JP2023088193A - Estimation device, estimation method, estimation program, and learning model generation device - Google Patents

Abstract

To provide an estimation device, an estimation method, an estimation program, and a learning model generation device capable of estimating a posture state of a wearer of shoes without impairing foot comfort.SOLUTION: An estimation device 11 detects, at a detection unit, electric characteristics between a plurality of detection points 75 in shoes 12 having a conductive urethane 22 as a conductive flexible material whose electric characteristics change according to a change in an imparted pressure. An estimation unit estimates a posture state of a wearer of the shoes 12 from the electric characteristics of the shoes 12 using a learning model. The estimation unit inputs the electric characteristics to the learning model that has learned so as to output posture state information with the electric characteristics as input, using the electric characteristics when a pressure is given to the shoes 12 and posture state information indicating the posture state of the wearer of the shoes 12 that gives a pressure to the shoes 12 as learning data. The learning model is learned so as to output the posture state of the wearer of the shoes 12 corresponding to the input electric characteristics.SELECTED DRAWING: Figure 8

Description

The present disclosure relates to an estimating device, an estimating method, an estimating program, and a learning model generating device.

2. Description of the Related Art Conventionally, a shape change occurring in an object is detected, and the detection result is used to estimate the state of a person or an object that deforms the object. In terms of detecting a shape change that occurs in an object, it is difficult to detect the deformation of the object without disturbing the deformation of the object. In addition, since strain sensors used for detecting deformation of a rigid body such as metal deformation are difficult to use for articles, a special detection device is required to detect deformation of an object. For example, there is known a technique of measuring the displacement and vibration of an object with a camera, obtaining a deformed image, and extracting the amount of deformation (see, for example, Patent Document 1). Also known is a technique of measuring the pressure distribution applied to a wheelchair, a cushion, a bed, etc., using a sheet-like pressure-sensitive sensor (see, for example, Non-Patent Document 1).

WO2017/029905

https://www.nitta.co.jp/product/sensor/bpms/

However, although a person maintains or changes a predetermined posture, it is difficult for a strain sensor or the like to detect deformation without interfering with the deformation of the member, and image processing requires a large-scale device. Also, invisible parts such as the soles of the feet where the load is concentrated cannot be detected by image processing. Furthermore, when a sheet-like sensor or the like is provided on the shoe, there is a possibility that it may affect the comfort of wearing.

An object of the present disclosure is to provide an estimating device, an estimating method, an estimating program, and a learning model generating device capable of estimating the posture state of a wearer of shoes without impairing comfort.

In order to achieve the above object, the first aspect is
Electrical characteristics between a plurality of predetermined detection points on the flexible material of a shoe provided with a flexible material that is electrically conductive and whose electrical characteristics change according to changes in applied pressure at predetermined regions a detection unit that detects
Inputting the time-series electrical characteristics using the time-series electrical characteristics when pressure is applied to the flexible material and the posture state information of the wearer of the shoe applying pressure to the flexible material as learning data. and inputting the time-series electrical characteristics detected by the detection unit to the learning model trained to output the posture state information, and wearing the shoes corresponding to the input time-series electrical characteristics an estimation unit that estimates posture state information of a person;
is an estimation device including

A second aspect is the estimation device of the first aspect,
The estimation unit estimates the center of gravity or weight shift of the shoe wearer.

A third aspect is the estimation device of the first aspect or the second aspect,
A control unit that performs predetermined control according to the posture state information estimated by the estimation unit is further included.

A fourth aspect is the estimating device according to any one aspect of the first aspect to the third aspect,
An output unit that outputs at least one of the posture state information estimated by the estimation unit and a warning based on the posture state information is further included.

A fifth aspect is the estimation device according to any one aspect of the first aspect to the fourth aspect,
The learning model includes a model generated by learning using a network by reservoir computing using the flexible material as a reservoir and using the reservoir.

The sixth aspect is
A computer is provided with a flexible material which has electrical conductivity and whose electrical characteristics change according to a change in applied pressure at a predetermined portion, and between a plurality of predetermined detection points on the flexible material of the shoe. detect electrical properties,
Inputting the time-series electrical characteristics using the time-series electrical characteristics when pressure is applied to the flexible material and the posture state information of the wearer of the shoe applying pressure to the flexible material as learning data. and inputting the detected time-series electrical characteristics to a learning model trained to output the posture state information, and determining the posture state of the wearer of the shoe corresponding to the input time-series electrical characteristics It is an estimation method for estimating information.

The seventh aspect is
A shoe having a flexible material that is electrically conductive and whose electrical characteristics change according to a change in applied pressure at a predetermined portion, and between a plurality of predetermined detection points on the flexible material. detect electrical properties,
Inputting the time-series electrical characteristics using the time-series electrical characteristics when pressure is applied to the flexible material and the posture state information of the wearer of the shoe applying pressure to the flexible material as learning data. and inputting the detected time-series electrical characteristics to a learning model trained to output the posture state information, and determining the posture state of the wearer of the shoe corresponding to the input time-series electrical characteristics It is an estimation program for executing the process of estimating information.

The eighth aspect is
Electrical characteristics between a plurality of predetermined detection points on the flexible material of a shoe provided in predetermined regions with a flexible material that is conductive and whose electrical characteristics change according to changes in applied pressure and posture state information indicating the posture state of the wearer of the shoe applying pressure to the flexible material;
Based on the acquisition result of the acquisition unit, time-series electrical characteristics when pressure is applied to the flexible material are input, and posture state information indicating the posture state of the wearer applying pressure to the flexible material is output. a learning model generation unit that generates a learning model;
is a learning model generation device including

ADVANTAGE OF THE INVENTION According to this indication, it has the effect that the posture state of the wearer of shoes can be estimated, without impairing comfort.

It is a figure which shows the structure of the estimation apparatus which concerns on embodiment. It is a figure which shows arrangement|positioning of the electroconductive urethane which concerns on embodiment. 1 is a diagram showing a conceptual configuration of a learning model generation device according to an embodiment; FIG. It is a figure which shows the functional structure of the learning process part which concerns on embodiment. It is a figure which shows the other functional structure of the learning process part which concerns on embodiment. It is a figure which shows the electrical structure of the estimation apparatus which concerns on embodiment. It is a flow chart which shows a flow of presumed processing concerning an embodiment. FIG. 4 is a diagram showing a posture state estimation device as a specific example of the estimation device according to the embodiment; FIG. 3 is a diagram showing a detailed configuration of a posture state estimation device; FIG. 4 is a diagram related to a conductive member according to the embodiment; FIG. 4 is a diagram related to a conductive member according to the embodiment; FIG. 4 is a diagram related to a conductive member according to the embodiment; 6 is a flowchart illustrating an example of learning data collection processing according to the embodiment; 6 is a flowchart showing an example of the flow of learning processing according to the embodiment; 6 is a flowchart showing a specific example of the flow of estimation processing according to the embodiment; It is a figure which shows the structural example of an example of the control apparatus which concerns on embodiment. 6 is a flowchart showing an example of the flow of drive processing according to the embodiment;

Hereinafter, embodiments for implementing the technology of the present disclosure will be described in detail with reference to the drawings. Components and processes having the same actions and functions are given the same reference numerals throughout the drawings, and overlapping descriptions may be omitted as appropriate. In addition, the present disclosure is not limited to the following embodiments, and can be implemented with appropriate modifications within the scope of the purpose of the present disclosure.

