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

Abstract

Provided is an information providing device comprising: an acquisition unit that acquires sensor data including acceleration and angular velocity measured by a measurement device mounted on footwear of a person to be managed in order to monitor the gait of the person to be managed on a daily basis and provide advice corresponding to the psychosomatic state of the person to be managed; an estimation unit that uses the acquired sensor data to estimate the psychosomatic state of the person to be managed; an advice information generation unit that generates advice information responsive to the psychosomatic state of the person to be managed; and an output unit that outputs the generated advice 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 according to gait are attracting attention. For example, technology has been developed that analyzes gait using sensor data measured by sensors mounted on footwear such as shoes. Using sensor data measured by sensors mounted on footwear may enable the daily physical and mental state of children (subjects under management) to be detected. Patent Literature 1 discloses a walking training support device that measures the walking state of a person undergoing walking training and visualizes the foot movements.

[0003] Japanese Patent Application Laid-Open No. 2006-204730

[0004] The method of Patent Document 1 has difficulty in measuring the walking state of a person undergoing walking training in daily life, and therefore, the method of Patent Document 1 cannot monitor the gait of the person being managed in daily life and provide appropriate advice according to the gait of the person being managed.

[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 monitor the gait of a person to be managed in daily life and provide advice according to the physical and mental state of the person to be managed.

[0006] An information providing device of one embodiment of the present disclosure includes an acquisition unit that acquires sensor data including acceleration and angular velocity measured by a measuring device mounted on the footwear of the managed person, an estimation unit that estimates the mental and physical state of the managed person using the acquired sensor data, an advice information generation unit that generates advice information according to the mental and physical state of the managed person, and an output unit that outputs the generated advice information.

[0007] In one aspect of the information provision method of the present disclosure, sensor data including acceleration and angular velocity measured by a measuring device mounted on the footwear of the person to be managed is acquired, the acquired sensor data is used to estimate the mental and physical state of the person to be managed, advice information corresponding to the mental and physical state of the person to be managed is generated, and the generated advice information is output.

[0008] A program of one embodiment of the present disclosure causes a computer to perform the following processes: acquire sensor data including acceleration and angular velocity measured by a measuring device mounted on the footwear of the person to be managed; use the acquired sensor data to estimate the physical and mental state of the person to be managed; generate advice information according to the physical and mental state of the person to be managed; and output the generated advice information.

[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 monitor the gait of a person to be managed in daily life and provide advice according to the physical and mental state of the person to be managed.

[0010] 1 is a block diagram illustrating an example of a configuration of an information providing system according to the present disclosure. FIG. 1 is a block diagram illustrating an example of a configuration of a measurement device provided in the information providing system according to the present disclosure. FIG. 2 is a conceptual diagram illustrating an example of an arrangement of a measurement device provided in the information providing system according to the present disclosure. FIG. 3 is a conceptual diagram illustrating an example of a coordinate system set in the measurement device provided in the information providing system according to the present disclosure. FIG. 4 is a conceptual diagram for explaining a human body plane. FIG. 5 is a block diagram illustrating an example of a configuration of an information providing system provided in the information providing system according to the present disclosure. FIG. 6 is a conceptual diagram for explaining a gait cycle. FIG. 7 is a conceptual diagram for explaining an example of estimation of a mental and physical state by an information providing system provided in the information providing system according to the present disclosure. FIG. 8 is a conceptual diagram for explaining an example of generation of advice information by an information providing system provided in the information providing system according to the present disclosure. FIG. 9 is a flowchart for explaining an example of operation of the information providing system according to the present disclosure. FIG. 10 is a flowchart for explaining an example of gait index calculation processing by the information providing system according to the present disclosure. FIG. 11 is a conceptual diagram illustrating the correlation between a business operator, a manager (guardian), a managed person (child), and an expert according to the present disclosure. FIG. 12 is a conceptual diagram illustrating a display example of advice information output from the information providing system according to the present disclosure. FIG. 13 is a conceptual diagram illustrating a display example of reservation information corresponding to advice information output from the information providing system according to the present disclosure. FIG. 14 is a conceptual diagram illustrating a display example of advice information output from the information providing system according to the present disclosure. Fig. 1 is a conceptual diagram showing an example of display of reservation information according to advice information output from an information providing system in the present disclosure; Fig. 2 is a block diagram showing an example of a configuration of an information providing device in the present disclosure; Fig. 3 is a flowchart for explaining an example of an operation of an information providing device in the present disclosure; Fig. 4 is a block diagram showing an example of a hardware configuration for executing processing in 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 example of an information provision system according to the first embodiment will be described with reference to the drawings. The information provision system of this embodiment estimates the physical and mental state of a person to be managed using sensor data related to foot movements measured as the person walks. The information provision system of this embodiment provides advice (advice information) according to the estimated physical and mental state to the manager of the person to be managed. In the following, an example of the person to be managed is a child. The manager is the child's guardian. The method of this embodiment can be applied to any person, not just children. For example, the method of this embodiment can be applied to the management of patients in a hospital or the like, or the management of residents in a nursing home or the like.

[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 attached to the footwear of a child (person to be managed). For example, the functions of the information provision device 12 are implemented in a mobile device carried by the guardian (person to be managed), or in a server or cloud connected to the mobile device carried by the guardian via a network. 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 (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. For example, a piezoelectric, piezo-resistive, or capacitance type sensor can be used as the acceleration sensor 111. There are no limitations on the sensor used as the acceleration sensor 111 as long as it can measure acceleration.

[0016] The angular velocity sensor 112 is a sensor that measures angular velocity around three axes (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. For example, a vibration type or capacitance type sensor can be used as the angular velocity sensor 112. There are no limitations on the sensor used as the angular velocity sensor 112 as long as it can measure angular velocity.

[0017] The sensor 110 is realized by, for example, an inertial measurement unit (IMU) that measures acceleration and angular velocity. An example of an inertial measurement unit is an IMU (Inertial Measurement Unit). The IMU includes an acceleration sensor 111 that measures acceleration in three axial directions and an angular velocity sensor 112 that measures angular velocity around three axes. The sensor 110 may be realized by an inertial measurement unit such as a VG (Vertical Gyro) or an AHRS (Attitude Heading Reference System). The sensor 110 may also be realized by a GPS / INS (Global Positioning System / Inertial Navigation System). The sensor 110 may be realized by a device other than an inertial measurement unit as long as it can measure physical quantities related to foot movement.

[0018] FIG. 3 is a conceptual diagram showing an example in which the measurement device 10 is placed in 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 shoe 100. The measurement device 10 may be detachable from the shoe 100 or may not be detachable from the shoe 100. The measurement device 10 may be placed at a position other than the back of the arch of the foot as long as it can measure sensor data related to foot movement. The measurement device 10 may also be placed in socks worn by the person to be managed or in an accessory such as an anklet worn by the person to be managed. The measurement device 10 may also be attached directly to the foot or embedded in the foot. The measurement device 10 may also be placed in one of the shoes 100 as long as it can measure data used for estimation.

[0019] In the example of FIG. 3 , a local coordinate system is set with the measurement device 10 (sensor 110) as the reference. The local coordinate system 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 and right feet. For example, if sensors 110 manufactured to the same specifications are placed in the left and right shoes 100, the up-down orientation (Z-axis direction) of the sensors 110 placed in the left and right shoes 100 will be the same. In this case, the three axes of the local coordinate system set for the sensor data originating from the left foot and the three axes of the local coordinate system set for the sensor data originating from the right foot are the same for the left and right feet. 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. Alternatively, different coordinate systems may be set for the left and right feet.

