JP6943326B2 - Training equipment, its methods, and programs - Google Patents

Description

The present invention provides a technique for collecting data for analyzing perceptual information contributing to motor control from a subject playing sports, an analysis technique for analyzing the data, and perceptual information contributing to motor control. Based on techniques for training the subject to improve their athletic performance.

In order to properly acquire the basic movements of sports, it is important for the actor to objectively grasp his / her own physical movements.

Patent Document 1 discloses a technique that makes it possible to objectively grasp the physical movement of an actor when compared with a model by outputting the current exercise state of the actor with respect to the reference exercise state.

Japanese Unexamined Patent Publication No. 2016-150119

For example, when considering the batting movement of baseball, a person reaches the destination of the ball based on various perceptual information such as the movement of the opponent throwing the ball as well as the trajectory and speed of the ball coming toward him. It predicts the arrival time and plans the content and timing of the action (exercise) to be taken by oneself, and controls the exercise. Various perceptual information can be acquired in the real environment, but it is considered important for motor control to process meaningful perceptual information quickly and accurately. However, motion control such as batting is performed in a short period of time, and necessary perceptual information is collected almost unconsciously to make a judgment. Therefore, it is difficult for oneself to grasp what information is used to decide the exercise. In addition, it is difficult to objectively grasp the difference in perceptual information used for motor control between a person who is good at batting and a person who is not good at batting.

In Patent Document 1, by presenting the location and timing of moving muscles with sound or video, it is possible to objectively grasp the difference between a model and one's own exercise regarding which muscle to move, when and how. It is possible to train to match the model. However, in order to carry out such a target exercise in a real sports environment, it is necessary to appropriately recognize and judge the situation and select an appropriate target exercise. In order to do so, it is necessary to understand what kind of information is perceived to control movement (that is, the processing in the brain before it is expressed as body movement), and such perceived information is clarified. If this happens, it will lead to effective training to improve athletic performance.

An object of the present invention is to provide an analysis data acquisition device for objectively analyzing what perceptual information has a large contribution to motor control. Furthermore, it is an object of the present invention to provide an analysis device that analyzes perceptual information that greatly contributes to motor control using analysis data, and a training device for training a subject to be able to handle the perceptual information. ..

In order to solve the above problem, according to one aspect of the present invention, the training device differs between the motion information used when presenting the motion of an object as an image and the motion with a high evaluation and a motion with a low evaluation of the motion. A storage unit that stores change movement type information that represents a certain type of movement information and attention movement type information that is information that associates attention area and attention visual information that identifies the attention area and attention time in a virtual image, and training. Training target exercise information, which is the type of exercise to be trained by the subject, is given, and based on the attention exercise type information, the training condition setting that outputs the attention visual information associated with the change exercise type information corresponding to the training target exercise information. The second video compositing unit includes a unit and a second video compositing unit that synthesizes a virtual image in which the opponent displayed in the virtual image moves against the laws of physics using motion information. It is characterized by synthesizing a virtual image in which a visual effect that emphasizes a region of interest specified by the visual information of interest is superimposed in the vicinity of the time of interest included in the visual information of interest.
In order to solve the above problem, according to another aspect of the present invention, the training device differs between the motion information used when presenting the motion of the object as an image and the motion with a high evaluation and the motion with a low evaluation of the motion. A storage unit that stores the change movement type information representing the type of certain movement information and the attention movement type information which is the information associated with the attention area and the attention visual information which is the information for specifying the attention area and the attention time in the virtual image. A training condition in which training target exercise information, which is a type of exercise to be trained by a trainee, is given, and based on the attention exercise type information, attention visual information associated with the change exercise type information corresponding to the training target exercise information is output. A second video including a setting unit and a second video compositing unit that synthesizes a virtual image in which the movement of the other party's body and the movement of an object accompanying the other's body movement do not match in accordance with the laws of physics using motion information. The synthesizing unit is characterized in synthesizing a virtual image in which a visual effect that emphasizes a region of interest specified by the visual information of interest is superimposed in the vicinity of the time of interest included in the visual information of interest in the virtual image.

In order to solve the above-mentioned problems, according to another aspect of the present invention, the training method includes motion information used when presenting the motion of an object as an image in a storage unit, and motion with a high evaluation of motion. Changes that represent the type of exercise information that differs from low exercise The attention exercise type information that is information that associates the attention area and attention visual information that identifies the attention area and attention time in the virtual image is stored. The training target exercise information, which is the type of exercise to be trained by the trainee, is given, and the attention associated with the change exercise type information corresponding to the training target exercise information based on the attention exercise type information. The second video includes a training condition setting step for outputting visual information and a second video synthesis step for synthesizing a virtual video in which the opponent displayed in the virtual video moves against the laws of physics using the motion information. The synthesizing step is characterized in that a virtual image in which a visual effect that emphasizes a region of interest specified by the visual information of interest is superimposed is synthesized in the vicinity of the time of interest included in the visual information of interest in the virtual image.
In order to solve the above-mentioned problems, according to another aspect of the present invention, the training method includes motion information used when presenting the motion of an object as an image in a storage unit, and motion with a high evaluation of motion. Changes that represent the type of exercise information that differs from low exercise The attention exercise type information that is information that associates the attention area and attention visual information that identifies the attention area and attention time in the virtual image is stored. The training target exercise information, which is the type of exercise to be trained by the trainee, is given, and the attention associated with the change exercise type information corresponding to the training target exercise information based on the attention exercise type information. A training condition setting step that outputs visual information, and a second video composition step that uses motion information to synthesize a virtual image in which the body movement of the other party and the movement of an object accompanying the body movement of the other party do not match in light of the physical law. Including It is characterized by.

According to the analysis data collecting device of the present invention, it is possible to collect analysis data for objectively analyzing what perceptual information has a large contribution to motor control. Further, according to the analysis device of the present invention, it is possible to estimate perceptual information that greatly contributes to motor control by using the analysis data. Further, according to the training device of the present invention, it is possible to train the subject so that the subject can handle the perceptual information by using the perceptual information having a large contribution to the motor control.

The block diagram of the training system which concerns on 1st Embodiment. The functional block diagram of the data acquisition apparatus for analysis which concerns on 1st Embodiment. The figure which shows the example of the processing flow of the analysis data acquisition apparatus which concerns on 1st Embodiment. The functional block diagram of the analysis apparatus which concerns on 1st Embodiment. The figure which shows the example of the processing flow of the analysis apparatus which concerns on 1st Embodiment. The functional block diagram of the training apparatus which concerns on 1st Embodiment. The figure which shows the example of the processing flow of the training apparatus which concerns on 1st Embodiment.

