system

Abstract

The system according to this embodiment aims to efficiently analyze an athlete's exercise form and provide specific feedback. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, and a provision unit. The collection unit collects exercise forms. The analysis unit analyzes the forms collected by the collection unit. The provision unit provides feedback based on the analysis results obtained by the analysis unit.

Description

Technical Field

[0001] The technology of the present disclosure relates to a system.

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that it is difficult to efficiently analyze the movement form of an athlete and provide specific feedback.

[0005] The system according to the embodiment aims to efficiently analyze the movement form of an athlete and provide specific feedback.

Means for Solving the Problems

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and a provision unit. The collection unit collects the movement form. The analysis unit analyzes the form collected by the collection unit. The provision unit provides feedback based on the analysis result obtained by the analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently analyze an athlete's exercise form and provide specific feedback. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

[0011] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), or TPU (Tensor Processing Unit).

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0013] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0014] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

[0016] [First Embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] As shown in FIG. 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0023] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The sports form analyzer agent according to an embodiment of the present invention is a system that utilizes multimodal AI to analyze an athlete's exercise form and provide specific feedback for technique improvement. This system videos the athlete's exercise form, and the AI ​​analyzes the video feed. The AI ​​uses a deep learning model to suggest form optimizations. Next, it provides real-time feedback based on the analysis results. Furthermore, it performs customizable analysis tailored to individual athletes. This system is suitable for a wide range of targets, including sports teams, training facilities, and school sports departments. For example, it can meet the needs of athletes seeking technique improvement, thereby improving the quality and efficiency of training. It can also reduce the risk of injury due to incorrect form and support sustainable improvement of athletic performance. Thus, the sports form analyzer agent can efficiently analyze an athlete's exercise form and provide specific feedback for technique improvement.

[0029] The sports form analyzer agent according to this embodiment comprises a data collection unit, an analysis unit, and a data provision unit. The data collection unit collects the athlete's exercise form. The data collection unit, for example, films the athlete's exercise form using a video camera. The data collection unit can either fix the video camera in place or hold it by hand. The data collection unit can also, for example, use multiple cameras to film the exercise form from different angles. The analysis unit analyzes the exercise form collected by the data collection unit. The analysis unit performs the analysis of the exercise form using, for example, a deep learning model. The analysis unit uses a large amount of exercise form data to train the deep learning model. The analysis unit analyzes each part of the exercise form and proposes optimizations for the form. The data provision unit provides feedback based on the analysis results obtained by the analysis unit. The data provision unit provides feedback in real time, for example. The data provision unit can provide feedback by voice or by text. The data provision unit can also, for example, send feedback to the athlete's smartphone. As a result, the sports form analyzer agent according to this embodiment can efficiently collect, analyze, and provide feedback on the athlete's exercise form.

[0030] The data collection unit collects the athlete's movement form. For example, the data collection unit uses a video camera to film the athlete's movement form. The video camera has high resolution and can accurately capture the athlete's subtle movements and posture. The data collection unit can either fix the video camera in place or use it handheld. A fixed camera provides stable footage from a specific position, while a handheld camera can flexibly change its position according to movement. The data collection unit can also use multiple cameras to film the movement form from different angles. This allows for a multi-faceted capture of the athlete's movements and the collection of more detailed data. For example, simultaneously collecting footage from the front, side, and rear allows for a comprehensive analysis of the athlete's overall form. Furthermore, the data collection unit can record the athlete's movements in three dimensions using not only cameras but also motion capture systems and wearable sensors. Motion capture systems capture the athlete's joint and muscle movements with high precision, while wearable sensors collect data such as acceleration and angular velocity in real time. This allows the data collection unit to collect diverse data on the athlete's movement form and provide it to the analysis unit.

[0031] The analysis unit analyzes the movement forms collected by the data collection unit. For example, the analysis unit uses a deep learning model to analyze the movement forms. The deep learning model employs a neural network with numerous parameters to analyze the athlete's movements with high accuracy. The analysis unit uses a large amount of movement form data to train the deep learning model. The training data includes data from various sports and movements, allowing the model to learn diverse movement forms. The analysis unit analyzes each part of the movement form and proposes optimizations. Specifically, it analyzes the athlete's posture, joint angles, muscle movements, etc., in detail to identify areas for improvement to promote efficient movement and reduce the risk of injury. For example, in running form analysis, it can evaluate foot landing position, knee angle, hip movement, etc., and propose the optimal running form. Furthermore, the analysis unit can evaluate the athlete's progress by comparing it with past data and measure the effectiveness of training. This allows the analysis unit to analyze the athlete's movement form in detail and provide optimal feedback tailored to individual needs.