In the present disclosure, a person is a concept that includes at least one of a human body and an object that can stimulate an object with a physical quantity. In the following description, a concept including a person and an object will be collectively referred to as a person without distinguishing between at least one of a human body and an object. In other words, a single human body and an object, and a combination of a human body and an object are collectively referred to as a person.

First, referring to FIGS. 1 to 7, a flexible material imparted with conductivity to which the technology of the present disclosure is applied and a state estimation process for estimating the state of the flexible material on the application side using the flexible material. explain.

<Flexible material>
In the present disclosure, the term “flexible material” is a concept that includes materials that are at least partially deformable such as bending, and is a soft elastic body such as a rubber material, a structure having at least one of a fibrous and mesh-like skeleton. It contains a body and a structure in which multiple microscopic air bubbles are scattered. Examples of these structures include polymeric materials such as urethane materials. Also, in the present disclosure, a flexible material imparted with conductivity is used. The term "flexible material to which electrical conductivity is imparted" is a concept that includes materials having electrical conductivity, materials obtained by imparting electrical conductivity to a flexible material to impart electrical conductivity, and materials in which the flexible material has electrical conductivity. including. Polymer materials such as urethane are suitable for the flexible material that imparts electrical conductivity. In the following description, as an example of a flexible material to which conductivity is imparted, a member formed by blending and infiltrating (also referred to as impregnation) a conductive material into all or part of a urethane material will be referred to as "conductive urethane." will be named and explained. The conductive urethane can be formed by either blending or soaking (impregnation) of the conductive material, and can be formed by blending or soaking (impregnating) the conductive material, or by blending and soaking (impregnating) the conductive material. can be formed in combination with For example, when the conductive urethane obtained by infiltration (impregnation) has higher conductivity than the conductive urethane obtained by blending, it is preferable to form the conductive urethane by infiltration (impregnation).

Conductive urethane has the function of changing its electrical properties according to a given physical quantity. An example of a physical quantity that causes a function of changing electrical properties is a stimulus value based on a pressure value that indicates a stimulus due to pressure that deforms a structure such as bending (hereinafter referred to as pressure stimulus). In addition, the pressure stimulus includes the application of pressure to a predetermined site and the application of pressure by distribution of pressure within a predetermined range. Further, other examples of the physical quantity include a stimulus value such as a water content indicating a stimulus (hereinafter referred to as material stimulus) that changes (transforms) the properties of the material by applying moisture or the like. Conductive urethane changes its electrical properties according to given physical quantities. An example of a physical quantity representing this electrical property is an electrical resistance value. Other examples include voltage values or current values.

By giving conductivity to a flexible material having a predetermined volume, conductive urethane exhibits electrical characteristics (that is, changes in electrical resistance) according to given physical quantities. It can be regarded as a volume resistance value. In conductive urethane, the electrical paths are linked in a complicated manner, and for example, the electrical paths expand and contract according to deformation. It may also exhibit behavior in which the electrical path is temporarily disconnected, and behavior in which a different connection than before occurs. Therefore, the conductive urethane deforms according to the magnitude and distribution of stimuli (pressure stimuli and material stimuli) by given physical quantities between positions separated by a predetermined distance (for example, the positions of detection points where electrodes are arranged). It exhibits different electrical properties depending on the temperature and alteration. For this reason, the electrical characteristics change according to the magnitude and distribution of the physical quantity applied to the conductive urethane.

By using conductive urethane, it is not necessary to provide a detection point such as an electrode at a target location for deformation and deterioration. Detecting points such as electrodes may be provided at at least two arbitrary locations on either side of the location where the conductive urethane is stimulated by a physical quantity (for example, FIG. 1).

Further, more than two detection points may be used in order to improve the detection accuracy of the electrical properties of the conductive urethane. Also, the conductive urethane of the present disclosure may be formed of a conductive urethane group in which a plurality of conductive urethane pieces are arranged with the conductive urethane 22 shown in FIG. 1 as one piece of conductive urethane. In this case, the electrical characteristics may be detected for each of the plurality of conductive urethane pieces, or the electrical characteristics of the plurality of conductive urethane pieces may be synthesized and detected. When the electrical characteristics are detected for each of a plurality of conductive urethane pieces, the electrical characteristics such as the electrical resistance value can be detected for each arrangement portion (for example, detection sets #1 to #n). As another example, the detection range on the conductive urethane 22 may be divided, a detection point may be provided for each divided detection range, and the electrical characteristics may be detected for each detection range.

<Estimation device>
Next, an example of an estimating device for estimating the state of the application side with respect to the conductive urethane using the conductive urethane will be described.

FIG. 1 shows an example of the configuration of an estimation device 1 capable of executing estimation processing for estimating the state of the granting side. The estimating device 1 includes an estimating unit 5 and is connected to the object 2 so that the electrical characteristics of the conductive urethane 22 are input. The estimation device 1 estimates the state of the application side of the conductive urethane 22 contained in the object 2 . The estimating device 1 can be realized by a computer having a CPU as an executing device that executes the processing described later.

The above-described deformation and alteration of the conductive urethane occur in physical quantities given to the conductive urethane in chronological order. The physical quantity given in this time series depends on the state of the giver. Therefore, the electrical properties of the conductive urethane that change in time series correspond to the state of the physical quantity imparted to the conductive urethane. For example, when a pressure stimulus that deforms conductive urethane or a material stimulus that modifies conductive urethane is applied, the electrical properties of conductive urethane that change in time series indicate the position, distribution, and magnitude of the pressure stimulus. corresponds to the state of the side. Therefore, it is possible to estimate the state of the application side to the conductive urethane from the electrical properties of the conductive urethane that change in time series.

The estimating device 1 estimates and outputs an unknown granting-side state using a learned learning model 51 by an estimating process to be described later. This makes it possible to identify the state of the application side to the object 2 without using a special device or a large-sized device or directly measuring the deformation and deterioration of the conductive urethane 22 contained in the object. The learning model 51 is learned by inputting the state of the application side with respect to the object 2 and the electrical characteristics of the object 2 (that is, the electrical characteristics such as the electrical resistance value of the conductive urethane 22 placed on the object 2). be. Learning of the learning model 51 will be described later.

