Information processing device, information processing method, and program

Abstract

The present invention achieves an information processing device capable of improving the play of an athlete by feeding back movements of the athlete. The information processing device comprises: an acquisition means for acquiring input data including at least one among image data, voice data, or tactile data; and a generation means for generating, with reference to the input data, control data to be supplied to one or a plurality of tactile devices worn by a player.

Description

Information processing device, information processing method, and program

[0001] The present disclosure relates to an information processing device, an information processing method, and a program.

[0002] There is known a technique for providing feedback on a user's actions via a device used by the user. For example, Patent Literature 1 describes a technique for providing feedback on the user's movements at a location where the device is worn by the user via the device.

[0003] Japan Special Table No. 2020-504883

[0004] In the technology described in Patent Document 1, only the user's movements at the location where the device is worn are the subject of feedback. For example, the movements of an athlete in a sport can be more effectively understood by using images or audio. However, this technology cannot include information related to images or audio as feedback, which limits its contribution to improving the athlete's play.

[0005] The present disclosure has been made in consideration of the above-mentioned problems, and one exemplary purpose thereof is to provide a technology that improves an athlete's play by providing feedback on the athlete's movements.

[0006] An information processing device according to an exemplary aspect of the present disclosure includes an acquisition means for acquiring input data including at least one of image data, audio data, and tactile data, and a generation means for generating control data to be supplied to one or more tactile devices worn by an athlete by referring to the input data.

[0007] An information processing method according to an exemplary aspect of the present disclosure includes an information processing device acquiring input data including at least one of image data, audio data, and tactile data, and generating control data to be supplied to one or more tactile devices worn by an athlete by referring to the input data.

[0008] A program according to one exemplary aspect of the present disclosure is a program that causes a computer to function as an information processing device, the program causing the computer to acquire input data including at least one of image data, audio data, and tactile data, and to generate control data by referring to the input data to be supplied to one or more tactile devices worn by the athlete.

[0009] According to one exemplary aspect of the present disclosure, an exemplary effect is achieved in that a technique can be provided that improves an athlete's play by providing feedback on the athlete's movements.

[0010] FIG. 1 is a block diagram showing a configuration of an information processing device according to the present disclosure. FIG. 2 is a flow diagram showing a flow of an information processing method according to the present disclosure. FIG. 3 is a block diagram showing a configuration of an information processing system according to the present disclosure. FIG. 4 is a block diagram showing an application example of the information processing system according to the present disclosure. FIG. 5 is a block diagram showing an application example of the information processing system according to the present disclosure. FIG. 6 is a diagram for explaining an example of processing by an information processing device according to the present disclosure. FIG. 7 is a diagram for explaining an example of processing by an information processing system according to the present disclosure. FIG. 8 is a diagram for explaining an example of processing by an information processing system according to the present disclosure. FIG. 9 is a block diagram showing a configuration of a computer functioning as an information processing device according to the present disclosure.

[0011] The following are examples of embodiments of the present invention. However, the present invention is not limited to the exemplary embodiments shown below, and various modifications are possible within the scope of the claims. For example, embodiments obtained by appropriately combining the technical means employed in the exemplary embodiments shown below may also be included in the scope of the present invention. Furthermore, embodiments obtained by appropriately omitting some of the technical means employed in the exemplary embodiments shown below may also be included in the scope of the present invention. Furthermore, the effects mentioned in the exemplary embodiments shown below are examples of effects expected in the exemplary embodiments, and do not define the scope of the present invention. In other words, embodiments that do not exhibit the effects mentioned in the exemplary embodiments shown below may also be included in the scope of the present invention.

[0012] [First Exemplary Embodiment] A first exemplary embodiment, which is an example of an embodiment of the present invention, will be described in detail with reference to the drawings. This exemplary embodiment is a basic form for each of the exemplary embodiments described below. Note that the scope of application of each technical means employed in this exemplary embodiment is not limited to this exemplary embodiment. That is, each technical means employed in this exemplary embodiment can also be employed in other exemplary embodiments included in the present disclosure, to the extent that no particular technical obstacles arise. Furthermore, each technical means shown in the drawings referenced to explain this exemplary embodiment can also be employed in other exemplary embodiments included in the present disclosure, to the extent that no particular technical obstacles arise.

[0013] (Configuration of information processing device 1) The configuration of the information processing device 1 according to this exemplary embodiment will be described with reference to Fig. 1. Fig. 1 is a block diagram showing the configuration of the information processing device 1. As shown in Fig. 1, the information processing device 1 includes an acquisition unit 11 and a generation unit 12.

[0014] (Acquisition Unit 11) The acquisition unit 11 acquires input data including at least one of image data, audio data, and tactile data. The tactile data may be, for example, data provided by a tactile sensor capable of detecting contact with an object, and may be data relating to the contact of an object with the tactile sensor.

[0015] (Generation Unit 12) The generation unit 12 generates control data to be supplied to one or more haptic devices worn by the athlete, by referring to the input data acquired by the acquisition unit 11. The haptic device may be, for example, a device that includes components used for driving and generates a motion that can be recognized by the athlete wearing the device through the sense of touch. The control data may be, for example, data used to control the haptic device. Furthermore, the haptic device may further include, for example, a tactile sensor that supplies tactile data to the acquisition unit 11.

[0016] (Effects of Information Processing Device 1) As described above, the information processing device 1 is configured to acquire input data including at least one of image data, audio data, and haptic data, and generate control data to be supplied to one or more haptic devices worn by the athlete by referring to the input data. Therefore, the information processing device 1 has the effect of improving the athlete's play by providing feedback on the athlete's movements.

[0017] (Flow of Information Processing Method S1) Next, the flow of information processing method S1 according to this exemplary embodiment will be described with reference to Fig. 2. Fig. 2 is a flow diagram showing the flow of information processing method S1. As shown in Fig. 2, information processing method S1 includes an acquisition process (acquisition step) S11 and a generation process (generation step) S12. Information processing method S1 is executed, as an example, in the information processing device 1 described above.

[0018] (Step S11) In step S11, the acquisition unit 11 of the information processing device 1 acquires input data including at least one of image data, audio data, and tactile data.

[0019] (Step S12) In step S12, the generation unit 12 of the information processing device 1 generates control data to be supplied to one or more haptic devices worn by the athlete, by referring to the input data acquired by the acquisition unit 11 in step S11.

[0020] (Effects of Information Processing Method S1) As described above, information processing method S1 is configured to acquire input data including at least one of image data, audio data, and haptic data, and generate control data to be supplied to one or more haptic devices worn by the athlete by referring to the input data. Therefore, information processing method S1 has the effect of improving the athlete's play by providing feedback on the athlete's movements.

