Walking posture state estimation system, walking posture state estimation method, walking posture state estimation apparatus, and control program

Abstract

To provide a walking posture state estimation system capable of more accurately estimating a walking posture state, a walking posture state estimation method, a walking posture state estimation apparatus, and a control program.SOLUTION: A walking posture estimation system comprises: a sole information acquisition device which acquires static information related to a user's sole; a wearable device which is wearable on the user and acquires dynamic information acquirable when the user walks; and an information processing device connected to the sole information acquisition device and the wearable device. The information processing device includes: a reception part which receives the static information and the dynamic information; a storage part which stores a learned model capable of outputting the user's walking posture state estimated based on the static information and the dynamic information.SELECTED DRAWING: Figure 7

Description

Technical Field

[0001] The present disclosure relates to a walking posture state estimation system, a walking posture state estimation method, a walking posture state estimation device, and a control program.

Background Art

[0002] Conventionally, systems have been developed that analyze a user's walking style, postural habits, etc. and propose improvement methods. In such systems, in order to propose appropriate improvement methods, it is necessary to estimate the user's walking style, postural habits, etc. more accurately.

[0003] Patent Document 1 discloses a motion posture derivation device that is worn on a user's body and derives a walking or running posture.

Prior Art Documents

Patent Documents

[0004]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0005] There is a need to provide a system capable of estimating a walking posture state more accurately.

[0006] The purpose of the walking posture state estimation system, the walking posture state estimation method, the walking posture state estimation device, and the control program is to enable more accurate estimation of the walking posture state.

Means for Solving the Problems

[0007] The walking posture estimation system according to the embodiment includes a sole information acquisition device that acquires static information regarding the sole of the user, a wearable device that can be worn by the user and acquires dynamic information obtained when the user walks, and an information processing device connected to the sole information acquisition device and the wearable device. The information processing device includes a reception unit that receives the static information and the dynamic information, a storage unit that stores a learned model capable of outputting the walking posture state of the user estimated based on the static information and the dynamic information, and an output unit that outputs the walking posture state.

[0008] In the walking posture estimation system according to the embodiment, it further preferably includes a second wearable device that can be worn by the user and acquires second dynamic information obtained when the user walks. The first wearable device preferably includes a second dynamic information reception unit that receives the second dynamic information from the second wearable device, and a transmission unit that transmits the dynamic information for a predetermined number of steps of the user and the second dynamic information to the information processing device.

[0009] In the walking posture estimation system according to the embodiment, the walking posture state preferably includes any one or more of the state of the step width, the state of the center of gravity position, the state of the swinging leg, the state of the supporting leg, and the state of the hip joint.

[0010] In the walking posture estimation system according to the embodiment, the learned model preferably includes a first learned model capable of outputting a first walking posture state estimated based on the static information, and a second learned model capable of outputting a second walking posture state estimated based on the dynamic information.

[0011] In the walking posture estimation system according to the embodiment, the learned model is preferably a single learned model capable of outputting the walking posture state estimated based on the static information and the dynamic information.

[0012] In the walking posture estimation system according to the embodiment, it is preferable that the information processing device inputs the information based on the static information to the first layer of the first learned model included in the learned model, and inputs the information based on the dynamic information to the second layer different from the first layer of the second learned model included in the learned model, thereby obtaining the walking posture state.

[0013] In the walking posture estimation system according to the embodiment, the walking posture state preferably includes the information output from the first layer of the learned model when the information based on the static information is input to the learned model, and the information output from the second layer different from the first layer of the learned model when the information based on the dynamic information is input to the learned model.

[0014] The walking posture estimation method according to the embodiment acquires static information regarding the user's sole by a sole information acquisition device, acquires dynamic information obtained when the user walks by a wearable device wearable by the user, and uses a learned model capable of outputting the walking posture state of the user estimated based on the static information and the dynamic information to estimate the walking posture state based on the acquired static information and dynamic information, and outputs the walking posture state.

[0015] The walking posture estimation device according to the embodiment includes an acquisition unit that acquires static information and dynamic information from a static information sensor that measures static information regarding the user's sole and a dynamic information sensor that measures dynamic information obtained when the user walks, an estimation unit that uses a learned model capable of outputting the walking posture state of the user estimated based on the static information and the dynamic information and acquires the walking posture state estimated based on the acquired static information and dynamic information, and an output unit that outputs the walking posture state.

[0016] The walking posture estimation method according to the embodiment acquires static information and dynamic information from a static information sensor that measures static information regarding the sole of the user's foot and a dynamic information sensor that measures dynamic information obtained when the user walks, and uses a learned model capable of outputting the walking posture state of the user estimated based on the static information and the dynamic information, acquires the walking posture state estimated based on the acquired static information and dynamic information, and outputs the walking posture state.

[0017] The control program according to the embodiment is a control program for a walking posture state estimation device, which acquires static information and dynamic information from a static information sensor that measures static information regarding the sole of the user's foot and a dynamic information sensor that measures dynamic information obtained when the user walks, and uses a learned model capable of outputting the walking posture state of the user estimated based on the static information and the dynamic information, acquires the walking posture state estimated based on the acquired static information and dynamic information, and causes the walking posture state estimation device to output the walking posture state.

[0018] The walking posture estimation device according to the embodiment includes an acquisition unit that acquires static information from a static information sensor that measures static information regarding the sole of the user's foot, a sensor that measures dynamic information obtained when the user walks, an estimation unit that uses a learned model capable of outputting the walking posture state of the user estimated based on the static information and the dynamic information, acquires the walking posture state estimated based on the acquired static information and dynamic information, and an output unit that outputs the walking posture state.

[0019] The walking posture estimation method according to the embodiment is a walking posture state estimation method by a walking posture state estimation device having a sensor that measures dynamic information obtained when the user walks, which acquires static information from a static information sensor that measures static information regarding the sole of the user's foot, uses a learned model capable of outputting the walking posture state of the user estimated based on the static information and the dynamic information, acquires the walking posture state estimated based on the acquired static information and dynamic information, and outputs the walking posture state.

[0020] The control program according to the embodiment is a control program for a walking posture state estimation device having a sensor that measures dynamic information obtained when a user walks. The control program acquires static information from a static information sensor that measures static information regarding the user's sole, and uses a learned model capable of outputting the walking posture state of the user estimated based on the static information and the dynamic information, acquires the walking posture state estimated based on the acquired static information and dynamic information, and causes the walking posture state estimation device to output the walking posture state.

Advantages of the Invention

[0021] The walking posture state estimation system, the walking posture state estimation method, the walking posture state estimation device, and the control program can estimate the walking posture state with higher accuracy.

Brief Description of the Drawings

[0022]

Figure 1

Figure 2

Figure 3

Figure 4

Figure 5

Figure 6

Figure 7

Figure 8

Figure 9

Modes for Carrying Out the Invention

[0023] Hereinafter, a walking posture state estimation system, a walking posture state estimation method, a walking posture state estimation device, and a control program according to one aspect of the embodiment will be described with reference to the drawings. However, note that the technical scope of the present invention is not limited to those embodiments, and extends to the invention described in the claims and equivalents thereof.

[0024] FIG. 1 is a diagram showing a schematic configuration of a walking posture state estimation system 1 according to an embodiment.

[0025] As shown in FIG. 1, the walking posture state estimation system 1 includes a first wearable device 100, one or more second wearable devices 200, one or more sole information acquisition devices 300, an information processing device 400, a server device 500, and the like. The first wearable device 100, each second wearable device 200, and each sole information acquisition device 300 are worn and used by the user. The information processing device 400 is used by an administrator or the like who manages the user's health. The server device 500 is, for example, a server arranged on a cloud network.