It should be noted that the conductive urethane 22 can be placed on a flexible member 21 to form the object 2 (FIG. 2). The object 2 composed of the member 21 on which the conductive urethane 22 is arranged includes an electrical property detection portion 76 . The conductive urethane 22 may be placed on at least part of the member 21, and may be placed inside or outside. In addition, the conductive urethane may be arranged so that the state of the application side to the conductive urethane can be estimated, for example, it may be arranged so as to be able to contact the person directly or indirectly, or both.

FIG. 2 shows an arrangement example of the conductive urethane 22 on the object 2. As shown in FIG. The conductive urethane 22 may be formed so as to completely fill the inside of the member 21 so that the AA cross section of the object 2 is shown as an object cross section 2-1. Further, as shown in the object cross section 2-2, the conductive urethane 22 may be formed on one side (surface side) inside the member 21, and as shown in the object cross section 2-3, the conductive urethane 22 may be formed on the member 21 A conductive urethane 22 may be formed on the other side (rear side) inside the . Furthermore, as shown in the cross section 2-4 of the object, a conductive urethane 22 may be formed in a part of the inside of the member 21. FIG. Further, as shown in the object cross section 2-5, the conductive urethane 22 may be separately arranged outside the surface side of the member 21, and as shown in the object cross section 2-6, the other side ( back side). When the conductive urethane 22 is arranged outside the member 21, the conductive urethane 22 and the member 21 may be simply laminated, or the conductive urethane 22 and the member 21 may be integrated by adhesion or the like. Even if the conductive urethane 22 is arranged outside the member 21, the flexibility of the member 21 is not hindered because the conductive urethane 22 is a conductive urethane member.

As shown in FIG. 1, the conductive urethane 22 detects the electrical properties of the conductive urethane 22 (that is, the volume resistance value, which is the electrical resistance value) by signals from at least two detection points 75 arranged at a distance. ). In the example of FIG. 1, a detection set #1 is shown for detecting electrical characteristics (time-series electrical resistance values) from signals from two detection points 75 arranged diagonally on the conductive urethane 22. there is The number and arrangement of the detection points 75 are not limited to the positions shown in FIG. good. The electrical characteristics of the conductive urethane 22 can be obtained by connecting an electrical characteristics detector 76 for detecting electrical characteristics (for example, a volume resistance value, which is an electrical resistance value) to the detection point 75 and using the output thereof.

In this embodiment, since the conductive urethane 22 is used as the sensor, for example, when a person intervenes, the sense of discomfort given to the person is extremely small compared to a conventional sensor. Therefore, it is possible to perform measurement and estimation of the state of the granting side at the same time without damaging the state of the granting side of the person during the measurement. This is an advantage over conventional sensors that separately perform measurement and state estimation on the application side, and is particularly advantageous in estimation based on long-term measurement evaluation that follows chronological changes.

The estimating unit 5 is a functional unit that is connected to the object 2 and estimates the state of the applying side using the learning model 51 based on the electrical characteristics that change according to at least one of deformation and alteration of the conductive urethane 22. be. Specifically, time-series input data 4 representing the magnitude of electrical resistance (eg, electrical resistance value) in the conductive urethane 22 is input to the estimation unit 5 . The input data 4 corresponds to the state data 3 indicating the state of the application side with respect to the object 2, for example, the state related to the behavior of the person in contact with the object 2, such as the posture and movement of the person. For example, when a person contacts the object 2, the contact is made in a predetermined state such as a posture, and the conductive urethane 22 constituting the object 2 is stimulated (at least pressure stimulation and material stimulation) in response to the state. On the other hand) is given as a physical quantity, and the electrical properties of the conductive urethane 22 change. Therefore, the electrical characteristics of the conductive urethane 22 that change in time series indicated by the input data 4 correspond to the state of the object 2, ie, the conductive urethane 22 on the application side. The estimating unit 5 also outputs output data 6 representing the state of the applying side corresponding to the electrical characteristics of the conductive urethane 22 that change in time series, as an estimation result using the learned learning model 51 .

The learning model 51 has completed learning to derive the output data 6 representing the state of the applying side from the electric resistance (input data 4) of the conductive urethane 22 that changes due to the stimulus (pressure stimulus and material stimulus) given as a physical quantity. is a model. The learning model 51 is, for example, a model that defines a trained neural network, and is expressed as a set of information on weights (strengths) of connections between nodes (neurons) that make up the neural network.

<Learning processing>
Next, learning processing for generating the learning model 51 will be described.

FIG. 3 shows a conceptual configuration of a learning model generation device that generates the learning model 51. As shown in FIG. The learning model generation device includes a learning processing unit 52 . The learning model generation device can be configured including a computer having a CPU (not shown), and is executed as a learning processing unit 52 by a learning data collection process and a learning model generation process executed by the CPU to generate a learning model 51. .

<Learning data collection processing>
In the learning data collection process, the learning processing unit 52 learns a large amount of input data 4 obtained by measuring the electrical characteristics (for example, electrical resistance) of the conductive urethane 22 in chronological order using the state data 3 representing the state of the application side as a label. Collect as data. Therefore, the learning data includes a large amount of sets of input data 4 indicating electrical characteristics and state data 3 indicating the state of the application side corresponding to the input data 4 .

Specifically, in the learning data collection process, stimulation (pressure stimulation and material stimulation) according to the state of the application side when the state of the object 2 (that is, the state of the application side with respect to the conductive urethane 22) is formed An electrical characteristic (for example, an electrical resistance value) that changes depending on the time is acquired in chronological order. Next, state data 3 is given as a label to the acquired time-series electrical characteristics (input data 4), and the set of state data 3 and input data 4 reaches a predetermined number or a predetermined time. Repeat the process until A set of the state data 3 indicating the state of the application side and the time-series electrical characteristics (input data 4) of the conductive urethane 22 acquired for each state of the application side becomes the learning data. The state data 3 in the learning data is stored in a memory (not shown) so as to be handled as output data 6 indicating the state of the giving side in which the estimation result is correct in the learning process described later.

The learning data may be associated with time-series information by adding information indicating the measurement time to each electrical resistance value (input data 4) of the conductive urethane 22 . In this case, information indicating the time of measurement may be added to a set of time-series electrical resistance values in the conductive urethane 22 to associate the time-series information with the period determined as the state of the application side.

An example of the learning data described above is shown in the following table. Table 1 is an example of a data set in which time-series electrical resistance value data (r) and state data (R) indicating the state of the application side are associated as learning data regarding the state of the application side of the conductive urethane 22. be.