[0020] 4 is a conceptual diagram illustrating a local coordinate system (x-axis, y-axis, z-axis) set in the measurement device 10 (sensor 110) installed on the back of the arch of the foot, and a world coordinate system (x-axis, y-axis, z-axis) set relative to the ground. In the world coordinate system (x-axis, y-axis, z-axis), the lateral direction of the managed person is set as the x-axis direction, the direction of the managed person's back is set as the y-axis direction, and the direction of gravity is set as the z-axis direction. 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 managed person's walking.

[0021] FIG. 5 is a conceptual diagram illustrating human body planes set for the human body. In this embodiment, a sagittal plane that divides the body into left and right, a coronal plane that divides the body into front and back, and a horizontal plane that divides the body horizontally are defined. Note that, as shown in FIG. 5 , when the user is standing upright with the centerline of the feet pointing in the direction of travel, the world coordinate system and the local coordinate system are assumed to coincide. In this embodiment, a rotation in the sagittal plane about the X-axis (x-axis) as the rotation axis is defined as roll, a rotation in the coronal plane about the Y-axis (y-axis) as the rotation axis is defined as pitch, and a rotation in the horizontal plane about the Z-axis (z-axis) as the rotation axis is defined as yaw. Furthermore, a rotation angle in the sagittal plane about the X-axis (x-axis) as the rotation axis is defined as roll angle, a rotation angle in the coronal plane about the Y-axis (y-axis) as the rotation axis is defined as pitch angle, and a rotation angle in the horizontal plane about the Z-axis (z-axis) as the rotation axis is defined as yaw angle.

[0022] The control unit 113 (control means) is a controller that controls the measuring device 10. For example, the control unit 113 is realized by a microcomputer or microcontroller that performs overall control and data processing of the measuring device 10. For example, the control unit 113 includes a CPU (Central Processing Unit), RAM (Random Access Memory), ROM (Read Only Memory), flash memory, etc.

[0023] The control unit 113 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 walking of the managed person. For example, the control unit 113 starts measuring the sensor data starting from the point in time when it is detected that either the left or right foot has started to move in the direction of travel after both feet have remained at the same vertical height for a predetermined period of time. The control unit 113 may also be configured to start measuring the sensor data at a predetermined timing.

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

[0025] The sensor data includes acceleration data converted into digital data and angular velocity data converted into digital data. The acceleration data includes acceleration vectors in three axial directions. The angular velocity data includes angular velocity vectors around three axes. The acceleration data and angular velocity data are associated with the time at which they were acquired. The control unit 113 may also apply corrections to the acceleration data and angular velocity data, such as correction for mounting error, temperature correction, and linearity correction. For example, the control unit 113 may calculate any of the gait indices and feature amounts described below. In this case, the measurement device 10 outputs the calculated gait indices and feature amounts to the information provision device 12.

[0026] 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 in a storage unit (not shown) and transmit the sensor data stored in the storage unit 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.

[0027] 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 be configured to transmit the sensor data to the information providing device 12 via a wired connection such as a cable.

[0028] 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 powered by wire or 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. In this case, by placing footwear equipped with the measuring device 10 on the wireless power supply device, the measuring device 10 can be appropriately charged when not in use.

[0029] 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 mental and physical state estimation unit 125, an advice information generation unit 126, and an output unit 127. The waveform processing unit 122, the gait index calculation unit 123, and the mental and physical state estimation unit 125 constitute the estimation unit 15. The waveform processing unit 122 and the gait index calculation unit 123 constitute the calculation unit 13.

[0030] The acquisition unit 121 (acquisition means) acquires sensor data from the measurement device 10 mounted on the footwear of the managed person. The acquisition unit 121 receives the sensor data from the measurement device 10 via wireless communication. 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. For example, the acquisition unit 121 may add location information of the managed person's mobile device (not shown), which is the source of the sensor data, to the sensor data. For example, the location information is measured by a function of a GPS (Global Positioning System) installed in the mobile terminal.

[0031] The acquisition unit 121 also acquires attribute data of the managed person. The attribute data includes gender, date of birth, height, and weight. The date of birth is converted to age. The gender, date of birth (age), height, and weight included in the attribute data are also called physical information. For example, the attribute data is input via an input device (not shown). For example, the attribute data is input via a terminal device used by the administrator. For example, the attribute data may be input via a mobile terminal used by the managed person. For example, the attribute data may be stored in advance in the storage unit 124. The attribute data may be updated at any timing in response to input by the managed person or the administrator.

[0032] 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 axes (spatial acceleration) and angular velocity around three axes (spatial angular velocity) 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 time series data for one walking cycle starting from the timing of a heel strike and ending at the timing of the next heel strike as walking waveform data.

[0033] FIG. 7 is a conceptual diagram for explaining a step cycle based on the right foot. The step cycle based on the left foot is similar to that of the right foot. The horizontal axis of FIG. 7 represents one step cycle of the right foot, starting from the point when the heel of the right foot hits the ground and ending from the point when the heel of the right foot hits the ground again. The horizontal axis of FIG. 7 is normalized with the step cycle set to 100% (percent). Normalizing one step cycle to 100% is called first normalization. One step cycle of one leg is broadly divided into a stance phase and a swing phase. The stance phase is a 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 a 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.

[0034] As shown in Figure 7, multiple events occur during walking. The multiple events that occur during walking are also called walking events. P1 represents an event in which the heel of the right foot touches the ground (heel strike) (HS: Heel Strike). P2 represents an event in which the toe of the left foot leaves the ground while the sole of the right foot remains on the ground (OTO: Opposite Toe Off). P3 represents an event in which the heel of the right foot rises (heel rise) while the sole of the right foot remains on the ground (HR: Heel Rise). P4 represents an event in which the heel of the left foot touches the ground (opposite heel strike) (OHS: Opposite Heel Strike). P5 represents an event in which the toe of the right foot leaves the ground while the sole of the left foot remains on the ground (TO: Toe Off). P6 represents an event in which the left and right feet cross (foot crossing) with the sole of the left foot touching the ground (FA: Foot Adjacent). P7 represents an event in which the tibia of the right foot is approximately perpendicular to the ground (TV: Tibia Vertical) with the sole of the left foot touching the ground. P8 represents an event in which the heel of the right foot touches the ground (heel strike) (HS: Heel Strike). P8 corresponds to the end point of the walking cycle that begins with P1 and the start point of the next walking cycle. Note that the walking events shown in Figure 7 are merely examples and do not limit the events that occur during walking or the names of these events.

[0035] 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.

[0036] The waveform processing unit 122 normalizes the time of the extracted walking waveform data for one step cycle to a walking cycle of 0 to 100% (first normalization). The timing of 1%, 10%, or the like included in the walking cycle of 0 to 100% is also called a walking phase. Furthermore, the waveform processing unit 122 normalizes the first-normalized walking waveform data for one step cycle so that the stance phase is 60% and the swing phase is 40% (second normalization). Second-normalizing the walking waveform data can reduce discrepancies in the walking phases from which feature values ​​are extracted. The waveform processing unit 122 outputs the normalized walking waveform data to the gait index calculation unit 123.

[0037] For example, the waveform processing unit 122 extracts gait waveform data for one step gait cycle using the forward acceleration (Y-direction acceleration). The waveform processing unit 122 normalizes the extracted gait waveform data for one step gait cycle. With respect to accelerations and angular velocities other than the forward acceleration (Y-direction acceleration), the waveform processing unit 122 extracts and normalizes gait waveform data for one step gait cycle in accordance with the gait cycle of the forward acceleration (Y-direction acceleration). The waveform processing unit 122 may also generate time series data of angles around three axes by integrating time series data of angular velocities around three axes. In this case, the waveform processing unit 122 extracts and normalizes gait waveform data for one step gait cycle for angles around three axes in accordance with the gait cycle of the forward acceleration (Y-direction acceleration).