[0021] Second Exemplary Embodiment A second exemplary embodiment, which is an example of an embodiment of the present invention, will be described in detail with reference to the drawings. Components having the same functions as those described in the above exemplary embodiment will be denoted by the same reference numerals, and their description will be omitted as appropriate. The scope of application of each technical means employed in this exemplary embodiment is not limited to this exemplary embodiment. That is, each technical means employed in this exemplary embodiment can also be employed in other exemplary embodiments included in the present disclosure, to the extent that no particular technical hindrance occurs. Furthermore, each technical means shown in each drawing referenced to describe this exemplary embodiment can also be employed in other exemplary embodiments included in the present disclosure, to the extent that no particular technical hindrance occurs.

[0022] (Problems with the Prior Art) First, we will explain the problems with the prior art. For example, volleyball training is conducted through the coach's movements, and skill instruction is limited to visual and auditory interaction. However, volleyball is a sport that emphasizes the strength of each individual player. Therefore, while endurance, power conversion, and pitching are essential, instruction is limited to information via audio and visual means. There is no data recorded in real time regarding the transmission of ball force or pitching (hitting). Therefore, it remains uncertain whether a player's technique is optimal and sufficient to win a game.

[0023] There is no exact measure of force or other tactile or tactile parameters that are important in a volleyball game. Throwing (hitting) the ball harder can result in higher scores and potentially win the game. However, identifying the boundaries of such physical performance has been difficult, making a complete analysis impossible.

[0024] Overview of the Present Exemplary Embodiment Training a volleyball team is challenging due to the speed and dynamics of each player and the team as a whole. The coach of such a team aims to improve the team's endurance and increase their chances of winning matches, and he or she wants to utilize technology to help achieve this goal. However, opposing teams are similarly thinking and trying to win matches, making the competition intense and players quickly becoming fatigued. Furthermore, training is intense, and finding optimal preparation and motivation is not easy. Currently, coaches are single-player and must determine strategies and methods on their own without additional data or assistance. As a result, training and preparing for the next match are challenging and time-consuming. In addition, players often make decisions on their own to improve their performance, which sometimes leads to injuries or missed shots. Therefore, neither the coach nor the team may know what to improve in training to win the next match. Changing strategies or rotating players is possible, but these decisions are often biased and based on a lack of evidence or data. Furthermore, opposing teams sometimes successfully implement strategies that are difficult to understand. To paraphrase the above, lack of data and evidence makes it difficult to identify optimal strategies and training preparations.

[0025] The configuration according to this exemplary embodiment may, for example, involve simultaneously recording multimodal data consisting of audio, video, tactile, etc., analyzing it using AI-based technology, and providing real-time tactile feedback to the athlete via a haptic device.

[0026] (Configuration of Information Processing System 100A) Next, the configuration of the information processing system 100A according to this exemplary embodiment will be described with reference to FIGS. 3 to 5. FIG. 3 is a block diagram showing the configuration of the information processing system 100A. As shown in FIG. 3, the information processing system 100A includes, as an example, an information processing device 1A, an information processing device 2A, a camera 16, a microphone 17, a haptic device 18, and an input / output device 19. Note that, as an example, the information processing device 2A has the same configuration as the information processing device 1A described below.

[0027] (Camera 16) The camera 16 captures an image of a subject and supplies image data including the subject in its angle of view to the information processing device 1A or other devices included in the information processing system 100A. As an example, the camera 16 may be composed of one or more cameras installed in the competition area. For example, the camera 16 may be composed of a front camera that captures images of the competition area from the front, a rear camera that captures images of the competition area from the opposite side to the front camera, and other cameras that capture images of important locations in the competition area.

[0028] The camera 16 may also include a camera attached to the body of an athlete, coach, or referee. As an example, the camera 16 may include a camera attached to or above the head of an athlete, coach, or referee. Such a camera is suitable for capturing images of the entire playing area, and by acquiring image data from such a camera, the information processing device 1A can record and analyze the entire playing area.

[0029] (Microphone 17) The microphone 17 collects sounds in the target area and supplies sound data containing the sounds to the information processing device 1A included in the information processing system 100A or other devices. As an example, the microphone 17 may be composed of one or more microphones installed in the competition area. For example, the microphone 17 may be composed of a front microphone that collects sounds in the competition area from the front, a back microphone that collects sounds in the competition area from the opposite side of the front microphone, and other microphones that collect sounds in important locations in the competition area.

[0030] The microphone 17 may also include a microphone attached to the body of an athlete, coach, or referee. As an example, the microphone 17 may include a microphone attached to the head of an athlete, coach, or referee. Such a microphone is suitable for collecting sounds from a specific subject, and by acquiring sound data from such a microphone, the information processing device 1A can suitably perform recording and analysis related to the subject.

[0031] (Haptic device 18) The haptic device 18 is, for example, a device that includes components used for driving and generates an action that can be recognized by an athlete or the like wearing the device through the sense of touch. Here, the control data supplied to the haptic device 18 from the information processing device 1A included in the information processing system 100A or another device may be data used to control the haptic device 18.

[0032] The haptic device 18 may be attached to the body of an athlete, etc. Specifically, the haptic device 18 may be attached to an arm, hand, leg, finger, footwear, etc. of the athlete, etc.

[0033] As an example, the tactile device 18 further includes a tactile sensor capable of detecting contact with an object, and supplies tactile data, which is data relating to the contact of an object with the tactile sensor, to the information processing device 1A or other device included in the information processing system 100A.

[0034] Such tactile devices are suitable for detecting objects that come into contact with athletes, etc., and by acquiring tactile data from such tactile devices, the information processing device 1A can suitably perform recording and analysis of the athletes, etc.

[0035] As an example, the component used to drive the haptic device 18 may be a motor that generates vibrations. The control data supplied to the haptic device 18 may include, for example, information regarding the pattern of an electrical signal that drives a motor or the like provided in the haptic device 18. Furthermore, the control data supplied to the haptic device 18 may include, for example, information regarding a vibration pattern generated by a motor or the like provided in the haptic device 18. Furthermore, the vibration pattern may include, for example, information transmitted from the information processing device 1A to the athlete via the haptic device 18.

[0036] Such a tactile device is suitable for generating actions that can be recognized by athletes and the like through their sense of touch, and by supplying control data to such a tactile device, the information processing device 1A can suitably transmit information to the athletes and the like through their sense of touch.

[0037] (Input / output device 19) The input / output device 19 receives instructions from a user, supplies information indicating the received instructions to the information processing device 1A or other devices, and presents information obtained from the information processing device 1A or other devices to the user.