It should be noted that the electrical characteristics detected by the conductive urethane 22 (temporal characteristics based on time-series electrical resistance value data) can be regarded as characteristic patterns relating to the state of the conductive urethane 22 on the application side. That is, different stimuli are applied to the conductive urethane 22 in chronological order depending on the state of the application side of the conductive urethane 22 . Therefore, it is considered that the time-series electrical characteristics within a predetermined period of time appear as characteristic electrical characteristics with respect to the state of the application side. Therefore, the pattern (for example, the distribution shape of time-series electrical resistance values in the electrical characteristics) shown in the electrical characteristics (time characteristics based on time-series electrical resistance value data) detected by the conductive urethane 22 varies depending on the state of the application side. It corresponds and functions effectively in the learning process which will be described later.

<Learning model generation processing>
Next, learning model generation processing will be described. The learning model generation device shown in FIG. 3 generates a learning model 51 using the learning data described above by a learning model generation process in the learning processing unit 52 .

FIG. 4 is a diagram showing the functional configuration of the learning processing unit 52, that is, the functional configuration of a CPU (not shown) regarding learning model generation processing executed by the learning processing unit 52. As shown in FIG. A CPU (not shown) of the learning processing unit 52 operates as functional units of the generator 54 and the computing unit 56 . The generator 54 has a function of generating an output in consideration of the sequential relationship of the electrical resistance values obtained in time series as an input.

The learning processing unit 52 uses, as learning data, the above-described input data 4 (for example, an electrical resistance value) and output data 6, which is the state data 3 indicating the state of the stimulus applied to the conductive urethane 22. A large number of sets are held in a memory (not shown).

The generator 54 includes an input layer 540, an intermediate layer 542, and an output layer 544 to form a known neural network (NN). Since the neural network itself is a known technology, detailed description is omitted, but the intermediate layer 542 includes a large number of node groups (neuron groups) having inter-node connections and feedback connections. Data from the input layer 540 is input to the intermediate layer 542 , and data resulting from the operation of the intermediate layer 542 is output to the output layer 544 .

The generator 54 is a neural network that generates generated output data 6A as data representing the state of the application side or data close to the state of the application side from the inputted input data 4 (eg, electrical resistance value). The generated output data 6A is data obtained by estimating, from the input data 4, the state of the applying side in which the conductive urethane 22 is stimulated. The generator 54 generates generated output data representing a state close to the state of the granting side from the input data 4 input in chronological order. The generator 54 learns using a large number of input data 4 so that it can generate the generated output data 6A that is close to the state of the applying side such as a person or the like to whom the stimulus is given to the object 2, that is, the conductive urethane 22. Become. In another aspect, the electrical characteristics, which are the input data 4 input in chronological order, are regarded as a pattern, and by learning the pattern, the object 2, that is, the person or the like to whom the stimulus is applied to the conductive urethane 22, can be applied to the subject. generated output data 6A close to the state of .

The calculator 56 is a calculator that compares the generated output data 6A with the output data 6 of the learning data and calculates the error of the comparison result. The learning processing unit 52 inputs the generated output data 6A and the output data 6 of the learning data to the calculator 56 . In response to this, the calculator 56 calculates the error between the generated output data 6A and the output data 6 of the learning data, and outputs a signal indicating the calculation result.

The learning processing unit 52 performs learning of the generator 54 that tunes the weight parameter of the connection between nodes based on the error calculated by the calculator 56 . Specifically, the weight parameter of the connection between the nodes of the input layer 540 and the hidden layer 542 in the generator 54, the weight parameter of the connection between the nodes in the hidden layer 542, and the node of the hidden layer 542 and the output layer 544 Each of the weight parameters of the connections between is fed back to the generator 54 using techniques such as gradient descent and error backpropagation. That is, with the output data 6 of the learning data as a target, the connections between all nodes are optimized so as to minimize the error between the generated output data 6A and the output data 6 of the learning data.

Note that the generator 54 may use a recursive neural network having a function of generating an output in consideration of the context of time-series inputs, or may use another method.

The learning processing unit 52 generates the learning model 51 using the learning data described above by the learning model generation process. The learning model 51 is expressed as a set of information of weight parameters (weights or strengths) of connections between nodes of learning results, and is stored in a memory (not shown).

Specifically, the learning processing unit 52 executes learning model generation processing according to the following procedure. In the first learning process, input data 4 (electrical characteristics) labeled with information indicating the state of the application side, which is learning data obtained as a result of chronological measurement, is obtained. In the second learning process, a learning model 51 is generated using learning data obtained as a result of time-series measurement. That is, a set of information on weight parameters (weights or strengths) of connections between nodes is obtained as a result of learning using a large amount of learning data as described above. Then, in the third learning process, data expressed as a set of information of weight parameters (weights or strengths) of connections between nodes of learning results is stored as a learning model 51 .

Then, the estimation apparatus 1 uses the learned generator 54 (that is, data expressed as a set of information of weight parameters of connections between nodes of learning results) as the learning model 51 . If a sufficiently learned learning model 51 is used, it is also possible to identify the state of the applying side from the time-series electrical characteristics of the object 2, that is, the conductive urethane 22 (for example, the electrical resistance value characteristics that change in time series). Not impossible.

<PRC>
By the way, in the conductive urethane 22, as described above, the electrical paths are intricately linked, and the electrical paths are expanded, contracted, expanded, contracted, temporarily disconnected, and changed (deformed) such as new connections, and changes in the properties of the material. It shows behavior according to (transformation). As a result, the conductive urethane 22 behaves with different electrical properties depending on the applied stimulus (eg, pressure stimulus). This allows the conductive urethane 22 to be treated as a reservoir that stores data regarding deformation of the conductive urethane 22 . That is, the estimation device 1 can apply the conductive urethane 22 to a network model (hereinafter referred to as PRCN) called physical reservoir computing (PRC). Since PRC and PRCN are known technologies, detailed description thereof will be omitted, but PRC and PRCN are suitable for estimating information on deformation and deterioration of the conductive urethane 22 .

FIG. 5 shows an example of the functional configuration of the learning processing unit 52 to which PRCN is applied. The learning processing unit 52 applying PRCN includes an input reservoir layer 541 and an estimation layer 545 . The input reservoir layer 541 corresponds to the conductive urethane 22 included in the object 2 . That is, the learning processing unit 52 to which the PRCN is applied treats the target object 2 including the conductive urethane 22 as a reservoir for storing data on deformation and deterioration of the target object 2 including the conductive urethane 22 and learns. The conductive urethane 22 becomes an electrical characteristic (electrical resistance value) according to each of various stimuli, functions as an input layer for inputting the electrical resistance value, and stores data on deformation and alteration of the conductive urethane 22. Acts as a reservoir layer. Since the conductive urethane 22 outputs different electrical characteristics (input data 4) according to the stimulus given by the state of the applying side such as a person, the estimation layer 545 calculates the electrical resistance value of the given conductive urethane 22. It is possible to deduce the state of the unknown grantor from Therefore, the estimation layer 545 should be learned in the learning process in the learning processing unit 52 to which the PRCN is applied.

