JP7493721B2 - Adaptability assessment device, adaptability assessment method, and adaptability assessment program - Google Patents

Description

This invention relates to an adaptability assessment device, an adaptability assessment method, and an adaptability assessment program.

There is an approach that uses a system (game) to evaluate the adaptability of a user performing a target exercise in which the situation changes (see, for example, non-patent document 1). In this approach, the parameters used to evaluate adaptability are the score when the target exercise is achieved, and the number of successes.

Toru Saito, Yasuyoshi Asakawa, “Relationship between balance function, cognitive attention, and executive function and performance ability in a physical game” Physical Therapy Supplement, vol. 2013, p. 519, 2014.

However, Non-Patent Document 1 does not take into account changes in the situation of the target exercise, and therefore cannot correctly evaluate adaptability.

The objective of this invention is to correctly evaluate a user's adaptability to changes in the situation of the targeted exercise.

In order to achieve the above object, the adaptability evaluation device of the present invention, which evaluates the adaptability of a user to a target exercise that can be judged as a success or failure when performed by the user and changes as a predetermined condition changes, includes a condition control unit that sets the predetermined condition, a measurement unit that measures motion information about the user each time the user performs the target exercise under the set conditions a predetermined number of times, an exercise content evaluation calculation unit that calculates a reproducibility evaluation value that indicates how well the most recently measured motion information is reproduced based on the motion information and an efficiency evaluation value that indicates the degree of movement of the user when performing the target exercise, a target exercise judgment unit that calculates a success rate of the target exercise based on the number of times that the user performs the target exercise and is judged to be successful, and an adaptability evaluation calculation unit that calculates an adaptability evaluation value that indicates the adaptability of the user to the target exercise based on the reproducibility evaluation value, the efficiency evaluation value, and the success rate.

According to one aspect of the present invention, it is possible to provide an adaptability assessment device, an adaptability assessment method, and an adaptability assessment program that can correctly assess a user's adaptability to changes in the situation of the targeted exercise.

FIG. 1 is a schematic diagram showing an example of an adaptability assessment device according to an embodiment of the present invention, a virtual reality device, and a user performing a target action. FIG. 2 is a block diagram showing an example of a hardware configuration of the adaptability assessment device. FIG. 3 shows a functional configuration diagram of the adaptability assessment device according to this embodiment. FIG. 4 is a flowchart showing an example of the operation of the adaptability assessment device in this embodiment for assessing the adaptability of a user. FIG. 5 is a flowchart illustrating step S105 in FIG. 4 in more detail.

Hereinafter, an embodiment of the present invention will be described with reference to the drawings.
[composition]
FIG. 1 is a schematic diagram showing an example of an adaptability assessment device 10 according to an embodiment of the present invention, a virtual reality device 11, and a user 20 performing a target action.

The adaptability assessment device 10 has the user 20 perform a target exercise. The target exercise may be any exercise that the adaptability assessment device 10 can judge as a success or failure as a result of the execution. For example, if the target exercise is to stand on one leg for 5 seconds, standing on one leg for 5 seconds or more is a success, and if both feet touch the ground in less than 5 seconds, it is a failure. In the following, the target exercise is described as an exercise in which the user 20 grabs any moving object that moves regularly, such as falling or flying, with his/her right hand. In this case, if the user 20 grabs the flying object with his/her right hand, it is judged as a success, and if he/she cannot grab it, it is judged as a failure. Note that the hand that performs the target exercise may be the user's dominant hand, that is, it is of course possible to perform the target exercise using the left hand instead of the right hand. The target exercise is performed, for example, using a virtual reality device 11 that operates in conjunction with the adaptability assessment device 10 and can provide a virtual reality environment that can be projected into a digital space. That is, the user 20 performs a target movement while viewing a moving object in a virtual reality environment. The virtual reality device 11 may be a general device that can provide a virtual reality environment. The adaptability assessment device 10 can be connected to the virtual reality device 11 by wire or wirelessly.