[0032] The service provider provides feedback based on the analysis results obtained by the analysis unit. For example, the service provider can provide feedback in real time. Real-time feedback allows athletes to immediately correct their form during training, supporting effective training. The service provider can also provide feedback via voice or text. Voice feedback helps athletes understand areas for form correction without distracting their visual attention during training. Text feedback provides detailed analysis results and specific areas for improvement, which athletes can review later. The service provider can also send feedback to the athlete's smartphone. Through a smartphone app, athletes can review detailed analysis results after training and adjust their training plan. Furthermore, the service provider can visualize the feedback. For example, displaying the athlete's exercise form as a 3D model and visually showing areas for improvement can deepen understanding. This allows the service provider to provide effective feedback to athletes and help them optimize their exercise form.

[0033] The service provider can provide real-time feedback. For example, the service provider can provide feedback to an athlete while they are exercising. By providing real-time feedback, the service provider can enable athletes to immediately correct their form. The service provider can, for example, use voice feedback to give instructions to athletes in real time. The service provider can use low-latency communication technology to provide real-time feedback. This enables immediate form correction by providing real-time feedback.

[0034] The analysis unit can perform customizable analyses tailored to individual athletes. For example, it can create individualized analysis models based on an athlete's past exercise data. By performing customizable analyses, the analysis unit can provide feedback tailored to the athlete's characteristics. For example, it can adjust analysis parameters according to the athlete's physique and athletic ability. The analysis unit can use athlete profile information to perform customizable analyses. This allows for more effective feedback by providing analyses tailored to individual athletes.

[0035] The collection unit can collect video feeds. For example, the collection unit can use a video camera to film an athlete's movement form. The collection unit can collect video feeds in high resolution. For example, the collection unit can use a 4K resolution video camera to collect detailed movement form. The collection unit can collect video feeds at a high frame rate. For example, the collection unit can collect video feeds at 60 frames per second. This allows for detailed analysis of movement form by collecting video feeds.

[0036] The analysis unit can propose form optimization using a deep learning model. For example, the analysis unit extracts features of the movement form using a convolutional neural network (CNN). The analysis unit uses a large amount of movement form data to train the deep learning model. For example, the analysis unit analyzes each part of the movement form and proposes form optimization. By using a deep learning model, the analysis unit can perform form optimization with high accuracy. This means that form optimization can be performed with high accuracy by using a deep learning model.

[0037] The system can immediately communicate corrections to athletes if it detects form errors. For example, it can detect form errors in real time and communicate corrections to athletes. The system can use deep learning models to detect form errors. For example, it can use convolutional neural networks (CNNs) to detect form errors. If the system detects form errors, it can communicate corrections to athletes using voice feedback. This reduces the risk of injury by allowing for immediate correction of form errors.

[0038] The data collection unit can analyze an athlete's past form data and select the optimal data collection method. For example, the data collection unit can collect video from the most effective angle based on past form data. The data collection unit can focus on collecting specific movements based on past form data. The data collection unit can collect form in different environments based on past form data. This enables effective form collection by selecting the optimal data collection method based on past form data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI.

[0039] The collection unit can filter video footage based on the athlete's current training status and goals. For example, the collection unit can prioritize collecting specific movements based on the athlete's training goals. The collection unit can track changes in form based on the athlete's current training status. The collection unit can collect videos of specific training sessions based on the athlete's goals. This allows for effective form collection by filtering videos based on training status and goals. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI.

[0040] The collection unit can prioritize the collection of highly relevant forms by considering the athlete's geographical location information during video collection. For example, if an athlete is at a specific training facility, the collection unit will prioritize the collection of forms taken at that facility. If an athlete is training in a different environment, the collection unit can prioritize the collection of forms taken in that environment. If an athlete is at a specific geographical location, the collection unit can prioritize the collection of forms taken at that location. This allows for the analysis of forms according to the environment by collecting forms while considering geographical location information. Some or all of the above processing in the collection unit may be performed using AI, for example, or without using AI.

[0041] The collection unit can analyze an athlete's social media activity and collect relevant forms when collecting videos. For example, the collection unit can collect videos based on training content shared by the athlete on social media. The collection unit can collect videos by referencing forms of other athletes that the athlete follows on social media. The collection unit can collect videos of specific training sessions based on the athlete's social media activity. This allows for form analysis tailored to the athlete's activity by analyzing social media activity and collecting forms. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI.