[0026] The first wearable device 100 and each second wearable device 200, and the first wearable device 100 and each sole information acquisition device 300 are connected to be communicable with each other via a wireless network such as Bluetooth (registered trademark) or wireless LAN (Local Area Network). Also, the first wearable device 100, the information processing device 400, and the server device 500 are connected to be communicable with each other via a network N. The network N is a wired network such as the Internet or an intranet. The network N may also be a wireless network such as a wireless LAN (Local Area Network). Further, each second wearable device 200, each sole information acquisition device 300, the information processing device 400, and the server device 500 may be connected to be communicable with each other via the network N.

[0027] FIG. 2 is a diagram showing a schematic configuration of the first wearable device 100.

[0028] The first wearable device 100 is an example of a walking state estimation device. The first wearable device 100 is a terminal device that can be worn by a user, such as a multifunctional mobile phone (so-called smartphone), a tablet PC, or the like. The first wearable device 100 includes a first input device 101, a first display device 102, a first communication device 103, a first interface device 104, a first sensor 105, a first storage device 110, a first processing device 120, and the like. The first input device 101, the first display device 102, the first communication device 103, the first interface device 104, the first sensor 105, the first storage device 110, and the first processing device 120 are interconnected via a CPU (Central Processing Unit) bus or the like.

[0029] The first input device 101 has an input device such as a touch panel type and an interface circuit that acquires a signal from the input device, and outputs an operation signal corresponding to a user's input operation.

[0030] The first display device 102 is an example of an output unit. The first display device 102 has a display including liquid crystal, organic EL (Electro-Luminescence), or the like, and an interface circuit that outputs image data to the display, and displays the image data on the display.

[0031] The first communication device 103 is an example of an output unit. The first communication device 103 has an antenna that transmits and receives wireless signals, and a wireless communication interface circuit according to a communication protocol such as wireless LAN. The first communication device 103 communicates and connects with the network N according to a communication standard such as wireless LAN. The first communication device 103 sends the data received from the information processing device 400, the server device 500, or the like to the first processing device 120 via the network N. Also, the first communication device 103 transmits the data received from the first processing device 120 to the information processing device 400, the server device 500, or the like via the network N.

[0032] The first interface device 104 has an antenna for transmitting and receiving radio signals and a wireless communication interface circuit according to a communication protocol such as Bluetooth (registered trademark). The first interface device 104 communicatively connects with each second wearable device 200 according to a communication standard such as Bluetooth (registered trademark). The first interface device 104 sends the data received from each second wearable device 200 or each sole information acquisition device 300 to the first processing device 120. Also, the first interface device 104 transmits the data received from the first processing device 120 to each second wearable device 200 or each sole information acquisition device 300.

[0033] The first sensor 105 is an example of a sensor and a motion information sensor. The first sensor 105 includes an acceleration sensor that measures the acceleration in three axial directions applied to the first wearable device 100, a gyro sensor that measures the angular velocity in three axial directions applied to the first wearable device 100, and the like. The acceleration sensor and the gyro sensor output a measurement signal indicating the measured acceleration and angular velocity to the first processing device 120.

[0034] Note that the first sensor 105 may include a geomagnetic sensor that detects the geomagnetism applied to the first wearable device 100, that is, the movement of the user wearing the first wearable device 100. Also, the first sensor 105 may include a barometric pressure sensor that detects the barometric pressure around the first wearable device 100, that is, the movement of the user wearing the first wearable device 100 in the vertical direction. Further, the first sensor 105 may include a GPS (Global Positioning System) sensor that detects the position of the first wearable device 100, that is, the movement of the user wearing the first wearable device 100. In that case, the first sensor 105 outputs a measurement signal indicating the detected geomagnetism, barometric pressure, or position to the first processing device 120.

[0035] The first storage device 110 is an example of a storage unit. The first storage device 110 includes a memory device such as a RAM (Random Access Memory) or a ROM (Read Only Memory), a fixed disk device such as a hard disk, or a portable storage device such as a flexible disk or an optical disk. Further, various computer programs, databases, tables, etc. used for various processes of the first wearable device 100 are stored in the first storage device 110. The computer program may be installed in the first storage device 110 using a known setup program or the like from a computer-readable portable recording medium. The portable recording medium is, for example, a CD-ROM (compact disc read only memory), a DVD-ROM (digital versatile disc read only memory), or the like. The computer program may be stored in a recording medium of a predetermined server and installed via the network N.

[0036] The first processing device 120 operates based on a program stored in advance in the first storage device 110. The first processing device 120 is, for example, a CPU. As the first processing device 120, a DSP (digital signal processor), LSI (large scale integration), ASIC (Application Specific Integrated Circuit), FPGA (Field-Programmable Gate Array), etc. may be used. The first processing device 120 is connected to the first input device 101, the first display device 102, the first communication device 103, the first interface device 104, the first sensor 105, the first storage device 110, etc., and controls each device. The first processing device 120 acquires information measured by the first sensor 105 when the user walks. Further, the first processing device 120 acquires information measured by the second wearable device 200 when the user who holds the second wearable device 200 walks, from the second wearable device 200 via the first interface device 104. Further, the first processing device 120 acquires information regarding the user's sole from the sole information acquisition device 300 via the first interface device 104. The first processing device 120 transmits the acquired pieces of information to the information processing device 400 via the first communication device 103.

[0037] The first processing device 120 reads a computer program stored in the first storage device 110 and operates according to the read computer program. Thereby, the first processing device 120 functions as the first acquisition unit 121 and the first transmission unit 122. The first acquisition unit 121 is an example of the second dynamic information reception unit, and the first transmission unit 122 is an example of the transmission unit.

[0038] Figure 3 is a diagram showing a schematic configuration of the second wearable device 200.

[0039] The second wearable device 200 is a terminal device wearable by a user. For example, the second wearable device 200 is a wristwatch-type wearable computer worn on the user's wrist. Also, the second wearable device 200 is an earphone-type wearable computer worn on the user's ear. The second wearable device 200 includes a second input device 201, a second display device 202, a second communication device 203, a second interface device 204, a second sensor 205, a second storage device 210, a second processing device 220, and the like. The second input device 201, the second display device 202, the second communication device 203, the second interface device 204, the second sensor 205, the second storage device 210, and the second processing device 220 are interconnected via a CPU bus or the like.

[0040] The second input device 201 includes an input device such as a button and an interface circuit that acquires a signal from the input device, and outputs an operation signal according to a user's input operation. The second input device 201 may be omitted.

[0041] The second display device 202 includes a display including liquid crystal, organic EL, etc. and an interface circuit that outputs image data to the display, and displays the image data on the display. The second display device 202 may have an LED (Light Emitting Diode) or the like, and may notify the user by lighting or extinguishing. The second display device 202 may be omitted.

[0042] The second communication device 203 includes an antenna that transmits and receives wireless signals and a wireless communication interface circuit according to a communication protocol such as a wireless LAN. The second communication device 203 communicatively connects to the network N according to a communication standard such as a wireless LAN. The second communication device 203 sends data received from the information processing device 400 or the server device 500 or the like via the network N to the second processing device 220. Also, the second communication device 203 transmits data received from the second processing device 220 to the information processing device 400 or the server device 500 or the like via the network N.

[0043] The second interface device 204 includes an antenna for transmitting and receiving wireless signals and a wireless communication interface circuit according to a communication protocol such as Bluetooth (registered trademark). The second interface device 204 communicatively connects to the first wearable device 100 according to a communication standard such as Bluetooth (registered trademark). The second interface device 204 sends the data received from the first wearable device 100 to the second processing device 220. Also, the second interface device 204 transmits the data received from the second processing device 220 to the first wearable device 100.

[0044] The second sensor 205 is an example of a dynamic information sensor. The second sensor 205 includes an acceleration sensor that measures the three-axis acceleration applied to the second wearable device 200, a gyro sensor that measures the three-axis angular velocity applied to the second wearable device 200, and the like. The acceleration sensor and the gyro sensor output measurement signals indicating the measured acceleration and angular velocity to the second processing device 220.