The adaptability assessment device 10 further acquires and analyzes motion information of each body part of the user 20 when the user 20 is performing a target exercise in the virtual reality environment provided by the virtual reality device 11. The adaptability assessment device 10 measures and analyzes, for example, three-dimensional position data and gaze of the right hand of the user 20 performing the target exercise as motion information. The analysis method will be described later. The measured motion information will be described below using three-dimensional position data and gaze data of the right arm as an example, but is not limited to these data and can of course be replaced with three-dimensional position data of other body parts such as the head, elbows, shoulders, waist, trunk, knees, and feet.

The user 20 performs a target movement in the virtual reality environment a predetermined number of times, e.g., N times, where N is any positive integer.

Figure 2 is a block diagram showing an example of the hardware configuration of the adaptability assessment device 10.

The adaptability assessment device 10 has a hardware processor 101, such as a CPU (Central Processing Unit) or an MPU (Micro Processing Unit). A program memory 102, a data memory 103, a communication interface 104, and an input/output interface 105 are connected to the hardware processor 101 via a bus 106.

The program memory 102 can be used as a storage medium, a non-transient tangible computer-readable storage medium, for example, a combination of a non-volatile memory such as a hard disk drive (HDD) or a solid state drive (SSD) that can be written and read at any time, and a non-volatile memory such as a read only memory (ROM). The program memory 102 stores programs necessary for executing various processes. That is, the calculation function units in each part of the functional configuration described below can be realized by reading and executing the programs stored in the program memory 102 by the processor 101. Note that some or all of these processing function units may be realized in various other forms, including integrated circuits such as application specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or graphics processing units (GPUs).

The data memory 103 is a storage that uses, as a tangible computer-readable storage medium, for example, a combination of the above-mentioned non-volatile memory and a volatile memory such as a RAM (Random Access Memory). The data memory 103 is used to store data acquired and generated in the process of the processor 101 executing a program and performing various processes. The data memory 103 has, for example, repeatability parameters and efficiency parameters that are targets for the measured three-dimensional position data of the right hand and the gaze data. The repeatability parameters and efficiency parameters will be described in detail later. That is, an area is allocated in the data memory 103 for storing various data as appropriate in the process of performing various processes.

The communication interface 104 may include, for example, one or more wired or wireless communication modules. For example, the communication interface 104 may include a wireless communication module for wirelessly connecting to a Wi-Fi access point or a mobile phone base station. Furthermore, the communication interface 104 may also include a wireless communication module for wirelessly connecting to a virtual reality device 11 or the like using short-range wireless technology.

The input/output interface 105 is an interface with the user interface device 107. Note that in FIG. 2, the "user interface device" is written as the "user IF device."

The user interface device 107 includes an input device 1071 and an output device 1072. The input device 1071 is, for example, an input detection sheet using an electrostatic or pressure method that is placed on the display screen of the display device that is the output device 1072, and outputs the touch position of the user 20 to the processor 101 via the input/output interface 105. The output device 1072 is, for example, a display device that uses liquid crystal, organic EL (Electro Luminescence), etc., and displays images and messages according to signals input from the input/output interface 105.

The output device 1072 may also be a virtual reality device 11 that is glasses or goggles capable of providing a virtual reality environment to the user 20. It is also possible to display an image in the glasses or goggles according to a signal input from the input/output interface 105, thereby providing the user 20 with a virtual reality environment.

The input/output interface 105 may also be connected to a sensor 108. The sensor 108 may transmit acquired data to the processor 101 via the input/output interface 105. The sensor 108 may be, for example, a sensor capable of measuring the position of the user 20 when the user 20 performs a target movement and acquiring three-dimensional position data indicating the position. The sensor 108 may also include a sensor arranged in the virtual reality device 11 capable of measuring the line of sight of the user 20 and acquiring line of sight data indicating the movement of the line of sight. The sensor 108 may be, for example, a distance sensor, an acceleration sensor, a line of sight detection sensor, or the like, which is capable of measuring three-dimensional position data of the user 20 relative to the movement of the user 20, and a sensor capable of measuring the line of sight data of the user 20. For example, an acceleration sensor may be attached to the right arm of the user 20, and the movement of the right arm of the user 20 may be detected by the acceleration sensor, thereby measuring three-dimensional position data regarding the movement of the right arm. The three-dimensional position data and gaze data measured by the sensor 108 may be reflected in the movement of the user 20 in the virtual reality environment by the output device 1072 via the input/output interface 105. The sensor 108 may be disposed in the adaptability assessment device 10, or may be disposed as a device separate from the adaptability assessment device 10. The sensor 108 may be replaced with one or more cameras. That is, if one or more cameras can measure the three-dimensional position data and the gaze of the user 20 when the user 20 performs the target exercise, the sensor 108 can be replaced with one or more cameras. In addition, the sensor 108 may be any other sensor that can measure the three-dimensional position data and gaze data of the user 20 when the user 20 performs the target exercise, and is not limited to the above-mentioned example.