[0038] As an example, the input / output device 19 is configured to include at least one of input / output devices such as a keyboard, a mouse, a display, a printer, and a touch panel. For example, the input / output device 19 visually presents information acquired from the information processing device 1A or other devices to the user via a touch panel. The input / output device 19 may also be configured to display a GUI (Graphical User Interface) on the touch panel and acquire instructions from the user as input to the GUI.

[0039] Furthermore, the input / output device 19 may be configured to include, for example, a head-mounted display or an eyeglass-type display, and to present information acquired from the information processing device 1A or another device to the user as virtual reality or mixed reality by displaying the information on the head-mounted display or eyeglass-type display. More specifically, the input / output device 19 may be configured to present a digital copy of the athlete generated by the generation unit 12 (described later) as virtual reality or mixed reality.

[0040] 3, the information processing device 1A includes a control unit 10A, a storage unit 14A, and a communication unit 15A. The information processing device 1A may also include a configuration equivalent to the input / output device 19 described above.

[0041] (Communication Unit 15A) The communication unit 15A communicates with devices external to the information processing device 1A. As an example, the communication unit 15A communicates with the camera 16, the microphone 17, the haptic device 18, the input / output device 19, and the information processing device 2A. The communication unit 15A transmits data supplied from the control unit 10A to the input / output device 19 and the information processing device 2A, and supplies data received from the camera 16, the microphone 17, the haptic device 18, the input / output device 19, and the information processing device 2A to the control unit 10A.

[0042] (Memory Unit 14A) The memory unit 14A stores various types of data referenced by the control unit 10A and various types of data generated by the control unit 10A. As an example, the memory unit 14A stores the following: Input data IN referenced by the control unit 10A Athlete movement data PA referenced by the control unit 10A Analysis results RR, which are an example of the results of processing by the control unit 10A Library data LI referenced by the control unit 10A Control data COD, which are an example of the results of processing by the control unit 10A Presentation data DD, which are an example of the results of processing by the control unit 10A Learned model LM used in processing by the control unit 10A.

[0043] For example, the input data IN may be multimodal data including at least one of image data IM, audio data AU, and haptic data HAP. Furthermore, the input data IN may include at least one of image data captured by the camera 16, audio data collected by the microphone 17, and haptic data detected by the haptic device 18. The image data IM, audio data AU, and haptic data HAP included in the input data IN may be synchronized with each other in real time, for example. Here, for example, at least one of the image data IM, audio data AU, and haptic data HAP included in the input data IN may include information regarding the movements of one or more athletes.

[0044] The image data IM may be, for example, data representing an image including people such as athletes or other objects present in the competition area and its surroundings within the field of view. The image data IM may also be, for example, data captured by the camera 16.

[0045] The audio data AU may be, for example, data including sounds generated by athletes in and around the competition area. The audio data AU may also be, for example, sounds collected by the microphone 17.

[0046] The tactile data HAP is, for example, data relating to contact of an object with a tactile sensor capable of detecting contact of an object. Here, the tactile sensor may, for example, be provided in a tactile device 18 worn by an athlete or the like.

[0047] The athlete movement data PA is, for example, data indicating information about the movements of one or more athletes included in at least one of the image data IM, the audio data AU, and the haptic data HAP in the input data IN. For example, the athlete movement data PA may be data indicating the results of the generation unit 12 detecting the movements of one or more athletes in the input data using a machine learning model (for example, a learned model LM).

[0048] The analysis result RR includes information indicating the results of the analysis process performed by the generation unit 12, which will be described later. As an example, the analysis result RR may include the results of an analysis process of the performance of at least one of one or more athletes, with reference to information related to the movements of one or more athletes in the input data. In addition, as an example, the analysis process is performed on each of multiple areas (area 1, area 2, ...) in the competition area indicated by the image data (not shown).

[0049] As will be described later, the analysis result RR may also include gap information GI. Here, the gap information GI is, for example, information including: information regarding the difference between the athlete's performance and the comparison performance.

[0050] The library data LI may be, for example, a library of training data for one or more athletes. For example, the library data LI may include information on the improvement of the athletes' skills in each training item.

[0051] The control data COD is, for example, data used to control the operation of the haptic device 18. For example, the control data COD may include information regarding a vibration pattern generated by a motor or the like provided in the haptic device 18. Furthermore, the vibration pattern may include, for example, information transmitted from the information processing device 1A to the athlete via the haptic device 18. Here, the control data COD may include, for example, information regarding the pattern of an electrical signal that vibrates a motor or the like provided in the haptic device 18.

[0052] The presentation data DD may be, for example, data for presenting a digital copy of the athlete in virtual reality or mixed reality, where the digital copy may be, for example, a data set that represents (or simulates) the athlete's movements in two or three dimensions.

[0053] As an example, the model LM has a plurality of parameters learned based on a deep learning algorithm and detects the actions of one or more players in the input data IN.

[0054] (Control Unit 10A) As shown in FIG. 3, the control unit 10A includes an acquisition unit 11, a generation unit 12, and an output unit 13.

[0055] (Acquisition Unit 11) The acquisition unit 11 acquires the above-mentioned input data IN. The acquisition unit 11 may also acquire, as the input data IN, at least one of image data IM captured by the camera 16, audio data AU collected by the microphone 17, and haptic data HAP detected by the haptic device 18. The acquisition unit 11 may also be configured to acquire instructions from the user input via the input / output device 19.

[0056] (Generation Unit 12) The generation unit 12 generates control data COD to be supplied to one or more haptic devices 18 worn by the athlete, by referring to the input data IN. As described above, for example, the control data COD may include information regarding a vibration pattern generated by a motor or the like provided in the haptic device 18. Furthermore, the vibration pattern may include, for example, information transmitted from the information processing device 1A to the athlete via the haptic device 18.

[0057] For example, the generation unit 12 may generate the control data COD in accordance with the actions of one or more athletes included in the input data IN. Here, the generation unit 12 may generate the control data COD as feedback for the actions of one or more athletes. The information processing device 1A having a feedback loop will be described later with reference to FIG. 6.

[0058] Furthermore, as an example, the generation unit 12 may detect the movements of one or more athletes in the input data IN using a machine learning model (for example, a learned model LM) and generate the control data COD by referring to the detection results. Here, the specific algorithm used by the generation unit 12 to detect the movements of the athletes is not limited to this exemplary embodiment, but as an example, the process of detecting the movements of the athletes may use an algorithm based on machine learning.

[0059] Furthermore, the generation unit 12 may generate the control data COD by referring to library data LI, which is a library related to the training of one or more athletes. The control data COD generated by the generation unit 12 by referring to the library data LI may contribute to improving the athletes' skills in each training item, for example. The generation of the control data COD by referring to the library data LI will be described later with reference to FIGS. 7 and 8.