Hereinafter, embodiments of the present invention will be described. In the drawings used in the following description, the same reference numerals are given to the components having the same function and the steps for performing the same processing, and duplicate description is omitted. In the following explanation, the symbol "^" etc. used in the text should be written directly above the character immediately before, but due to the limitation of the text notation, it should be written immediately after the character. In the formula, these symbols are described in their original positions. Further, unless otherwise specified, the processing performed for each element of the vector or the matrix shall be applied to all the elements of the vector or the matrix.

<First Embodiment>
FIG. 1 shows a configuration diagram of a training system according to the first embodiment. The training system includes an analysis data acquisition device 100, an analysis device 200, and a training device 300.

Each of the analysis data collection device 100, the analysis device 200, and the training device 300 is a known or dedicated computer having, for example, a central processing unit (CPU), a main storage device (RAM: Random Access Memory), and the like. It is a special device configured by loading a special program into the memory. Each of the analysis data acquisition device 100, the analysis device 200, and the training device 300 executes each process under the control of, for example, the central processing unit. The data input to each of the analysis data collection device 100, the analysis device 200, and the training device 300 and the data obtained by each process are stored in, for example, the main storage device, and the data stored in the main storage device is necessary. It is read out to the central arithmetic processing device according to the above and used for other processing. At least a part of each processing unit of the analysis data acquisition device 100, the analysis device 200, and the training device 300 may be configured by hardware such as an integrated circuit. Each storage unit included in each of the analysis data collection device 100, the analysis device 200, and the training device 300 includes, for example, a main storage device such as RAM (Random Access Memory), a hard disk, an optical disk, or a flash memory (Flash Memory). It can be configured by an auxiliary storage device composed of semiconductor memory elements or middleware such as a relational database or a key value store.

Further, the training system includes a video display unit 91 that displays an image on the analysis target person 93 when collecting analysis data, a sensor 92, and a video display unit 81 that displays an image on the training target person 83 when training. But it may be. The video display units 81 and 91 are, for example, head-mounted displays, and are attached to the analysis target person 93 and the training target person 83. The head-mounted display is a device for the training target person 83 and the analysis target person 93 to wear on the head and watch video and audio. As for the head-mounted display, for example, the display is provided in a range corresponding to the field of view when the training target person 83 and the analysis target person 93 are worn, and the headphones are provided at positions corresponding to both ears. The head-mounted display is equipped with various sensors such as an acceleration sensor and a displacement sensor, and can recognize the position and posture of the head when worn by the training target person 83 and the analysis target person 93. As a result, the head-mounted display can present images that match the viewpoints of the training target person 83 and the analysis target person 93. However, the video display units 81 and 91 may be display devices other than the head-mounted display. For example, it may be a 3D display, a multi-faceted box-shaped projection screen (for example, CAVE: Cave Automatic Virtual Environment), or the like. In this case, various sensors such as an acceleration sensor and a displacement sensor mounted on the head-mounted display may be separately attached to the training target person 83 and the analysis target person 93 as necessary.

[Data collection device for analysis 100]
The analysis data collecting device 100 of the present embodiment uses a sports system using virtual reality, and presents an image based on the actual environment and an image of an environment that cannot be reproduced in the actual environment to the analysis target person as a virtual reality image. Exercise information and exercise evaluation information indicating the evaluation of the exercise are acquired from the analysis target person who exercises while watching the virtual image, and the image identification information which is the information for identifying the presented virtual image, the exercise information of the subject, and the exercise It generates analysis data associated with the evaluation information reaction.

FIG. 2 shows a functional block diagram of the analysis data collection device 100, and FIG. 3 shows an example of its processing flow.

The analysis data acquisition device 100 includes a storage unit 101, a video condition setting unit 102, a video synthesis unit 103, an evaluation information acquisition unit 106, and an analysis data generation unit 107.

<Memory unit 101>
The storage unit 101 stores in advance motion information (each object in the virtual image) of each object and an image of the surrounding environment, which are the bases when the image composition unit 103 synthesizes the image. It is assumed that the movement information of each object is stored in association with the "type of movement" for each "type of movement" of the object.

For example, when trying to acquire analysis data for a baseball batter, an image in which the pitcher throws a ball as a virtual image and the ball flies toward the analysis target (batter) is displayed on the image display unit 91. It will be. In this case, the motion information of each object stored in the storage unit 101 is
(1) Pitcher movement information An image of the pitcher's pitching form for each pitcher's movement (throwing style and type) (for example, an image of an overhand throw pitcher throwing a straight, an image of a side throw pitcher throwing a curve, and throwing a slider Underslow pitcher footage, etc.)
(2) Ball movement information A series of ball position information for each ball movement (ball trajectory), that is, a series of position information indicating where the ball is displayed in each frame (for example, flying straight). A series of ball position information that comes out, a series of ball position information that flies on a curve, a series of ball position information that flies with a slider, etc.).
Is. In addition, the series of position information starts with the position information indicating the position where the ball is displayed at the next time (frame) based on the time when the ball leaves the pitcher's hand, and the position of the ball at a later time. The information is arranged in chronological order.

Also, instead of (2), the image of the ball for each movement of the ball (ball trajectory) (the image of a straight ball flying toward the target, and the curved ball toward the target). An image of flying, an image of a slider ball flying toward the target person, etc.) may be stored as ball movement information. In short, the motion information may be any information indicating the motion of the object when the virtual image is generated by the image compositing unit 103, which will be described later.

<Video condition setting unit 102>
The video condition setting unit 102 sets the conditions for the virtual video to be synthesized by the video compositing unit 103 (S102). As described above, the analysis data acquisition device 100 of the present embodiment displays "an image of an environment that cannot be reproduced in an actual environment" on the image display unit 91, but as this "environment that cannot be reproduced in an actual environment". Specifically, what kind of data is handled is set as a video condition (hereinafter, also referred to as "video specific information"). In other words, the video specific information is information for specifying what kind of setting the presented virtual video is generated, that is, information for specifying the presented virtual video. As long as a unique ID is assigned to each video condition, that ID may be used, or the video condition itself (for example, in the example of baseball described later, it is a pair of throwing form and ball movement (straight, curve), etc.). good.