[0038] The waveform processor 122 may extract / normalize the gait waveform data for one step gait cycle using acceleration / angular velocity other than the forward acceleration (Y-direction acceleration). For example, the waveform processor 122 may detect heel strike and toe lift from time series data of vertical acceleration (Z-direction acceleration) (not shown). The timing of heel strike is the timing of a steep minimum peak that appears in the time series data of vertical acceleration (Z-direction acceleration). At the timing of the steep minimum peak, the value of vertical acceleration (Z-direction acceleration) becomes approximately zero. The minimum peak that marks the timing of heel strike corresponds to the minimum peak of the gait waveform data for one step gait cycle. The interval between consecutive heel strikes is the gait cycle. The timing of toe lift is the timing of an inflection point in the time series data of vertical acceleration (Z-direction acceleration) that gradually increases after passing through a period of small fluctuation following the maximum peak immediately after heel strike. The waveform processing unit 122 may also extract / normalize gait waveform data for one walking cycle using both forward acceleration (Y-direction acceleration) and vertical acceleration (Z-direction acceleration).The waveform processing unit 122 may also extract / normalize gait waveform data for one walking cycle using acceleration, angular velocity, angle, etc. other than forward acceleration (Y-direction acceleration) and vertical acceleration (Z-direction acceleration).

[0039] For example, the waveform processing unit 122 may extract, from the walking waveform data, feature quantities (physical ability feature quantities) used to estimate physical abilities. 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. Specific physical ability feature quantities will not be described. 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. When physical ability feature quantities are used to estimate the mental and physical state, the waveform processing unit 122 outputs the extracted physical ability feature quantities to the mental and physical state estimation unit 125.

[0040] The gait index calculation unit 123 (gait index calculation means) acquires normalized gait waveform data from the waveform processing unit 122. The gait index calculation unit 123 calculates gait indices used to estimate physical ability using the normalized gait waveform data. There are no particular limitations on the gait indices to be calculated, as long as they can be calculated using normalized gait waveform data. For example, the gait index calculation unit 123 calculates gait indices related to distance, height, angle, speed, time, frailty level, CPEI (Center of Pressure Exclusion Index), etc. Representative gait indices are listed below. Specific methods for calculating the following gait indices will not be described.

[0041] 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.

[0042] 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.

[0043] 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.

[0044] 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.

[0045] For example, the gait index calculation unit 123 calculates a frailty level or a center of pressure exclusion index (CPEI) as a gait index. The frailty level is an estimated value of a frailty state according to a walking state. For example, the gait index calculation unit 123 estimates an index as a frailty level according to a determination result such as not frail, possibly frail, or highly likely frail. The CPEI indicates an estimated value of the rate at which the center of foot pressure acting on the ground expands during the stance phase.

[0046] The memory unit 124 (storage means) stores a mental / physical state estimation model (described later) that estimates the mental / physical state of the managed subject using gait indices extracted from the gait waveform data. For example, the mental / physical state includes physical conditions such as flat feet and floating toes. For example, the mental / physical state includes mental conditions such as bipolar disorder. The memory unit 124 stores a mental / physical state estimation model that has learned the mental / physical states of multiple subjects. The mental / physical state estimation model outputs an index indicating the mental / physical state (mental / physical state index) in response to input of the gait indices extracted from the gait waveform data. For example, the mental / physical state index is an expression that represents the physical or mental state. For example, the mental / physical state estimation model may output a mental / physical state score indicating the degree of a specific mental / physical state as an index related to the mental / physical state in response to input of the gait indices extracted from the gait waveform data. For example, the mental / physical state score is a score expressed using a numerical value or symbol that indicates the degree of a physical condition such as flat feet or floating toes or a mental condition such as bipolar disorder. For example, the mental and physical state estimation model may be configured to output a mental and physical state score related to the mental and physical state of the managed person, including attribute data of the managed person. Also, the mental and physical state estimation model may be configured to output a mental and physical state score related to the mental and physical state, including physical ability feature amounts extracted from gait waveform data.

[0047] The storage unit 124 also stores an advice information generation model (described later) that outputs advice information including advice to an administrator who manages the managed subject in response to input of indicators output from the mental and physical state estimation model. For example, the advice information generation model is a model that is trained using a data set of mental and physical state indicators and advice corresponding to the indicators as training data. For example, the advice information generation model is a model that is trained to output advice information regarding the mental and physical state in response to input of the mental and physical state indicators. For example, the advice information generation model outputs advice information including an introduction to an expert on the mental and physical state in response to input of the mental and physical state indicators. For example, the advice information generation model outputs advice information including advice regarding the mental and physical state in response to input of the mental and physical state indicators. For example, the advice information generation model may be a model customized for each type of mental and physical state. The advice information generation model may be a model that generates advice information including advice based on rules in response to input of mental and physical state indicators related to the managed subject. For example, the advice information generation model may include a large-scale language model that generates sentences including advice in response to input of mental and physical state indicators related to the managed subject.

[0048] The storage unit 124 stores the mental and physical state estimation model and the advice information generation model trained for a plurality of subjects. For example, the mental and physical state estimation model and the advice information generation model may be stored in the storage unit 124 when the product is shipped from a factory. The mental and physical state estimation model and the advice information generation model may also be stored in the storage unit 124 when the information providing device 12 is calibrated. For example, the mental and physical state estimation model and the advice information generation model stored in a storage device (not shown) such as an external server may be used. In this case, it is only necessary to access the mental and physical state estimation model and the advice information generation model via an interface (not shown) connected to the storage device.

[0049] The storage unit 124 also stores attribute data of the managed person. The attribute data includes gender, date of birth (age), height, and weight. The attribute data may be updated at any time. Furthermore, the storage unit 124 may store health checkup data of the managed person. The health checkup data can be a factor in improving the estimation accuracy of mental and physical condition indicators and advice information. For example, the health checkup data of the managed person includes at least one of the examination items specified in Article 6 of the Enforcement Regulations of the School Health and Safety Act. The health checkup data of the managed person may also include examination items not specified in Article 6 of the Enforcement Regulations of the School Health and Safety Act.

[0050] The mental and physical state estimation unit 125 (mental and physical state estimation means) acquires gait indices generated from walking waveform data from the waveform processing unit 122. If necessary, the mental and physical state estimation unit 125 acquires attribute data stored in the storage unit 124. The mental and physical state estimation unit 125 estimates mental and physical state indices using the gait indices. The mental and physical state estimation unit 125 inputs the gait indices of the managed person to the mental and physical state estimation model stored in the storage unit 124. For example, the mental and physical state estimation unit 125 estimates mental and physical state indices related to physical conditions such as flat feet and floating toes, and mental conditions such as bipolar disorder. The estimation of mental and physical state indices by the mental and physical state estimation unit 125 will be described later. The mental and physical state estimation unit 125 outputs the mental and physical state indices output from the mental and physical state estimation model to the advice information generation unit 126.

[0051] FIG. 8 is a conceptual diagram showing an example of mental and physical state index estimation by the mental and physical state estimation unit 125. The mental and physical state estimation unit 125 inputs attribute data and gait indexes used to estimate the mental and physical state of the managed person to the mental and physical state estimation model 150. The mental and physical state estimation model 150 receives attribute data and gait indexes used to estimate a specific mental and physical state. In response to the input of the attribute data and gait index, the mental and physical state estimation model 150 outputs a mental and physical state index related to a specific mental and physical state. For example, the mental and physical state estimation model 150 may include multiple models that estimate mental and physical state indexes for each mental and physical state. For example, the mental and physical state estimation model 150 may be a single model that collectively outputs mental and physical state indexes related to multiple mental and physical states.

