JP2005111178A - Motion training display system - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To constitute a system for learning a desired motion by generating a human body model of a person based on his or her personal information such as height, weight, muscular strength or the like, visually displaying a difference between the desired and actual motions, thereafter generating a training plan suited to the person and display the same. <P>SOLUTION: The motion training system comprises a human body model generating portion 4 for generating a human body model based on the personal information of a trainee's physical characteristic, a motion data track portion 5 for extracting motion data from the moving images of the trainee and storing the same therein, a data integration portion 71 for generating the motion of a human body based on the data in the human body model generating portion 4 and the motion data track portion 5, a motion database 6 for storing model motions therein, a desired motion selecting portion 1 for selecting data in the motion database 6, and a display 8 for displaying image data in the data integration portion 71 and the motion database 6. <P>COPYRIGHT: (C)2005,JPO&NCIPI

Description

  The present invention relates to an operation training display system capable of supporting training for executing a desired operation (operation of a sports player, operation of a healthy person, etc.) as an object of acquisition.

  As a system for actually experiencing a desired movement, there is a simulated experience system (Japanese Patent Laid-Open No. 10-340039). In this system, an actuator is attached to each joint of the experience person, a desired range of motion of the joint can be set, and the desired motion can be actually experienced.

  As a system for presenting a training method, there is a golf practice target achievement system (Japanese Patent Laid-Open No. 2002-248187). This system can set practice targets based on personal information such as muscle strength and physical strength, and can compare the actual form and practice goals during exercise exercises to evaluate achievement.

Further, as a system for diagnosing an athlete's swing form, comparing it with a reference swing form, and giving appropriate advice to the athlete so that the difference can be minimized, a swing form diagnostic apparatus (Japanese Patent Laid-Open No. 3-12180). ) Has been proposed.
JP-A-10-340039 JP 2002-248187 A Japanese Patent Laid-Open No. 3-12180

  However, these systems have the following problems. The pseudo-experience system of Patent Document 1 allows an experiencer to attach an actuator and actually experience a desired movement. Therefore, if the range of motion of the desired motion is wider than the range of motion of the experiencer, the experience is excessive. Will be burdensome, and in the worst case, it may cause trouble. Furthermore, the troublesomeness caused by mounting the actuator also occurs. The golf practice target attainment system of Patent Document 2 is specialized for golf, and training effect simulation cannot be performed. Moreover, neither system displays in three dimensions, and it is difficult to visually grasp the difference between the target action and the actual action. The swing form diagnostic apparatus of Patent Document 3 does not use personal information of physical characteristics such as the height, weight, and muscle strength of the experience person.

  The present invention has been made in view of the above points, and generates a personal human body model based on personal information such as height, weight, muscle strength, etc., and visually shows the difference between the desired motion and its own actual motion. It is an object of the present invention to construct and provide a system capable of generating and presenting a training plan suitable for an individual and simulating and presenting the effect thereof.

  In order to solve the above problems, the motion training display system of the present invention generates a human body model based on personal information of physical characteristics such as height, weight, muscle strength, etc. of a person to be exercised as shown in FIG. The human body model generation unit 4, the motion data is extracted from the exercise image of the person to be trained, and the motion data track unit 5 is stored. The data of the human body model generation unit 4 and the motion data track unit 5 is used to determine the operation of the human body model. A data integration unit 71 to be generated, a motion database 6 that stores exemplary motions, a desired operation selection unit 1 that selects data in the motion database 6, video data of the data integration unit 71, and video data of the motion database 6 And a display unit 8 for displaying.

According to the first aspect of the present invention, the person to be practiced can compare the current movement reflecting his / her physical characteristics with the display of the model movement of the desired movement.
According to the second aspect of the present invention, the model movement of the desired movement is also corrected so as to be consistent with the body shape of the person to be practiced. Can be compared, and the correction point can be grasped more intuitively.

According to the invention of claim 3, the person to be practiced can specifically recognize the difference between the model action and the own action.
According to the invention of claim 4, it is possible to present a training plan that is optimal for the individual's physical characteristics.

According to the invention of claim 5, by simulating and displaying the effect when the exercise target person executes the presented training plan, the exercise object person visually shows how the body changes. It becomes possible to have a motivation for training.
According to the invention of claim 6, the person to be practiced can compare the past movement with the current movement, and can confirm the progress of the practice.