[0045] The second storage device 210 includes a memory device such as a RAM or a ROM, a fixed disk device such as a hard disk, or a portable storage device such as a flexible disk or an optical disk. Also, various computer programs, databases, tables, etc. used for various processes of the second wearable device 200 are stored in the second storage device 210. The computer program may be installed in the second storage device 210 using a known setup program or the like from a computer-readable portable recording medium. The portable recording medium is, for example, a CD-ROM, a DVD-ROM, or the like. The computer program may be stored in a recording medium possessed by a predetermined server and installed via the network N.

[0046] The second processing device 220 operates based on a program stored in advance in the second storage device 210. The second processing device 220 is, for example, a CPU. As the second processing device 220, a DSP, LSI, ASIC, FPGA, or the like may be used. The second processing device 220 is connected to the second input device 201, the second display device 202, the second communication device 203, the second interface device 204, the second sensor 205, the second storage device 210, etc., and controls each device. The second processing device 220 acquires information measured by the second sensor 205 when the user walks, and transmits it to the first wearable device 100 via the second interface device 204.

[0047] The second processing device 220 reads a computer program stored in the second storage device 210, and operates according to the read computer program. Thereby, the second processing device 220 functions as the second acquisition unit 221 and the second transmission unit 222.

[0048] FIG. 4 is a diagram showing a schematic configuration of the sole information acquisition device 300.

[0049] The sole information acquisition device 300 is a terminal device wearable by a user. The sole information acquisition device 300 is an insole (inner sole) type wearable computer worn inside the user's shoe. The sole information acquisition device 300 includes a third input device 301, a third display device 302, a third communication device 303, a third interface device 304, a third sensor 305, a third storage device 310, a third processing device 320, etc. The third input device 301, the third display device 302, the third communication device 303, the third interface device 304, the third sensor 305, the third storage device 310, and the third processing device 320 are interconnected via a CPU bus or the like.

[0050] The third input device 301 has an input device such as a button and an interface circuit that acquires a signal from the input device, and outputs an operation signal according to a user's input operation. The third input device 301 may be omitted.

[0051] The third display device 302 has a display including a liquid crystal, an organic EL, etc. and an interface circuit that outputs image data to the display, and displays the image data on the display. The third display device 302 may have an LED or the like and may notify the user by lighting or extinguishing. The third display device 302 may be omitted.

[0052] The third communication device 303 has an antenna that transmits and receives radio signals and a wireless communication interface circuit according to a communication protocol such as a wireless LAN. The third communication device 303 communicates and connects with the network N according to a communication standard such as a wireless LAN. The third communication device 303 sends the data received from the information processing device 400 or the server device 500 etc. via the network N to the third processing device 320. Also, the third communication device 303 transmits the data received from the third processing device 320 to the information processing device 400 or the server device 500 etc. via the network N.

[0053] The third interface device 304 has an antenna that transmits and receives radio signals and a wireless communication interface circuit according to a communication protocol such as Bluetooth (registered trademark). The third interface device 304 communicates and connects with the first wearable device 100 according to a communication standard such as Bluetooth (registered trademark). The third interface device 304 sends the data received from the first wearable device 100 to the third processing device 320. Also, the third interface device 304 transmits the data received from the third processing device 320 to the first wearable device 100.

[0054] The third sensor 305 is an example of a static information sensor. The third sensor 305 includes an insole type pressure sensor. The insole type pressure sensor is a resistance change type or capacitance change type pressure sensor arranged in one dimension or two dimensions. The third sensor 305 measures the magnitude of the pressure applied to each position on the user's sole by each pressure sensor, generates sole data with the measured pressure magnitude as the gradation value of each pixel, and outputs it to the third processing device 320. The sole data is, for example, a sole image with the magnitude of the pressure measured by each pressure sensor as the gradation value of each pixel. The sole data may also be, for example, a sole vector with the pressure measured by each pressure sensor as an element.

[0055] The third storage device 310 has a memory device such as a RAM or a ROM, a fixed disk device such as a hard disk, or a portable storage device such as a flexible disk or an optical disk. Also, stored in the third storage device 310 are computer programs, databases, tables, etc. used for various processes of the sole information acquisition device 300. The computer program may be installed in the third storage device 310 using a known setup program etc. from a computer-readable portable recording medium. The portable recording medium is, for example, a CD-ROM, a DVD-ROM, etc. The computer program may be stored in a recording medium of a predetermined server and installed via the network N.

[0056] The third processing device 320 operates based on a program stored in the third storage device 310 in advance. The third processing device 320 is, for example, a CPU. As the third processing device 320, a DSP, an LSI, an ASIC, an FPGA, etc. may be used. The third processing device 320 is connected to the third input device 301, the third display device 302, the third communication device 303, the third interface device 304, the third sensor 305, the third storage device 310, etc., and controls each device. The third processing device 320 acquires the information measured by the third sensor 305 when the user walks, and transmits it to the first wearable device 100 via the third interface device 304.

[0057] The third processing device 320 reads the computer program stored in the third storage device 310 and operates according to the read computer program. As a result, the third processing device 320 functions as a third acquisition unit 321 and a third transmission unit 322.

[0058] FIG. 5 is a diagram showing a schematic configuration of the information processing apparatus 400.

[0059] The information processing apparatus 400 is an example of a walking posture state estimation apparatus. The information processing apparatus 400 includes a fourth communication device 401, a fourth storage device 410, a fourth processing device 420, and the like. The fourth communication device 401, the fourth storage device 410, and the fourth processing device 420 are mutually connected via a CPU bus or the like.

[0060] The fourth communication device 401 is an example of an output unit. The fourth communication device 401 has a wired communication interface circuit according to a communication protocol such as TCP / IP. The fourth communication device 401 communicatively connects to the network N according to a communication standard such as Ethernet (registered trademark). The fourth communication device 401 sends data received from the first wearable device 100, the second wearable device 200, the sole information acquisition device 300, the server device 500, or the like via the network N to the fourth processing device 420. The fourth communication device 401 transmits data received from the fourth processing device 420 to the first wearable device 100, the second wearable device 200, the sole information acquisition device 300, the server device 500, or the like via the network N. Note that the fourth communication device 401 may have an antenna that transmits and receives radio signals and a wireless communication interface circuit according to a communication protocol such as a wireless LAN, and communicatively connect to the network N according to a communication standard such as a wireless LAN.

[0061] The fourth storage device 410 is an example of a storage unit. The fourth storage device 410 includes a memory device such as a RAM or a ROM, a fixed disk device such as a hard disk, or a portable storage device such as a flexible disk or an optical disk. Also, various computer programs, databases, tables, etc. used for various processes of the information processing device 400 are stored in the fourth storage device 410. The computer program may be installed in the fourth storage device 410 from a computer-readable portable recording medium such as a CD-ROM or a DVD-ROM using a known setup program or the like. The computer program may be stored in a recording medium possessed by a predetermined server and installed via the network N.

[0062] Stored in the fourth storage device 410 as data are a learned model 411, a data table 412, etc. The learned model 411 is a model for estimating the walking posture state of a user wearing the first wearable device 100, the second wearable device 200, and the sole information acquisition device 300. Stored in the data table 412 is each piece of information measured by the first sensor 105 of the first wearable device 100, the second sensor 205 of the second wearable device 200, or the third sensor 305 of the sole information acquisition device 300. Details of the data table 412 will be described later.

[0063] The fourth processing device 420 operates based on a program stored in the fourth storage device 410 in advance. The fourth processing device 420 is, for example, a CPU. As the fourth processing device 420, a DSP, an LSI, an ASIC, an FPGA, etc. may be used. The fourth processing device 420 is connected to the fourth communication device 401, the fourth storage device 410, etc. and controls each device.

[0064] The fourth processing device 420 reads a computer program stored in the fourth storage device 410 and operates according to the read computer program. Thereby, the fourth processing device 420 functions as a fourth acquisition unit 421, an estimation unit 422, and an output control unit 423.