In addition, the virtual reality device 11 can have a hardware configuration similar to that of the adaptability assessment device 10, except that it does not have a sensor capable of acquiring three-dimensional position data, and therefore a description thereof will be omitted here.

Figure 3 shows a functional configuration diagram of the adaptability assessment device 10 according to this embodiment.

The adaptability assessment device 10 includes a condition control unit 301, a measurement unit 302, an exercise content evaluation calculation unit 303, a target exercise determination unit 304, and an adaptability assessment calculation unit 305.

The condition control unit 301 includes an object characteristic control unit 306, an environment characteristic control unit 307, and a motion characteristic control unit 308. The condition control unit 301 can set and control each condition of the target motion. The conditions include, for example, object characteristics, environment characteristics, and motion characteristics. The object characteristics are the size, weight, shape, etc. of the flying object. The environment characteristics are the area in which the speed and acceleration of the flying object change. Here, the area in which the acceleration changes is the area in which the moving object accelerates or decelerates when it reaches a certain position, or the area in which the moving object increases its speed at a constant acceleration and the acceleration is multiplied by a constant when it reaches a certain speed. The motion characteristics are the exercise load, the magnitude of the motion, etc. Note that changing the magnitude of the motion in the motion characteristics means that the magnitude of the motion when the user 20 moves his/her arm in the virtual reality environment becomes smaller or larger in the virtual reality environment. Each condition can be set by the condition control unit 301 based on an instruction from the administrator of the adaptability assessment device 10 and can be stored in the data memory 103. Note that, although an example is shown here in which the target exercise is performed in a virtual reality environment, the condition control unit 301 can of course control according to the conditions stored in the data memory 103 even if the target exercise is not performed in a virtual reality environment.

The object characteristic control unit 306 can change the object characteristics each time the user 20 performs the target exercise N times. The environment characteristic control unit 307 can change the environment characteristics each time the user 20 performs the target exercise N times. The motion characteristic control unit 308 can change the motion characteristics each time the user 20 performs the target exercise N times. That is, the object characteristic control unit 306, the environment characteristic control unit 307, and the motion characteristic control unit 308 control the moving object according to the conditions set before the user 20 performs the target exercise. Note that, in each of the N exercise executions, all of the object characteristic control unit 306, the environment characteristic control unit 307, and the motion characteristic control unit 308 may change the characteristics, or it is also possible to change only one of the characteristics.

The measurement unit 302 includes a motion measurement unit 309 and a gaze measurement unit 310. The measurement unit 302 measures motion information each time the user 20 performs a target motion N times. The motion information is, for example, three-dimensional position data and gaze data of the user 20's right hand.

The motion measurement unit 309 measures three-dimensional position data regarding the motion of the user 20 when the user 20 performs each targeted exercise. The gaze measurement unit 310 measures gaze data, which is the movement of the user's gaze, when the user 20 performs each targeted exercise. The motion measurement unit 309 and the gaze measurement unit 310 measure three-dimensional position data and gaze data of the user 20 consisting of the same sample size n each time the user 20 performs the targeted exercise. That is, the motion measurement unit 309 and the gaze measurement unit 310 perform measurements n times using the sensor 108 each time the user 20 performs the targeted exercise, and store each data in the data memory 103.

The exercise content evaluation calculation unit 303 includes a reproducibility evaluation calculation unit 311 and an efficiency evaluation calculation unit 312.

The repeatability evaluation calculation unit 311 calculates a repeatability evaluation value based on the movement information acquired the i-th time and the movement information acquired the i-1th time. Here, each piece of movement information includes n pieces of three-dimensional position data and gaze data, and i is an integer that satisfies 2<i≦N. In other words, the repeatability evaluation value is an index that indicates how well the most recently measured movement information is reproduced. The method for calculating the repeatability evaluation value will be described later.