"Environment that cannot be reproduced in the real environment" means an image in which the opponent moves against the laws of physics displayed in the virtual image, or an object such as a ball that is drawn out according to the opponent's body movement and the opponent's body movement. It is an image in which the movement of is not consistent with the laws of physics. Taking the case of baseball as an example, the following image is shown.
(1) The image of the throwing form when the pitcher throws a straight ball and the image of the ball approaching the target along the trajectory of the curve are combined (that is, the throwing form and the trajectory of the thrown ball do not match. picture).
(2) An image in which the trajectory of the ball thrown by the pitcher follows a straight trajectory halfway, but then suddenly switches to a movement along a curved trajectory (that is, the physics law that the trajectory of the ball in the first half follows). , The image that the physical law that the trajectory of the ball in the latter half follows is different).
(3) An image in which a ball coming from a pitcher toward himself disappears in a part of the orbit (that is, an image in which an object disappears where it cannot disappear in the actual environment).

That is, the video condition setting unit 102 identifies the movement of each object (including a person) in the video displayed as a virtual video on the video display unit 91 (a video presenting an environment that cannot be reproduced in the real environment). Is set as a video condition (S102) and output to the video compositing unit 103.

In the above-mentioned example of baseball (1), a set of information that specifies the movement of the pitcher and the ball (straight, curve) is set as a video condition (video specific information).

The video condition may be given by an external input, or the video condition may be generated by randomly selecting one from a plurality of movements prepared in advance for each object. For example, in the above-mentioned baseball example, straights, curves, sliders, etc. are defined in advance as pitcher movements, and straights, curves, sliders, etc. are defined as ball movements in a list. Select movements one by one (curve, slider) and set a set of information that identifies the movement as a condition of the image.

The motion information and the video identification information stored in the storage unit 101 may be appropriately prepared according to the virtual video presented to the analysis target person. For example, in the case of an image in which the trajectory of the ball in (2) moves against the laws of physics, the motion information of the ball thrown by the pitcher along the trajectory of a straight, curve, slider, etc. is prepared halfway. (Stored in the storage unit 101 in advance), prepare motion information along the trajectory of straights, curves, sliders, etc. from the middle (stored in the storage unit 101 in advance), and the trajectory and the middle of the process. The combination with the trajectory from is set as the video specific information that can be set. Also, for example, (3) in the case of an image in which the ball coming from the pitcher toward himself disappears in a part of the orbit, from point A to point B on the orbit, from point B to point C, ..., the movement information on which the ball disappears is prepared (stored in the storage unit 101 in advance), and the information for specifying the disappearing section is set as the video identification information that can be set.

In addition to the movement of each object, information for specifying the exercise environment (raining / sunny, dark / bright) may be added as a video condition (video specific information).

In addition to the images of the environment that cannot be reproduced in the actual environment, in order to present the images based on the actual environment to the analysis target as virtual reality images, the images that move without violating the laws of physics and the body movements of the other party. Information that identifies an image in which the movement of an object such as a ball that is delivered in accordance with the body movement of the other party matches with the physical law may also be added as the image identification information. For example, in the above-mentioned example of baseball (1), a set of information specifying the movement such as the movement of the pitcher and the ball (straight, straight) may be set as the video identification information, and in the example of (2), the pitcher. A set of information that specifies the movement, such as the trajectory of the ball thrown by the ball up to the middle and the trajectory from the middle (straight, straight), may be set as the video identification information, or in the example of (3), from the pitcher. The motion information that the ball coming toward itself does not disappear may be set as the image specific information.

<Video compositing unit 103>
The video compositing unit 103 receives video specific information as input, and generates a video (virtual video) that synthesizes a video in which each object (including a person) moves according to the video conditions set by the video condition setting unit 102 (S103). , Is output to the video display unit 91. When the video condition includes information on the exercise environment, the video is processed according to the environment and output to the video display unit 91.

Explaining with the above-mentioned baseball example, the image compositing unit 103 has a pitching form image corresponding to the pitcher's movement under the condition of the image output from the image condition setting unit 102 and a ball image corresponding to the ball movement. Is read from the storage unit 101, and the combined video is output to the video display unit 91 as a virtual video. Specifically, for example, in the image of the throwing form read from the storage unit 101, the position of the hand throwing the ball (initial position) of the frame image (throwing start frame image) corresponding to the moment when the ball leaves the hand. ) Is superimposed on the image of the ball to generate an image. The image of the frame at the next time is the image of the frame next to the pitching start frame image in the video of the pitching form, and the first position information in the series of ball position information read from the storage unit 101 is the ball of the previous frame. Generates an image in which the image of the ball is superimposed on the position corrected according to the position of. A virtual image is generated by performing the same processing for subsequent frames.

The above-mentioned image display unit 91 displays the virtual image synthesized by the image composition unit 103 on the internal display and presents it to the analysis target person 93 (S104).

The sensor 92 is a predetermined part of the body of the analysis target person 93 who moves while viewing the virtual image presented on the image display unit 91, or / and an object (bat, racket, or the like, which is attached to the sensor 92) held by the analysis target person 93. It is attached to an object), acquires sensor data (S105), and outputs it. This acquired sensor data will be referred to as motion information. As the sensor 92, a sensor for detecting various motion information such as an acceleration sensor, a displacement sensor, and a motion sensor, or various biological information such as heartbeat, electrocardiogram, respiration, myoelectricity, and dynamic movement of the eye are acquired. Sensors and the like can be considered. It is assumed that one or more sensor data acquired from one or more of these sensors is acquired as motion information.

<Evaluation information acquisition unit 106>
The evaluation information acquisition unit 106 evaluates the exercise performed by the subject exercising while viewing the virtual image presented on the image display unit 91 (in other words, the exercise performed by the analysis subject while the virtual image is presented). The information indicating the above is acquired as exercise evaluation information (S106) and output.

"Exercise evaluation information" includes, for example, whether or not batting was successful (whether or not the ball was hit back, whether it was a hit, whether it was a home run, or whether it was a missed shot, etc.) and the skill of batting movement (what kind of timing). Information indicating the movement state that affects the subject's biological reaction (eye movement, heartbeat, etc.) during the exercise (for example, beanball (toward the head)) Information indicating that the ball) was thrown). The attention state (degree and range of attention) and psychological state of the subject during the exercise may be added as exercise evaluation information.

The motion evaluation information may be input from the analysis target person 93 or a third party by external input, or the evaluation value is calculated based on the motion information acquired by the sensor 92 and the video information displayed by the video display unit 91. However, it may be used as exercise evaluation information.

