertising-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 advertising-related information generation unit is: The information providing device according to claim 2, which uses a health policy estimation model that outputs the advertising information in response to the input of the disease risk score and attribute data to select the advertising information corresponding to the disease risk score of the target person.

4. The aforementioned health policy estimation model is The information providing device according to claim 3, which selects advertising information including advertisements that correspond to the disease status of the aforementioned target person.

5. The aforementioned advertising-related information generation unit is: The information providing device according to claim 3, which selects advertising information including advertisements that are appropriate to the health status of the subject.

6. The aforementioned advertising information is, The information providing device according to claim 3, which is optimized for the attributes of the aforementioned target person.

7. 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 advertising-related information optimized for the advertising provider on the screen of a terminal device used by the advertising provider.

8. The terminal device of the advertiser, The advertising-related information is distributed to the mobile device owned by the aforementioned person. The aforementioned mobile terminal is The information provision system according to claim 7, which displays an advertisement included in the advertising information on a screen during the activity time period included in the advertising-related information.

9. Computers Time-series data of sensor data measured by measuring devices installed in the subject's footwear, and the subject's activity period are acquired. Using the acquired sensor data, the disease risk of the subject is estimated. Using the estimated disease risk of the target individual and the attribute data of the target individual that has been registered in advance, advertising information including advertisements tailored to the target individual is selected. The system generates ad-related information that associates selected ad information with the target audience's activity time periods. A method for providing information that outputs generated advertising-related information.

10. A process to acquire time-series data of sensor data measured by a measuring device installed in the subject's footwear, and the subject's activity period. Using the acquired sensor data, a process is performed to estimate the disease risk of the subject, A process that selects advertising information, including advertisements, that are relevant to the target audience, using the estimated disease risk of the target audience and the attribute data of the target audience that has been registered in advance. A process that generates advertising-related information by associating selected advertising information with the target person's activity time period, A program that processes and outputs generated advertising-related information, and instructs a computer to execute this process.