[0065] FIG. 6 is a schematic diagram showing an example of the data structure of the data table 412.

[0066] In the data table 412, the angular velocity, acceleration, geomagnetism, atmospheric pressure (not shown), position (not shown), or sole data acquired from the first wearable device 100, the second wearable device 200, or the sole information acquisition device 300 are stored in association with the identification information (device ID) of each device and the acquisition time at which each data was acquired.

[0067] FIG. 7 is a sequence showing an example of the operation of the estimation process in the walking posture state estimation system 1.

[0068] Hereinafter, an example of the operation of the estimation process will be described with reference to the flowchart shown in FIG. 7. The flowchart of the operation described below is mainly executed in cooperation with each element of each device by each processing device of each device based on a program stored in each storage device of each device of the walking posture state estimation system 1 in advance.

[0069] First, the third acquisition unit 321 of each sole information acquisition device 300 acquires a measurement signal from the third sensor 305. The third acquisition unit 321 acquires sole data indicating the magnitude of the pressure applied to each position of the user's sole shown in the measurement signal as static information regarding the user's sole (step S101). The static information indicates information regarding static alignment. Alignment indicates the arrangement of bones or joints, particularly morphological features such as posture, lateral curvature, kyphosis, bow legs or knock knees, the femoral neck-shaft angle or anteversion angle, and tibial torsion. Static alignment indicates the alignment in the natural standing position (an upright position where the face faces forward in the standing posture, both upper limbs hang along the trunk, the radial edge of the forearm faces forward, and the lower limbs are parallel with the toes facing forward). When an abnormality occurs in the static alignment, it affects the magnitude of the pressure applied to each position of the sole. The walking posture state estimation system 1 can accurately estimate the static alignment by using the sole data as static information. The third acquisition unit 321 periodically acquires the static information.

[0070] Next, the third transmission unit 322 transmits the static information acquired by the third acquisition unit 321, together with the device ID of the sole information acquisition device 300 and the acquisition time when the static information was acquired, to the first wearable device 100 via the third interface device 304 (step S102). The third transmission unit 322 transmits the static information to the first wearable device 100 every time the third acquisition unit 321 acquires the static information. The third transmission unit 322 may also transmit a plurality of pieces of static information acquired by the third acquisition unit 321 to the first wearable device 100 at an arbitrary timing after aggregating them.

[0071] Next, the first acquisition unit 121 of the first wearable device 100 acquires the static information, the device ID, and the acquisition time from each sole information acquisition device 300 via the first interface device 104 (step S103).

[0072] Next, the second acquisition unit 221 of each second wearable device 200 acquires a measurement signal from the second sensor 205. The second acquisition unit 221 acquires the acceleration and angular velocity in the three-axis directions applied to the second wearable device 200 indicated by the measurement signal as second dynamic information obtained when the user walks (step S104). The second dynamic information indicates information related to dynamic alignment. Dynamic alignment indicates the alignment during movement. When an abnormality occurs in the dynamic alignment in each part of the body, it affects the acceleration and angular velocity applied to each part. The walking posture state estimation system 1 can accurately estimate the dynamic alignment at that part by using the acceleration and angular velocity applied to the part where the second wearable device 200 is worn as the second dynamic information. The second acquisition unit 221 periodically acquires the second dynamic information.

[0073] Next, the second transmission unit 222 transmits the second dynamic information acquired by the second acquisition unit 221, together with the device ID of the second wearable device 200 and the acquisition time when the second dynamic information was acquired, to the first wearable device 100 via the second interface device 204 (step S105). The second transmission unit 222 transmits the second dynamic information to the first wearable device 100 every time the second acquisition unit 221 acquires the second dynamic information. The second transmission unit 222 may also transmit a plurality of pieces of second dynamic information acquired by the second acquisition unit 221 to the first wearable device 100 at an arbitrary timing after aggregating them.

[0074] Next, the first acquisition unit 121 of the first wearable device 100 acquires the second dynamic information, the device ID, and the acquisition time from the second wearable device 200 via the first interface device 104 (step S106).

[0075] Next, the first acquisition unit 121 acquires a measurement signal from the first sensor 105. The first acquisition unit 121 acquires the acceleration, angular velocity, geomagnetism, air pressure, or position in the three-axis directions applied to the first wearable device 100 indicated by the measurement signal as first dynamic information obtained when the user walks (step S107). The first dynamic information is an example of dynamic information. The first dynamic information is different from the second dynamic information and indicates information related to dynamic alignment. By using the acceleration, angular velocity, geomagnetism, air pressure, or position applied to the part where the first wearable device 100 is worn as the first dynamic information, the walking posture state estimation system 1 can accurately estimate the dynamic alignment at that part. The first acquisition unit 121 periodically acquires the first dynamic information.

[0076] Next, the first transmission unit 122 transmits the static information, the first dynamic information, and the second dynamic information acquired by the first acquisition unit 121 to the information processing device 400 via the first communication device 103, together with the device ID and acquisition time corresponding to each piece of information (step S108). The first transmission unit 122 analyzes a plurality of continuously measured first dynamic information and second dynamic information, and detects the maximum and minimum values in the first dynamic information and second dynamic information arranged in time series. The first transmission unit 122 detects the vibration periods of the first wearable device 100 and the second wearable device 200 based on the detected maximum and minimum values. The first transmission unit 122 specifies the swing interval of the legs of the user wearing the first wearable device 100 and the second wearable device 200 based on the detected vibration period. Then, the first transmission unit 122 transmits the first dynamic information and the second dynamic information for a predetermined number of steps (for example, 10 steps) of the user and the static information acquired during the same period to the information processing device 400.

[0077] Thereby, as will be described later, when the input information to be input to the learned model 411 is information for a predetermined number of steps, the information processing device 400 does not need to extract data for a predetermined number of steps from the acquired static information, first dynamic information, and second dynamic information. The walking posture state estimation system 1 can reduce the processing load in the information processing device 400, and can reduce the processing load as a whole.

[0078] Note that the first transmission unit 122 may transmit the static information, the first dynamic information, or the second dynamic information to the information processing device 400 every time the first acquisition unit 121 acquires the static information, the first dynamic information, or the second dynamic information. Further, the first transmission unit 122 may collectively transmit the plurality of static information, first dynamic information, and second dynamic information acquired by the first acquisition unit 121 to the information processing device 400 at an arbitrary timing.

[0079] Next, the fourth acquisition unit 421 of the information processing apparatus 400 acquires the static information, the first dynamic information, the second dynamic information, the device ID, and the acquisition time from the first wearable device 100 via the fourth communication device 401 (step S109). The fourth acquisition unit 421 stores the acquired static information, the first dynamic information, and the second dynamic information in the data table 412 in association with the corresponding device ID and acquisition time.

[0080] Next, the estimation unit 422 estimates the walking posture state of the user wearing the sole information acquisition device 300, the first wearable device 100, and the second wearable device 200 based on the static information, the first dynamic information, and the second dynamic information acquired by the third acquisition unit 321 (step S110). The estimation unit 422 estimates the walking posture state of the user using the learned model 411.

[0081] The learned model 411 includes one or more learned models. The learned model 411 is pre-generated by the information processing apparatus 400, the server apparatus 500, or the like. The learned model 411 is pre-trained by supervised learning such as deep learning or support vector machine. The learned model 411 may be pre-trained by random forest or the like. The learned model 411 is trained to output the walking posture state of the user wearing the sole information acquisition device 300, the first wearable device 100, or the second wearable device 200 that generated the input information when the input information is input.