[0060] The generation unit 12 may also refer to the control data COD to generate presentation data DD for presenting a digital copy of the athlete in virtual reality or mixed reality. Here, the digital copy is, for example, a data set that represents (or simulates) the athlete's movements in two or three dimensions, and is presented as virtual reality or mixed reality via the input / output device 19.

[0061] As shown in FIG. 3, the generating unit 12 also includes an analyzing unit 12-1.

[0062] (Analysis unit 12-1) The analysis unit 12-1 performs an analysis process by referring to information relating to the movements of one or more athletes in the input data. As an example, the analysis unit 12-1 performs an analysis process by referring to information relating to the movements of one or more athletes in the input data IN, and analyzes the performance of at least one of the one or more athletes. Here, the generation unit 12 may generate control data COD by referring to the analysis results by the analysis unit 12-1.

[0063] The analysis results by the analysis unit 12-1 may also include information regarding the difference between the athlete's performance and a comparison performance. Here, the comparison performance may be the athlete's ideal performance, or a provisional performance aimed at approaching the ideal performance.

[0064] As an example, the performance for comparison may be configured to be set in advance by a user such as a coach or trainer by referring to: - multiple past performances of the athlete - multiple past performances of the team to which the athlete belongs, etc. Alternatively, a learning process may be applied to the above-mentioned model LM or other machine learning model by referring to: - multiple past performances of the athlete - multiple past performances of the team to which the athlete belongs, and the learned model may output the performance for comparison.

[0065] The analysis results by the analysis unit 12-1 may also include information to support the coach's decision-making regarding each of one or more athletes.

[0066] Here, the information to support the coach's decision-making for each of one or more athletes may be output by a machine-learned model. As an example, the above-mentioned model LM or another machine-learning model may be trained in advance by referring to: - multiple past performances of the athlete - multiple past performances of the team to which the athlete belongs - multiple past performances of the team the athlete is currently competing against, etc., and the trained model may be input with: - the athlete's current performance - multiple current performances of the team to which the athlete belongs - multiple current performances of the team the athlete is currently competing against, etc., thereby outputting information to support the coach's decision-making for each of the one or more athletes.

[0067] The analysis results by the analysis unit 12-1 may also include items that the athlete should improve upon. For example, such items may be generated by a process similar to or identical to the process for generating information to support coach decision-making described above.

[0068] (Output unit 13) The output unit 13 outputs the results of the analysis process performed by the generation unit 12. More specifically, the output unit 13 supplies the results of the analysis process performed by the generation unit 12 to the input / output device 19 and controls the input / output device 19 to output the results. The output unit 13 also stores the results of the analysis process performed by the generation unit 12 in the storage unit 14A.

[0069] The output unit 13 outputs, for example, the virtual reality or mixed reality indicated by the presentation data. As partially described above, the presentation data DD generated by the generation unit 12 may be presented as the virtual reality or mixed reality via a touch panel, a head-mounted display, or an eyeglass-type display, for example.

[0070] (Information Processing Device 2A) As an example, the information processing device 2A has a configuration similar to that of the information processing device 1A described above. However, the user of the information processing device 2A may be different from the user of the information processing device 1A. As an example, the information processing device 1A may be installed on-premise (for example, in a facility having a competition area), and the information processing device 2A may be installed remotely (in a remote location). When grounded in this way, the information processing device 1A can exchange information with athletes, teams, coaches (including managers and trainers), referees, etc. participating in the competition, and the information processing device 2A can exchange information with other coaches (including managers and trainers), other referees, etc. located in remote locations.

[0071] (Application Example 1 of Information Processing System 100A) Next, Application Example 1 of the information processing system 100A will be described with reference to FIG. 4 . FIG. 4 is a diagram illustrating Application Example 1 of the information processing system 100A. As shown in FIG. 4 , in this example, at least two cameras (16-1, 16-2) and at least two microphones (17-1, 17-2) are installed in the competition area CA. In addition, in this example, at least two haptic devices (18-1, 18-2) are attached to the players competing in the competition area CA. As shown in FIG. 4 , the competition area CA is composed of, for example, an area CA1 where player P1 plays and an area CA2 where player P2 plays. As shown in FIG. 4 , area CA1 is composed of three areas, and area CA2 is also composed of three areas. Each of these multiple areas is identified by the generation unit 12 described above, and each area is associated with the results of analysis of the players in that area.

[0072] 4, an input / output device 19 is placed on the premises and used by an operator or coach who is a user on the premises. The user can input instructions to the information processing device 1A and view the results of the analysis performed by the information processing device 1A via the input / output device 19.

[0073] 4, the information processing device 2A is placed in a remote location and used by an operator or coach who is a user in the remote location. The user can input instructions to the information processing device 2A or the information processing device 1A and view the results of the analysis performed by the information processing device 2A or the information processing device 1A.

[0074] Note that communication between the devices constituting information processing system 100A is performed via a wide area network, such as Tactile Internet, 5G, Beyond 5G, or 6G, for example. In such a configuration, there is almost no communication delay, and even a coach in a remote location can check the analysis results in real time, allowing instructions to be given to the athlete or team in real time and the analysis results to be stored in a database (e.g., storage unit 14A) for future evaluation.

[0075] (Application Example 2 of Information Processing System 100A) Next, Application Example 2 of the information processing system 100A will be described with reference to FIG. 5 . FIG. 5 is a diagram showing Application Example 2 of the information processing system 100A. As shown in FIG. 5 , in this example, the camera 16 includes a front camera ("Front" in the group of cameras shown in FIG. 5 ), a rear camera ("Back" in the group of cameras shown in FIG. 5 ), and side cameras ("Side A" and "Side B" in the group of cameras shown in FIG. 5 ). The microphones 17 include a front microphone ("Front" in the group of microphones shown in FIG. 5 ), a rear microphone ("Back" in the group of microphones shown in FIG. 5 ), and side microphones ("Side A" and "Side B" in the group of microphones shown in FIG. 5 ). The haptic devices 18 include haptic devices worn by each player ("Player A," "Player B," "Player C," and "Player D" in the group of haptic devices shown in FIG. 5 ).

[0076] The video data captured by the cameras, the sound data collected by the microphones, and the haptic data detected by the haptic devices constitute input data IN. The input data IN is input to a trained model LM (AI in FIG. 5 ) in the generation unit 12 of the information processing device 1A (or 2A). An operator or coach, who is the user of the information processing device 1A (or 2A), inputs instructions to the information processing device 1A (or 2A) via the input / output device 19 (GUI in FIG. 5 ) and visually views the analysis result information output by the information processing device 1A (or 2A). As described above, the analysis results may include, for example, the athlete's performance, information to support the coach's decision-making, and areas for improvement for the athlete.