<Configuration of estimation device>
Next, an example of a specific configuration of the estimation device 1 described above will be further described. FIG. 6 shows an example of the electrical configuration of the estimation device 1. As shown in FIG. The estimating device 1 shown in FIG. 6 includes a computer as an execution device that executes processing for realizing the various functions described above. The estimation device 1 described above can be realized by causing a computer to execute a program representing each function described above.

A computer functioning as the estimation device 1 includes a computer main body 100 . The computer main body 100 includes a CPU 102 , a RAM 104 such as a volatile memory, a ROM 106 , an auxiliary storage device 108 such as a hard disk drive (HDD), and an input/output interface (I/O) 110 . These CPU 102, RAM 104, ROM 106, auxiliary storage device 108, and input/output I/O 110 are connected via a bus 112 so as to exchange data and commands with each other. Also, the input/output I/O 110 is connected to a communication unit 114 for communicating with an external device, an operation display unit 116 such as a display and a keyboard, and a detection unit 118 . The detection unit 118 has a function of acquiring input data 4 (electrical characteristics such as time-series electrical resistance values) from the object 2 including the conductive urethane 22 . That is, the detection unit 118 includes the object 2 on which the conductive urethane 22 is arranged, and can acquire the input data 4 from the electrical characteristic detection unit 76 connected to the detection point 75 in the conductive urethane 22. be. Note that the detection unit 118 may be connected via the communication unit 114 .

The auxiliary storage device 108 stores a control program 108P for causing the computer main body 100 to function as the estimation device 1 as an example of the estimation device of the present disclosure. The CPU 102 reads the control program 108P from the auxiliary storage device 108, develops it in the RAM 104, and executes processing. Thereby, the computer main body 100 that has executed the control program 108P operates as the estimation device 1. FIG.

The auxiliary storage device 108 stores a learning model 108M including the learning model 51 and data 108D including various data. The control program 108P may be provided by a recording medium such as a CD-ROM.

<Estimation processing>
Next, the estimation processing in the estimation device 1 implemented by a computer will be further described. FIG. 7 shows an example of the flow of estimation processing by the control program 108P executed by the computer main body 100. As shown in FIG. The estimation process shown in FIG. 7 is executed by the CPU 102 when the computer main body 100 is powered on. The CPU 102 reads the control program 108P from the auxiliary storage device 108, develops it in the RAM 104, and executes processing.

First, CPU 102 acquires learning model 51 by reading learning model 51 from learning model 108M in auxiliary storage device 108 and developing it in RAM 104 (step S200). Specifically, a network model (see FIG. 4 and FIG. 5), which is represented as a learning model 51 and is a connection between nodes based on weight parameters, is developed in RAM 104 to realize connections between nodes based on weight parameters. A learning model 51 is constructed.

Next, the CPU 102 acquires the unknown input data 4 (electrical characteristics) for estimating the state of the applied side due to the stimulus applied to the conductive urethane 22 in time series via the detection unit 118 (step S202). Next, the CPU 102 uses the learning model 51 to estimate the output data 6 (unknown state of the giving side) corresponding to the acquired input data 4 (step S204). Then, the CPU 102 outputs the output data 6 (state of the provision side) of the estimation result via the communication section 114 (step S206), and ends this processing routine.

Thus, according to the estimation device 1, it is possible to estimate the unknown state of the applying side from the electrical resistance value of the conductive urethane 22. FIG. Specifically, the estimating device 1 can estimate the state of the applying side of a person or the like from the input data 4 (electrical characteristics) that change according to the stimulus given to the conductive urethane 22 depending on the state of the applying side. It becomes possible. That is, it is possible to estimate the state of the applying side, such as a person, without using a special device or a large-sized device or directly measuring the deformation of the flexible member.

Subsequently, the estimation of the estimation device 1 according to this embodiment will be described with a specific example. Below, an example of the posture state estimation device 11 that estimates the posture state of the wearer of the shoe by providing the conductive urethane 22, which is an example of the above-mentioned "flexible material imparted with conductivity", to the shoe will be described. FIG. 8 is a diagram showing a posture state estimation device as a specific example of the estimation device according to this embodiment, and FIG. 9 is a diagram showing a detailed configuration example of the posture state estimation device 11. As shown in FIG.

Note that shoes are a type of footwear, and refer to those that wrap around the feet. In addition, as footwear, except for the sole, it is fixed with a string or belt (so-called "sandals"), short shoes, and long boots with a mouth that extends above the ankle. (so-called “boots”). "Shoes" refers to footwear to which the heel portion is fixed. In the example of FIG. 8 , an example in which the conductive urethane 22 is provided on the shoe 12 is shown, but the conductive urethane 22 may be provided on footwear other than the shoe 12 . Also, the wearer of the shoes may be a person or a robot.

The posture state estimation device 11 in the example of FIG. A detector 118 is provided on the bottom of the shoe 12 . The detection section 118 includes the conductive urethane 22 and a detection unit 78 including the electrical property detection section 76 .

The conductive urethane 22 is molded into a shape that matches the insole or sole portion of the shoe 12 . That is, the conductive urethane 22 is given a pressure stimulus as a physical quantity from the soles of the wearer's feet of the shoe 12, and the electrical characteristics of the conductive urethane 22 change. Therefore, the conductive urethane 22 outputs a pattern represented by characteristic electrical characteristics (time characteristics based on time-series electrical resistance value data) according to the posture state such as the center of gravity and weight shift of the wearer of the shoe 12 . In this embodiment, the sensor itself that detects the pressure stimulus by the wearer of the shoe 12 is the conductive urethane 22 that includes a urethane member as a conductive flexible material. Compared to this, the feeling of discomfort when wearing the shoes 12 is extremely small. Therefore, it is possible to measure and evaluate the wearing comfort at the same time without impairing the wearing comfort during the measurement. This is an advantage over conventional sensors that separately perform measurement and wear comfort evaluation, and is particularly advantageous in long-term measurement and evaluation involving time-series changes.