[0042] The analysis unit can adjust the level of detail of the analysis based on the importance of the form during the analysis. For example, the analysis unit can perform a detailed analysis for important forms and a simplified analysis for less important forms. The analysis unit can adjust the depth of the analysis according to the importance of the form. This allows for effective feedback by adjusting the level of detail of the analysis according to the importance of the form. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI.

[0043] The analysis unit can apply different analysis algorithms depending on the form category during analysis. For example, the analysis unit can apply a running-specific analysis algorithm to a running form. The analysis unit can apply a swimming-specific analysis algorithm to a swimming form. The analysis unit can apply a shooting-specific analysis algorithm to a basketball shooting form. By applying an analysis algorithm appropriate to the form category, more accurate analysis becomes possible. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without using AI.

[0044] The analysis unit can determine the priority of analysis based on when the forms were collected. For example, the analysis unit may prioritize the analysis of the most recent form data. The analysis unit may also prioritize the analysis of form data from important training sessions. The analysis unit can analyze current form data while referring to past form data. This allows for prioritizing the analysis of the most recent form data by determining the priority of analysis based on when the forms were collected. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without using AI.

[0045] The analysis unit can adjust the order of analysis based on the relationships between forms during the analysis process. For example, the analysis unit can prioritize the analysis of important forms. The analysis unit can prioritize the analysis of highly relevant forms. The analysis unit can adjust the order of analysis according to the relationships between forms. This allows for effective analysis by adjusting the order of analysis based on the relationships between forms. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without using AI.

[0046] The feedback provider can adjust the level of detail of the feedback based on the importance of the form when providing feedback. For example, the provider can provide detailed feedback for important forms, and concise feedback for less important forms. The provider can adjust the depth of the feedback according to the importance of the form. This allows for the provision of effective feedback by adjusting the level of detail according to the importance of the form. Some or all of the above processing in the feedback provider may be performed using AI, for example, or without using AI.

[0047] The service provider can apply different feedback algorithms depending on the form category when providing feedback. For example, the service provider can apply a running-specific feedback algorithm to a running form. The service provider can apply a swimming-specific feedback algorithm to a swimming form. The service provider can apply a shooting-specific feedback algorithm to a basketball shooting form. By applying a feedback algorithm appropriate to the form category, more accurate feedback becomes possible. Some or all of the above processing in the service provider may be performed using AI, for example, or without using AI.

[0048] The service provider can prioritize feedback based on when the forms were collected. For example, the service provider can provide feedback based on the most recent form data. The service provider can provide feedback based on form data from important training sessions. The service provider can provide feedback based on current form data, while also referring to past form data. This allows for the provision of feedback based on the most recent form data by prioritizing feedback based on when the forms were collected. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI.

[0049] The feedback provider can adjust the order of feedback based on the relevance of the forms when providing feedback. For example, the provider can prioritize providing feedback to important forms. The provider can prioritize providing feedback to highly relevant forms. The provider can adjust the order of feedback according to the relevance of the forms. This allows for the provision of effective feedback by adjusting the order of feedback based on the relevance of the forms. Some or all of the above processing in the feedback provider may be performed using AI, for example, or not using AI.

[0050] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0051] The data collection unit can analyze an athlete's past training data and select the optimal collection method. For example, it can collect video from the most effective angles based on past data. It can also focus on collecting specific movements. Furthermore, it can collect form in different environments. This allows for effective form collection by selecting the optimal collection method based on past data.

[0052] The analysis unit can adjust the level of detail of the analysis based on the importance of the form. For example, it can perform a detailed analysis on important forms and a simplified analysis on less important forms. Furthermore, it can adjust the depth of the analysis according to the importance of the form. This allows for the provision of effective feedback by adjusting the level of detail of the analysis according to the importance of the form.

[0053] The feedback system can apply different feedback algorithms depending on the form category when providing feedback. For example, a running-specific feedback algorithm can be applied to a running form, a swimming-specific feedback algorithm to a swimming form, and a basketball shooting form-specific feedback algorithm to a shooting form. By applying a feedback algorithm appropriate to the form category, more accurate feedback becomes possible.

[0054] The data collection unit can prioritize the collection of highly relevant forms by considering the athlete's geographical location during video acquisition. For example, if an athlete is at a specific training facility, the system will prioritize collecting forms from that facility. If the athlete is training in a different environment, the system will prioritize collecting forms from that environment. Furthermore, if the athlete is at a specific geographical location, the system will prioritize collecting forms from that location. This allows for the analysis of forms tailored to the environment by considering geographical location information during form collection.