[0077] In this example, as described above, the camera 16 is configured with an array of multiple cameras, including a front camera, a rear camera, and side cameras. Images captured by these cameras are then recorded. The generation unit 12 can track and record the movements of each player (athlete) by referencing these images. Furthermore, the generation unit 12 can track and record the coordination of the entire team by analyzing the image data acquired by the camera 16. It is also preferable to install cameras on the court in order to observe the entire court at once.

[0078] In this example, as described above, the microphone 17 is configured as an array of multiple microphones, including front microphones, back microphones, and side microphones. Sounds collected by these microphones are recorded. The coordination and conversations of the players (athletes) are recorded for real-time analysis and future use. In this way, referring to audio data in addition to video data is advantageous for the information processing device 1A to analyze the real-time coordination of the entire team and for coaches to analyze team performance.

[0079] In this example, as described above, the haptic device 18 is configured by a combination of multiple haptic input devices. Each player (athlete) can wear these haptic input devices on their arms, hands, legs, fingers, footwear, etc. These haptic input devices can record haptic data related to swing, force, angle, movement, and other tactile sensations. This haptic data may be used, for example, to improve team coordination. For example, in volleyball training, ball handling, ball pitching, ball blocking, and player positioning can be analyzed and optimized to improve the transfer of force to the ball. The haptic data includes data related to the force and torque of each player's ball handling, and referencing this data is useful for coaches to observe players and fine-tune their movements.

[0080] (Processing Example 1 by Information Processing Device 1A) FIG. 6 is a diagram showing Processing Example 1 by the information processing device 1A. More specifically, FIG. 6 is a diagram showing a processing example when the information processing device 1A is equipped with a feedback loop. As shown in FIG. 6, the information processing device 1A may be, for example, a tactile training system equipped with a feedback loop. TR in FIG. 6 is a start / stop sequence triggered by a coach, for example, when the coach requests assistance from AI in the information processing device 1A. As shown in FIG. 6, when the coach requests the start of processing in sequence TR, the acquisition unit 11 acquires input data IN. As described above, the input data IN may include information regarding the actions of one or more athletes.

[0081] Next, in FIG. 6 , the process proceeds to processing in each of the generation unit 12 and the analysis unit 12-1. As an example, the generation unit 12 may detect the movements of one or more athletes in the input data IN using a machine learning model. As a specific example, in FIG. 6 , the generation unit 12 executes a pose estimation process PE and a tile identification process TD for the athletes using a machine learning model LM, respectively, to detect the athletes' movements. The tiles in the tile identification process TD may be, for example, each of multiple areas defined by lines or the like in the competition area where the athletes compete. In the case of the competition area CA in the example of FIG. 4 , the tiles may be each of the three areas that make up area CA1 and each of the three areas that make up area CA2. Furthermore, the tile identification process TD may be a process for identifying tiles that make up the competition area. The pose estimation process PE and the tile identification process TD described above may each use, for example, known techniques.

[0082] As one example, the analysis unit 12-1 may execute an analysis process of the performance of at least one or more athletes by referring to information about the movements of the one or more athletes in the input data IN. Here, the analysis unit 12-1 may execute an analysis process by referring to at least one of the information about the movements of the athletes in the input data IN acquired by the acquisition unit 11 and the detection results of the movements of the athletes in the input data IN detected by the generation unit 12 using a machine learning model.

[0083] Furthermore, the analysis unit 12-1 may execute a performance analysis process for each action included in the action group PA in a sport. The actions included in the action group PA in a sport may be, for example, actions related to skills in the sport. Specific examples of actions included in the action group PA in a sport include serve, dig, set, spike, block, rotation, etc., as shown in FIG. 6 . Here, for example, the coach may use the AI ​​assistant to decide which action of the actions included in the action group PA in a sport to focus on or which actions to analyze in parallel.

[0084] Next, FIG. 6 illustrates processing in the tactile training system TS. The tactile training system TS may be set to either enabled (YES) or disabled (NO), for example, depending on the user's settings. If the tactile training system TS is enabled (YES), the generation unit 12 may generate the control data COD, for example, by referring to the analysis results of the analysis unit 12-1. At this time, the athlete may train the relevant body part via a haptic device 18 worn on the body. The information processing device 1A may also respond to the athlete's movements via tactile feedback to the athlete's body, for example, to improve swing, rotation, or power transmission. Specific examples of body parts to be trained include the hand (HND) or arm (ARM), as shown in FIG. 6. Examples of tactile training systems for the hand (HND) or arm (ARM) will be described below with reference to FIG. 7 or FIG. 8, respectively. If the tactile training system TS is disabled (NO), the information processing device 1A may, for example, provide visual or auditory feedback to the athlete of the analysis results from the analysis unit 12-1.

[0085] The athlete's response RES to the above-mentioned feedback may be recorded as part of the input data IN after undergoing analysis processing AN, for example. The analysis processing AN may improve the assistance provided by the AI ​​and fine-tune the training, for example. Note that the analysis processing AN may be executed by, for example, a configuration such as the analysis unit 12-1 in the information processing device 1A, or a configuration in another device.

[0086] (Processing Example 1 by Information Processing System 100A) FIG. 7 is a diagram showing processing example 1 by the information processing system 100A. More specifically, FIG. 7 is a diagram showing an example of processing by a tactile training system for an athlete's hands. In the example of FIG. 7, the hand tactile training system TS provided in the information processing device 1A (or 2A) may include settings related to training sessions for one or more athletes. The settings related to training sessions for one or more athletes in the example of FIG. 7 may define a training library, which will be described later. Furthermore, the settings related to the training sessions may be related to, for example, a training session TR or a self-practice session SP. A training session TR may be related to, for example, the movements of multiple athletes. A self-practice session SP may be related to specialized training for one skill of one athlete. A specific example of a self-practice session SP is practice using the hands to improve pitching or blocking.

[0087] In the example of FIG. 7 , the haptic training system TS is connected to a hand-related haptic device 18 via a controller CON. The configuration of the controller CON may be included in the generation unit 12, for example. The controller CON may generate the control data COD by, for example, referencing libraries related to the training of one or more athletes. As specific examples of libraries referenced by the controller CON, FIG. 7 shows a warm-up haptic library LIW and a practice haptic library LIP. The warm-up haptic library LIW may relate to, for example, training of at least one of the left hand, right hand, fingers, and thumb. The practice haptic library LIP may relate to, for example, training of at least one of the serve, dig, set, spike, and block.

[0088] The hand-related haptic device 18 may include, for example, a left-hand glove HAPL, a left-hand controller HAPCL, a right-hand glove HAPR, and a right-hand controller HAPCR, as shown in Fig. 7 . The left-hand glove HAPL and the right-hand glove HAPR may include, for example, vibrotactile motors. The left-hand controller HAPCL and the right-hand controller HAPCR may supply signals related to the control of the vibrotactile motors included in the left-hand glove HAPL and the right-hand glove HAPR, for example. In the example of Fig. 7 , the left-hand glove HAPL and the right-hand glove HAPR include vibrotactile motors shown in black at positions corresponding to the fingertips and the palm, respectively.