The efficiency evaluation calculation unit 312 calculates an efficiency evaluation value based on the exercise information acquired the i-th time. The efficiency evaluation value is an index that indicates the degree of movement of the user 20 when performing the target exercise, and indicates whether the target exercise was performed with efficient movement. The method of calculating the efficiency evaluation value will be described later.

The target motion determination unit 304 includes a target motion success determination unit 313 and a target motion success rate calculation unit 314.

The target motion success determination unit 313 determines whether the target motion of the user 20 is successful as a result of the user 20 performing the target motion. For example, if the user 20 is able to grasp the moving object with his/her right hand, the target motion success determination unit 313 determines that the target motion is successful.

The target exercise success rate calculation unit 314 calculates the success rate of the target exercise by dividing the number of times determined to be successful by the target exercise success determination unit 313 by the number of times the user 20 performed the target exercise.

The adaptability evaluation calculation unit 305 calculates the adaptability based on the reproducibility evaluation value and efficiency evaluation value calculated by the exercise content evaluation calculation unit 303 and the success rate calculated by the target exercise determination unit 304.

In addition, the adaptability assessment device 10 can present the adaptability calculated by the adaptability assessment calculation unit 305 to the user 20 or an administrator of the adaptability assessment device 10 (not shown) via the output device 1072.

[motion]
4 is a flowchart showing an example of the operation of the adaptability assessment device 10 in this embodiment for evaluating the adaptability of the user 20. The processor 101 of the adaptability assessment device 10 reads out and executes the adaptability assessment program stored in the program memory 102, thereby realizing the operation of this flowchart. This flowchart is started when the adaptability assessment device 10 receives an instruction from the user 20 or an administrator of the adaptability assessment device 10 to evaluate the adaptability of the user 20 to exercise. The instruction includes information on the conditions for performing the target exercise and information such as the number of times the target exercise is performed. The information on the conditions is, for example, an instruction on how to set the object characteristics, the environment characteristics, and the exercise characteristics. When performing the target exercise, the adaptability assessment device 10 may notify the user 20 of the start.

The condition control unit 301 sets each condition based on the information about the conditions (step S101). In accordance with the instructions, the condition control unit 301 sets the object characteristics, the environment characteristics, and the motion characteristics using the object characteristics control unit 306, the environment characteristics control unit 307, and the motion characteristics control unit 308, respectively. These conditions can be set to any condition within the range in which the user 20 can perform the target motion, for example, within the range in which the user 20 can grasp the moving object with his/her right hand. Note that when the conditions are the same, the condition control unit 301 controls the moving object so that it behaves in the same way according to the conditions. In this step S101, the condition control unit 301 sets a condition that changes one of the object characteristics, the environment characteristics, and the motion characteristics for each exercise.

The adaptability assessment device 10 has the user 20 execute a target movement by projecting a moving object that behaves according to the conditions set in step S101 onto the digital environment using the virtual reality device 11. During execution of this movement, the measurement unit 302 measures the movement information of the user 20 using the movement measurement unit 309 and the gaze measurement unit 310 (step S102). The movement information is, for example, three-dimensional position data and gaze data of the right arm of the user 20. For example, the three-dimensional position data H _i and gaze data E _i of the user 20 when executing the i-th target movement, which is an arbitrary positive integer, can be obtained as shown in the following formula.

where x, y, z are the z, y, z coordinate components of the right hand, e.g.,

is the first measured value of the x-coordinate component in the i-th execution of the target movement. In other words, the three-dimensional position data and gaze data of user 20 expressed by Equation 1 and Equation 2 indicate that the three-dimensional position data and gaze data are measured n times in time series when user 20 executes the target movement. Here, n is any positive integer.

The desired motion determination unit 304 determines whether the user 20 has succeeded in the desired motion in this i-th motion execution (step S103). If the desired motion is to grasp a moving object with the right hand of the user 20, the desired motion determination unit 304 determines that the motion is successful if the user 20 is able to grasp the moving object with the right hand, and determines that the motion is unsuccessful if the user is unable to grasp the moving object.