[0055] The analysis unit can determine the priority of analysis based on when the forms were collected. For example, it can prioritize the analysis of the most recent form data. It can also prioritize the analysis of form data from important training sessions. Furthermore, it can analyze current form data while referring to past form data. This allows for prioritizing the analysis based on when the forms were collected, thereby prioritizing the analysis of the most recent form data.

[0056] The following briefly describes the processing flow for example form 1.

[0057] Step 1: The collection unit collects the athlete's exercise form. The collection unit films the athlete's exercise form, for example, using a video camera. The collection unit can either fix the video camera in place or hold it by hand. The collection unit can also, for example, use multiple cameras to film the exercise form from different angles. Step 2: The analysis unit analyzes the exercise form collected by the data collection unit. The analysis unit performs the exercise form analysis using, for example, a deep learning model. The analysis unit uses a large amount of exercise form data to train the deep learning model. The analysis unit analyzes each part of the exercise form and proposes optimizations for the form. Step 3: The service provider provides feedback based on the analysis results obtained by the analysis unit. The service provider provides feedback in real time, for example. The service provider can provide feedback via voice or text. The service provider can also send feedback to the athlete's smartphone, for example.

[0058] (Example of form 2) The sports form analyzer agent according to an embodiment of the present invention is a system that utilizes multimodal AI to analyze an athlete's exercise form and provide specific feedback for technique improvement. This system videos the athlete's exercise form, and the AI ​​analyzes the video feed. The AI ​​uses a deep learning model to suggest form optimizations. Next, it provides real-time feedback based on the analysis results. Furthermore, it performs customizable analysis tailored to individual athletes. This system is suitable for a wide range of targets, including sports teams, training facilities, and school sports departments. For example, it can meet the needs of athletes seeking technique improvement, thereby improving the quality and efficiency of training. It can also reduce the risk of injury due to incorrect form and support sustainable improvement of athletic performance. Thus, the sports form analyzer agent can efficiently analyze an athlete's exercise form and provide specific feedback for technique improvement.

[0059] The sports form analyzer agent according to this embodiment comprises a data collection unit, an analysis unit, and a data provision unit. The data collection unit collects the athlete's exercise form. The data collection unit, for example, films the athlete's exercise form using a video camera. The data collection unit can either fix the video camera in place or hold it by hand. The data collection unit can also, for example, use multiple cameras to film the exercise form from different angles. The analysis unit analyzes the exercise form collected by the data collection unit. The analysis unit performs the analysis of the exercise form using, for example, a deep learning model. The analysis unit uses a large amount of exercise form data to train the deep learning model. The analysis unit analyzes each part of the exercise form and proposes optimizations for the form. The data provision unit provides feedback based on the analysis results obtained by the analysis unit. The data provision unit provides feedback in real time, for example. The data provision unit can provide feedback by voice or by text. The data provision unit can also, for example, send feedback to the athlete's smartphone. As a result, the sports form analyzer agent according to this embodiment can efficiently collect, analyze, and provide feedback on the athlete's exercise form.

[0060] The data collection unit collects the athlete's movement form. For example, the data collection unit uses a video camera to film the athlete's movement form. The video camera has high resolution and can accurately capture the athlete's subtle movements and posture. The data collection unit can either fix the video camera in place or use it handheld. A fixed camera provides stable footage from a specific position, while a handheld camera can flexibly change its position according to movement. The data collection unit can also use multiple cameras to film the movement form from different angles. This allows for a multi-faceted capture of the athlete's movements and the collection of more detailed data. For example, simultaneously collecting footage from the front, side, and rear allows for a comprehensive analysis of the athlete's overall form. Furthermore, the data collection unit can record the athlete's movements in three dimensions using not only cameras but also motion capture systems and wearable sensors. Motion capture systems capture the athlete's joint and muscle movements with high precision, while wearable sensors collect data such as acceleration and angular velocity in real time. This allows the data collection unit to collect diverse data on the athlete's movement form and provide it to the analysis unit.

[0061] The analysis unit analyzes the movement forms collected by the data collection unit. For example, the analysis unit uses a deep learning model to analyze the movement forms. The deep learning model employs a neural network with numerous parameters to analyze the athlete's movements with high accuracy. The analysis unit uses a large amount of movement form data to train the deep learning model. The training data includes data from various sports and movements, allowing the model to learn diverse movement forms. The analysis unit analyzes each part of the movement form and proposes optimizations. Specifically, it analyzes the athlete's posture, joint angles, muscle movements, etc., in detail to identify areas for improvement to promote efficient movement and reduce the risk of injury. For example, in running form analysis, it can evaluate foot landing position, knee angle, hip movement, etc., and propose the optimal running form. Furthermore, the analysis unit can evaluate the athlete's progress by comparing it with past data and measure the effectiveness of training. This allows the analysis unit to analyze the athlete's movement form in detail and provide optimal feedback tailored to individual needs.