The number of detection points 75 of the conductive urethane 22 may be more than the number of detection points (two) shown in FIGS. As an example, the conductive urethane 22 may be formed by arranging one or more rows of a plurality of conductive urethane pieces each having a detection point, and the electrical characteristics may be detected for each of the plurality of conductive urethane pieces. good. For example, the conductive urethane pieces 23 (FIG. 10) may be arranged to form the conductive urethane 22 (FIGS. 11 and 12). The example shown in FIG. 10 includes a first detection set #1 that detects an electrical resistance value from a signal from a detection point 75A arranged diagonally with a distance, and another detection set #1 arranged diagonally. A second detection set #2 is shown which detects the electrical resistance value from the signal from point 75B. In the example shown in FIG. 11, the conductive urethane pieces 23 (FIG. 10) are arranged (4×1) in the longitudinal direction of the shoe 12 to form the conductive urethane 22, and sequentially from the first detection set #1. It shows constructing an eighth detection set #8. Furthermore, in the example shown in FIG. 12, the conductive urethane pieces 23 (FIG. 10) each adopt the first detection set #1 and are arranged (4×2) in the longitudinal direction and width direction of the shoe 12 to form the conductive urethane pieces 22 . to form the first detection set #1 to the eighth detection set #8. 10 to 12 show the shape of the conductive urethane 22 in a rectangular shape, but the shape of the shoe 12 is preferable.

As another example, the detection range on the conductive urethane 22 may be divided, a detection point may be provided for each divided detection range, and the electrical characteristics may be detected for each detection range. For example, a region corresponding to the size of the conductive urethane piece 23 shown in FIGS. What is necessary is just to detect an electrical characteristic.

The detection unit 78 includes, for example, the electrical characteristic detection section 76 provided in the heel portion of the shoe 12 and connected to the detection point 75 provided in the conductive urethane 22, the communication section 80, the electricity storage section 82, and the power generation section 84. I have.

The electrical characteristic detector 76 detects the electrical characteristic input from the detection point 75 and outputs the detection result to the communication section 80 .

The communication unit 80 communicates with the mobile terminal device 30 such as a smartphone, and transmits detection results of physical quantities representing electrical characteristics obtained from the detection points 75 of the electrical characteristics detection unit 76 to the mobile terminal device 30 . The communication unit 80 communicates with the mobile terminal device 30 by short-range wireless communication such as Wi-Fi (registered trademark) or Bluetooth (registered trademark).

The power storage unit 82 supplies power for the electrical property detection unit 76 to detect electrical properties and power for the communication unit 80 to communicate with the mobile terminal device 30 . Various types of rechargeable batteries, capacitors, and the like are applied to the power storage unit 82 , and are charged with power generated by the power generation unit 84 .

Power generation unit 84 generates power by various well-known methods, and charges power storage unit 82 by supplying the generated power to power storage unit 82 . For example, a coil and a magnet may be used to generate electricity by causing the magnet to relatively move within the coil in accordance with the rocking motion of the shoe 12 . Alternatively, power may be generated using a power generation element or the like that converts energy such as light, heat, pressure, or vibration into electric power.

On the other hand, the mobile terminal device 30 includes the computer main body 100, the communication section 114, and the operation display section 116 described above.

The mobile terminal device 30 functions as the estimation device 1, and the computer main body 100 uses the learned learning model 51 to estimate the posture of the wearer of the shoes 12 from the electrical characteristics of the conductive urethane 22 provided on the shoes 12. presume. Here, an example of estimating the posture state of the wearer of the shoe 12 by providing the conductive urethane 22 on the insole or sole portion of the shoe 12 will be described. It may be provided on the inner side of the foot to estimate the internal pressure, or may be provided on the outside of the instep or the heel of the foot to estimate the external pressure.

The communication unit 114 communicates with the communication unit 80 of the shoe 12 and acquires from the shoe 12 the detection result of the physical quantity representing the electrical characteristic obtained from the detection point 75 of the electrical characteristic detection unit 76 . The communication unit 114 communicates with the mobile terminal device 30 by short-range wireless communication such as Wi-Fi (registered trademark) or Bluetooth (registered trademark).

The operation display unit 116 corresponds to an example of the output unit, and displays the posture state of the wearer of the shoe 12 estimated by the computer main body 110 .

The estimation processing in the portable terminal device 30 uses the learned model 51 to determine the unknown posture state of the wearer of the shoes 12. Estimates and outputs the attitude state of This makes it possible to identify the posture of the wearer of the shoe 12 without using a special or large-sized device or directly measuring the deformation of the conductive urethane 22 contained in the shoe 12 . Therefore, in order to estimate the posture of the wearer of the shoes 12 from the electrical properties of the conductive urethane 22, the mobile terminal device 30 is equipped with the electrical properties of the conductive urethane 22 to estimate the posture of the wearer of the shoes 12. A learning model 51 is stored in the auxiliary storage device 108 .

The learning model 51 uses the electrical resistance (input data 4) of the conductive urethane 22, which changes due to pressure stimulation from the wearer of the shoes 12, to determine the posture of the wearer of the shoes 12, that is, the center of gravity and the center of gravity of the wearer of the shoes 12. This is a model that has been trained to derive the state (output data 6) of the wearer of the shoe 12, which indicates the posture state such as weight shift. The learning model 51 is, for example, a model that defines the above-described trained neural network, and is expressed as a set of information on weights (strengths) of connections between nodes (neurons) that constitute the neural network.

The learning model 51 is generated by learning processing of the learning processing unit 52 (FIG. 3). The learning processing unit 52 performs learning processing using the electrical characteristics (input data 4) of the conductive urethane 22 that change due to the pressure stimulation caused by the posture of the wearer of the shoe 12 (state data 3). That is, a set of a large amount of data obtained by measuring the electrical resistance of the conductive urethane 22 in chronological order using the posture of the wearer of the shoe 12 as a label is used as learning data. Specifically, the learning data is a set of input data including an electrical resistance value (input data 4) and information indicating the posture state of the wearer of the shoe 12 corresponding to the input data (output data 6). contains a large amount of Here, time-series information is associated with each of the electrical resistance values (input data 4) of the conductive urethane 22 by adding information indicating the measurement time. In this case, for a period determined as the posture state (state data 3) of the wearer of the shoe 12, time-series information is associated with a set of time-series electrical resistance values in the conductive urethane 22 by adding information indicating the measurement time. may

Next, the learning processing section 52 will be described. In the learning process performed by the learning processing unit 52, the posture state of the wearer of the shoe 12 (state data 3) and the electrical resistance value of the conductive urethane 22 (input data 4) are used as learning data. For example, when the wearer of the shoes 12 maintains or moves a posture state indicating behavior such as a posture or movement resulting in a predetermined center of gravity or weight shift, the electrical resistance value at that time is detected and learned in association with the posture state. data.

Note that the posture state includes a state indicating the behavior of the wearer of the shoe 12 in chronological order. The state showing this chronological behavior includes a static state in which the movement of the wearer of the shoe 12 is stable and a dynamic state in which a plurality of postures and movements change in chronological order. Also, the electrical characteristics (that is, the volume resistance value, which is the electrical resistance value) can be detected by connecting the electrical characteristics detection section 76 to the detection point 75 .