[0082] The input information is static information acquired by the sole information acquisition device 300, first dynamic information acquired by the first wearable device 100, and / or second dynamic information acquired by the second wearable device 200. The input information may be information generated based on the static information, the first dynamic information, and / or the second dynamic information. For example, the input information is a set of static information, first dynamic information, and / or second dynamic information for a predetermined number of consecutive steps or a predetermined period (e.g., 5 seconds) (a vector arranged one-dimensionally or two-dimensionally with the magnitude of the pressure measured by each pressure sensor, or the acceleration or angular velocity measured by each acceleration sensor or each gyro sensor as elements). The input information may be statistical values such as the average value, median value, maximum value, or minimum value of the static information, the first dynamic information, and / or the second dynamic information for a predetermined number of consecutive steps or a predetermined period. Further, the input information may be a quaternion calculated from the angular velocity, acceleration, or geomagnetism included in the first dynamic information and / or the second dynamic information.

[0083] Also, the input information may be predetermined components (such as the first principal component and / or the second principal component, etc.) calculated by principal component analysis for the sole data or the three-axis angular velocity or acceleration included in the static information, the first dynamic information, and / or the second dynamic information. In that case, when the contribution rate of the first principal component is greater than a predetermined ratio (e.g., 80%), the first principal component is used as the input information, and when the contribution rate of the first principal component is equal to or less than the predetermined ratio, the second principal component may be used as the input information. Further, the principal component analysis may be executed after noise is removed using a known noise processing technique. In these cases, a model pre-trained by a random forest or the like is used as the learned model 411. In these cases, a model pre-trained by a neural network or the like may be used as the learned model 411. Thereby, the walking posture state estimation system 1 can further improve the accuracy of the learned model 411.

[0084] Further, the input information may include an image of a spectrum obtained by performing a short-time Fourier transform or a wavelet transform on each angular velocity and / or acceleration in three axial directions included in the first dynamic information and / or the second dynamic information. An image of a spectrum obtained by performing a short-time Fourier transform or a wavelet transform on each angular velocity and / or acceleration in three axial directions included in the first dynamic information is an example of the first image data and the image data. An image of a spectrum obtained by performing a short-time Fourier transform or a wavelet transform on each angular velocity and / or acceleration in three axial directions included in the second dynamic information is an example of the second image data and the image data. In these cases, as the learned model 411, a model pre-trained by a random forest or the like is used. In these cases, as the learned model 411, a model pre-trained by a neural network or the like may be used. Thereby, the walking posture state estimation system 1 can further improve the accuracy of the learned model 411.

[0085] Further, the input information may be a feature vector including the gradation value (pressure) of each pixel of the sole image or each element of the sole vector, and the angular velocity, acceleration, geomagnetism, atmospheric pressure, and / or position.

[0086] The walking posture state output from the learned model 411 includes five elements. The first element indicates the state of the step length, particularly whether the user's step length is sufficient or insufficient. The second element indicates the state of the center of gravity position, particularly whether the user's center of gravity is normal, leaning forward, or leaning backward. The third element indicates the state of the leading leg, particularly whether the user is not kicking, dragging, or kicking the leg to be put forward. The fourth element indicates the state of the supporting leg, particularly whether the user's axis leg is tensed or not. The fifth element indicates the state of the hip joint, particularly whether the user's hip joint is open or not. The first element is an element related to the premise of the walking posture. The second and third elements are elements related to the axis in the front-rear direction. The fourth and fifth elements are elements related to the axis of rotation (in the left-right direction). The walking posture state does not have to include all the elements from the first element to the fifth element, and may include at least one element.

[0087] The walking posture state output from the learned model 411 is not limited to information directly indicating each element from the first element to the fifth element, and may be a predetermined feature amount related to each element from the first element to the fifth element.

[0088] The learned model 411 includes, for example, learned models corresponding to each sole information acquisition device 300, the first wearable device 100, and each second wearable device 200. That is, the learned model 411 includes a first learned model, a second learned model, and a third learned model. The first learned model can output a first walking posture state estimated based on static information acquired from each sole information acquisition device 300. The second learned model can output a second walking posture state estimated based on first dynamic information acquired from the first wearable device 100. The third learned model can output a third walking posture state estimated based on second dynamic information acquired from each second wearable device 200.

[0089] Alternatively, the second learned model may be able to output a first walking posture state estimated based on first image data based on the first dynamic information acquired from the first wearable device 100. The third learned model may be able to output a second walking posture state estimated based on second image data based on the second dynamic information acquired from each second wearable device 200.

[0090] Thereby, the walking posture state estimation system 1 can estimate the walking posture state for each body part on which the sole information acquisition device 300, the first wearable device 100, or the second wearable device 200 is worn, and comprehensively estimate the walking posture state of the user based on the walking posture states estimated for each part.

[0091] In these cases, the estimation unit 422 may obtain the walking posture state by inputting the input information based on the static information into the first layer of the first pre-trained model, inputting the input information based on the first dynamic information into the second layer of the second pre-trained model, and inputting the input information based on the second dynamic information into the third layer of the third pre-trained model. This first layer is a specific layer among the input layer or the intermediate layer. This second layer is a layer different from the first layer and is a specific layer among the input layer or the intermediate layer. This third layer is a layer different from the first layer and / or the second layer and is a specific layer among the input layer or the intermediate layer. Thereby, the walking posture state estimation system 1 can flexibly change the number of processing layers (number of processing steps) in each pre-trained model, and can suppress an increase in processing time and processing load.

[0092] Alternatively, the estimation unit 422 may obtain the first output information, the second output information, and the third output information as the walking posture state. The first output information is the information output from the first layer of the first pre-trained model when the input information based on the static information is input into the first pre-trained model. The second output information is the information output from the second layer of the second pre-trained model when the input information based on the first dynamic information is input into the second pre-trained model. The third output information is the information output from the third layer of the third pre-trained model when the input information based on the second dynamic information is input into the third pre-trained model. This first layer is a specific layer among the intermediate layer or the output layer. This second layer is a layer different from the first layer and is a specific layer among the intermediate layer or the output layer. This third layer is a layer different from the first layer and / or the second layer and is a specific layer among the intermediate layer or the output layer. Thereby, the walking posture state estimation system 1 can flexibly change the number of processing layers (number of processing steps) in each pre-trained model, and can suppress an increase in processing time and processing load.

[0093] Note that the learned model 411 may be generated to correspond to a plurality of devices among the sole information acquisition device 300, the first wearable device 100, and the second wearable device 200. In particular, the learned model 411 may be generated to correspond to all of the sole information acquisition device 300, the first wearable device 100, and the second wearable device 200. That is, the learned model 411 includes a single learned model capable of outputting a walking posture state estimated based on static information acquired from the sole information acquisition device 300, first dynamic information acquired from the first wearable device 100, and second dynamic information acquired from each second wearable device 200.

[0094] Alternatively, the learned model 411 includes a single learned model capable of outputting a walking posture state estimated based on each static information acquired from each sole information acquisition device 300 and image data based on the first dynamic information and each second dynamic information acquired from the first wearable device 100 and each second wearable device 200.

[0095] In these cases, the learned model 411 is learned to output a walking posture state when a vector including static information, first dynamic information, and second dynamic information acquired from a plurality of wearable devices at mutually corresponding times (substantially matching times) is input.

[0096] Thereby, the walking posture state estimation system 1 can comprehensively estimate the walking posture state based on the information acquired from each sole information acquisition device 300, the first wearable device 100, and each second wearable device 200.

[0097] Further, the learned model 411 includes learned models corresponding to each of the first to fifth elements. That is, the learned model 411 includes a plurality of learned models capable of outputting information regarding each of the first to fifth elements. The learned model 411 may include a single learned model capable of collectively outputting information regarding each of the first to fifth elements.

[0098] Further, the learned model 411 may include an upstream learned model and a downstream learned model arranged in series. In that case, the upstream learned model is learned to output a predetermined feature amount when input information is input, and the downstream learned model is learned to output a walking posture state when the feature amount output from the intermediate layer or the output layer of the upstream learned model is input. The predetermined feature amount is a feature amount related to the input information or a feature amount related to the walking posture state.