[0062] The service provider provides feedback based on the analysis results obtained by the analysis unit. For example, the service provider can provide feedback in real time. Real-time feedback allows athletes to immediately correct their form during training, supporting effective training. The service provider can also provide feedback via voice or text. Voice feedback helps athletes understand areas for form correction without distracting their visual attention during training. Text feedback provides detailed analysis results and specific areas for improvement, which athletes can review later. The service provider can also send feedback to the athlete's smartphone. Through a smartphone app, athletes can review detailed analysis results after training and adjust their training plan. Furthermore, the service provider can visualize the feedback. For example, displaying the athlete's exercise form as a 3D model and visually showing areas for improvement can deepen understanding. This allows the service provider to provide effective feedback to athletes and help them optimize their exercise form.

[0063] The service provider can provide real-time feedback. For example, the service provider can provide feedback to an athlete while they are exercising. By providing real-time feedback, the service provider can enable athletes to immediately correct their form. The service provider can, for example, use voice feedback to give instructions to athletes in real time. The service provider can use low-latency communication technology to provide real-time feedback. This enables immediate form correction by providing real-time feedback.

[0064] The analysis unit can perform customizable analyses tailored to individual athletes. For example, it can create individualized analysis models based on an athlete's past exercise data. By performing customizable analyses, the analysis unit can provide feedback tailored to the athlete's characteristics. For example, it can adjust analysis parameters according to the athlete's physique and athletic ability. The analysis unit can use athlete profile information to perform customizable analyses. This allows for more effective feedback by providing analyses tailored to individual athletes.

[0065] The collection unit can collect video feeds. For example, the collection unit can use a video camera to film an athlete's movement form. The collection unit can collect video feeds in high resolution. For example, the collection unit can use a 4K resolution video camera to collect detailed movement form. The collection unit can collect video feeds at a high frame rate. For example, the collection unit can collect video feeds at 60 frames per second. This allows for detailed analysis of movement form by collecting video feeds.

[0066] The analysis unit can propose form optimization using a deep learning model. For example, the analysis unit extracts features of the movement form using a convolutional neural network (CNN). The analysis unit uses a large amount of movement form data to train the deep learning model. For example, the analysis unit analyzes each part of the movement form and proposes form optimization. By using a deep learning model, the analysis unit can perform form optimization with high accuracy. This means that form optimization can be performed with high accuracy by using a deep learning model.

[0067] The system can immediately communicate corrections to athletes if it detects form errors. For example, it can detect form errors in real time and communicate corrections to athletes. The system can use deep learning models to detect form errors. For example, it can use convolutional neural networks (CNNs) to detect form errors. If the system detects form errors, it can communicate corrections to athletes using voice feedback. This reduces the risk of injury by allowing for immediate correction of form errors.

[0068] The data collection unit can estimate the athlete's emotions and adjust the timing of video collection based on the estimated emotions. For example, the data collection unit can collect video when the athlete is focused to capture the most effective form. It can also collect video when the athlete is relaxed to capture a natural form. It can collect video when the athlete is tired to detect a breakdown in form. By adjusting the timing of video collection according to the athlete's emotions, the optimal form can be captured. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0069] The data collection unit can analyze an athlete's past form data and select the optimal data collection method. For example, the data collection unit can collect video from the most effective angle based on past form data. The data collection unit can focus on collecting specific movements based on past form data. The data collection unit can collect form in different environments based on past form data. This enables effective form collection by selecting the optimal data collection method based on past form data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI.

[0070] The collection unit can filter video footage based on the athlete's current training status and goals. For example, the collection unit can prioritize collecting specific movements based on the athlete's training goals. The collection unit can track changes in form based on the athlete's current training status. The collection unit can collect videos of specific training sessions based on the athlete's goals. This allows for effective form collection by filtering videos based on training status and goals. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI.

[0071] The collection unit can estimate the athlete's emotions and determine the priority of videos to collect based on the estimated emotions. For example, the collection unit can prioritize collecting videos of the athlete when they are focused, when they are relaxed, or when they are tired. By prioritizing videos according to the athlete's emotions, it is possible to capture their optimal form. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0072] The collection unit can prioritize the collection of highly relevant forms by considering the athlete's geographical location information during video collection. For example, if an athlete is at a specific training facility, the collection unit will prioritize the collection of forms taken at that facility. If an athlete is training in a different environment, the collection unit can prioritize the collection of forms taken in that environment. If an athlete is at a specific geographical location, the collection unit can prioritize the collection of forms taken at that location. This allows for the analysis of forms according to the environment by collecting forms while considering geographical location information. Some or all of the above processing in the collection unit may be performed using AI, for example, or without using AI.