[0052] For example, the mental and physical state estimation model 150 estimates a mental and physical state index related to flatfoot. When symptoms of flatfoot are present, the angle of the foot twists toward the arch when the sole of the foot touches the ground. That is, the symptoms of flatfoot are manifested in the angle around the axis of travel (in the coronal plane) when the sole of the foot touches the ground. Furthermore, when symptoms of flatfoot are present, the foot pressure center trajectory approaches the central axis of the foot, resulting in a smaller CPEI. Therefore, by training the mental and physical state estimation model 150 with training data in which the angle around the axis of travel (in the coronal plane) when the sole of the foot touches the ground and the CPEI are used as explanatory variables and the degree of flatfoot is used as the objective variable, a mental and physical state index related to flatfoot can be estimated. Furthermore, the mental and physical state estimation model 150 that estimates a mental and physical state index related to flatfoot may be a model trained using one of the angle around the axis of travel (in the coronal plane) when the sole of the foot touches the ground and the CPEI as an explanatory variable. The mental and physical condition estimation model 150 that estimates the mental and physical condition index related to flat feet may be a model trained using data other than the angle around the axis of travel (in the coronal plane) when the sole of the foot touches the ground and the CPEI as explanatory variables. For example, the mental and physical condition estimation model 150 outputs a mental and physical condition index that indicates that the person to be managed may have flat feet.

[0053] For example, the mental and physical state estimation model 150 estimates a mental and physical state index related to floating toes. When there are signs of floating toes, the toes leave the ground earlier, resulting in a shallower plantar flexion angle at the time of toe-off. Furthermore, when there are signs of floating toes, the person is unable to push off the ground as strongly, resulting in smaller forward acceleration and vertical acceleration at the time of foot push-off. Therefore, if the mental and physical state estimation model 150 learns training data in which the plantar flexion angle at the time of toe-off, the forward acceleration and vertical acceleration at the time of push-off are used as explanatory variables, and the degree of floating foot is used as the objective variable, a mental and physical state index related to floating foot can be estimated. Furthermore, the mental and physical state estimation model 150 related to floating foot may be a model trained using any of the plantar flexion angle at the time of toe-off, the forward acceleration at the time of push-off, and the vertical acceleration at the time of push-off as an explanatory variable. The mental and physical condition estimation model 150 that estimates the mental and physical condition index related to flat feet may be a model that has been trained using data other than the plantar flexion angle at the timing of toe-off and the forward acceleration and vertical acceleration at the timing of push-off as explanatory variables. For example, the mental and physical condition estimation model 150 outputs a mental and physical condition index that indicates that the person to be managed may have floating foot.

[0054] For example, the mental and physical state estimation model 150 estimates mental and physical state indices related to mental states such as manic depression. Mental states such as manic depression are manifested in gait and posture. For example, when a person is in a manic depression state and walking slowly and listlessly, they tend to look down and their walking speed and stride length become smaller. Furthermore, walking in this manner shortens the heel rocker time and DST, and reduces the vertical acceleration and angular velocity around the left-right axis. Therefore, by having the mental and physical state estimation model 150 learn training data that uses walking speed, stride length, heel rocker time, DST, vertical acceleration, and angular velocity around the left-right axis as explanatory variables and the degree of manic depression as a response variable, it is possible to estimate mental and physical state indices related to manic depression. Furthermore, the mental and physical state estimation model 150 that estimates mental and physical state indices related to manic depression may be a model trained using any of walking speed, stride length, heel rocker time, DST, vertical acceleration, and angular velocity around the left-right axis as explanatory variables. The mental and physical state estimation model 150 that estimates the mental and physical state index related to bipolar disorder may be a model trained using data other than walking speed, stride length, heel rocker time, DST, vertical acceleration, and angular velocity around the left and right axes as explanatory variables. For example, the mental and physical state estimation model 150 outputs a mental and physical state index that indicates that the person to be managed may have bipolar disorder.

[0055] As the amount of data used for estimation increases, the accuracy of estimation of the mental and physical state index by the mental and physical state estimation model 150 improves. For example, the mental and physical state estimation model 150 may be configured to output a mental and physical state index in response to input of medical examination data, attribute data, and a gait index. When attribute data is included in the medical examination data items, the mental and physical state estimation model 150 may be configured to output a mental and physical state index in response to input of the medical examination data and the gait index.

[0056] The mental and physical state estimation model 150 may be stored in an external storage device constructed on a cloud, a server, or the like. In this case, the mental and physical state estimation unit 125 uses the mental and physical state estimation model 150 via an interface (not shown) connected to the storage device. The mental and physical state estimation model 150 is a machine learning model. For example, the mental and physical state estimation model 150 is a model trained using training data in which attribute data and gait indices related to multiple subjects are used as explanatory variables and a mental and physical state indices related to a specific mental and physical state are used as objective variables. For example, the mental and physical state 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.

[0057] For example, the mental and physical state estimation model 150 is generated by learning using a linear regression algorithm. For example, the mental and physical state estimation model 150 is generated by learning using a support vector machine (SVM) algorithm. For example, the mental and physical state estimation model 150 is generated by learning using a Gaussian process regression (GPR) algorithm. For example, the mental and physical state estimation model 150 is generated by learning using a random forest (RF) algorithm. For example, the mental and physical state estimation model 150 may be generated by unsupervised learning that classifies the mental and physical state of the managed person according to attribute data and gait indices. There are no particular limitations on the algorithm used to train the mental and physical state estimation model 150.

[0058] For example, the mental and physical state estimation model 150 may be a machine learning model such as an incomplete heterogeneous variational autoencoder or a random forest. If the model is an incomplete heterogeneous variational autoencoder, the mental and physical state of the person to be managed can be estimated even if there are some defects in the attribute data or gait indicators.

[0059] The advice information generation unit 126 (advice information estimation means) acquires the mental and physical state index estimated by the mental and physical state estimation unit 125. The advice information generation unit 126 estimates advice information according to the mental and physical state index. The advice information generation unit 126 may be configured to estimate advice information according to the mental and physical state index, including health checkup data. The advice information generation unit 126 outputs the estimated advice information.

[0060] FIG. 9 is a conceptual diagram showing an example of advice information estimated by the advice information generation unit 126. The advice information generation unit 126 inputs mental and physical state indices used for estimating advice information to the advice information estimation model 160. In response to the input of the mental and physical state indices, the advice information estimation model 160 outputs advice information related to a specific mental and physical state. In the example of FIG. 9, a single piece of advice information is output from the advice information estimation model 160 in response to the input of a single mental and physical state index. The advice information estimation model 160 may be configured to output a plurality of pieces of advice information in response to the input of a single mental and physical state index. Furthermore, the advice information estimation model 160 may be configured to output a single piece of advice information in response to the input of a plurality of mental and physical state indices. Furthermore, the advice information estimation model 160 may be configured to output a plurality of pieces of advice information in response to the input of a plurality of mental and physical state indices.

[0061] When a physical and mental condition index indicating that the managed person may have flat feet, floating gait, or the like is input, the advice information estimation model 160 outputs advice information including advice regarding flat feet, floating gait, or the like. For example, the advice information estimation model 160 outputs advice information including advice indicating that the managed person may have flat feet, floating gait, or the like to a terminal device carried by the administrator of the managed person. For example, the advice information estimation model 160 may output advice information including advice that the managed person has a foot abnormality to a terminal device carried by the administrator of the managed person. For example, the advice information estimation model 160 outputs advice information including contact information for a foot care specialist. For example, the advice information estimation model 160 may output advice information including contact information for an osteopathic clinic or specialty store that can arrange for special insoles to be placed in shoes.

[0062] When a mental and physical state index indicating that the managed person may have bipolar disorder is input, the advice information estimation model 160 outputs advice information including advice on bipolar disorder, etc. For example, the advice information estimation model 160 outputs advice information including advice indicating that the managed person may have bipolar disorder to a terminal device carried by the manager of the managed person. For example, the advice information estimation model 160 may output advice information including advice that the managed person is not feeling well to a terminal device carried by the manager of the managed person. For example, the advice information estimation model 160 outputs advice information including contact information for a psychiatrist or a counselor. For example, the advice information estimation model 160 may output advice information including contact information for a temple or other place where one can experience Zen meditation or other forms of meditation.