In the example of baseball, the result of hit determination as to whether or not the ball hits the bat is exercised based on the motion information of the bad swung by the analysis target 93 and the position information of the ball displayed on the video display unit 91. It may be used as evaluation information. For example, the evaluation information acquisition unit 106 receives the video identification information from the video condition setting unit 102, and retrieves the ball movement information (series of ball position information) from the storage unit 101 based on the video identification information. Further, the evaluation information acquisition unit 106 receives motion information indicating the position of the bat from the sensor 92 attached to the bat. The evaluation information acquisition unit 106 determines whether or not the ball hits the bat based on the movement information of the ball and the movement information indicating the position of the bat. If it hits, a high evaluation value may be set, and if it does not hit, a low evaluation value may be set as exercise evaluation information.

<Data generation unit for analysis 107>
The analysis data generation unit 107 inputs the image identification information corresponding to the image condition set by the image condition setting unit 102, the motion information output by the sensor 92, and the motion evaluation information output by the evaluation information acquisition unit 106. Then, the information associated with these information is used as analysis data and output to the analysis device 200. The analysis data collection device 100 stores analysis data in a storage unit (not shown), and a set of analysis data is stored in the storage unit 201 of the analysis device 200, which will be described later.

[Analyzer 200]
The analysis device 200 of the present embodiment improves exercise performance by using a set of analysis data (analysis data set) acquired for different video conditions and different subjects using the above-mentioned analysis data collection device 100. Estimate perceptual information that can contribute (information indicating which movement in the image should be focused on, hereinafter also referred to as "focused visual information").

In FIG. 1, it seems that the analysis data acquired by the analysis data collection device 100 is input to the sequential analysis device 200, but the present invention is not limited to this. It is assumed that the analysis data acquired by the analysis data collection device 100 at various places, various subjects, and different dates and times is stored in the storage unit in advance, and the set of analysis data stored in the storage unit is stored in the analysis device. It is assumed that 200 can be read and used.

FIG. 4 shows a functional block diagram of the analyzer 200, and FIG. 5 shows an example of its processing flow.

The analysis device 200 includes a storage unit 201, a first analysis unit 202, and a second analysis unit 203.

The analysis device 200 receives a set of analysis data collected by the analysis data collection device 100 and stores it in the storage unit 201.

<Memory unit 201>
It is assumed that the storage unit 201 stores a set of analysis data. The set of analysis data includes at least (1) multiple analysis data acquired from the same analysis target person for videos corresponding to a plurality of different video specific information, and (2) video corresponding to the same video specific information. It shall include any of a plurality of analysis data obtained from a plurality of different analysis subjects.

<First analysis unit 202>
The first analysis unit 202 takes out a set of analysis data from the storage unit 201, estimates motion information having a high correlation with the motion evaluation information based on the set of analysis data (S202), and types of the estimated motion information. Outputs information that identifies. The estimated result will be referred to as "analysis target motion information". Therefore, the motion information to be analyzed specifies the types of various biological information (biological responses) such as acceleration sensor, displacement sensor, output value of motion sensor, heartbeat, electrocardiogram, respiration, myoelectricity, and dynamic movement of the eye. Information etc. The motion information to be analyzed can be said to be information that specifies the type of sensor used when obtaining these output values and various biological information (biological responses).

Specifically, when a plurality of analysis data acquired from the same or a plurality of analysis subjects are analyzed for a video corresponding to the same video specific information, when the motion evaluation is high (when it is successful) and the motion evaluation is performed. It is estimated that the category of motion information (for example, information that identifies the type of sensor) that has a large difference from when is low (when it cannot be done well) is the motion information to be analyzed. For example, a category of motion information (for example, a sensor) in which the time at which the peak value is taken differs greatly depending on whether the motion evaluation is high or low, or the time-series pattern of the motion information differs greatly depending on whether the evaluation is high or low. Information that identifies the type, etc.) is used as the exercise information to be analyzed. It should be noted that the motion information to be analyzed is information that can be identified even if the sensors are of the same type, including the part (where the sensor is attached) from which the motion information is acquired.

In addition, (1) when a plurality of analysis data acquired from the same analysis target person are analyzed for videos corresponding to a plurality of different video specific information, there is a difference between when the motion evaluation is high and when the motion evaluation is low. A large category of motion information (for example, information that identifies the type of sensor) is estimated as the motion information to be analyzed.

Alternatively, (2) when a plurality of analysis data acquired from a plurality of different analysis subjects are analyzed for a video corresponding to the same video specific information, a subject with a high exercise evaluation (a subject with a high exercise performance: a professional). Analyzing the categories of exercise information (for example, information that identifies the type of sensor) where there is a large difference between subjects with low exercise evaluation (targets with low exercise performance: beginners, substitute athletes, etc.) Use as target exercise information.

The movement information to be analyzed may be one movement information category or a combination of a plurality of movement information categories.

When there are a plurality of analysis data, the analysis target motion information may be obtained by performing the above analysis on the analysis data obtained by averaging these data, or the analysis target motion information may be obtained for each analysis data. The majority of the obtained results may be taken, and the most ones may be prioritized as the final analysis target exercise information.

By the above-mentioned processing, a plurality of analysis target motion information corresponding to a plurality of video specific information is acquired from a set of analysis data.

<Second analysis unit 203>
The second analysis unit 203 uses the analysis target motion information and the video identification information included in the set of analysis data stored in the storage unit 201, and the motion information corresponding to the analysis target motion information included in the virtual image. Information that identifies a video partial region (a partial region with movement in a virtual video) that has a high correlation with is estimated as "attention visual information" (S203), and information that associates the analysis target motion information with the attention visual information (information that is associated with the attention visual information). (Also called attention movement type information) is obtained and output. For example, the visual information of interest includes the time at which the behavior indicated by the motion information corresponding to the motion information to be analyzed changes (hereinafter referred to as “change time”) and a partial region in the virtual image that moves near the change time. It includes at least information indicating (a video partial region having a high correlation with the motion information corresponding to the motion information to be analyzed). The information indicating the partial area is information that can identify a moving partial area in the frame, that is, information that specifies the position and size of the partial area in the frame. For example, the information indicating the partial region moves in the vicinity of the time (also referred to as the fluctuation time) in which the movement information corresponding to the analysis target movement information fluctuates greatly in the virtual image included in the analysis data when the movement evaluation is high. It may be information that identifies a certain subregion. The visual information of interest is the association of the fluctuation time with the information indicating the partial area. In addition, the visual information of interest may include video identification information that identifies the video information used to specify a partial region in the virtual video.

Here, the analysis data set used by the second analysis unit 203 is the same as the analysis data set used by the first analysis unit 202 to obtain the analysis target motion information.