[0073] The collection unit can analyze an athlete's social media activity and collect relevant forms when collecting videos. For example, the collection unit can collect videos based on training content shared by the athlete on social media. The collection unit can collect videos by referencing forms of other athletes that the athlete follows on social media. The collection unit can collect videos of specific training sessions based on the athlete's social media activity. This allows for form analysis tailored to the athlete's activity by analyzing social media activity and collecting forms. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI.

[0074] The analysis unit can estimate the athlete's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the athlete is focused, the analysis unit can provide detailed analysis results. If the athlete is relaxed, the analysis unit can provide concise analysis results. If the athlete is tired, the analysis unit can provide concise analysis results. By adjusting the presentation of the analysis according to the athlete's emotions, more effective feedback can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0075] The analysis unit can adjust the level of detail of the analysis based on the importance of the form during the analysis. For example, the analysis unit can perform a detailed analysis for important forms and a simplified analysis for less important forms. The analysis unit can adjust the depth of the analysis according to the importance of the form. This allows for effective feedback by adjusting the level of detail of the analysis according to the importance of the form. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI.

[0076] The analysis unit can apply different analysis algorithms depending on the form category during analysis. For example, the analysis unit can apply a running-specific analysis algorithm to a running form. The analysis unit can apply a swimming-specific analysis algorithm to a swimming form. The analysis unit can apply a shooting-specific analysis algorithm to a basketball shooting form. By applying an analysis algorithm appropriate to the form category, more accurate analysis becomes possible. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without using AI.

[0077] The analysis unit can estimate the athlete's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the athlete is focused, the analysis unit can perform a detailed analysis. If the athlete is relaxed, the analysis unit can perform a concise analysis. If the athlete is tired, the analysis unit can perform a concise analysis. By adjusting the length of the analysis according to the athlete's emotions, more effective feedback can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0078] The analysis unit can determine the priority of analysis based on when the forms were collected. For example, the analysis unit may prioritize the analysis of the most recent form data. The analysis unit may also prioritize the analysis of form data from important training sessions. The analysis unit can analyze current form data while referring to past form data. This allows for prioritizing the analysis of the most recent form data by determining the priority of analysis based on when the forms were collected. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without using AI.

[0079] The analysis unit can adjust the order of analysis based on the relationships between forms during the analysis process. For example, the analysis unit can prioritize the analysis of important forms. The analysis unit can prioritize the analysis of highly relevant forms. The analysis unit can adjust the order of analysis according to the relationships between forms. This allows for effective analysis by adjusting the order of analysis based on the relationships between forms. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without using AI.

[0080] The system can estimate the athlete's emotions and adjust the way feedback is presented based on the estimated emotions. For example, if the athlete is focused, the system can provide detailed feedback. If the athlete is relaxed, the system can provide concise feedback. If the athlete is tired, the system can provide to the point. By adjusting the way feedback is presented according to the athlete's emotions, more effective feedback can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0081] The feedback provider can adjust the level of detail of the feedback based on the importance of the form when providing feedback. For example, the provider can provide detailed feedback for important forms, and concise feedback for less important forms. The provider can adjust the depth of the feedback according to the importance of the form. This allows for the provision of effective feedback by adjusting the level of detail according to the importance of the form. Some or all of the above processing in the feedback provider may be performed using AI, for example, or without using AI.

[0082] The service provider can apply different feedback algorithms depending on the form category when providing feedback. For example, the service provider can apply a running-specific feedback algorithm to a running form. The service provider can apply a swimming-specific feedback algorithm to a swimming form. The service provider can apply a shooting-specific feedback algorithm to a basketball shooting form. By applying a feedback algorithm appropriate to the form category, more accurate feedback becomes possible. Some or all of the above processing in the service provider may be performed using AI, for example, or without using AI.

[0083] The feedback system can estimate the athlete's emotions and adjust the length of the feedback based on the estimated emotions. For example, if the athlete is focused, the system can provide detailed feedback. If the athlete is relaxed, the system can provide concise feedback. If the athlete is tired, the system can provide to the point. By adjusting the length of the feedback according to the athlete's emotions, more effective feedback can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0084] The service provider can prioritize feedback based on when the forms were collected. For example, the service provider can provide feedback based on the most recent form data. The service provider can provide feedback based on form data from important training sessions. The service provider can provide feedback based on current form data, while also referring to past form data. This allows for the provision of feedback based on the most recent form data by prioritizing feedback based on when the forms were collected. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI.