[0099] Further, the learned model 411 may include any combination of the above-described learned models. For example, the learned model 411 includes a first upstream learned model, a second upstream learned model, a third upstream learned model, and a downstream learned model. The first upstream learned model is learned to output a predetermined feature amount when the first principal component of the sole data is input. The second upstream learned model is learned to output a predetermined feature amount when the second principal component of the sole data is input. The third upstream learned model is learned to output a predetermined feature amount when the first principal component of the angular velocity or the acceleration is input. Alternatively, the third upstream learned model is learned to output a predetermined feature amount when an image of a spectrum converted by a short-time Fourier transform or a wavelet transform with respect to the angular velocity and / or the acceleration is input. The downstream learned model is learned to output a walking posture state when a feature vector including data output from the output layer or the intermediate layer of the first upstream learned model, the second upstream learned model, and the third upstream learned model is input. Thereby, the walking posture state estimation system 1 can further improve the accuracy of the learned model 411.

[0100] Alternatively, the learned model 411 includes a first upstream learned model, a second upstream learned model, and a downstream learned model. The first upstream learned model is learned to output a predetermined feature amount when sole data is input. The second upstream learned model is learned to output a predetermined feature amount when an angular velocity or an acceleration is input. The downstream learned model is learned to output a walking posture state when a feature vector including data output from an output layer or an intermediate layer of the first upstream learned model and the second upstream learned model is input. Thereby, the walking posture state estimation system 1 can further improve the accuracy of the learned model 411.

[0101] Thus, the learned model 411 is provided to be able to output an estimated walking posture state of a user wearing the sole information acquisition device 300, the first wearable device 100, and the second wearable device 200.

[0102] The learning data of the learned model 411 includes a combination of pre-generated input information and the walking posture state of a user wearing the sole information acquisition device 300, the first wearable device 100, and the second wearable device 200 that generated the input information. Each learning data is created by an expert who has viewed a moving image of a user wearing the device that generated the static information, the first dynamic information, or the second dynamic information when the static information, the first dynamic information, or the second dynamic information is generated by each device.

[0103] Each piece of learning data is composed of, for example, a set of static information, first dynamic information, or second dynamic information for a predetermined number of consecutive steps or a predetermined period, and a combination with a walking posture state. In that case, each piece of learning data may be generated such that a part of the static information, first dynamic information, or second dynamic information included in each piece of learning data overlaps. That is, the first piece of learning data may include information for a predetermined number of steps or a predetermined period from the first time, and the second piece of learning data may include data for a predetermined number of steps or a predetermined period from the second time immediately after the first time (for example, 1 second later). Thereby, the walking posture state estimation system 1 can efficiently generate the learned model 411 from a small amount of sample data, and can reduce the time and labor required for generating the learned model 411.

[0104] When the learned model 411 is pre-learned by a random forest or the like, each piece of learning data is composed of, for example, a combination of static information (the gradation value of each pixel of the sole image or each element of the sole vector), a vector obtained by arranging the first dynamic information or the second dynamic information in one dimension for a predetermined number of consecutive steps or a predetermined period, and a walking posture state. Each piece of learning data may include only the angular velocity in the three-axis directions, for example, among the first dynamic information or the second dynamic information. The walking posture state estimation system 1 can improve the accuracy of the learned model 411 by generating the learned model 411 based on both the angular velocity and the acceleration. On the other hand, the walking posture state estimation system 1 can reduce the time and labor required for generating the learned model 411 by generating the learned model 411 based only on the angular velocity. Also in these cases, each piece of learning data may be generated such that a part of the static information, first dynamic information, or second dynamic information included in each piece of learning data overlaps.

[0105] The estimation unit 422 converts the static information, the first dynamic information, and the second dynamic information acquired in step S109 into a format corresponding to the learned model 411, and inputs them into the learned model 411. Based on the information output from the learned model 411, the estimation unit 422 estimates the walking posture state of the user wearing the sole information acquisition device 300, the first wearable device 100, and the second wearable device 200. The estimation unit 422 estimates the walking posture state of the user for each element included in the walking posture state.

[0106] When the information output from the learned model 411 is a feature amount, the information processing device 400 stores in advance in the fourth storage device 410 a table or formula indicating the relationship between the feature amount and the walking posture state. The estimation unit 422 refers to the table or formula stored in the fourth storage device 410, and specifies the walking posture state of the user corresponding to the feature amount output from the learned model 411.

[0107] Also, when the learned model 411 includes a plurality of learned models corresponding to each sole information acquisition device 300, the first wearable device 100, and each second wearable device 200, the estimation unit 422 determines that the walking posture state output from the most learned models among the walking posture states output from each learned model is the walking posture state of the user.

[0108] In this way, the estimation unit 422 estimates the walking posture state of the user based on the acquired static information, the first dynamic information, and the second dynamic information by using the learned model 411. That is, the estimation unit 422 uses the learned model 411 to acquire the walking posture state estimated based on the acquired static information, the first dynamic information, and the second dynamic information.

[0109] Next, the output control unit 423 outputs the estimated walking posture state of the user wearing the sole information acquisition device 300, the first wearable device 100, and the second wearable device 200 by transmitting it to the first wearable device 100 via the fourth communication device 401 (step S111).

[0110] Next, the first acquisition unit 121 of the first wearable device 100 acquires the estimated walking posture state of the user by receiving it from the information processing device 400 via the first communication device 103 (step S112).

[0111] Next, the first acquisition unit 121 outputs the acquired walking posture state by displaying it on the first display device 102 to notify the user (step S113), and ends a series of steps. Thereby, the user can recognize their own walking posture state.

[0112] Note that one of steps S104 to S106 and S107 may be omitted, and the first acquisition unit 121 may estimate the walking posture state based on only the static information and one of the first dynamic information and the second dynamic information. The first wearable device 100 can estimate the walking posture state at low cost and with low load without using the second wearable device 200 by estimating the walking posture state based on the static information and the first dynamic information. On the other hand, the first wearable device 100 can increase the degree of freedom of the body part for acquiring the second dynamic information and can estimate the walking posture state with high accuracy by estimating the walking posture state based on the static information and the second dynamic information. Also, in step S110, the static information and each dynamic information used for estimating the walking posture state do not have to be information acquired at the same time. In particular, the static information may be information acquired when the user is stationary (such as in a natural standing position) instead of when the user is walking.

[0113] Also, in step S111, the output control unit 423 of the information processing device 400 may transmit the estimated walking posture state of the user wearing the sole information acquisition device 300, the first wearable device 100, and the second wearable device 200 to the administrator device owned by the administrator who manages the user's health. In that case, the administrator device notifies the administrator by displaying the received walking posture state. The administrator can recognize the walking posture state of the user and propose improvement of the walking posture state to the user.

[0114] Also, the timings at which the processes of step S101, step S104, and step S107 are executed may be arbitrary and may be simultaneous. For example, when the first acquisition unit 121 of the first wearable device 100 receives an instruction to start measurement from the user using the first input device 101, it transmits a measurement request signal requesting the start of measurement to the sole information acquisition device 300 and the second wearable device 200 via the first interface device 104, and acquires first dynamic information. On the other hand, when the third acquisition unit 321 of the sole information acquisition device 300 receives a measurement request signal from the first wearable device 100 via the third interface device 304, it acquires static information and transmits it to the first wearable device 100 via the third interface device 304. Also, when the second acquisition unit 221 of the second wearable device 200 receives a measurement request signal from the first wearable device 100 via the second interface device 204, it acquires second dynamic information and transmits it to the first wearable device 100 via the second interface device 204. The first acquisition unit 121 repeats the above processes until it receives an instruction to end measurement from the user using the first input device 101, and then executes the process of step S108.