Specifically, the learning processing unit 52 described above executes learning data collection processing and learning processing. FIG. 13 shows an example of learning data collection processing executed by a CPU (not shown). In step S100, the learning processing unit 52 gives stimulus to the wearer of the shoes 12 provided with the conductive urethane 22 by instructing the posture state. Get values in chronological order. In the next step S104, the acquired time-series electrical resistance values are labeled with the posture state (state data 3) and stored. The learning processing unit 52 continues until the set of the posture state (state data 3) of the wearer of the shoe 12 and the electrical resistance value of the conductive urethane 22 reaches a predetermined number or a predetermined time (step A negative determination is made until an affirmative determination is made in S106), and the above processing is repeated. As a result, the learning processing unit 52 can acquire and store the electrical resistance value of the conductive urethane 22 in chronological order for each posture state of the wearer of the shoe 12. A set of time-series electrical resistance values of the conductive urethane 22 for each posture state is learning data.

By the way, the posture state of the wearer of the shoes 12 is determined by the relative positional relationship of each part of the sole of the wearer of the shoes 12, the distribution of pressure stimulation by each part, the magnitude, and the change of each physical quantity such as frequency. Identifiable by, at least in part, such as maintenance. Therefore, some of these time-series physical quantities are considered to include features indicating the posture of the wearer of the shoe 12 . In this embodiment, by using the conductive urethane 22, it is possible to detect the electrical characteristics (volume resistance) reflecting these physical quantities in chronological order.

An example of learning data for learning the learning model 51 for estimating the posture state of the wearer of the shoe 12 from the electrical properties of the conductive urethane 22 is shown in a table. Table 2 is an example of data in which time-series electrical resistance value data (r) and posture state values are associated with each other as learning data relating to the posture state. Table 2 shows an example of data in which the state value of the center of gravity is associated as the posture state value, but instead of the state value of the center of gravity, the state value of weight shift may be associated. Further, Table 3 shows an example of data in which posture states corresponding to the center of gravity, weight shift, etc. are associated with each other. In addition, considering that the left and right shoes 12 are linked, Table 4 shows correspondence between a set of characteristic data (J) indicating time-series electrical resistance values detected by the left and right shoes 12 and posture state values. This is an example of attached data. Any of the characteristic data (J) included in this set includes the feature of the posture state, that is, the feature pattern. All characteristic data (J) are used as learning data. For example, a plurality of characteristic data (J) detected for the left and right shoes 12 and posture state values (in Table 4, values indicating a "walking" motion) are used as learning data.

As the attitude state value associated with the time-series electrical resistance value data, attitude state values other than those described above may be applied. For example, a posture state value related to a fall such as a posture with a possibility of a fall or a probability of a possibility of a fall may be applied.

In the present embodiment, the learning data collected in this manner is used to generate the learning model 51 through learning processing by the learning processing unit 52 .

FIG. 14 shows an example of the flow of learning processing. In step S110, the learning processing unit 52 acquires input data 4 (electrical resistance) labeled with information indicating the posture state of the wearer of the shoe 12, which is learning data obtained as a result of chronological measurement. In step S112, the learning processing unit 52 generates the learning model 51 using learning data obtained as a result of time-series measurement. That is, a set of information on weight parameters (weights or strengths) of connections between nodes is obtained as a result of learning using a large amount of learning data as described above. Then, in step S114, data expressed as a set of information on weight parameters (weights or strengths) of connections between nodes of learning results is stored as a learning model 51. FIG.

Note that the processing by the learning processing unit 52 is an example of the processing of the learning model generation device of the present disclosure. Also, posture state estimation device 11 is an example of an estimation unit and an estimation device of the present disclosure. The output data 6, which is information indicating the posture state, is an example of the posture state information of the present disclosure.

Then, the mobile terminal device 30 estimates the posture state of the wearer of the shoe 12 using the learning model 51 generated by the learning processing unit 52 . Specifically, the same estimation processing as in FIG. 7 described above is performed, but instead of outputting the output data of the estimation result (S206), as shown in FIG. Display (S205). For example, the estimated posture state may be displayed as text, or the transition of the estimated posture state may be displayed as an animation. When estimating a posture state value related to a fall as a posture state value, if the estimated posture state value is a predetermined posture or a predetermined threshold value or more probability, a warning is output by sound, display, or the like. good too. As a result, when a person receiving rehabilitation or nursing care puts on the shoes 12, the person assisting the person can prevent the person from falling. Alternatively, both the estimated posture state and a warning based on the posture state may be output. Note that the estimation process illustrated in FIG. 15 is an example of the process performed by the estimation method of the present disclosure.

As described above, according to the present disclosure, from the input data 4 (electrical resistance) that changes according to the pressure stimulation given from the foot of the wearer of the shoe 12 to the conductive urethane 22, the It is possible to estimate the posture state of the wearer. That is, it is possible to easily estimate the unknown posture state of the wearer of the shoe 12 without using a large-scale device such as a special device or a large-sized device or directly measuring the deformation of the flexible member. Become.

In addition, the electrical characteristics of each detection set change depending on the posture of the wearer of the shoes 12, and the posture is reflected in the amount of time-series change in the electrical characteristics. The posture state of the wearer of the shoe 12 can be estimated from the resistance value. That is, by using the learning model described above, the posture of the wearer of the shoe 12 can be identified and the posture of the wearer of the shoe 12 can be estimated even in various postures.

In the posture state estimation device 11 using the learning model 51 learned by the learning process described above, by inputting the electrical characteristics of the conductive urethane 22 in various unknown posture states, the posture of the wearer of the corresponding shoe 12 is calculated. It was confirmed that the state can be estimated.

It should be noted that the estimation of the center of gravity and weight shift described above functions effectively in robot control, for example. For example, when the robot is in a stationary state, there are cases where the center of gravity or weight shifts due to a person hugging the robot or coming into contact with an obstacle. There is also a possibility that the robot may fall over due to the movement of the center of gravity or weight from the stationary state.

Therefore, the above-mentioned conductive urethane 22 is placed on the foot part of the robot, for example, the sole, and the movement of the center of gravity and the movement of the weight are detected, and the physical quantity related to the movement (for example, the movement speed or the movement acceleration) is derived. When the derived physical quantity exceeds a predetermined threshold, it is estimated that the robot is likely to fall. Then, when a state indicating that the robot is likely to fall over is estimated, as secondary control, for example, the robot is controlled to move the center of gravity in the opposite direction, such as by changing the posture of the robot. By doing so, it is possible to support the fall prevention of the robot.