The adaptability assessment device 10 determines whether the user 20 has performed the target exercise N times (step S104). If it is determined that the user 20 has not performed the target exercise N times, the process returns to step S102. Note that the condition control unit 301 sets different conditions for the i+1th condition setting to be returned to in this way from the ith condition setting, i.e., changes the exercise situation. Also, if it is determined in step S103 that the user 20 has performed the target exercise N times, the process proceeds to step S104.

The adaptability assessment device 10 calculates an adaptability assessment value based on the measured three-dimensional position data and gaze data of the user 20 and the success rate of the target exercise determined in step S103 (step S105).

Figure 5 is a flowchart that explains step S104 in Figure 4 in more detail.

The exercise content evaluation calculation unit 303 of the adaptability assessment device 10 acquires the exercise information measured by the measurement unit 302 (step S201). As described above, the exercise information is, for example, three-dimensional position data and gaze data of the right arm of the user 20.

The repeatability evaluation calculation unit 311 of the adaptability evaluation device 10 calculates a repeatability evaluation value based on the acquired three-dimensional position data and gaze data (step S202). The repeatability evaluation calculation unit 311 calculates the similarity of movements between trials for the three-dimensional position data and gaze data using the following formula.

Furthermore, the reproducibility evaluation calculation unit 311 calculates a function f(p) with explanatory variables p=1,...,N for the similarities ^REH and ^REE expressed by Equation 4. The function f(p) is expressed by the following Equation 5 with α, β, γ, and δ as parameters.

Specifically, when p=i, RE _i ^H is the objective variable, and the error

The parameters (α, β, γ, δ) that minimize the above are found under the constraints of α≧0, β≧0, γ≧0, δ≧0. It is considered that the repeatability RE ^H and RE ^E continue to rise for the first few trials and then asymptotically approach a specific value after a certain number of trials. The repeatability evaluation calculation unit 311 obtains the repeatability characteristics of the user by extracting the rate at which repeatability rises, timing, and asymptotic value as feature quantities from a curve having such characteristics. The repeatability evaluation calculation unit 311 performs curve approximation using a sigmoid function shown in Equation 5 to extract the rate at which repeatability rises, timing, and asymptotic value. As for the obtained parameters, α is the rate at which repeatability rises, β is the timing of rise, and (γ-δ) is the asymptotic value. From the parameters (α, β, γ, δ) found above, the repeatability parameters are calculated as follows:

is obtained. The reproducibility evaluation calculation unit 311 calculates the reproducibility parameter obtained and the corresponding reproducibility parameter among the target reproducibility parameters for each body part stored in advance in the data memory 103.

Cosine similarity with

The target reproducibility parameter may be calculated from an ideal movement when performing the target exercise, or may be calculated from an average movement when performing other target exercises. The reproducibility evaluation calculation unit 311 then determines this calculated value as the reproducibility evaluation value.

The efficiency evaluation calculation unit 312 calculates an efficiency evaluation value based on the acquired three-dimensional position data and gaze data (step S203). The efficiency evaluation calculation unit 312 calculates the efficiency of each target movement based on the three-dimensional position data and gaze data using the following formula.

Furthermore, the efficiency evaluation calculation unit 312 calculates a function f(p) for the efficiencies EF ^H and EF ^E with explanatory variables p = 1,...,N. The function f(p) is the same as the above-described formula 5. Specifically, when p = i, EF _i ^H is the objective variable, and the error

The parameters (α, β, γ, δ) that minimize are found under the constraints of α≦0, β≧0, γ≧0, δ≧0. It is considered that the efficiencies EF ^H and EF ^E continue to decrease for the first few trials and then asymptotically approach a specific value after a certain number of trials. The efficiency evaluation calculation unit 312 obtains the efficiency characteristics of the user by extracting the rate at which the efficiency decreases, the timing, and the asymptotic value from a curve having such characteristics. In order to extract the rate at which the efficiency decreases, the timing, and the asymptotic value, the efficiency evaluation calculation unit 312 performs curve approximation using a sigmoid function shown in Equation 5. With regard to the obtained parameters, |α| is the rate at which the efficiency decreases, β is the timing of the decrease, and (γ-δ) is the asymptotic value. From the parameters (α, β, γ, δ) found above, the following is obtained as the efficiency parameters:

is obtained. The efficiency evaluation calculation unit 312 calculates the efficiency parameter obtained and the corresponding efficiency parameter of the target efficiency parameters for each body part stored in advance in the data memory 103.