[0063] The advice information estimation model 160 may be a model that outputs advice information including advice from an expert in response to an input of a mental and physical state index. For example, the advice information estimation model 160 may be a model that has been trained with training data in which a mental and physical state index is an explanatory variable and expert advice corresponding to the mental and physical state index is an objective variable. For example, the advice information estimation model 160 may be a model that generates advice information including advice from an expert such as a psychiatrist, an orthopedic surgeon, a certified psychologist, or a physical therapist in a rule-based manner in response to an input of a mental and physical state index.

[0064] For example, the advice information estimation model 160 may be a model that outputs advice information including contact information of an expert that is easily accessible from the residence of the managed person. In this case, the model may be configured so that location information of a mobile device carried by the managed person or the administrator is input to the advice information estimation model 160. The location information of the mobile device carried by the managed person or the administrator may be configured so that it is acquired by the acquisition unit 121. For example, the information providing device 12 may be configured so that the locations of experts such as specialist doctors, physical therapists, and certified psychologists are searched for by accessing structured data on information about the locations of experts.

[0065] The advice information estimation model 160 may be stored in an external storage device constructed on a cloud, a server, or the like. In this case, the advice information generation unit 126 uses the advice information estimation model 160 via an interface (not shown) connected to the storage device. The advice information estimation model 160 is a machine learning model. For example, the advice information estimation model 160 is a model trained using training data in which attribute data and gait indices related to a plurality of subjects are used as explanatory variables and a mental and physical state index related to a specific mental and physical state is used as a target variable. For example, the advice information estimation model 160 may be a model trained using training data in which gait waveform data of acceleration in three axial directions, angular velocities around three axes, and angles around three axes (posture angles) are used as explanatory variables.

[0066] For example, the advice information estimation model 160 is generated by learning using a linear regression algorithm. For example, the advice information estimation model 160 is generated by learning using a support vector machine (SVM) algorithm. For example, the advice information estimation model 160 is generated by learning using a Gaussian process regression (GPR) algorithm. For example, the advice information estimation model 160 is generated by learning using a random forest (RF) algorithm. For example, the advice information estimation model 160 may be generated by unsupervised learning that classifies the advice information of the managed person according to attribute data, gait indicators, and mental and physical state indicators. There are no particular limitations on the algorithm used to train the advice information estimation model 160.

[0067] For example, the advice information estimation model 160 may be a machine learning model such as an incomplete heterogeneous variational autoencoder or a random forest. If the model is an incomplete heterogeneous variational autoencoder, it is possible to estimate advice information about the managed person even if there are some defects in the attribute data or gait indicators.

[0068] The output unit 127 (output means) outputs the advice information estimated by the advice information generation unit 126. For example, the output unit 127 outputs the advice information to a mobile terminal carried by an administrator who manages the managed users. For example, the output unit 127 may display the advice information of the managed users on the screen of the administrator's mobile terminal. For example, the output unit 127 may output the advice information to an external system or the like that uses the advice information. There are no particular limitations on the use of the output advice information.

[0069] For example, the information providing device 12 is connected to an external system, such as a cloud or a server, via a mobile terminal (not shown) carried by the managed user. The mobile terminal is a portable communication device. For example, the mobile terminal is a portable communication device having a communication function, such as a smartphone, a smart watch, or a mobile phone. For example, the information providing device 12 is connected to the mobile terminal via wireless communication. For example, the information providing device 12 is connected to the mobile terminal via a wireless communication function (not shown) conforming to standards such as Bluetooth (registered trademark) or Wi-Fi (registered trademark). Note that the communication function of the information providing device 12 may conform to standards other than Bluetooth (registered trademark) or Wi-Fi (registered trademark). For example, the information providing device 12 may be connected to the mobile terminal via a wired connection, such as a cable. The advice information may be used by an application installed on the mobile terminal. In this case, the mobile terminal executes a process using the advice information using application software or the like installed on the mobile terminal.

[0070] (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. 10 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. 10, 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. 10 may be the information providing device 12.

[0071] 10 , first, the acquiring unit 121 acquires time-series data of sensor data measured by the measuring device 10 mounted on the footwear of the person to be managed (step S11). The sensor data includes acceleration in three axial directions and angular velocity around three axes.

[0072] 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. 11 ).

[0073] Next, the mental and physical state estimation unit 125 estimates the physical state of the managed person using the attribute data and gait indices (step S13).

[0074] Next, the advice information generating unit 126 generates advice information according to the estimated physical and mental state of the managed person (step S14).

[0075] Next, the output unit 127 outputs the generated advice information (step S15). For example, the output unit 127 outputs the advice information to a mobile terminal (not shown) carried by the administrator of the managed entity. For example, the output unit 127 may output the advice information to an external system that uses the advice information. For example, the output unit 127 may display the advice information on the screen of the mobile terminal of the managed entity.

[0076] [Gait Index Calculation Process] Next, the gait index calculation process (step S12 in FIG. 10 ) by the calculation unit 13 of the information provision device 12 will be described with reference to the drawings. FIG. 11 is a flowchart for explaining an example of the operation of the calculation unit 13. In describing the process according to the flowchart in FIG. 11 , the components of the calculation unit 13 will be described as the actors performing the operations. The actors performing the process according to the flowchart in FIG. 11 may be the information provision device 12 or the calculation unit 13.

[0077] 11, 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.

[0078] 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%.

[0079] 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.

[0080] (Application Examples) Next, application examples according to this embodiment will be described with reference to the drawings. In the application examples below, relationships between a business that provides a service using the information provision system 1, an administrator (guardian) who uses the service, a person to be managed (child), and an employee who is equivalent to an expert will be shown. Three application examples (application examples 1 and 2) will be described below. The application examples below are merely examples and do not limit the application examples of the information provision system 1.

[0081] Figure 12 is a conceptual diagram showing the correlation between the business operator, administrator (guardian), person under management (child), and expert. The business operator provides the administrator (guardian) with a service using the information provision system 1. Based on a contract concluded with the guardian, the business operator provides the guardian with advice information estimated based on the child's physical condition. The guardian pays the business operator a usage fee for the service using the information provision system 1. If the child's health checkup data is used to estimate the advice information, the guardian provides the child's health checkup data to the business operator. The contract between the business operator and the guardian clarifies rules regarding the handling of personal information and appropriate data management. The business operator clearly explains that the advice information is for reference only and does not guarantee medical accuracy or completeness.

[0082] A mobile terminal (not shown) used by a guardian downloads the advice information from the provider's cloud server. The guardian monitors the child's physical and mental condition according to the content of the advice information provided by the provider. The guardian refers to the advice information included in the advice information and considers how to respond to the child's physical and mental condition. The guardian also considers how to respond to the child's physical and mental condition according to the content of the advice information provided by the provider. The guardian provides advice to the child in their daily lives. The guardian refers to information about experts included in the advice information and consults with an expert as needed. The guardian also refers to information about experts included in the advice information and has the child examined by an expert. The guardian pays the expert a consultation fee.

[0083] A child is a child being raised by a guardian. The child is loaned or provided with a special insole equipped with the measuring device 10 by a business operator who has a contract with the guardian. The child wears shoes equipped with the special insole and carries a mobile terminal (not shown) capable of communicating with the measuring device 10 while walking around. The mobile terminal uploads sensor data measured by the measuring device 10 to the business operator's cloud server. The sensor data uploaded to the cloud server is used to estimate the child's physical and mental state and generate advice information.