For example, the second analysis unit 203 changes the behavior of each analysis data in the analysis data set to the behavior indicated by the motion information acquired from the specific sensor 92 corresponding to the analysis target motion information obtained by the first analysis unit 202. Estimates a partial area in the virtual image (the image displayed on the image display unit 91 of the analysis data acquisition device 100) that has moved in the vicinity of the time when the above occurs. This makes it possible to correlate which part of the image moves (partial area in the image) with which movement or movement of the biological response (motion information corresponding to the movement information to be analyzed) has a high correlation. ..

The second analysis unit 203 includes, for example, a partial region in the virtual image having a high correlation (presumed) with the change time, the motion information corresponding to the motion information to be analyzed thus obtained, and a virtual including the partial region. The video identification information for specifying the video may be the visual information of interest. In this case, the attention visual information and the analysis target movement information are output as "information relating the analysis target movement information and the attention visual information". In this case, the visual information of interest includes the image-specific information because it is necessary as the information for specifying the virtual image generated for training by the training device described later.

For example, the following processing can be performed.
(1) Regarding the sequence of motion information in the analysis data acquired by the sensor corresponding to the analysis target motion information obtained by the first analysis unit 202, the time when the sensor data fluctuates greatly between adjacent times is set as the change time. Extract.
(2) Regarding the video identification information indicated by the analysis data for which the change time was obtained in (1) above, the vicinity of the change time in the video specified by the video identification information (the video output from the video synthesis unit 103 described above). The images of the two frames at different times are compared, and the partial region in the image in which there is movement between the frames is calculated as the feature of the frame at the change time.

When the above (1) and (2) are performed for each analysis data in the analysis target data set for each type of exercise information indicated by the analysis target exercise information, the partial region and change of the image having a high correlation with each exercise information type. The time can be extracted. If the subregions and change times extracted for each analysis data are different, a majority vote may be taken in the entire analysis data, and a predetermined number of subregions and change times may be selected from the largest number.

The exercise information corresponding to the analysis target exercise information obtained by the first analysis unit 202 differs between when the exercise evaluation is high (or person) and when the exercise evaluation is low (or person).

In the second analysis unit 203, focusing on the movements and the movements of the biological response that differ depending on whether the motion evaluation is high or low, the partial region of the moving image around the time when the movements or the biological response is large is generated. , It is presumed that it is the video (visual) information that contributed to the determination of the movement and the biological response in the brain.

For example, if a person with high batting accuracy in a real environment loses accuracy in batting for an image in which the throwing form and the trajectory of the ball do not match, the perceived information of at least one of the throwing form or the trajectory of the ball moves. It is considered that it greatly contributes to control and biological response. Therefore, the analysis data when batting in the actual environment by this method is used as the analysis data when the motion evaluation is high, and the analysis data when batting is performed on the image in which the throwing form and the trajectory of the ball do not match is the motion evaluation. If the extracted attention visual information is "the section where the throwing form is moving (before the ball leaves the hand)" by analyzing it as analysis data when the value is low, the player with high athletic performance will have the trajectory of the ball. It is thought that the trajectory of the ball is predicted to some extent from the throwing form before looking at. In particular, it is possible to grasp at what point in time the movement of the pitching form the judgment is made by the attention visual information. Similarly, if the visual information of interest is "the section where the ball flies away from the pitcher's hand", the trajectory of the ball in that section contributes to determining motion control (batting timing and movement) and biological response. You can see that you are doing it.

Alternatively, (3) of the image condition setting unit 102 shows an example of erasing the image of the ball in a part section on the trajectory of the ball, but in such an image, the analysis data when the motion evaluation is low. It can be presumed that the information on the section where the ball is erased in the video corresponding to the above may greatly contribute to the movement control and the biological response.

The analysis data acquired by the analysis data collection device 100 is not limited to the method used by the analysis device 200, and it is also possible to manually analyze perceptual information (visual information of interest) that can contribute to the improvement of motor performance. be.

[Training device 300]
The training device 300 is a training device 300 for training so that the trainee can acquire the visual information corresponding to the visual information of interest.

In FIG. 1, the information output from the analysis device 200 is sequentially input to the training device 300, and it seems that the training device 300 is executed, but the present invention is not limited to this. In general, information that associates the analysis target motion information with the attention visual information is obtained in advance by the analyzer 200 or another manual method, and the information that associates the analysis target motion information with the attention visual information is stored in the storage unit. In the training device 300, the analysis target exercise information and the attention visual information stored in the storage unit are stored in the storage unit at the place where the analysis data is acquired, the acquired target person, the place different from the acquired date and time, the target person, and the date and time. It is assumed that the information associated with is read and executed.

FIG. 6 shows a functional block diagram of the training device 300, and FIG. 7 shows an example of its processing flow.

The training device 300 includes a storage unit 301, a training condition setting unit 302, and a second video compositing unit 303.

<Memory unit 301>
The storage unit 301 stores in advance motion information (each object in the virtual image) of each object and an image of the surrounding environment, which are the bases when the image is synthesized by the second image compositing unit 303. It is assumed that the movement information of each object is stored in association with the "type of movement" for each "type of movement" of the object. The motion information is information used when presenting the motion of an object as an image. Basically, it is the same as that stored in the storage unit 101 of the analysis data collection device.

Further, it is assumed that the storage unit 301 stores information relating the analysis target motion information and the attention visual information. This is the analysis target motion information (type of sensor) obtained by the above-mentioned analysis device 200 or another manual method, and the attention visual information which is the visual information highly correlated with the fluctuation of the analysis target motion information. Information to be associated. The exercise information to be analyzed is information indicating the type of exercise information in which there is a difference between exercise with a high evaluation of exercise and exercise with a low evaluation, and is also referred to as change exercise type information.

<Training condition setting unit 302>
The training condition setting unit 302 sets the analysis target exercise information to be trained by the training target person as the training target exercise information (S302), and sets the analysis target exercise information stored in the storage unit 301 as the information for associating the attention visual information. Based on this, the attention visual information associated with the analysis target exercise information corresponding to the set training target exercise information is output. For example, the analysis data obtained for the trainee by the first analysis unit 202 of the above-mentioned analysis device 200 (or manual analysis, etc.) and the analysis data obtained for the person with higher exercise performance than the trainee. An analysis target exercise with a large difference is obtained, and this is set as training target exercise information. When a plurality of analysis target exercise information is extracted, one of them to be trained may be selected and set.

Alternatively, when the analysis data acquired by the first analysis unit 202 of the above-mentioned analysis device 200 (or manual analysis or the like) when the exercise evaluation of the trainee is high and when the exercise evaluation of the trainee is low. An analysis target exercise having a large difference from the acquired analysis data may be obtained and set as training target exercise information.