[0115] The inventor evaluated the learned model 411 for all of the first to fifth elements, and confirmed that the accuracy rate of the information output from the learned model 411 was 93% or more, and the accuracy was sufficiently high. That is, it was confirmed that the walking posture state estimation system 1 can estimate the user's walking posture state with higher accuracy by using the learned model 411. Further, the walking posture state estimation system 1 can calculate the walking posture state for various combinations of various types of parameters with high accuracy and efficiency by using the learned model 411. Further, the walking posture state estimation system 1 can identify the walking posture state without storing the corresponding walking posture states for various combinations of various types of parameters by using the learned model 411, so that the storage capacity of the storage device can be reduced. Further, the walking posture state estimation system 1 can identify the walking posture state without performing complicated determination according to various combinations of various types of parameters by using the learned model 411, so that the processing time and processing load of the estimation process can be reduced.

[0116] Hereinafter, the technical significance of the walking posture state including the first to fifth elements will be described.

[0117] The main power source of the walking motion is the thigh lift, and the main power source of the running motion is the toe kick. Therefore, the walking motion and the running motion, that is, the motion of running slowly to the limit, are fundamentally different motions, and clearly changing the motion between the walking motion and the running motion is related to the user's health. In order to kick with the toes, the ball of the thumb touches the floor, the foot turns inward, and the arch disappears. As a result, the bones of the foot become loose as a whole, and the impact on the foot is easily transmitted to the knee or the waist. Since the running motion involves a burden on the foot, it is not desirable to perform a running motion that places a burden on the foot while walking.

[0118] The walking posture state estimation system 1 can identify whether a user is performing an appropriate walking motion without running by estimating each element from the first element to the fifth element as the walking posture state of the user. In particular, each element from the first element to the fifth element is related to each other, and in the order from the first element to the fifth element, as the user's posture is improved, the user can easily improve the walking posture.

[0119] First of all, it should not be the case that the stride is too short and the walking speed is too slow. The stride needs to be guaranteed to be a certain length or more. By including, as a first element indicating whether the user's stride is sufficient or insufficient, in the walking posture state, the walking posture state estimation system 1 can ensure that the user's stride is a certain length or more.

[0120] Next, when the user's posture is not straight, the user has no choice but to run. When the user stands straight with the heel as the center of gravity, the user can lift the leg using the thigh, but when in a forward-leaning posture, the upper body covers the leg, making it difficult to lift the leg using the thigh, and the user has no choice but to kick with the toes. By including, as a second element indicating whether the user's center of gravity is normal, forward-leaning, or backward-leaning, as an element related to the axis in the front-rear direction, in the walking posture state, the walking posture state estimation system 1 can confirm whether the user's posture is kept straight.

[0121] Next, even if the user's center of gravity is straight, if the thigh lift is not performed correctly, the user has to run. Even if the user's center of gravity is straight and the foot rises without kicking with the toes, kicking with the toes will cause violent vertical movement. Also, even though the user's center of gravity is straight, since the user does not have the habit of raising the leg with the thigh, the user may drag the foot. By including, as an element related to the axis in the front-rear direction, a third element indicating whether the user is not kicking, dragging, or kicking the foot that the user puts out, the walking posture state estimation system 1 can confirm whether the user's thigh lift is performed correctly.

[0122] Next, even if the axis in the front-rear direction is correct, if the rotation (left-right direction) axis is not correct, the appropriate walking motion is not performed. If the knee goes inside with respect to the toes, the knee gets in the way and the opposite foot does not come out correctly. That is, when the axis foot is tense or the hip joint is not open, the foot that wants to come out does not rise correctly. On the other hand, when the user rotates the hip joint, a space for the opposite foot to come out is secured, the foot is likely to rise, and the thigh lift is performed correctly. By including, as an element related to the rotation (left-right direction) axis, a fourth element indicating whether the user's axis foot is tense or not and a fifth element indicating whether the user's hip joint is open or not in the walking posture state, the walking posture state estimation system 1 can confirm whether the rotation axis is correct.

[0123] Generally, it is said that the repetition of dynamic alignment accumulates in static alignment, and diagnostic indices tend to be established based only on static alignment. However, the equivalence between dynamic alignment and static alignment is only 5 to 60%. Also, when diagnostic indices are determined based only on static alignment, it takes a long time until the improvement effect of guidance becomes clear. The walking posture state estimation system 1 can confirm whether both dynamic alignment and static alignment are improved by estimating the walking posture state of the user using a combination of dynamic information and static information, and can more accurately confirm whether the alignment of the user is normal. Further, the walking posture state estimation system 1 can confirm the improvement effect of guidance in a shorter period by estimating the walking posture state of the user using a combination of dynamic information and static information.

[0124] As described in detail above, the walking posture state estimation system 1 estimates the walking posture state of the user using the learned model 411 based on the static information acquired by the sole information acquisition device 300 and the first dynamic information acquired by the first wearable device 100. Thereby, the walking posture state estimation system 1 can estimate the walking posture state of the user with higher accuracy.

[0125] FIG. 8 is a sequence showing an example of the operation of the estimation process in the walking posture state estimation system 1 according to another embodiment.

[0126] Hereinafter, an example of the operation of the estimation process according to the present embodiment will be described with reference to the flowchart shown in FIG. 8. Note that the flow of the operation described below is mainly executed in cooperation with each element of each device by each processing device of each device based on a program stored in each storage device of each device included in the walking posture state estimation system 1 in advance. In the present embodiment, the learned model 411 is stored in the server device 500. Since the processes of steps S201 to S209 and S216 to S218 in FIG. 8 are the same as the processes of steps S101 to S109 and S111 to S113 in FIG. 7, the description thereof will be omitted, and hereinafter, only steps S210 to S215 will be described.

[0127] In step S210, the estimation unit 422 of the information processing apparatus 400 transmits a request signal for requesting transmission of the user's walking posture information to the server apparatus 500 via the fourth communication apparatus 401 (step S210). The request signal includes the static information acquired in step S106 and converted into a format corresponding to the learned model 411, the first dynamic information, and the second dynamic information.

[0128] Next, the server apparatus 500 receives the request signal from the information processing apparatus 400 (step S211). Next, the server apparatus 500 inputs the static information, the first dynamic information, and the second dynamic information included in the received request signal into the learned model 411, and acquires the output information output from the learned model 411 (step S212). Next, the server apparatus 500 transmits the acquired output information to the information processing apparatus 400 (step S213).

[0129] Next, the estimation unit 422 of the information processing apparatus 400 acquires the output information by receiving it from the server apparatus 500 via the fourth communication apparatus 401 (step S214). Next, the estimation unit 422 estimates the walking posture state of the user wearing the first wearable apparatus 100 and the second wearable apparatus 200 based on the acquired output information (step S215). The estimation unit 422 estimates the walking posture state of the user in the same manner as the process of step S110 in FIG. 7.

[0130] As described in detail above, even when the learned model 411 is stored in a server apparatus 500 other than the information processing apparatus 400, the walking posture state estimation system 1 can estimate the walking posture state of the user with higher accuracy.

[0131] The walking posture state estimation system 1 can estimate the walking posture state of a user by using the learned model 411 stored in the server device 500, i.e., the latest learned model updated by the server device 500. Also, the walking posture state estimation system 1 can reduce the storage capacity of the information processing device 400 by using the learned model 411 stored in the server device 500. On the other hand, the information processing device 400 can estimate the walking posture state of the user even when the communication connection with the server device 500 is disconnected by using the learned model 411 stored in its own device. Further, the information processing device 400 can reduce the communication volume between the information processing device 400 and the server device 500 by using the learned model 411 stored in its own device.

[0132] FIG. 9 is a sequence showing an example of the operation of the estimation process in the walking posture state estimation system 1 according to still another embodiment.