[0089] 7 , the controller CON may generate control data COD by referencing at least one of a practice haptic library LIP and a warm-up haptic library LIW. Furthermore, the controller CON may, for example, supply the generated control data COD to a vibratory haptic motor provided in each of the left-hand glove HAPL and the right-hand glove HAPR via the left-hand controller HAPCL and the right-hand controller HAPCR. The vibratory haptic motor may, for example, be driven in accordance with the supplied control data COD to generate vibrations, thereby transmitting tactile information to the athlete wearing the haptic device 18.

[0090] (Processing Example 2 by Information Processing System 100A) FIG. 8 is a diagram showing processing example 2 by the information processing system 100A. More specifically, FIG. 8 is a diagram showing a processing example of a tactile training system for an athlete's arm. In the example of FIG. 8, the arm tactile training system TS provided in the information processing device 1A (or 2A) may include settings related to training sessions of one or more athletes, similar to the hand tactile training system TS in the example of FIG. 7. The settings related to training sessions of one or more athletes in the example of FIG. 8 are similar to those described with reference to FIG. 7, and therefore will not be described here.

[0091] In the example of FIG. 8 , the haptic training system TS is connected to the arm-related haptic device 18 via a controller CON. The configuration of the controller CON may be included in the generation unit 12, for example. The controller CON may generate the control data COD by, for example, referencing a library related to training for one or more athletes. As specific examples of libraries referenced by the controller CON, FIG. 8 shows a haptic library LIS related to strategy training and a haptic library LIT related to team play. The haptic library LIS related to strategy training may be related to training for one athlete, two athletes, or three or more athletes. The haptic library LIT related to team play may be related to training for at least one of defense, offense, arm swing, and blocking, for example.

[0092] The arm-related haptic device 18 may include, for example, a vibrotactile motor HAPM attached to a location via an arm band, and a left-arm or right-arm controller HAPC, as shown in Fig. 8. The left-arm or right-arm controller HAPC may, for example, supply a signal related to the control of the vibrotactile motor HAPM.

[0093] 8 , the controller CON may generate control data COD by referencing at least one of a haptic library LIS related to strategy training and a haptic library LIT related to team play. Furthermore, the controller CON may, for example, supply the generated control data COD to the vibrotactile motor HAPM via the left-arm or right-arm controller HAPC. The vibrotactile motor HAPM may, for example, be driven in accordance with the supplied control data COD to generate vibrations, thereby transmitting tactile information to the athlete wearing the haptic device 18.

[0094] (Processing Example 3 by Information Processing System 100A) FIG. 9 is a diagram illustrating Processing Example 3 by the information processing system 100A. More specifically, FIG. 9 is a diagram illustrating an example of output of virtual reality in the information processing system 100A. As described above with reference to FIG. 3 , the generation unit 12 may, for example, refer to the control data COD to generate presentation data DD for presenting a digital copy of a player as virtual reality or mixed reality. At this time, the output unit 13 may, for example, output the virtual reality or mixed reality indicated by the presentation data DD. Furthermore, the output unit 13 may, for example, output the virtual reality or mixed reality indicated by the presentation data DD via the input / output device 19, as shown in FIG. 9 . In the example of FIG. 9 , a virtual image representing the presence of a player wearing gloves in a room is displayed on the input / output device 19. The virtual reality or mixed reality indicated by the presentation data DD may include, for example, a scenario in which the player is present. Specifically, the scenario in which the player is present may be, for example, related to a match, or may include information regarding the actions of other team members. For example, the output unit 13 may present the virtual reality or mixed reality indicated by the presentation data DD to the player through at least one of the visual, auditory, and tactile sensations generated by the haptic device 18. Here, the tactile sensation generated by the haptic device 18 may indicate, for example, pitching or blocking of the ball.

[0095] (Effects of Information Processing System 100A) The information processing system 100A configured as described above acquires input data including at least one of image data, audio data, and haptic data, and generates control data to be supplied to one or more haptic devices worn by the athlete by referring to the input data. This configuration allows the athlete's play to be improved by providing feedback on the athlete's movements.

[0096] For example, the information processing system 100A can help sports athletes maintain motivation during training sessions and improve their skills. Furthermore, the information processing system 100A can directly provide new stimuli to the athlete's body by using the sense of touch during training. The use of the sense of touch allows the athlete to select the correct strategy or path.

[0097] Using haptic feedback can reduce injuries, which is essential when athletes are costly and the performance of the entire team depends on them. Incorrect movements, such as throwing a ball, can be identified and corrected with immediate haptic feedback.

[0098] Coaches can gain a deeper understanding of player performance if they can further analyze tactile data. Skills such as jumping, force balance, and torque are essential for improving player performance. Coaches can access such data in real time and in playback mode via the information processing system 100A. Coaches can identify problems and provide skill improvements, thereby improving game outcomes.

[0099] (Other Application Examples) The application examples of the information processing system 100A are not limited to volleyball, but can also be used in the fields of sports such as basketball, baseball, soccer, rugby, etc. As one example, the information processing system 100A can record and analyze defensive and offensive strategies, and players (athletes) can receive guidance via the haptic device through training sessions on how to decide on a course of action or how to avoid incorrect strategies.

[0100] The information processing system 100A supports recording of force and torque data during sports and training sessions. Such recorded data is important for improving an athlete's skills. Such recorded data also aids in health care after an injury, helping to improve the healing process for a faster recovery. The information processing system 100A can contribute to the athlete's training and recovery through haptic feedback during the injury recovery process.

[0101] The information processing system 100A supports tactile sensations in virtual reality (VR) or mixed reality (MR) online games. In addition to visual and audio information, tactile information completes the entire immersive experience. This allows game providers to provide new sensations and connect customers with new experiences.

[0102] The information processing system 100A can provide a tactile sensation that can be perceived even by athletes with disabilities, such as athletes with poor eyesight. Athletes with various disabilities can team up and play while feeling the action.

[0103] As described above, the information processing system 100A is also useful for training volleyball teams. By recording multimodal data such as audio, video, and tactile data, a feedback mechanism can be applied to improve each player's performance. The information processing system 100A provides a new method for improving volleyball team training. Players can be provided with immediate tactile feedback to focus on specific skills, such as pitching and blocking, that require specific forces or movements.

[0104] By recording multimodal data such as audio, video, and tactile data, the information processing system 100A allows each player to experience a more realistic replay of their play, making it more enjoyable and improving their performance. Using tactile data in training sessions provides new stimuli that can be enjoyed and recognized, increasing motivation.