Note that the posture state estimation device 11 of the present disclosure may further include a control device that performs predetermined control according to the estimated posture state. Here, a control device that performs predetermined control using the estimation result of posture state estimation device 11 will be described. FIG. 16 is a diagram showing an example configuration of a control device that performs predetermined control using the estimation result of the posture state estimation device 11 .

As an example of the control device 13 that performs predetermined control, a device such as a power assist device or an assistance device is applied, and controls an actuator 168 such as a motor based on the estimation result of the posture state estimation device 11 . That is, devices such as a power assist device and an assistance device can be controlled according to the posture of the wearer of the shoe 12 .

Specifically, as shown in FIG. 16, the control device 13 includes a computer main body 150, a communication section 164, and an operation display section 166, similarly to the portable terminal device 30. FIG. The computer main body 150 includes a CPU 152 , a RAM 154 such as a volatile memory, a ROM 156 , a hard disk drive (HDD) 158 and an input/output interface (I/O) 160 . These CPU 152, RAM 154, ROM 156, HDD 158, and input/output I/O 160 are connected via a bus 162 so as to exchange data and commands with each other. Also, the input/output I/O 160 is connected with a communication unit 164 for communicating with an external device, an operation display unit 166 such as a display and a keyboard, and an actuator 168 . Note that the control device 13 may have a form in which the operation display unit 166 is omitted.

The communication unit 164 communicates with the mobile terminal device 30 and acquires the posture state estimated by the mobile terminal device 30 . The communication unit 164 communicates with the mobile terminal device 30 by short-range wireless communication such as Wi-Fi (registered trademark) or Bluetooth (registered trademark).

The actuator 168 is, for example, a motor, a solenoid, or the like, and drives the actuator 168 to generate assistance. For example, when the control device 13 is a power assist device, it drives a motor to generate assistance for lifting a load or the like.

Next, a drive process for the actuator 168 executed by the computer main body 150 of the control device 13 will be described. FIG. 17 shows an example of the flow of drive processing. The CPU 152 acquires the estimation result of the posture state of the wearer of the shoe 12 from the portable terminal device 30 of the posture state estimation device 11 (S300).

Next, the CPU 152 determines the control amount of the actuator 168 based on the attitude state (S302). For example, when the estimated posture state value is "front of the center of gravity", a predetermined control amount for the drive direction is determined corresponding to the posture state of "front of the center of gravity", or if the estimated posture state value is " In the case of "bending down", a control amount of a predetermined driving direction is determined corresponding to the "bending" posture. Then, the CPU 152 drives the actuator 168 with the determined control amount (S304), and ends this processing routine.

In this manner, the control device 13 drives the actuator 168 with a control amount corresponding to the posture state of the wearer of the shoe 12 estimated by the posture state estimation device 11, thereby operating devices such as power assist devices and assistance devices. You can control it.

As described above, in the present disclosure, a case where conductive urethane is applied as an example of the flexible member has been described, but the flexible member is of course not limited to conductive urethane.

Moreover, the technical scope of the present disclosure is not limited to the scope described in the above embodiments. Various changes or improvements can be made to the above-described embodiments without departing from the scope of the invention, and forms with such changes or improvements are also included in the technical scope of the present disclosure.

Further, in the above embodiment, a case has been described in which the estimation process, the learning process, and the drive process are realized by a software configuration using a process using flowcharts, but the invention is not limited to this. It may be realized by a software configuration.

Also, part of the estimation device, for example, a neural network such as a learning model, may be configured as a hardware circuit.

1 estimation device 2 object 3 state data 4 input data 5 estimation unit 6 output data 6A generated output data 11 posture state estimation device 12 shoe 13 control device 22 conductive urethane 30 mobile terminal device 51 learning model 52 learning processing unit 54 generator 56 arithmetic unit 75 detection point 76 electrical characteristic detection unit 100 computer main body 150 computer main body 168 actuator

Claims (8)

Electrical characteristics between a plurality of predetermined detection points on the flexible material of a shoe provided in predetermined regions with a flexible material that is conductive and whose electrical characteristics change according to changes in applied pressure a detection unit that detects
Inputting the time-series electrical characteristics using the time-series electrical characteristics when pressure is applied to the flexible material and the posture state information of the wearer of the shoe applying pressure to the flexible material as learning data. and inputting the time-series electrical characteristics detected by the detection unit to the learning model trained to output the posture state information, and wearing the shoes corresponding to the input time-series electrical characteristics an estimation unit that estimates posture state information of a person;
estimator including The estimating device according to claim 1, wherein the estimating unit estimates the center of gravity or weight shift of the shoe wearer. 3. The estimation apparatus according to claim 1, further comprising a control section that performs predetermined control according to the posture state information estimated by the estimation section. The estimation device according to any one of claims 1 to 3, further comprising an output unit that outputs at least one of the posture state information estimated by the estimation unit and a warning based on the posture state information. The estimation according to any one of claims 1 to 4, wherein the learning model includes a model generated by learning using a network by reservoir computing using the flexible material as a reservoir. Device. A computer is provided with a flexible material which has electrical conductivity and whose electrical characteristics change according to a change in applied pressure at a predetermined portion, and between a plurality of predetermined detection points on the flexible material of the shoe. detect electrical properties,
Inputting the time-series electrical characteristics using the time-series electrical characteristics when pressure is applied to the flexible material and the posture state information of the wearer of the shoe applying pressure to the flexible material as learning data. and inputting the detected time-series electrical characteristics to a learning model trained to output the posture state information, and determining the posture state of the wearer of the shoe corresponding to the input time-series electrical characteristics An estimation method for estimating information. A shoe having a flexible material that is electrically conductive and whose electrical characteristics change according to a change in applied pressure at a predetermined portion, and between a plurality of predetermined detection points on the flexible material. detect electrical properties,
Inputting the time-series electrical characteristics using the time-series electrical characteristics when pressure is applied to the flexible material and the posture state information of the wearer of the shoe applying pressure to the flexible material as learning data. and inputting the detected time-series electrical characteristics to a learning model trained to output the posture state information, and determining the posture state of the wearer of the shoe corresponding to the input time-series electrical characteristics Estimating information An estimating program for executing a process. Electrical characteristics between a plurality of predetermined detection points on the flexible material of a shoe provided in predetermined regions with a flexible material that is conductive and whose electrical characteristics change according to changes in applied pressure and posture state information indicating the posture state of the wearer of the shoe applying pressure to the flexible material;
Based on the acquisition result of the acquisition unit, time-series electrical characteristics when pressure is applied to the flexible material are input, and posture state information indicating the posture state of the wearer applying pressure to the flexible material is output. a learning model generation unit that generates a learning model;
A learning model generator including

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