Information that identifies a sensor that measures the movement of the body part or accessory if the instructor or the like grasps the movement of the body part or accessory that the instructor or the like particularly wants to enhance the movement regardless of the analysis device 200. May be set as training target exercise information.

<Second video compositing unit 303>
The second video synthesizing unit 303 receives the attention visual information from the training condition setting unit 302, and synthesizes the virtual video for training by using the attention visual information. The virtual image for training is a virtual image that presents an environment that cannot be reproduced in a real environment, and is an image in which a partial area in the virtual image indicated by the visual information of interest is emphasized.

For example, the second video compositing unit 303 synthesizes a virtual video in the same manner as the video compositing unit 103 of the analysis data acquisition device 100. At this time, in the time frame near the change time included in the attention visual information received from the training condition setting unit 302, a virtual image superimposing a visual effect that emphasizes a partial area in the virtual image indicated by the attention visual information is synthesized. (S303), output to the image display unit 81.

In the video compositing unit 103 of the analysis data acquisition device 100, the video compositing conditions are given by the video condition setting unit 102, but the second video compositing unit 303 randomly sets the video conditions and analyzes under the set conditions. It is assumed that the virtual video can be synthesized by the same method as the video compositing unit 103 of the data acquisition device 100. In the randomly synthesized virtual image, training is performed to pay attention to the partial area indicated by the visual information of interest in the vicinity of the change time. It should be noted that the video specific information may be included in the visual information of interest, and the second video compositing unit 303 may synthesize the virtual video under the video conditions corresponding to the video specific information.

The image in which the visual effect that emphasizes the partial area in the virtual image indicated by the attention visual information set by the training condition setting unit 302 is superimposed is, for example, a visual effect that conspicuously surrounds the partial area in the virtual image. , This is an image in which the image in the area other than the relevant partial area is blurred or the information is reduced in the virtual image. In addition to imparting a certain visual effect, the reference image may be gradually changed according to the progress of the training from the reference image to the above image.

The above-mentioned video display unit 81 displays the virtual image synthesized by the second video compositing unit 303 on the internal display and presents it to the training target person 83 (S304).

The trainee 83 exercises while watching the image displayed on the image display unit 81. As a result, when and what kind of area a person with high exercise performance is visually recognizing and performing exercise control is highlighted in the virtual image, and the trainee 83 acquires visual information by paying attention to that part. And can be trained to start exercising.

<Effect>
With the above configuration, the analysis data collection device can collect analysis data to enable objective analysis of what perceived information has a large contribution to motor control, and the analysis device can collect analysis data. Can be used to estimate perceptual information that contributes significantly to motor control, and the training device should train the trainee to handle the perceptual information using perceptual information that contributes significantly to motor control. Can be done.

In this embodiment, by using a virtual image of an environment that cannot be reproduced in the actual environment, it is possible to determine which information the analysis target person uses to exercise, as compared with the case of using only the image based on the actual environment. It has the effect of emphasizing.

<Modification example>
In the present embodiment, the analysis data acquisition device 100, the analysis device 200, and the training device 300 have been described as separate devices, but these devices may be realized by one known or dedicated computer. Similarly, the video display units 81 and 91 may be realized by one video display unit (for example, a head-mounted display).

In the present embodiment, the output value (set of analysis data) of the analysis data collection device 100 is used by the analysis device 200, and the output value (attention visual information) of the analysis device 200 is used by the training device 300. However, each device does not necessarily require another device and can be used independently. For example, the output value (set of analysis data) of the analysis data collection device 100 does not necessarily have to be used in the analysis device 200, and as described above, perceptual information (attention visual information) that can manually contribute to the improvement of motor performance. ) Can also be analyzed. Further, in the analysis device 200, if there is data corresponding to a set of analysis data, it does not necessarily have to be the output value of the analysis data collection device 100. For example, the analysis data collection device presents only the "image based on the actual environment" to the analysis target person, and the analysis target person who exercises while watching the image provides the exercise information and the exercise evaluation information indicating the evaluation of the exercise. You may use a set of analysis data which is acquired and associates the video specific information with the exercise information of the target person and the exercise evaluation information reaction. Further, from the output value (visual information of interest) of the analysis device 200, a part to be noted at the time of training may be presented regardless of the training device. In the training device 300, even if the output value of the analysis device 200 is not used, if the part to be noted is known, the virtual image in which the visual effect emphasizing the part to be noted in the virtual image is superimposed may be synthesized.

In the present embodiment, an example of baseball batting is shown in which the exercise performed by the analysis target person while the virtual image is presented and the exercise to be trained by the training target person are other sports (softball). It goes without saying that it can be applied to batting (or cricket) and even competitive ball games. Furthermore, even in other exercises, by using a virtual image of an environment that cannot be reproduced in the actual environment, it is possible to determine which information the analysis target person uses to exercise, as compared with the case of using only the image based on the actual environment. Since it becomes easy to analyze, the analysis data collecting device and the analysis device of the present invention can be applied. In other exercises as well, it is possible to efficiently train the trainee by presenting a composite of a virtual image that superimposes a visual effect that emphasizes a partial area in the virtual image including the movement corresponding to the visual information of interest. Therefore, the training device of the present invention can be applied.

<Other variants>
The present invention is not limited to the above embodiments and modifications. For example, the various processes described above may not only be executed in chronological order according to the description, but may also be executed in parallel or individually as required by the processing capacity of the device that executes the processes. In addition, changes can be made as appropriate without departing from the spirit of the present invention.

<Programs and recording media>
Further, various processing functions in each device described in the above-described embodiment and modification may be realized by a computer. In that case, the processing content of the function that each device should have is described by the program. Then, by executing this program on the computer, various processing functions in each of the above devices are realized on the computer.

The program describing the processing content can be recorded on a computer-readable recording medium. The computer-readable recording medium may be, for example, a magnetic recording device, an optical disk, a photomagnetic recording medium, a semiconductor memory, or the like.

Further, the distribution of this program is performed, for example, by selling, transferring, renting, or the like a portable recording medium such as a DVD or a CD-ROM in which the program is recorded. Further, the program may be distributed by storing the program in the storage device of the server computer and transferring the program from the server computer to another computer via the network.

A computer that executes such a program first temporarily stores, for example, a program recorded on a portable recording medium or a program transferred from a server computer in its own storage unit. Then, when the process is executed, the computer reads the program stored in its own storage unit and executes the process according to the read program. Further, as another embodiment of this program, a computer may read the program directly from a portable recording medium and execute processing according to the program. Further, each time a program is transferred from the server computer to this computer, processing may be sequentially executed according to the received program. In addition, the above processing is executed by a so-called ASP (Application Service Provider) type service that realizes the processing function only by the execution instruction and result acquisition without transferring the program from the server computer to this computer. May be. The program shall include information used for processing by a computer and equivalent to the program (data that is not a direct command to the computer but has a property of defining the processing of the computer, etc.).