[0105] The information processing system 100A can record multimodal data, such as audio, video, and haptic data, useful for playback sessions in virtual reality (VR) or mixed reality (MR). Furthermore, a match or training session can be experienced in VR or MR via the information processing system 100A. As an example, a specific training session, such as a blocking skill, can be realized using data recorded by the present invention and can be played back in VR.

[0106] The information processing system 100A can remotely train an entire team using all necessary data, including audio, video, and haptics. Games can be played remotely and enjoyed by children, minors, and even people with disabilities. Using the information processing system 100A, players from different regions and with different skills can participate in training sessions or the Game of Life, for example, via haptics.

[0107] [Example of implementation by software] Some or all of the functions of the information processing devices 1, 1A, 2A, and input / output device 19 (hereinafter also referred to as "each of the above devices") may be implemented by hardware such as an integrated circuit (IC chip), or by software.

[0108] In the latter case, each of the above devices is realized by, for example, a computer that executes instructions of a program, which is software that realizes each function. An example of such a computer (hereinafter referred to as computer C) is shown in Figure 10. Figure 10 is a block diagram showing the hardware configuration of computer C that functions as each of the above devices.

[0109] The computer C includes at least one processor C1 and at least one memory C2. The memory C2 stores a program P for causing the computer C to function as each of the above-mentioned devices. In the computer C, the processor C1 reads and executes the program P from the memory C2, thereby realizing the functions of each of the above-mentioned devices.

[0110] The processor C1 may be, for example, a central processing unit (CPU), a graphics processing unit (GPU), a digital signal processor (DSP), a micro processing unit (MPU), a floating point number processing unit (FPU), a physics processing unit (PPU), a tensor processing unit (TPU), a quantum processor, a microcontroller, or a combination thereof. The memory C2 may be, for example, a flash memory, a hard disk drive (HDD), a solid state drive (SSD), or a combination thereof.

[0111] The computer C may further include a RAM (Random Access Memory) for expanding the program P during execution and for temporarily storing various data. The computer C may also include a communication interface for transmitting and receiving data to and from other devices. The computer C may also include an input / output interface for connecting input / output devices such as a keyboard, a mouse, a display, and a printer.

[0112] The program P can also be recorded on a non-transitory, tangible recording medium M that can be read by the computer C. Such a recording medium M can be, for example, a tape, a disk, a card, a semiconductor memory, or a programmable logic circuit. The computer C can acquire the program P via such a recording medium M. The program P can also be transmitted via a transmission medium. Such a transmission medium can be, for example, a communication network or broadcast waves. The computer C can also acquire the program P via such a transmission medium.

[0113] [Appendix A] This disclosure includes the techniques described in the following appendices. However, the present invention is not limited to the techniques described in the following appendices, and various modifications are possible within the scope of the claims.

[0114] (Appendix A1) An information processing device comprising: an acquisition means for acquiring input data including at least one of image data, audio data, and tactile data; and a generation means for generating control data to be supplied to one or more tactile devices worn by an athlete, by referring to the input data.

[0115] (Appendix A2) The information processing device according to appendix A1, wherein the input data includes information relating to the actions of one or more athletes, and the generating means generates the control data in accordance with the actions of the one or more athletes.

[0116] (Supplementary Note A3) The information processing device according to Supplementary Note A2, wherein the generating means detects actions of the one or more players in the input data using a machine learning model, and generates the control data by referring to a detection result.

[0117] (Appendix A4) The information processing device described in any one of Appendices A2 or A3, wherein the generation means further performs an analysis process of performance of at least any of the one or more athletes by referring to information related to the movements of the one or more athletes in the input data, and generates the control data by referring to the analysis results.

[0118] (Appendix A5) The information processing device according to Appendix A4, further comprising: an output unit that outputs a result of the analysis process performed by the generation unit.

[0119] (Appendix A6) The information processing device according to Appendix A4, wherein the generating means generates the control data by further referring to a library related to training of the one or more athletes.

[0120] (Appendix A7) The information processing device described in Appendix A5, wherein the generation means further generates presentation data for presenting a digital copy of the athlete as virtual reality or mixed reality by referring to the control data, and the output means outputs the virtual reality or mixed reality indicated by the presentation data.

[0121] (Appendix A8) The information processing device according to Appendix A4, wherein the analysis results include information relating to a difference between the athlete's performance and a comparison performance.

[0122] (Appendix A9) The information processing device according to Appendix A4, wherein the analysis results include information for supporting a coach's decision-making for each of the one or more athletes.

[0123] [Appendix B] This disclosure includes the techniques described in the following appendices. However, the present invention is not limited to the techniques described in the following appendices, and various modifications are possible within the scope of the claims.

[0124] (Appendix B1) An information processing method including: an acquisition process in which at least one processor acquires input data including at least one of image data, audio data, and tactile data; and a generation process in which the at least one processor generates control data to be supplied to one or more tactile devices worn by an athlete, by referring to the input data.

[0125] (Appendix B2) An information processing method described in Appendix B1, wherein the input data includes information regarding the movements of one or more athletes, and in the generation process, the at least one processor generates the control data in accordance with the movements of the one or more athletes.

[0126] (Appendix B3) The information processing method described in Appendix B2, wherein in the generation process, the at least one processor detects the movements of the one or more players in the input data using a machine learning model, and generates the control data by referring to the detection results.

[0127] (Appendix B4) An information processing method described in either one of Appendices B2 or B3, wherein the at least one processor further performs an analysis process of performance of at least any of the one or more athletes by referring to information relating to the movements of the one or more athletes in the input data, and generates the control data by referring to the analysis results.

[0128] (Supplementary Note B5) The information processing method according to Supplementary Note B4, further comprising an output process in which the at least one processor outputs a result of the analysis process performed by the generation process.

[0129] (Supplementary Note B6) The information processing method according to Supplementary Note B4, wherein in the generation process, the at least one processor generates the control data by further referring to a library related to training of the one or more athletes.

[0130] (Appendix B7) An information processing method as described in Appendix B5, wherein in the generation process, the at least one processor further generates presentation data for presenting a digital copy of the athlete as virtual reality or mixed reality by referring to the control data, and in the output process, the at least one processor outputs the virtual reality or mixed reality indicated by the presentation data.

[0131] (Appendix B8) The information processing method according to Appendix B4, wherein the analysis results include information relating to the difference between the athlete's performance and a comparison performance.

[0132] (Appendix B9) The information processing method according to Appendix B4, wherein the analysis results include information for supporting a coach's decision-making for each of the one or more athletes.

[0133] [Appendix C] This disclosure includes the techniques described in the following appendices. However, the present invention is not limited to the techniques described in the following appendices, and various modifications are possible within the scope of the claims.