[0085] The feedback provider can adjust the order of feedback based on the relevance of the forms when providing feedback. For example, the provider can prioritize providing feedback to important forms. The provider can prioritize providing feedback to highly relevant forms. The provider can adjust the order of feedback according to the relevance of the forms. This allows for the provision of effective feedback by adjusting the order of feedback based on the relevance of the forms. Some or all of the above processing in the feedback provider may be performed using AI, for example, or not using AI.

[0086] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0087] The analysis unit can estimate the athlete's emotions and adjust the timing of the analysis based on those estimates. For example, it can perform analysis when the athlete is focused to provide the most effective feedback. When the athlete is relaxed, it can prioritize rest without performing analysis. Similarly, when the athlete is tired, it can encourage rest without performing analysis. This allows for optimal feedback by adjusting the timing of analysis according to the athlete's emotions.

[0088] The system can estimate the athlete's emotions and adjust the content of the feedback based on those estimates. For example, when the athlete is focused, it can provide detailed feedback. When the athlete is relaxed, it can provide concise feedback. When the athlete is tired, it can provide feedback that gets straight to the point. By adjusting the content of the feedback according to the athlete's emotions, it is possible to provide more effective feedback.

[0089] The capture unit can estimate the athlete's emotions and adjust the video capture method based on those estimates. For example, when the athlete is focused, multiple cameras can be used to capture detailed video. When the athlete is relaxed, a single camera can be used to capture natural video. Furthermore, when the athlete is tired, rest can be prioritized without video capture. This allows for the capture of optimal video by adjusting the video capture method according to the athlete's emotions.

[0090] The analysis unit can estimate the athlete's emotions and adjust the depth of the analysis based on those estimates. For example, when the athlete is focused, a detailed analysis can be performed. When the athlete is relaxed, a concise analysis can be performed. Furthermore, when the athlete is tired, a focused analysis can be performed. By adjusting the depth of the analysis according to the athlete's emotions, more effective feedback can be provided.

[0091] The system can estimate the athlete's emotions and adjust the timing of feedback based on those estimates. For example, when an athlete is focused, feedback can be provided immediately. When an athlete is relaxed, feedback can be delayed. Furthermore, when an athlete is tired, feedback can be withheld to encourage rest. This allows for optimal feedback delivery by adjusting the timing of feedback according to the athlete's emotions.

[0092] The data collection unit can analyze an athlete's past training data and select the optimal collection method. For example, it can collect video from the most effective angles based on past data. It can also focus on collecting specific movements. Furthermore, it can collect form in different environments. This allows for effective form collection by selecting the optimal collection method based on past data.

[0093] The analysis unit can adjust the level of detail of the analysis based on the importance of the form. For example, it can perform a detailed analysis on important forms and a simplified analysis on less important forms. Furthermore, it can adjust the depth of the analysis according to the importance of the form. This allows for the provision of effective feedback by adjusting the level of detail of the analysis according to the importance of the form.

[0094] The feedback system can apply different feedback algorithms depending on the form category when providing feedback. For example, a running-specific feedback algorithm can be applied to a running form, a swimming-specific feedback algorithm to a swimming form, and a basketball shooting form-specific feedback algorithm to a shooting form. By applying a feedback algorithm appropriate to the form category, more accurate feedback becomes possible.

[0095] The data collection unit can prioritize the collection of highly relevant forms by considering the athlete's geographical location during video acquisition. For example, if an athlete is at a specific training facility, the system will prioritize collecting forms from that facility. If the athlete is training in a different environment, the system will prioritize collecting forms from that environment. Furthermore, if the athlete is at a specific geographical location, the system will prioritize collecting forms from that location. This allows for the analysis of forms tailored to the environment by considering geographical location information during form collection.

[0096] The analysis unit can determine the priority of analysis based on when the forms were collected. For example, it can prioritize the analysis of the most recent form data. It can also prioritize the analysis of form data from important training sessions. Furthermore, it can analyze current form data while referring to past form data. This allows for prioritizing the analysis based on when the forms were collected, thereby prioritizing the analysis of the most recent form data.

[0097] The following briefly describes the processing flow for example form 2.