Further, although each device is configured by executing a predetermined program on a computer, at least a part of these processing contents may be realized by hardware.

Claims (13)

The motion information used when presenting the motion of an object as an image, the motion information to be analyzed indicating the type of motion information that differs between the motion with high evaluation and the motion with low evaluation, and the attention area and attention time in the virtual image. A storage unit that stores attention movement type information that is information associated with attention visual information that is identification information, and a storage unit that stores attention movement type information.
Based on the target motion classification information stored before term memory unit, a training condition setting unit for outputting an attention visual information associated with the analysis object motion information to be trained trainees,
It includes a second video compositing unit that synthesizes a virtual video in which the opponent displayed in the virtual video moves against the laws of physics using the motion information.
The second video compositing unit synthesizes a virtual image in which a visual effect that emphasizes a region of interest specified by the visual information of interest is superimposed in the vicinity of the time of interest included in the visual information of interest in the virtual image. A featured training device. The motion information used when presenting the motion of an object as an image, the motion information to be analyzed indicating the type of motion information that differs between the motion with high evaluation and the motion with low evaluation, and the attention area and attention time in the virtual image. A storage unit that stores attention movement type information that is information associated with attention visual information that is identification information, and a storage unit that stores attention movement type information.
Based on the target motion classification information stored before term memory unit, a training condition setting unit for outputting an attention visual information associated with the analysis object motion information to be trained trainees,
It includes a second image synthesizing unit that synthesizes a virtual image in which the body movement of the other party and the movement of the object accompanying the body movement of the other party do not match in light of the laws of physics using the movement information.
The second video compositing unit synthesizes a virtual image in which a visual effect that emphasizes a region of interest specified by the visual information of interest is superimposed in the vicinity of the time of interest included in the visual information of interest in the virtual image. A featured training device. The training device according to claim 1.
The other party displayed in the virtual image moves against the laws of physics The other party displayed in the virtual image is the law of physics based on the image identification information that specifies the movement of the object included in the virtual image and the above-mentioned image identification information. Evaluation of the motion information obtained from the sensor attached to the analysis target person or its accessory, which is presented with a synthesized virtual image that moves against, and the motion performed by the analysis target person while the virtual image is presented. The set of data associated with the exercise evaluation information indicating is used as the set of analysis data.
In the set of analysis data, the motion information of the analysis data in the analysis data corresponding to when the motion evaluation is high and the motion of the analysis data in the analysis data corresponding to when the motion evaluation is low. The information that identifies the sensor corresponding to the motion information that differs from the information is used as the analysis target motion information .
In the virtual image included in the analysis data when the motion evaluation is high, the information for identifying a partial region having motion in the vicinity of the fluctuation time, which is the time when the motion information corresponding to the analysis target motion information fluctuates greatly, is described above. As an area of interest
The attention visual information is
The fluctuation time is associated with the attention area as the attention time.
A training device characterized by that. The training device according to claim 2.
The body movements of the other party and the movements of the object accompanying the body movements of the other party do not match in light of the laws of physics. The motion information obtained from the sensor attached to the analysis target person or its accessory, which is presented with a synthesized virtual image in which the motion and the motion of the object accompanying the physical motion of the other party do not match in light of the laws of physics, and the virtual A set of data associated with the movement evaluation information indicating the evaluation of the movement performed by the analysis target person in the state where the image is presented is defined as a set of analysis data.
In the set of analysis data, the motion information of the analysis data in the analysis data corresponding to when the motion evaluation is high and the motion of the analysis data in the analysis data corresponding to when the motion evaluation is low. The information that identifies the sensor corresponding to the motion information that differs from the information is used as the analysis target motion information .
In the virtual image included in the analysis data when the motion evaluation is high, the information for identifying a partial region having motion in the vicinity of the fluctuation time, which is the time when the motion information corresponding to the analysis target motion information fluctuates greatly, is described above. As an area of interest
The attention visual information is
The fluctuation time is associated with the attention area as the attention time.
A training device characterized by that. The training device according to claim 1.
The other party displayed in the virtual image moves against the physical law The other party displayed in the virtual image is the physical law based on the video identification information that specifies the movement of the object included in the virtual image and the video identification information. Evaluation of the movement information obtained from the sensor attached to the analysis target person or its accessory, which is presented with a synthesized virtual image that moves against the above, and the movement performed by the analysis target person while the virtual image is presented. A storage unit that stores a set of data for analysis and a storage unit that stores a set of data associated with the exercise evaluation information indicating
Information that identifies a sensor that corresponds to the motion information that differs between the motion information of the analysis target person when the motion evaluation is high and the motion information of the analysis target person when the motion evaluation is low in the set of analysis data. The first analysis unit, which obtains the above-mentioned analysis target motion information,
In the virtual image included in the analysis data when the motion evaluation is high, information for identifying a partial region of motion in the vicinity of the fluctuation time, which is the time when the motion information corresponding to the analysis target motion information fluctuates greatly, is obtained. , information identifying the partial area and the change time as the attention visual information, and a second analysis unit for obtaining an attention motion type information is information that associates with the target visual information and the analysis object motion information,
Training equipment further equipped with. The training device according to claim 2.
The body movements of the other party and the movements of the object accompanying the body movements of the other party do not match in light of the laws of physics. The motion information obtained from the sensor attached to the analysis target person or its accessory, which is presented with a synthesized virtual image in which the motion and the motion of the object accompanying the body motion of the other party do not match in light of the laws of physics, and the virtual A storage unit that stores a set of data for analysis that associates a set of data associated with the movement evaluation information indicating the evaluation of the movement performed by the analysis target person in the state where the image is presented, and a storage unit that stores the set of data for analysis.
Information that identifies a sensor that corresponds to the motion information that differs between the motion information of the analysis target person when the motion evaluation is high and the motion information of the analysis target person when the motion evaluation is low in the set of analysis data. The first analysis unit, which obtains the above-mentioned analysis target motion information,
In the virtual image included in the analysis data when the motion evaluation is high, information for identifying a partial region of motion in the vicinity of the fluctuation time, which is the time when the motion information corresponding to the analysis target motion information fluctuates greatly, is obtained. , information identifying the partial area and the change time as the attention visual information, and a second analysis unit for obtaining an attention motion type information is information that associates with the target visual information and the analysis object motion information,
Training equipment further equipped with. It is a training method performed by the training device.