[0134] (Appendix C1) A program that causes a computer to function as an information processing device, causing the computer to function as: an acquisition means that acquires input data including at least one of image data, audio data, and tactile data; and a generation means that generates control data to be supplied to one or more tactile devices worn by the athlete, by referring to the input data.

[0135] (Appendix C2) The information processing program according to Appendix C1, wherein the input data includes information relating to the actions of one or more athletes, and the generating means generates the control data in accordance with the actions of the one or more athletes.

[0136] (Supplementary Note C3) The information processing program according to Supplementary Note C2, wherein the generating means detects actions of the one or more players in the input data using a machine learning model, and generates the control data by referring to a detection result.

[0137] (Appendix C4) The information processing program described in any one of Appendices C2 or C3, wherein the generation means further performs an analysis process of the performance of at least one of the one or more athletes by referring to information regarding the movements of the one or more athletes in the input data, and generates the control data by referring to the analysis results.

[0138] (Supplementary Note C5) The information processing program according to Supplementary Note C4, which causes the computer to function as an output process that outputs a result of the analysis process by the generating means.

[0139] (Appendix C6) The information processing program according to Appendix C4, wherein the generating means generates the control data by further referring to a library related to training of the one or more athletes.

[0140] (Appendix C7) An information processing program described in Appendix C5, wherein the generation means further generates presentation data for presenting a digital copy of the athlete as virtual reality or mixed reality by referring to the control data, and the output means outputs the virtual reality or mixed reality indicated by the presentation data.

[0141] (Appendix C8) The information processing program according to Appendix C4, wherein the analysis results include information regarding the difference between the athlete's performance and a comparison performance.

[0142] (Appendix C9) The information processing program according to Appendix C4, wherein the analysis results include information for supporting a coach's decision-making for each of the one or more athletes.

[0143] [Appendix D] This disclosure includes the techniques described in the following appendices. However, the present invention is not limited to the techniques described in the following appendices, and various modifications are possible within the scope of the claims.

[0144] (Appendix D1) An information processing device comprising at least one processor, the at least one processor executing: an acquisition process for acquiring input data including at least one of image data, audio data, and haptic data; and a generation process for generating control data to be supplied to one or more haptic devices worn by an athlete, by referring to the input data.

[0145] The information processing device may further include a memory, and the memory may store a program for causing the at least one processor to execute each of the processes.

[0146] (Appendix D2) The information processing device described in Appendix D1, wherein the input data includes information regarding the movements of one or more athletes, and in the generation process, the at least one processor generates the control data in accordance with the movements of the one or more athletes.

[0147] (Appendix D3) The information processing device described in Appendix D2, wherein in the generation process, the at least one processor detects the movements of the one or more athletes in the input data using a machine learning model, and generates the control data by referring to the detection results.

[0148] (Appendix D4) An information processing device described in any one of appendices D2 or D3, wherein in the generation process, the at least one processor further performs an analysis process of the performance of at least any of the one or more athletes by referring to information regarding the movements of the one or more athletes in the input data, and generates the control data by referring to the analysis results.

[0149] (Supplementary Note D5) The information processing device according to Supplementary Note D4, wherein the at least one processor executes an output process that outputs a result of the analysis process performed by the generation process.

[0150] (Supplementary Note D6) The information processing device according to Supplementary Note D4, wherein in the generation process, the at least one processor generates the control data by further referring to a library related to training of the one or more athletes.

[0151] (Appendix D7) An information processing device as described in Appendix D5, wherein in the generation process, the at least one processor further generates presentation data for presenting a digital copy of the athlete as virtual reality or mixed reality by referring to the control data, and in the output process, the at least one processor outputs the virtual reality or mixed reality indicated by the presentation data.

[0152] (Appendix D8) The information processing device according to appendix D4, wherein the analysis results include information regarding the difference between the athlete's performance and a comparison performance.

[0153] (Appendix D9) The information processing device according to appendix D4, wherein the analysis results include information for supporting a coach's decision-making for each of the one or more athletes.

[0154] [Appendix E] This disclosure includes the techniques described in the following appendices. However, the present invention is not limited to the techniques described in the following appendices, and various modifications are possible within the scope of the claims.

[0155] (Appendix E1) A non-transitory recording medium having recorded thereon an information processing program that causes a computer to function as an information processing device, the information processing program causing the computer to execute an acquisition process that acquires input data including at least one of image data, audio data, and tactile data, and a generation process that generates control data by referring to the input data to be supplied to one or more tactile devices worn by the athlete.

[0156] REFERENCE SIGNS LIST 1, 1A, 2A ... Information processing device 100A ... Information processing system 16 ... Camera 17 ... Microphone 18 ... Haptic device 19 ... Input / output device 10A ... Control unit 11 ... Acquisition unit 12 ... Generation unit 13 ... Output unit

Claims

1. An information processing device comprising: an acquisition means for acquiring input data including at least one of image data, audio data, and tactile data; and a generation means for generating control data to be supplied to one or more tactile devices worn by an athlete, by referring to the input data.

2. An information processing device as described in claim 1, wherein the input data includes information regarding the movements of one or more athletes, and the generating means generates the control data in accordance with the movements of the one or more athletes.

3. The information processing device according to claim 2, wherein the generation means detects the movements of the one or more athletes in the input data using a machine learning model and generates the control data by referring to the detection results.

4. An information processing device as claimed in any one of claims 2 or 3, wherein the generation means further performs an analysis process of the performance of at least one of the one or more athletes by referring to information relating to the movements of the one or more athletes in the input data, and generates the control data by referring to the analysis results.

5. The information processing device according to claim 4, further comprising output means for outputting the results of the analysis processing performed by said generating means.

6. An information processing device according to claim 4, wherein said generating means generates said control data by further referring to a library relating to the training of said one or more athletes.

7. An information processing device as described in claim 5, wherein the generation means further generates presentation data for presenting a digital copy of the athlete as virtual reality or mixed reality by referring to the control data, and the output means outputs the virtual reality or mixed reality indicated by the presentation data.

8. The information processing device according to claim 4, wherein the analysis results include information regarding the difference between the athlete's performance and the performance for comparison.

9. The information processing device according to claim 4, wherein the analysis results include information to support a coach's decision-making regarding each of the one or more athletes.

10. An information processing method in which an information processing device acquires input data including at least one of image data, audio data, and tactile data, and generates control data to be supplied to one or more tactile devices worn by an athlete by referring to the input data.

11. A program that causes a computer to function as an information processing device, said program causing said computer to acquire input data including at least one of image data, audio data, and tactile data, and to generate control data to be supplied to one or more tactile devices worn by an athlete, by referring to the input data.

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