[0133] Hereinafter, an example of the operation of the estimation process according to the present embodiment will be described with reference to the flowchart shown in FIG. 9. The flow of the operation described below is mainly executed in cooperation with each element of each device by each processing device of each device based on the program stored in each storage device of each device included in the walking posture state estimation system 1 in advance. In the present embodiment, the learned model 411 and the data table 412 are stored in the first storage device 110 of the first wearable device 100. Also, the first processing device 120 functions as the first acquisition unit 121 and the first transmission unit 122, and functions as an estimation unit and an output control unit having the same functions as the estimation unit 422 and the output control unit 423. Since the processes of steps S301 to S306 and S309 in FIG. 9 are the same as the processes of steps S101 to S106 and S113 in FIG. 7, the description thereof will be omitted, and hereinafter, only steps S307 to S308 will be described.

[0134] In step S307, the first acquisition unit 121 of the first wearable device 100 acquires first dynamic information obtained when the user walks, in the same manner as the process in step S107 (step S307). The first acquisition unit 121 stores the acquired static information, first dynamic information, and second dynamic information in the data table 412 in association with the corresponding device ID and acquisition time.

[0135] Next, the estimation unit of the first wearable device 100 estimates the walking posture state of the user wearing the sole information acquisition device 300, the first wearable device 100, and the second wearable device 200 based on the static information, first dynamic information, and second dynamic information, in the same manner as the process in step S110 (step S308).

[0136] As described in detail above, the walking posture state estimation system 1 can estimate the walking posture state of the user with higher accuracy even when the first wearable device 100 estimates the walking posture state of the user.

[0137] Although the preferred embodiments have been described above, the embodiments are not limited thereto. For example, even when the first wearable device 100 estimates the walking posture state of the user, the first wearable device 100 may estimate the walking posture state of the user based on the output information of the learned model 411 stored in the server device 500.

[0138] Further, the sole information acquisition device 300 and / or the second wearable device 200 may directly transmit the static information and / or the second dynamic information to the information processing device 400 instead of transmitting it to the information processing device 400 via the first wearable device 100. Thereby, the walking posture state estimation system 1 can reduce the processing load on the first wearable device 100. On the other hand, by aggregating the static information from the sole information acquisition device 300 and the second dynamic information from the second wearable device 200 in the first wearable device 100, the walking posture state estimation system 1 can reduce the processing load on the information processing device 400.

[0139] Further, the walking posture state estimation system 1 may estimate the walking posture state based only on the sole data at the beginning, and estimate the walking posture state based on the acceleration and angular velocity thereafter.

[0140] In the walking posture state estimation system 1, the second wearable device 200 may be omitted. Also, in the walking posture state estimation system 1, a plurality of first wearable devices 100 and / or a plurality of information processing devices 400 may cooperate to share each step of the above-described respective processes.

Explanation of Reference Numerals

[0141] 1 walking posture state estimation system, 100 first wearable device, 105 first sensor, 110 first storage device, 121 first acquisition unit, 122 first transmission unit, 200 second wearable device, 205 second sensor, 300 sole information acquisition device, 305 third sensor, 400 information processing device, 401 fourth communication device, 410 fourth storage device, 411 learned model, 421 fourth acquisition unit, 422 estimation unit

Claims

1. A sole information acquisition device that acquires static information regarding the soles of a user's feet, A wearable device that can be worn by a user and acquires dynamic information obtained when the user walks, An information processing device connected to the sole information acquisition device and the wearable device, and having, The information processing device includes: A receiving unit that receives the static information and the dynamic information, A storage unit that stores a learned model capable of outputting a walking posture state of the user estimated based on the static information and the dynamic information, An output unit that outputs the walking posture state, A walking posture state estimation system, characterized by comprising the above.

2. Further comprising a second wearable device that can be worn by a user and acquires second dynamic information obtained when the user walks, The wearable device includes: A second dynamic information receiving unit that receives the second dynamic information from the second wearable device, A transmission unit that transmits the dynamic information and the second dynamic information for a predetermined number of steps of the user to the information processing device. The walking posture state estimation system according to Claim 1.

3. The walking posture state includes any one or more of a state of step width, a state of center of gravity position, a state of the swinging leg, a state of the supporting leg, and a state of the hip joint. The walking posture state estimation system according to Claim 1 or 2.

4. The learned model includes a first learned model capable of outputting a first walking posture state estimated based on the static information, and a second learned model capable of outputting a second walking posture state estimated based on the dynamic information. The walking posture state estimation system according to Claim 1 or 2.

5. The learned model is a single learned model capable of outputting the walking posture state estimated based on the static information and the dynamic information. The walking posture state estimation system according to Claim 1 or 2.

6. The information processing device inputs information based on the static information to a first layer of a first learned model included in the learned model, and inputs information based on the dynamic information to a second layer different from the first layer of a second learned model included in the learned model, thereby obtaining the walking posture state. The walking posture state estimation system according to Claim 1 or 2.

7. The walking posture state includes information output from the first layer of the learned model when information based on the static information is input to the learned model, and information output from a second layer different from the first layer of the learned model when information based on the dynamic information is input to the learned model. The walking posture state estimation system according to claim 1.

8. The sole information acquisition device acquires static information regarding the user's sole, The wearable device that can be worn by the user acquires dynamic information obtained when the user walks, Using a learned model capable of outputting the walking posture state of the user estimated based on the static information and the dynamic information, the walking posture state is estimated based on the acquired static information and the dynamic information, Outputting the walking posture state. A walking posture state estimation method characterized by the above.

9. An acquisition unit that acquires the static information and the dynamic information from a static information sensor that measures static information regarding the user's sole and a dynamic information sensor that measures dynamic information obtained when the user walks, An estimation unit that uses a learned model capable of outputting the walking posture state of the user estimated based on the static information and the dynamic information, and acquires the walking posture state estimated based on the acquired static information and the dynamic information, An output unit that outputs the walking posture state. A walking posture state estimation device characterized by comprising the above.

10. A static information sensor that measures static information regarding the user's sole and a dynamic information sensor that measures dynamic information obtained when the user walks acquire the static information and the dynamic information, Using a learned model capable of outputting the walking posture state of the user estimated based on the static information and the dynamic information, the walking posture state estimated based on the acquired static information and the dynamic information is acquired, Outputting the walking posture state. A walking posture state estimation method characterized by the above.

11. A control program for a walking posture state estimation device, A static information sensor that measures static information regarding the user's sole and a dynamic information sensor that measures dynamic information obtained when the user walks acquire the static information and the dynamic information, Using a learned model capable of outputting the walking posture state of the user estimated based on the static information and the dynamic information, the walking posture state estimated based on the acquired static information and the dynamic information is acquired, Outputting the walking posture state, A control program characterized by causing the walking posture state estimation device to execute the above.

12. An acquisition unit that acquires the static information from a static information sensor that measures static information regarding the user's sole, A sensor that measures dynamic information obtained when the user walks, An estimation unit that utilizes a learned model capable of outputting the walking posture state of the user estimated based on the static information and the dynamic information, and acquires the walking posture state estimated based on the acquired static information and the dynamic information, An output unit that outputs the walking posture state, A walking posture state estimation device characterized by having the above.

13. A walking posture state estimation method by a walking posture state estimation device having a sensor that measures dynamic information obtained when the user walks, Acquiring the static information from a static information sensor that measures static information regarding the user's sole, Utilizing a learned model capable of outputting the walking posture state of the user estimated based on the static information and the dynamic information, and acquiring the walking posture state estimated based on the acquired static information and the dynamic information, Outputting the walking posture state, A walking posture state estimation method characterized by the above.

14. A control program for a walking posture state estimation device having a sensor that measures dynamic information obtained when the user walks, Acquiring the static information from a static information sensor that measures static information regarding the user's sole, Utilizing a learned model capable of outputting the walking posture state of the user estimated based on the static information and the dynamic information, and acquiring the walking posture state estimated based on the acquired static information and the dynamic information, Outputting the walking posture state, A control program characterized by causing the walking posture state estimation device to execute the above.

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