[0084] The specialist pays a registration fee to the business to receive recommendations for consultations and examinations for children with physical and mental conditions in the specialist's area of ​​expertise. The specialist provides advice based on the child's physical condition in response to consultations from parents who contact the specialist based on the advice information. The specialist also examines children who are visited based on the advice information. The specialist examines the child and provides treatment based on the examination results. The specialist also provides appropriate advice to the parents based on the results of the child's examination.

[0085] [Application Example 1] FIG. 13 shows an example in which advice information according to the physical and mental condition of a child is displayed on the screen of a mobile terminal 170A of a guardian (Mr. A). In the example of FIG. 13, advice information stating, "Your child may have an abnormality in his / her foot. We suggest that you consult a foot care specialist," is displayed on the screen of the mobile terminal 170A. Advice information optimized for the person to be managed is displayed on the screen of the mobile terminal 170A. Also, in the example of FIG. 13, an address for accessing information on foot care specialists is displayed on the screen of the mobile terminal 170A. After checking the advice information displayed on the screen of the mobile terminal 170A, the guardian can access the information on foot care specialists depending on the content of the advice information. In other words, the advice information is information that supports the administrator in making decisions regarding the person to be managed.

[0086] 14 shows an example in which reservation information made by a parent in response to advice information is displayed on the screen of terminal device 180A used by a foot care specialist. The reservation information displayed on the screen of terminal device 180A reads, "A reservation has been accepted from Mr. A. He / she would like to visit the store around 14:00 on July 14th. Would you like to confirm the reservation?" For example, when the foot care specialist selects the reservation confirmation button, a reservation confirmation email is sent to mobile terminal 170A of the parent who made the reservation.

[0087] [Application Example 2] Fig. 15 shows an example in which advice information according to the physical and mental condition of a child is displayed on the screen of a mobile terminal 170B of a guardian (Mr. B). In the example of Fig. 15, advice information such as "Your child seems to be in poor health. We suggest that you consult a mental health professional" is displayed on the screen of the mobile terminal 170B. Advice information optimized for the person to be managed is displayed on the screen of the mobile terminal 170B. In addition, in the example of Fig. 15, an address for accessing information about a mental health professional is displayed on the screen of the mobile terminal 170B. After checking the advice information displayed on the screen of the mobile terminal 170B, the guardian can access the information about the mental health professional according to the content of the advice information. In other words, the advice information is information that supports the administrator in making decisions regarding the person to be managed.

[0088] 16 shows an example in which reservation information made by a guardian in response to advice information is displayed on the screen of terminal device 180B used by a mental health care professional. The reservation information displayed on the screen of terminal device 180B reads, "We have accepted a reservation from Mr. B. He would like to be seen at around 4:00 PM on June 21st. Would you like to confirm the reservation?" For example, when the mental health care professional selects the reservation confirmation button, a reservation confirmation email is sent to mobile terminal 170B of the guardian who made the reservation.

[0089] As described above, the information provision system of this embodiment includes a measurement device and an information provision device. The measurement device is installed in the footwear of at least one of the subjects. 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, an estimation unit, an advice information generation unit, and an output unit. The acquisition unit acquires sensor data including acceleration and angular velocity measured by the measurement device installed in the footwear of the subject. The estimation unit estimates the mental and physical state of the subject using the acquired sensor data. The advice information generation unit generates advice information including advice information corresponding to the mental and physical state of the subject. The output unit outputs the generated advice information.

[0090] The information providing device of this embodiment provides advice information according to the physical and mental state of the person to be managed, using sensor data including acceleration and angular velocity measured by a measuring device mounted on the footwear of the person to be managed. Therefore, according to this embodiment, it is possible to monitor the daily behavior of the person to be managed and provide advice according to the physical and mental state of the person to be managed.

[0091] Due to changes in the social environment, the number of children suffering from physical and mental disorders is increasing. Therefore, it is necessary to continuously monitor children's physical and mental conditions in their daily lives and encourage them to take action according to their condition at any given time. However, it is difficult for parents who are not experts to detect changes in their children's physical and mental conditions and encourage them to take appropriate action. Therefore, it is necessary to monitor children on a daily basis and provide advice according to the child's physical and mental condition from an expert's perspective. According to this embodiment, it is possible to recommend expert intervention according to the child's physical and mental condition.

[0092] In one aspect of the present embodiment, the estimation unit has a calculation unit and a mental and physical state estimation unit. The calculation unit calculates a gait index using sensor data. The mental and physical state estimation unit inputs data including the gait index calculated using the sensor data to a mental and physical state estimation model. The mental and physical state estimation model outputs a mental and physical state index indicating the degree of the mental and physical state in response to the input of the data including the gait index. The mental and physical state estimation unit estimates a mental and physical state in accordance with the mental and physical state index output from the mental and physical state estimation model. According to this aspect, by inputting data including the gait index calculated using sensor data to the mental and physical state estimation model, a mental and physical state can be estimated in accordance with the mental and physical state index.

[0093] In one aspect of this embodiment, the advice information generation unit estimates advice information according to the mental and physical state index of the managed person using an advice information estimation model. The advice information estimation model outputs advice information in response to an input of the mental and physical state index. According to this aspect, the advice information of the managed person can be estimated by inputting the mental and physical state index into the advice information generation model.

[0094] In one aspect of the present embodiment, the mental and physical state estimation unit estimates a mental and physical state including a mental and physical state index related to the foot condition using a mental and physical state estimation model. The mental and physical state estimation model outputs a mental and physical state index related to the foot condition in response to input of data including a gait index. The advice information generation unit estimates advice information corresponding to the mental and physical state index of the managed person using the advice information estimation model. The advice information estimation model outputs advice information including information about a foot specialist in response to input of the mental and physical state index related to the foot condition. According to this aspect, advice information corresponding to the foot condition of the managed person can be estimated by using an advice information generation model that outputs a mental and physical state index related to the foot condition.

[0095] In one aspect of the present embodiment, the mental and physical state estimation unit estimates a mental and physical state including a mental and physical state index related to a mental state using a mental and physical state estimation model. The mental and physical state estimation model outputs a mental and physical state index related to a mental state in response to input of data including a gait index. The advice information generation unit estimates advice information according to the mental and physical state index of the managed person using the advice information estimation model. The advice information estimation model outputs advice information including information about a mental care professional in response to input of the mental and physical state index related to the mental state. According to this aspect, advice information according to the mental state of the managed person can be estimated by using an advice information generation model that outputs a mental and physical state index related to a mental state.

[0096] In one aspect of the present embodiment, the mental and physical state estimation model and the advice information generation model are models trained using machine learning techniques. The mental and physical state estimation model includes an incomplete heterogeneous variational autoencoder. According to this aspect, even if there is some loss of data such as gait indicators, it is possible to estimate advice information corresponding to the mental and physical state of the person to be managed.

[0097] In one aspect of the present embodiment, the information providing device displays, on a screen of a terminal device used by an administrator of the managed entity, advice information optimized for the managed entity. According to this aspect, advice information estimated according to the mental and physical state of the managed entity can be provided in an optimized manner for the managed entity.

[0098] 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 first embodiment.

[0099] 17 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, an estimation unit 25, an advice information generation unit 26, and an output unit 27.

[0100] The acquisition unit 21 acquires sensor data including acceleration and angular velocity measured by a measuring device mounted on the footwear of the person to be managed. The estimation unit 25 estimates the mental and physical state of the person to be managed using the acquired sensor data. The advice information generation unit 26 generates advice information according to the mental and physical state of the person to be managed. The output unit 27 outputs the generated advice information.

[0101] (Operation) Next, the operation of the information providing device 20 will be described with reference to the drawings. Fig. 18 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. 18, 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. 18 may be the information providing device 20.

[0102] In FIG. 18, first, the acquisition unit 21 acquires sensor data including acceleration and angular velocity measured by a measurement device mounted on the footwear of the person to be managed (step S21).