In the storage unit, the motion information used when presenting the motion of the object as an image, the motion information to be analyzed indicating the type of motion information that differs between the motion with high evaluation and the motion with low evaluation, and the attention in the virtual image. It is assumed that the attention movement type information, which is the information associated with the attention visual information, which is the information for specifying the area and the attention time, is stored.
The training apparatus, on the basis of the stored target movement type information in the storage unit, and the training condition setting step of outputting the target visual information associated with the analysis object motion information to be trained trainees,
The training device includes a second video compositing step of synthesizing a virtual video in which the opponent displayed in the virtual video moves against the laws of physics using the motion information.
In the second video compositing step, a virtual image superimposing a visual effect that emphasizes the attention area specified by the attention visual information is synthesized in the vicinity of the attention time included in the attention visual information in the virtual image.
A training method characterized by that. It is a training method performed by the training device.
In the storage unit, the motion information used when presenting the motion of the object as an image, the motion information to be analyzed indicating the type of motion information that differs between the motion with high evaluation and the motion with low evaluation, and the attention in the virtual image. It is assumed that the attention movement type information, which is the information associated with the attention visual information, which is the information for specifying the area and the attention time, is stored.
The training apparatus, on the basis of the stored target movement type information in the storage unit, and the training condition setting step of outputting the target visual information associated with the analysis object motion information to be trained trainees,
The training device includes a second image synthesis step of synthesizing a virtual image in which the body movement of the other party and the movement of an object accompanying the body movement of the other party do not match in light of the laws of physics using the movement information.
In the second video compositing step, a virtual image superimposing a visual effect that emphasizes the attention area specified by the attention visual information is synthesized in the vicinity of the attention time included in the attention visual information in the virtual image.
A training method characterized by that. The training method according to claim 7.
The other party displayed in the virtual image moves against the laws of physics The other party displayed in the virtual image is the law of physics based on the image identification information that specifies the movement of the object included in the virtual image and the above-mentioned image identification information. Evaluation of the motion information obtained from the sensor attached to the analysis target person or its accessory, which is presented with a synthesized virtual image that moves against, and the motion performed by the analysis target person while the virtual image is presented. The set of data associated with the exercise evaluation information indicating is used as the set of analysis data.
In the set of analysis data, the motion information of the analysis data in the analysis data corresponding to when the motion evaluation is high and the motion of the analysis data in the analysis data corresponding to when the motion evaluation is low. The information that identifies the sensor corresponding to the motion information that differs from the information is used as the analysis target motion information .
In the virtual image included in the analysis data when the motion evaluation is high, the information for identifying a partial region having motion in the vicinity of the fluctuation time, which is the time when the motion information corresponding to the analysis target motion information fluctuates greatly, is described above. As an area of interest
The attention visual information is
The fluctuation time is associated with the attention area as the attention time.
A training method characterized by that. The training method according to claim 8.
The body movements of the other party and the movements of the object accompanying the body movements of the other party do not match in light of the laws of physics. The motion information obtained from the sensor attached to the analysis target person or its accessory, which is presented with a synthesized virtual image in which the motion and the motion of the object accompanying the physical motion of the other party do not match in light of the laws of physics, and the virtual A set of data associated with the movement evaluation information indicating the evaluation of the movement performed by the analysis target person in the state where the image is presented is defined as a set of analysis data.
In the set of analysis data, the motion information of the analysis data in the analysis data corresponding to when the motion evaluation is high and the motion of the analysis data in the analysis data corresponding to when the motion evaluation is low. The information that identifies the sensor corresponding to the motion information that differs from the information is used as the analysis target motion information .
In the virtual image included in the analysis data when the motion evaluation is high, the information for identifying a partial region having motion in the vicinity of the fluctuation time, which is the time when the motion information corresponding to the analysis target motion information fluctuates greatly, is described above. As an area of interest
The attention visual information is
The fluctuation time is associated with the attention area as the attention time.
A training method characterized by that. The training method according to claim 7.
In the storage unit, the image identification information that identifies the movement of the object included in the virtual image in which the other party moves against the physical law displayed in the virtual image and the image identification information are displayed in the virtual image based on the image identification information. The analysis target person is presented with a synthesized virtual image in which the other party moves against the laws of physics. It is assumed that the set of data for analysis is stored in the set of data associated with the motion evaluation information indicating the evaluation of the exercise performed by.
The training device corresponds to the movement information in which the movement information of the analysis target person when the movement evaluation is high and the movement information of the analysis target person when the movement evaluation is low in the set of the analysis data are different. The first analysis step of obtaining the information that identifies the sensor as the analysis target motion information, and
In the virtual image included in the analysis data when the exercise evaluation is high , the training device sets a partial region of motion in the vicinity of the fluctuation time, which is the time when the motion information corresponding to the analysis target motion information fluctuates greatly. Second, the information to be specified is obtained, and the information for specifying the fluctuation time and the partial region is used as the attention visual information, and the attention movement type information which is the information in which the analysis target motion information and the attention visual information are associated with each other is obtained. Analysis steps and
Training method to further prepare. The training method according to claim 8.
In the storage unit, the image identification information for specifying the movement of the object included in the virtual image in which the body movement of the other party and the movement of the object accompanying the body movement of the other party do not match in light of the physical law, and the image identification information Based on this, the movement obtained from the sensor attached to the person to be analyzed or its accessory is presented with a synthesized virtual image in which the movement of the other party's body and the movement of the object accompanying the other party's body movement do not match in light of the laws of physics. It is assumed that the set of data for analysis is stored as a set of data in which the information is associated with the motion evaluation information indicating the evaluation of the motion performed by the analysis target person while the virtual image is presented.
The training device corresponds to the movement information in which the movement information of the analysis target person when the movement evaluation is high and the movement information of the analysis target person when the movement evaluation is low in the set of the analysis data are different. The first analysis step of obtaining the information that identifies the sensor as the analysis target motion information, and
In the virtual image included in the analysis data when the exercise evaluation is high , the training device sets a partial region of motion in the vicinity of the fluctuation time, which is the time when the motion information corresponding to the analysis target motion information fluctuates greatly. Second, the information to be specified is obtained, and the information for specifying the fluctuation time and the partial region is used as the attention visual information, and the attention movement type information which is the information in which the analysis target motion information and the attention visual information are associated with each other is obtained. Analysis steps and
Training method to further prepare. A program for operating a computer as the training device according to any one of claims 1 to 6.

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