According to the seventh aspect of the present invention, it is possible to propose an optimal training plan for each person by feeding back personal information after training by a training subject who uses the system. In other words, the effect simulation execution unit and the training database can be customized for the individual subject to practice, and more effective training proposals and highly accurate predictions can be performed.
According to the eighth aspect of the present invention, even if the person to be practiced does not have the entire system, the operation training can be easily performed through the Internet.

  Next, an embodiment of the present invention will be described. An embodiment of the present invention is shown in FIG. The motion training display system of FIG. 1 includes a desired motion selection unit 1, a body shape data input unit 2, a motion data input unit 3, an exemplary video input unit 13, and a post-training information input unit 16 as input devices. The input device may be a keyboard or mouse of a personal computer, or may be a measuring device for body shape, physical strength and the like. For input of moving images, a CCD camera connected via USB may be used, or a moving image file shot by a digital video camera may be read via a recording medium or a cable. The computing device is a computer, and the human body model generation unit 4, the motion data track unit 5, the motion database 6, the data integration units 71 and 72, the motion correction unit 9, the data comparison unit 10, the training database 11, and the effect simulation execution unit. 12, a motion data track unit 14, a history database 15, a correction unit 17, an accounting data storage unit 18, and a payment data storage unit 19. Further, the display device includes a display unit 8. The input device and the display device exist on the user side, and the arithmetic device exists on the server side. The user side device is composed of a personal computer or the like having an Internet connection function, and has a function of holding data transmitted from the server side.

  Information input by an input device provided on the user side is transmitted to the server side via the Internet. Note that the user side device may be connected to the LAN, and in this case, information is transmitted to and received from the LAN via the router. The connection form of the Internet is not particularly limited, but is preferably a broadband connection so that transmission / reception of moving images can be performed without stress.

  First, as shown in FIGS. 2 and 3, the human body model generation unit 4 provided on the server side stores a standard human body shape (standard human body model) having a plurality of joints. A human body model is created by correcting the model based on the body shape data input from the body shape data input unit 2. FIG. 3 shows an image of human body model creation in the human body model generation unit 4. By adding your own face photograph, body type data such as height, weight, chest circumference, image data such as age, sex, body characteristics, clothes, etc. to a standard human body model prepared in advance, you can create your own doll Generate on top.

  Next, the motion data track unit 5 extracts motion data from the moving image input from the motion data input unit 3. As a specific method, feature points such as each joint point are selected from the moving image, and the relationship between the feature points (time series of position, rotation angle, and rotation direction) is extracted for each frame. This frame rate can be arbitrarily changed. If more accurate data is required, the frame rate may be increased, and if rough, it may be decreased.

  FIG. 4 is an explanatory diagram showing an outline of the motion data track unit 5. The frame rate setting unit sets a frame rate for extracting a plurality of still images from a moving image. For example, 30 frames, 15 frames, 10 frames, 5 frames, 3 frames, 2 frames, and 1 frame can be selected per second. A frame rate is selected according to the required accuracy, and a moving image is input from the motion data input unit 3. The input moving image is analyzed at a selected frame rate to extract a plurality of frames of still images. Preprocessing for image recognition (processing such as background image removal, noise removal, contour enhancement, etc.) is performed on each extracted image. Feature points such as joint points are extracted from each image, and each feature point is labeled. The correspondence relationship between feature points is analyzed among a plurality of images, and the relationship between feature points is extracted. Thereby, time-series data of the position of the feature point, the mutual rotation speed, and the rotation direction can be obtained.

  The motion data extraction algorithm is not limited to that shown in the figure, and commercially available software may be used as appropriate. As software for extracting motion data from a moving image, for example, there is a product called “krops” of GSPORT Co., Ltd.

  Both the human body model generated by the human body model generation unit 4 and the motion data generated by the motion data track unit 5 are output to the data integration unit 71. An outline of the data integration unit 71 is shown in FIG. In the data integration unit 71, the two data are integrated, and corresponding motion data is assigned to each joint of the human body model. That is, the human body model generated by the human body model generation unit 4 (length, thickness, shape data, etc. of each part of the human body) is added to the motion data generated by the motion data track unit 5 (position of each joint, each joint). Video data of a moving human body model is created by assigning the rotation angle and the rotation time series). This video data is created as three-dimensional data and displayed as a two-dimensional image observed from an arbitrary viewpoint. Since the user side device having the display unit 8 sends information about the viewpoint of observing the three-dimensional video via the Internet, the data of the two-dimensional image observed from the viewpoint is transmitted to the display unit 8 via the Internet. Sent. In addition, the video data of the human body model created by the data integration unit 71 is also output to the data comparison unit 10.