[0103] Next, the estimation unit 25 estimates the mental and physical state of the managed person using the acquired sensor data (step S22).

[0104] Next, the advice information generating unit 26 generates advice information including advice information according to the physical and mental state of the person to be managed (step S23).

[0105] Next, the output unit 27 outputs the generated advice information (step S24).

[0106] As described above, the information providing device of this embodiment provides advice information according to the physical and mental state of the person to be managed, using sensor data including acceleration and angular velocity measured by a measuring device mounted on the footwear of the person to be managed. Therefore, according to this embodiment, it is possible to monitor the daily behavior of the person to be managed and provide advice according to the physical and mental state of the person to be managed.

[0107] (Hardware) Next, a hardware configuration for executing the processes according to each embodiment of the present disclosure will be described with reference to the drawings. Here, an information processing device 90 (computer) shown in FIG. 19 is given as an example of such a hardware configuration. The information processing device 90 in FIG. 19 is an example configuration for executing the processes according to each embodiment and does not limit the scope of the present disclosure.

[0108] As shown in Fig. 19, 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. 19, 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.

[0109] 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 processing of each embodiment. The processor 91 executes the program loaded onto the main storage device 92. The processor 91 executes the program to execute the processing of each embodiment.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] The above is an example of a hardware configuration for enabling the processing according to each embodiment of the present invention. The hardware configuration in Fig. 19 is an example of a hardware configuration for executing the processing according to each embodiment, and does not limit the scope of the present invention. A program for causing a computer to execute the processing according to each embodiment is also included in the scope of the present invention.

[0117] 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.

[0118] The components of each embodiment may be combined in any manner, may be realized by software, or may be realized by a circuit.

[0119] 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.

[0120] Some or all of the above embodiments can be described as, but are not limited to, the following supplementary notes. (Supplementary Note 1) An information providing device comprising: an acquisition unit that acquires sensor data including acceleration and angular velocity measured by a measuring device mounted on footwear of a person to be managed; an estimation unit that estimates a mental and physical state of the person to be managed using the acquired sensor data; an advice information generation unit that generates advice information according to the mental and physical state of the person to be managed; and an output unit that outputs the generated advice information. (Supplementary Note 2) The information providing device according to Supplementary Note 1, wherein the estimation unit comprises: a calculation unit that calculates a gait index using the sensor data; and a mental and physical state estimation unit that inputs data including the gait index calculated using the sensor data into a mental and physical state estimation model that outputs a mental and physical state index indicating a degree of the mental and physical state in response to input of data including the gait index, and estimates the mental and physical state of the person to be managed according to the mental and physical state index output from the mental and physical state estimation model. (Supplementary Note 3) The information providing device according to Supplementary Note 2, wherein the advice information generation unit estimates the advice information corresponding to the mental and physical state index of the managed person using an advice information estimation model that outputs the advice information in response to an input of the mental and physical state index. (Supplementary Note 4) The information providing device according to Supplementary Note 3, wherein the mental and physical state estimation unit estimates the mental and physical state including the mental and physical state index related to the foot state using the mental and physical state estimation model that outputs the advice information related to a foot state in response to an input of data including the gait index. (Supplementary Note 5) The information providing device described in Supplementary Note 3, wherein the mental and physical state estimation unit estimates the mental and physical state including the mental and physical state index related to the mental state using the mental and physical state estimation model that outputs the mental and physical state index related to the mental state in response to input of data including the gait index, and the advice information generation unit estimates the advice information corresponding to the mental and physical state index of the managed person using an advice information estimation model that outputs the advice information including information about a mental care professional in response to input of the mental and physical state index related to the mental state.(Supplementary Note 6) The information provision device according to Supplementary Note 3, wherein the mental and physical state estimation model and the advisory information estimation model are models trained using a machine learning technique, and the mental and physical state estimation model includes an incomplete heterogeneous variational autoencoder. (Supplementary Note 7) An information provision system comprising: the information provision device according to any one of Supplements 1 to 6; and the measurement device, wherein the measurement device is attached to footwear of the managed person, 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. (Supplementary Note 8) The information provision system according to Supplementary Note 7, wherein the information provision device displays the advice information optimized for the managed person on a screen of a terminal device used by an administrator of the managed person. (Supplementary Note 9) An information provision method, wherein a computer acquires sensor data including acceleration and angular velocity measured by a measurement device attached to footwear of the managed person, estimates the mental and physical state of the managed person using the acquired sensor data, generates advice information according to the managed person's mental and physical state, and outputs the generated advice information. (Supplementary Note 10) The information provision method according to Supplementary Note 9, wherein a computer calculates a gait index using the sensor data, inputs data including the gait index calculated using the sensor data into a mental and physical state estimation model that outputs a mental and physical state index indicating a degree of the mental and physical state in response to input of data including the gait index, and estimates the mental and physical state of the managed person in response to the mental and physical state index output from the mental and physical state estimation model. (Supplementary Note 11) The information provision method according to Supplementary Note 10, wherein a computer estimates the advice information in response to the mental and physical state index of the managed person using an advice information estimation model that outputs the advice information in response to input of the mental and physical state index.(Supplementary Note 12) The information provision method according to Supplementary Note 11, wherein a computer estimates a mental and physical state including the mental and physical state index related to a foot condition using the mental and physical state estimation model that outputs the mental and physical state index related to a foot condition in response to input of data including the gait index, and estimates the advice information corresponding to the mental and physical state index of the managed person using an advice information estimation model that outputs the advice information including information on a foot specialist in response to input of the mental and physical state index related to the foot condition. (Supplementary Note 13) The information provision method according to Supplementary Note 11, wherein a computer estimates a mental and physical state including the mental and physical state index related to a mental state using the mental and physical state estimation model that outputs the mental and physical state index related to a mental state in response to input of data including the gait index, and estimates the advice information corresponding to the mental and physical state index of the managed person using an advice information estimation model that outputs the advice information including information on a mental care specialist in response to input of the mental and physical state index related to the mental state. (Supplementary Note 14) The information providing method according to Supplementary Note 11, wherein the mental and physical state estimation model and the advisory information estimation model are models trained using a machine learning technique, and the mental and physical state estimation model includes an incomplete heterogeneous variational autoencoder. (Supplementary Note 15) A computer-readable non-transitory recording medium having recorded thereon a program causing a computer to execute the following processes: acquiring sensor data including acceleration and angular velocity measured by a measuring device mounted on footwear of a person to be managed; estimating the mental and physical state of the person to be managed using the acquired sensor data; generating advice information according to the mental and physical state of the person to be managed; and outputting the generated advice information. (Supplementary Note 16) A computer-readable non-transitory recording medium according to Supplementary Note 15, having recorded thereon a program that causes a computer to execute the following processes: a process of calculating a gait index using the sensor data; a process of inputting data including the gait index into a mental / physical state estimation model that outputs a mental / physical state index indicating the degree of mental / physical state in response to input of data including the gait index; and a process of estimating the mental / physical state of the managed person in accordance with the mental / physical state index output from the mental / physical state estimation model.(Supplementary Note 17) A computer-readable non-transitory recording medium according to Supplementary Note 16, having recorded thereon a program that causes a computer to execute a process of estimating advice information corresponding to the mental and physical state index of the managed person, using an advice information estimation model that outputs the advice information in response to input of the mental and physical state index. (Supplementary Note 18) A computer-readable non-transitory recording medium according to Supplementary Note 17, having recorded thereon a program that causes a computer to execute the following processes: a process of estimating a mental and physical state including the mental and physical state index related to a foot condition, using the mental and physical state estimation model that outputs the mental and physical state index related to a foot condition in response to input of data including the gait index; and a process of estimating the advice information corresponding to the mental and physical state index of the managed person, using an advice information estimation model that outputs the advice information including information on a foot specialist in response to input of the mental and physical state index related to the foot condition. (Supplementary Note 19) The computer-readable non-transitory recording medium according to Supplementary Note 17, having recorded thereon a program that causes a computer to execute the following steps: a process of estimating a mental and physical state including the mental and physical state index related to a mental state, using the mental and physical state estimation model that outputs the mental and physical state index related to a mental state in response to input of data including the gait index; and a process of estimating advice information corresponding to the mental and physical state index of the managed person, using an advice information estimation model that outputs the advice information including information on a mental care professional in response to input of the mental and physical state index related to the mental state. (Supplementary Note 20) The computer-readable non-transitory recording medium according to Supplementary Note 17, wherein the mental and physical state estimation model and the advice information estimation model are models trained using a machine learning technique, and the mental and physical state estimation model includes an incomplete heterogeneous variational autoencoder.