Cosine similarity with

The efficiency evaluation calculation unit 312 then sets this calculated value as the efficiency evaluation value. Note that the efficiency parameter may be calculated from an ideal movement when performing the target exercise, or may be calculated from an average movement when performing other target exercises.

The target exercise success rate calculation unit 314 calculates the success rate S of the target exercise (step S204). Based on the result of step S103, the target exercise success rate calculation unit 314 calculates the success rate S of the target exercise based on how many times the user 20 succeeded in performing the target exercise out of N times that the user 20 performed the target exercise.

The adaptability evaluation calculation unit 305 calculates the adaptability evaluation value A based on the success rate S, the reproducibility evaluation value, and the efficiency evaluation value using the following formula 8 (step S205).

This adaptability evaluation value A is an indicator of the adaptability of the user 20 when any one of the conditions related to the object characteristics, the environmental characteristics, and the movement characteristics changes.

The adaptability evaluation device 10 determines whether it is necessary to perform the target exercise under other conditions based on the information included in the instruction received at the start (step S106). The adaptability evaluation device 10 can acquire the adaptability of the user 20 under the conditions by changing the conditions in various ways. If it is determined that it is necessary to perform the target exercise under other conditions with respect to the object characteristics, the environmental characteristics, and the motion characteristics, the adaptability evaluation device 10 returns to step S101. Here, for example, by changing the object characteristics in various ways, it is possible to acquire the adaptability evaluation value A (object) of the user 20 when the object characteristics are changed. Similarly, by changing the environmental characteristics or the motion characteristics in various ways, it is possible to acquire the adaptability evaluation value A (environment) or the adaptability evaluation value A (motion) of the user 20 when the environmental characteristics or the motion characteristics are changed. Furthermore, it is of course possible to acquire the adaptability of the user 20 by changing the object characteristics, the environmental characteristics, and the motion characteristics in various ways. Also, in step S106, if the adaptability evaluation device 10 determines that it is not necessary to perform the target exercise under other conditions, it ends the process. The adaptability assessment device 10 may present the adaptability assessment value A obtained by this process to the user 20 or an administrator of the adaptability assessment device 10 via the output device 1072.

[Action and Effect]
According to the above embodiment, when the conditions for the target exercise are changed by setting various conditions, it is possible to correctly evaluate the adaptability of the user 20 to the change in the situation.

[Other embodiments]
It should be noted that the present invention is not limited to the above-described embodiment.

For example, the conditions are changed each time the target exercise is performed, but the conditions may be changed every time an arbitrary number of exercises are performed. It is also acceptable to change the conditions only once during N exercises.

The method described in the above embodiment can be stored as a program (software means) that can be executed by a calculator (computer) on a recording medium such as a magnetic disk (floppy disk, hard disk, etc.), optical disk (CD-ROM, DVD, MO, etc.), semiconductor memory (ROM, RAM, flash memory, etc.), and can also be distributed by transmitting it via a communication medium. The program stored on the medium also includes a setting program that configures the software means (including not only execution programs but also tables and data structures) that the calculator executes. The computer that realizes this device reads the program recorded on the recording medium, and in some cases, configures the software means using the setting program, and executes the above-mentioned processing by controlling the operation of the software means. Note that the recording medium referred to in this specification is not limited to a storage medium for distribution, but also includes storage media such as a magnetic disk or semiconductor memory installed inside the computer or in a device connected via a network.

In short, this invention is not limited to the above-described embodiment, and various modifications can be made in the implementation stage without departing from the gist of the invention. Furthermore, the embodiments may be implemented in combination as appropriate as possible, in which case the combined effects can be obtained. Furthermore, the above-described embodiment includes inventions at various stages, and various inventions can be extracted by appropriate combinations of the multiple constituent elements disclosed.