  The motion database 6 stores motion data (positions of joints, rotation angles between joints, time series data of rotation directions) of professional athletes and healthy persons. The user can select a desired action from the desired action selection unit 1 by operating it. The selected motion data is output to the motion correction unit 9.

  The motion correction unit 9 corrects the motion data based on the user's body shape data. An outline of the motion correction unit 9 is shown in FIG. When the uncorrected motion data read from the motion database 6 is input to the motion correction unit 9, the motion correction unit 9 inputs the user's body shape data and compares the relative position data between the joint points of the body shape data. By doing so, the motion data of the model movement is corrected to individual data. For example, when the length from the neck to the waist of the user is different from that of the motion data of the exemplary motion, the relative position data from the neck to the waist of the motion data is corrected.

  If there is no significant difference in body shape, the rotation angle and direction are not corrected. On the other hand, if the body shape is too different, the difference is taken into account and the rotation angle and direction of the motion data are corrected. For example, if the user has a thick physique and the human whose model motion data has been extracted has a lean physique, if the motion data of the model motion is integrated into the user's human body model without correction, a part of the body There is a risk of sinking into the body. Therefore, it is determined whether or not each part of the human body model is embedded in another part when integrated, and the rotation angle and the rotation direction of the motion data are corrected. The corrected data is output to the data comparison unit 10.

  An outline of the data comparison unit 10 is shown in FIG. The data comparison unit 10 receives the integrated data of the user's human body model and the user's current motion data input from the data integration unit 71, the user's human body model input from the data integration unit 72, and an exemplary operation of the desired operation This is a comparative analysis of integrated data. Here, first, the difference between the rotation angle and rotation time series data of each joint is extracted. After that, the possible causes (differences in muscle strength, differences in flexibility, etc.) are calculated from the angular acceleration and rotation angle of each joint and analyzed. The comparatively analyzed data is input to the display unit 8, and the correction points are displayed blinking in red, or the two moving images are displayed in an overlapping manner so that the exercise subject can visually understand the correction points and time differences. I can do it. In addition, it indicates where the muscle strength and flexibility are insufficient by displaying the part in red. The image is shown in FIG.

  When the “Compare” button shown in FIG. 14A is operated, the current user action and the model action are displayed side by side. These are displayed as moving images and can be observed from an arbitrary viewpoint using the GUI in the lower left corner of the screen. Specifically, the movement can be observed from an arbitrary viewpoint three-dimensionally by moving the viewpoint position back and forth, left and right, rotating the line-of-sight direction to the left and right, and changing the elevation angle and depression angle. In FIG. 14 (a), with regard to the current operation of the user, the part requiring correction is indicated by a circle, but in the actual product, the part requiring correction is blinked in red. The above processing is performed in real time, and the subject of training can know the difference from the model operation in real time.

  Further, when the user's body is thicker than the person performing the exemplary action, the correction degree from the exemplary action is calculated as to which meat should be dropped, and the portion is displayed in red. The operation is shown in FIG. This shows in more detail the process of correcting the rotation angle and the rotation direction of each joint point of the motion data by comparing the body types such as the thick type and the thin type shown in FIG. The total number of parts to be compared is N, and the body types of parts n = 1, 2,. For the part n, if the difference in body type is greater than or equal to the reference, the rotation angle and rotation direction of each joint point in the motion data are corrected according to the difference in body type. If the degree of correction from the exemplary action is equal to or greater than the reference, the part n is stored as a body type correction required part. As a result, when the corrected motion data is created, a list of body part correction sites can be created at the same time.

  As shown in FIG. 14B, the list of all correction points is displayed in a text format on another screen of the display unit 8, so that the subject of training can easily grasp all the correction points. For example, the difference in “muscle strength” is analyzed from the difference in angular velocity of each joint, and it is displayed as “abdominal muscle is insufficient”. Also, the difference in “flexibility” is analyzed from the difference in the rotation angle of each joint, and it is displayed as “Insufficient knee flexibility”. In addition, the body part requiring correction is displayed as “Abdomen diet is required” according to the correction from the model action. Also, specific advice for correcting the operation is displayed in text, such as “bend the elbow 30 degrees more”. The comparatively analyzed data is also transmitted to the training database 11.