[0121] REFERENCE SIGNS LIST 1 Information provision system 10 Measurement device 12 Information provision device 13 Calculation unit 15 Estimation unit 20 Information provision device 21 Acquisition unit 25 Estimation unit 26 Advice information generation unit 27 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 Storage unit 125 Mental and physical state estimation unit 126 Advice information generation unit 127 Output unit

Claims

1. An information providing device comprising: an acquisition unit that acquires sensor data including acceleration and angular velocity measured by a measuring device mounted on the footwear of a person to be managed; an estimation unit that estimates the mental and physical state of the person to be managed using the acquired sensor data; an advice information generation unit that generates advice information according to the mental and physical state of the person to be managed; and an output unit that outputs the generated advice information.

2. The information providing device of claim 1, wherein the estimation unit comprises: a calculation unit that calculates a gait index using the sensor data; and a mental and physical state estimation unit that inputs data including the gait index calculated using the sensor data into a mental and physical state estimation model that outputs a mental and physical state index indicating the degree of the mental and physical state in response to input of data including the gait index, and estimates the mental and physical state of the managed person in accordance with the mental and physical state index output from the mental and physical state estimation model.

3. The information providing device of claim 2, wherein the advice information generation unit estimates the advice information corresponding to the mental and physical state index of the managed person using an advice information estimation model that outputs the advice information in response to an input of the mental and physical state index.

4. The information providing device of claim 3, wherein the mental and physical state estimation unit estimates a mental and physical state including the mental and physical state index related to the foot condition using the mental and physical state estimation model that outputs the mental and physical state index related to the foot condition in response to input of data including the gait index, and the advice information generation unit estimates the advice information corresponding to the mental and physical state index of the managed person using an advice information estimation model that outputs the advice information including information about a foot specialist in response to input of the mental and physical state index related to the foot condition.

5. The information providing device of claim 3, wherein the mental and physical state estimation unit estimates a mental and physical state including the mental and physical state index related to a mental state using the mental and physical state estimation model that outputs the mental and physical state index related to a mental state in response to input of data including the gait index, and the advice information generation unit estimates the advice information corresponding to the mental and physical state index of the managed person using an advice information estimation model that outputs the advice information including information about a mental care professional in response to input of the mental and physical state index related to a mental state.

6. The information providing device according to claim 3, wherein the mental and physical state estimation model and the advisory information estimation model are models trained using a machine learning technique, and the mental and physical state estimation model includes an incomplete heterogeneous variational autoencoder.

7. An information provision system comprising: an information provision device according to any one of claims 1 to 6; and the measuring device, wherein the measuring device is attached to the footwear of the person to be managed, 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.

8. An information providing system according to claim 7, wherein the information providing device displays the advice information optimized for the managed entity on a screen of a terminal device used by an administrator of the managed entity.

9. An information provision method in which a computer acquires sensor data including acceleration and angular velocity measured by a measuring device mounted on the footwear of a person to be managed, estimates the mental and physical state of the person to be managed using the acquired sensor data, generates advice information according to the mental and physical state of the person to be managed, and outputs the generated advice information.

10. An information provision method as described in claim 9, wherein a computer calculates a gait index using the sensor data, inputs data including the gait index calculated using the sensor data into a mental and physical state estimation model that outputs a mental and physical state index indicating the degree of the mental and physical state in response to the input of data including the gait index, and estimates the mental and physical state of the managed person in response to the mental and physical state index output from the mental and physical state estimation model.

11. The information providing method according to claim 10, wherein a computer estimates the advice information corresponding to the mental and physical state index of the managed person using an advice information estimation model that outputs the advice information in response to an input of the mental and physical state index.

12. An information provision method as described in claim 11, wherein a computer estimates a mental and physical state including the mental and physical condition index related to the foot condition using the mental and physical condition estimation model that outputs the mental and physical condition index related to the foot condition in response to input of data including the gait index, and estimates the advice information corresponding to the mental and physical condition index of the managed person using an advice information estimation model that outputs the advice information including information about a foot specialist in response to input of the mental and physical condition index related to the foot condition.

13. The information provision method described in claim 11, wherein a computer estimates a mental and physical state including the mental and physical state index related to a mental state using the mental and physical state estimation model that outputs the mental and physical state index related to a mental state in response to input of data including the gait index, and estimates the advice information corresponding to the mental and physical state index of the managed person using an advice information estimation model that outputs the advice information including information about a mental care professional in response to input of the mental and physical state index related to a mental state.

14. The information provision method described in claim 11, wherein the mental and physical state estimation model and the advisory information estimation model are models trained using a machine learning technique, and the mental and physical state estimation model includes an incomplete heterogeneous variational autoencoder.

15. A non-transitory computer-readable recording medium having recorded thereon a program that causes a computer to execute the following processes: acquiring sensor data including acceleration and angular velocity measured by a measuring device mounted on the footwear of the person to be managed; estimating the mental and physical state of the person to be managed using the acquired sensor data; generating advice information according to the mental and physical state of the person to be managed; and outputting the generated advice information.

16. A non-transitory computer-readable recording medium as described in claim 15, having recorded thereon a program that causes a computer to execute the following processes: a process of calculating a gait index using the sensor data; a process of inputting data including the gait index calculated using the sensor data into a mental / physical state estimation model that outputs a mental / physical state index indicating the degree of the mental / physical state in response to the input of data including the gait index; and a process of estimating the mental / physical state of the managed person in response to the mental / physical state index output from the mental / physical state estimation model.

17. A non-transitory computer-readable recording medium as described in claim 16, having recorded thereon a program that causes a computer to execute a process of estimating the advice information corresponding to the mental and physical state index of the managed person using an advice information estimation model that outputs the advice information in response to the input of the mental and physical state index.

18. A non-transitory computer-readable recording medium as described in claim 17, having recorded thereon a program that causes a computer to execute the following processes: a process of estimating a mental and physical state including the mental and physical state index related to the foot condition using the mental and physical state estimation model that outputs the mental and physical state index related to the foot condition in response to input of data including the gait index; and a process of estimating the advice information corresponding to the mental and physical state index of the managed person using an advice information estimation model that outputs the advice information including information about a foot specialist in response to input of the mental and physical state index related to the foot condition.

19. A non-transitory computer-readable recording medium as described in claim 17, having recorded thereon a program that causes a computer to execute the following processes: a process of estimating a mental and physical state including the mental and physical state index related to a mental state using the mental and physical state estimation model that outputs the mental and physical state index related to a mental state in response to input of data including the gait index; and a process of estimating the advice information corresponding to the mental and physical state index of the managed person using an advice information estimation model that outputs the advice information including information about a mental care professional in response to input of the mental and physical state index related to the mental state.

20. The computer-readable non-transitory recording medium of claim 17, wherein the mental and physical state estimation model and the advisory information estimation model are models trained using a machine learning technique, and the mental and physical state estimation model includes an incomplete heterogeneous variational autoencoder.

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