10...Adaptability evaluation device 11...Virtual reality device 20...User 101...Processor 102...Program memory 103...Data memory 104...Communication interface 105...Input/output interface 106...Bus 107...User interface device 1071...Input device 1072...Output device 108...Sensor 301...Condition control unit 302...Measurement unit 303...Movement content evaluation calculation unit 304...Desired motion judgment unit 305...Adaptability evaluation calculation unit 306...Object characteristic control unit 307...Environment characteristic control unit 308...Movement characteristic control unit 309...Movement measurement unit 310...Gaze measurement unit 311...Reproducibility evaluation calculation unit 312...Efficiency evaluation calculation unit 313...Desired motion success judgment unit 314...Desired motion success rate calculation unit

Claims (8)

1. An adaptability assessment device for assessing a user's adaptability to a target exercise, which is executed by a user and can be judged to be successful or unsuccessful, and which changes as a predetermined condition changes, comprising:
a condition control unit for setting the predetermined conditions;
a measurement unit that measures exercise information about the user each time the user performs the target exercise under the set conditions a predetermined number of times;
an exercise content evaluation calculation unit that calculates, based on the exercise information, a reproducibility evaluation value that indicates how well the most recently measured exercise information is reproduced and an efficiency evaluation value that indicates how little the user moves when performing the target exercise;
a target exercise determination unit that calculates a success rate of the target exercise based on the number of times determined to be successful as a result of the user performing the target exercise the predetermined number of times;
an adaptability evaluation calculation unit that calculates an adaptability evaluation value that represents the adaptability of the user with respect to the target exercise based on the reproducibility evaluation value, the efficiency evaluation value, and the success rate;
An adaptability assessment device comprising: The predetermined conditions include object characteristics, environmental characteristics, and motion characteristics;
the object characteristic being a size, weight, or shape of an object to be used in a desired motion;
the environmental characteristic is a region in which the speed or acceleration of the object changes;
The adaptability assessment device according to claim 1 , wherein the exercise characteristic is an exercise load or an intensity of the exercise on the user. The adaptability assessment device according to claim 2, wherein the condition control unit changes at least one of the object characteristics, the environmental characteristics, and the motion characteristics at least once. The exercise content evaluation calculation unit is
A target reproducibility parameter of the target motion is provided in advance,
A cosine similarity between the i-th motion information, which is greater than 1 and is equal to or smaller than the predetermined number of times, and the i-1-th motion information is set as an objective variable, and parameters of the function that minimize an error between the objective variable and a function having positive integers up to the predetermined number of times as explanatory variables are calculated;
4. The adaptability evaluation device according to claim 1, wherein a value calculated from a cosine similarity between the calculated parameter and the reproducibility parameter is set as the reproducibility evaluation value. The exercise content evaluation calculation unit is
A target efficiency parameter of the target exercise is provided in advance,
Calculating the absolute value of the motion information;
a parameter of the function that minimizes an error between the absolute value and a function having positive integers up to the predetermined number of times as explanatory variables,
5. The adaptability evaluation device according to claim 1, wherein a value calculated from a cosine similarity between the calculated parameter and the efficiency parameter is set as the efficiency evaluation value. The adaptability evaluation device according to any one of claims 1 to 5, wherein the adaptability evaluation calculation unit multiplies the larger of the reproducibility evaluation value and 0, the larger of the efficiency evaluation value and 0, and the success rate to obtain the adaptability evaluation value. 1. An adaptability assessment method for assessing a user's adaptability to a target exercise, the adaptability assessment method being executed by a processor of an adaptability assessment device, capable of determining whether a user's execution is a success or failure, and the adaptability of the user to a target exercise that changes as a predetermined condition changes, comprising:
Setting the predetermined condition;
When the user performs the target exercise under the set conditions a predetermined number of times, measuring exercise information about the user every time the user performs the target exercise;
Calculating a reproducibility evaluation value indicating how well the most recently measured motion information is reproduced based on the motion information, and an efficiency evaluation value indicating how little the user moves when performing the target motion;
Calculating a success rate of the target exercise based on the number of times determined to be successful as a result of the user performing the target exercise the predetermined number of times;
calculating an adaptability evaluation value representing the adaptability of the user to the target exercise based on the reproducibility evaluation value, the efficiency evaluation value, and the success rate;
The adaptive capacity assessment method includes: An adaptability evaluation program that causes a processor to function as each of the components of the adaptability evaluation device according to any one of claims 1 to 6.