  The training database 11 records muscular strength training methods, stretch methods, and diet methods of various parts of the body as motion data, and records the plans. When the “training plan” button shown in FIG. 14 (a) is operated, a training plan request command is sent to the server, and the training database 11 applies the correction point comparison analysis data input from the data comparison unit 10. The training plan is retrieved and sent to the data integration unit 71. In the data integration unit 71, the training motion data and the human body data are integrated and transmitted to the display unit 8. The display unit 8 displays the transmitted training method on the display as a moving image, and displays the training plan as text. The training plan data is also output to the effect simulation execution unit 12.

  A training plan creation process by the training database 11 is illustrated in FIG. The correction point comparison analysis data input from the data comparison unit 10, that is, a list of muscle strength, a portion with insufficient flexibility, and a body shape correction required portion are input, and one portion is selected from the list. If the muscle strength of the part is insufficient, a strength training method is searched and output. Further, if the flexibility of the part is insufficient, the method of stretching the part is searched and output. Also, if the body part needs to be corrected, the diet method for that part is retrieved and output. The same process is performed for all parts of the list. As a result, a training plan is created and displayed as text on the separate screen of the display unit 8 as shown in FIG. 14 (b), such as “first day: 30 times abdominal muscles × 4 sets, second day:. Is done. The training method is displayed as a moving image as shown in FIG. A moving image of this training method can also be observed from an arbitrary viewpoint using a GUI directly below the screen.

  The effect simulation execution unit 12 simulates the effect on the training plan data input from the training database 11. In other words, when training is performed according to this plan, it simulates how the body shape and movement are deformed at an arbitrary time, and the body shape data and motion data are created. These data are sent to the data integration unit 71, integrated with the human body model of the person to be practiced, and then sent to the display unit 8, as shown in FIG. 14 (c). Predicted user actions and model actions are displayed side by side. These are displayed as moving images and can be observed from an arbitrary viewpoint using the GUI in the lower left corner of the screen.

  The processing content of the effect simulation execution part 12 is illustrated in FIG. When the “effect prediction” button shown in FIG. 14A is pressed, training plan data such as a training method, a stretching method, and a diet method are acquired from the training database 11. In addition, information such as age, gender, and body type of the subject is acquired. Also, the simulation period set in the column “How many days later?” In FIG. If one part to be simulated is selected, and that part is a target for strength training, the training method x period is calculated using a prediction calculation formula created based on statistical data in advance according to the age, sex, etc. of the person to be trained. Predict and calculate the effect of increasing strength. Then, by correcting the angular velocity of the motion data after increasing strength in the increasing direction, a change in which the operation becomes sharp after training is simulated.

  Similarly, if the part to be simulated is the subject of stretching, a prediction calculation formula created based on statistical data in advance according to the age, sex, etc. of the person to be trained will increase the flexibility according to the stretching method × period Predict the effect. Then, by correcting the rotation angle of the motion data after increasing the flexibility in the increasing direction, a change in which the operation after the training becomes large is simulated.

  In addition, if the part to be simulated is a diet target, predictive calculation of the diet effect according to the diet method × period using the prediction formula created based on statistical data in advance according to the age, sex, etc. of the subject To do. Then, by correcting the thickness of the part of the human body model after the diet in a decreasing direction, a change in which the body shape after the diet becomes smart is simulated. By repeating the above processing for all the parts, it is simulated how the body shape and movement are deformed as a whole.

  The motion data track unit 14 converts the moving image input from the model moving image input unit 13 provided on the user side into motion data. The data is input to the motion database 6, and the motion database 6 can be updated as needed.

  The history database 15 stores the human body data and motion data input from the data integration unit 71 in association with each other, and outputs the data to the data comparison unit 10 when the data is called by the desired motion selection unit 1 on the user side. With this function, the practitioner can compare his / her previous movement, body shape with current movement, body shape, and confirm the degree of progress. If the effect simulation result and the actual state are different from each other, the data is sent to the correction unit 17 in order to review the presented training plan.

  The correction unit 17 compares the information from the history database 15 with the post-training information input by the user operating the post-training information input unit 16, and corrects the effect simulation execution unit 12 and the training database 11 from the data. . By doing so, it is possible to perform practice plans and effect simulations that are more suited to the individual subject to practice.

  The function of the correction unit 17 will be described with reference to FIG. The comparison unit in the figure shows the functions of the data comparison unit 10 described above, and the training plan (primary) is created by comparing the model movements, physical fitness, and movements of the trainer before training To do. The effect simulator in the figure corresponds to the effect simulation execution unit 12 described above, and simulates the body shape, physical strength, and movement of the practitioner after a predetermined period from the training plan (primary). When you actually train, the body shape, physical strength, and movement of the trainer after training may be different from the simulation results. Therefore, in order to obtain a desired training effect, a more appropriate training plan (secondary) is created in consideration of the model motion, the body shape, physical strength, motion, etc. of the trainer before training. In addition, the parameter of the prediction calculation formula of the effect simulator is corrected so that the effect can be predicted accurately. The above processing may be repeated so that a third, fourth,... Training plan can be created.

  Most of the systems described above are provided on the server side. By using a network-compatible program such as Java (registered trademark) script, only the necessary information is transferred to the network according to the request from the user side. It becomes possible to deliver via. Therefore, it is possible to use advanced three-dimensional CG even if the storage capacity of the machine on the user side and the computing capacity are small. Further, through the network, there are no restrictions on the place and time for using the system, and when the data is updated on the server side, each user can obtain the latest data in real time.

  Here, the update and provision of the motion database 6 and the training database 11 are not limited to server operators, and data providers who can participate freely on the network can provide motion data and training data of dancers and athletes belonging to the company. You may do it. The motion database 6 and the training database 11 do not necessarily have to be installed at the location where the server is installed, and may be arranged at any location on the network as long as they can be used under the monitoring of the server. In this way, by widely recruiting providers of motion data, training data, and human body data, it becomes possible to quickly construct an environment where abundant data can be used.

  In this case, in order to keep the quality of the data high, when the data provided by the data provider is used by the user, the user is charged and all or part of the collected revenue is provided to the data provider. To pay. Specifically, the user is charged when using the motion database 6, the data comparison unit 10, the training database 11, and the effect simulation execution unit 12. In FIG. 1, the part with the symbol “¥” means that the user can select whether or not to use these functions. The server is provided with a billing data storage unit 18 that stores data for billing the user, and a payment data storage unit 19 that stores data for paying a price to the data provider. After a certain period of time has passed since the data provider registered the data, check the frequency of use of the data, and take measures such as deleting data that has been used infrequently. Is possible.

  FIG. 12 exemplifies an algorithm for examining the data usage frequency. When the user logs in to the server, the user is identified by the user ID and password. If the user is a data provider, when the exemplary moving image input unit 13 of FIG. 1 is operated, the motion data track unit 14 is operated to load the exemplary moving image into the motion database 6 and the use frequency of the exemplary moving image. An initial value (for example, 0 times) is written in. If the user is a person to be practiced, when the desired motion selection unit 1 in FIG. 1 is operated and the model moving image in the motion database 6 is used, the usage frequency of the model moving image is incremented by one. Thereby, the usage frequency can be counted for each model video.

  FIG. 13 shows a configuration example of the payment data storage unit 19. The time is counted by a timer of the server computer, and a timer interrupt is generated every time a fixed time elapses. When a timer interrupt occurs, the period counter is incremented by one. If the value of the period counter is less than the reference value, the interrupt is terminated. If the value of the period counter is equal to or greater than the reference value, the period counter is reset and one model moving image registered in the motion database 6 is selected. The usage frequency of the model video is acquired, payment data is calculated by a calculation formula of usage frequency × unit price, and the data provider is notified by email. If the usage frequency is less than the predetermined number of times, the model video is deleted from the motion database 6 and the data provider is notified by e-mail because the usage frequency is low. If the usage frequency is greater than or equal to the predetermined number, the usage frequency count value is reset. After the above process is repeated for all model videos, the interrupt is terminated.

  When a new model video is registered in the motion database 6 in the flow of FIG. 12, instead of setting the initial value of the usage frequency to 0, a predetermined number of times (> 0) that is not a deletion target in the flow of FIG. Is set as the initial value, model videos that have not yet been registered will not be deleted because they are not used frequently, and initial payment as a price for registration will be paid when a certain period of time has passed after registration. be able to.

  The configuration example of the billing data storage unit 18 that stores data for billing the subject is omitted from illustration, but when the user operates a button for using a specific function, the function is charged. And a function for adding the unit price of the function to the user's billing data when the user who sees the display operates a button for approving the billing. The Note that a function may be added in which the details of the billing data are sent to the user by month by email.

  The reward paid to the data provider or the usage fee collected from the subject of training may be a point that is valid on the network or in the real world.

  The illustrated algorithm is merely an example, and it goes without saying that an arbitrary algorithm can be used as long as an equivalent function can be achieved within the scope of the claims.

  The present invention is intended for use by a person engaged in the manufacture / sales of health machinery and rehabilitation equipment to predict / evaluate training effects for purchasers of its own products, and a person engaged in the manufacture / sales of various sporting goods. It can be used for a purpose of providing a service for acquiring a professional player's form for a purchaser or a service for providing a service for a person who operates a music distribution business to learn the choreography of a popular singer for a CD purchaser.

It is explanatory drawing which shows the structure of one Embodiment of this invention. It is explanatory drawing for demonstrating the outline | summary of the human body model production | generation part used by this invention. It is principle explanatory drawing of the human body model production | generation by the human body model production | generation part used by this invention. It is explanatory drawing for demonstrating the outline | summary of the motion data track part used by this invention. It is explanatory drawing for demonstrating the outline | summary of the data integration part used by this invention. It is explanatory drawing for demonstrating the outline | summary of the motion correction part used by this invention. It is explanatory drawing which shows the principal part structure of the motion correction part used by this invention. It is explanatory drawing for demonstrating the outline | summary of the data comparison part used by this invention. It is explanatory drawing which shows the function of the training database used by this invention. It is explanatory drawing which shows the outline | summary of the effect simulation execution part used by this invention. It is explanatory drawing which shows the function of the correction part used by this invention. It is operation | movement explanatory drawing of the model moving image input part used by this invention, and a desired operation | movement selection part. It is operation | movement explanatory drawing of the payment data memory | storage part used by this invention. It is explanatory drawing which shows the image of the interface of the display part used by this invention.

Explanation of symbols

DESCRIPTION OF SYMBOLS 1 Desired motion selection part 2 Body type data input part 3 Motion data input part 4 Human body model production | generation part 5 Motion data track part 6 Motion database 71 Data integration part 72 Data integration part 8 Display part 9 Motion correction part 10 Data comparison part 11 Training database DESCRIPTION OF SYMBOLS 12 Effect simulation execution part 13 Model moving image input part 14 Motion data track part 15 History database 16 Information input part after training 17 Correction part 18 Billing data storage part 19 Payment data storage part

Claims (8)

A human body model generation unit that generates a human body model based on personal information of physical characteristics such as height, weight, muscle strength, etc. of a person to be exercised;
A motion data track section for extracting and storing motion data from the exercise image of the person to be practiced,
A data integration unit for generating an action of the human body model based on the data of the human body model generation unit and the motion data track unit;
A motion database that stores exemplary actions;
A desired motion selection unit for selecting data of the motion database;
An operation training display system comprising: video data of the data integration unit and a display unit for displaying video data of the motion database. The motion training display system according to claim 1, further comprising a motion correction unit that corrects the data of the motion database based on information of the human body model generation unit. 3. The motion training display system according to claim 1, further comprising a data comparison unit that compares data of the data integration unit and data of a motion database. 4. The training database according to claim 3, comprising a training database that associates the data of the comparison result by the data comparison unit with the training plan, searches the training database according to the data of the comparison result, and displays the training plan output from the training database. An operation training display system characterized by displaying in a department. 5. The apparatus according to claim 4, further comprising an effect simulation execution unit that creates training effect prediction data from training plan data output from the training database and personal information of a person to be practiced, and displays the effect prediction data on the display unit. A motion training display system characterized by that. The history database according to claim 1, further comprising: a history database that stores data output from the data integration unit as a history and outputs the history to the data comparison unit when the history is selected from the desired operation selection unit. A motion training display system characterized by that. The training database and the effect simulation execution according to claim 5 or 6, wherein the training information input unit for inputting the personal information or the exercise image of the training subject after training, and comparing the input training information with the effect prediction data An operation training display system comprising a correcting unit for correcting the unit. The motion training display system according to any one of claims 1 to 7, further comprising a body data input unit, a motion data input unit, and a display unit of a subject to be practiced on a client side, and the others on a server side. .

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