[0098] Step 1: The collection unit collects the athlete's exercise form. The collection unit films the athlete's exercise form, for example, using a video camera. The collection unit can either fix the video camera in place or hold it by hand. The collection unit can also, for example, use multiple cameras to film the exercise form from different angles. Step 2: The analysis unit analyzes the exercise form collected by the data collection unit. The analysis unit performs the exercise form analysis using, for example, a deep learning model. The analysis unit uses a large amount of exercise form data to train the deep learning model. The analysis unit analyzes each part of the exercise form and proposes optimizations for the form. Step 3: The service provider provides feedback based on the analysis results obtained by the analysis unit. The service provider provides feedback in real time, for example. The service provider can provide feedback via voice or text. The service provider can also send feedback to the athlete's smartphone, for example.

[0099] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0100] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0101] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0102] Each of the multiple elements described above, including the data collection unit, analysis unit, and data provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit uses the camera 42 of the smart device 14 to capture the athlete's exercise form. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and performs analysis of the exercise form using a deep learning model. The data provision unit is implemented by the control unit 46A of the smart device 14 and provides real-time feedback. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0103] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0104] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0105] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0106] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0107] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0108] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0109] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0110] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0111] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0112] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0113] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0114] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0115] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0116] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0117] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0118] Each of the multiple elements described above, including the data collection unit, analysis unit, and data provision unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit uses the camera 42 of the smart glasses 214 to capture the athlete's movement form. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and performs analysis of the movement form using a deep learning model. The data provision unit is implemented by the control unit 46A of the smart glasses 214 and provides real-time feedback. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0119] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0120] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0121] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0122] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0123] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0124] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0125] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0126] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0127] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0128] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0129] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0130] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0131] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0132] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0133] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0134] Each of the multiple elements described above, including the data collection unit, analysis unit, and data provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit uses the camera 42 of the headset terminal 314 to capture the athlete's exercise form. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and performs analysis of the exercise form using a deep learning model. The data provision unit is implemented by the control unit 46A of the headset terminal 314 and provides real-time feedback. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0135] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0136] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0137] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0138] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0139] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0140] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0141] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0142] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0143] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0144] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0145] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0146] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0147] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0148] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0149] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0150] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0151] Each of the multiple elements described above, including the data collection unit, analysis unit, and data provision unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the data collection unit uses the camera 42 of the robot 414 to capture the athlete's movement form. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and performs analysis of the movement form using a deep learning model. The data provision unit is implemented by the control unit 46A of the robot 414 and provides real-time feedback. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0152] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0153] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0154] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0155] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0156] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0157] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0158] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0159] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0160] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0161] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0162] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0163] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0164] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0165] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0166] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0167] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0168] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0169] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0170] (Note 1) A collection unit that collects exercise form data, An analysis unit analyzes the forms collected by the aforementioned collection unit, The system includes a providing unit that provides feedback based on the analysis results obtained by the analysis unit. A system characterized by the following features. (Note 2) The aforementioned supply unit is, Provide real-time feedback The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, We provide customizable analysis tailored to each individual athlete. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned collection unit is Collect video feeds The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned analysis unit, We propose form optimization using deep learning models. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned supply unit is, If a form error is detected, the athlete will be immediately informed of the necessary corrections. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is It estimates the athlete's emotions and adjusts the timing of the collected video based on the estimated emotions of the athlete. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze the athlete's past form data and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting videos, filtering is performed based on the athlete's current training status and goals. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is The system estimates the emotions of athletes and prioritizes which videos to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting video footage, the system prioritizes collecting highly relevant forms by considering the athlete's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is During video collection, analyze the athletes' social media activity and collect relevant forms. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, We estimate the athlete's emotions and adjust the representation of the analysis based on the estimated emotions of the athlete. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the form. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the form category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, The system estimates the athlete's emotions and adjusts the length of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the analysis priority is determined based on when the forms were collected. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the forms. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned supply unit is, The system estimates the athlete's emotions and adjusts the way feedback is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, When providing feedback, adjust the level of detail in the feedback based on the importance of the form. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, When providing feedback, different feedback algorithms are applied depending on the form category. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, The system estimates the athlete's emotions and adjusts the length of the feedback based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, When providing feedback, prioritize the feedback based on when the form was collected. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, When providing feedback, the order of feedback will be adjusted based on the relevance of the form. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0171] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A collection unit that collects exercise form data, An analysis unit analyzes the forms collected by the aforementioned collection unit, The system includes a providing unit that provides feedback based on the analysis results obtained by the analysis unit. A system characterized by the following features.

2. The aforementioned supply unit is, Provide real-time feedback The system according to feature 1.

3. The aforementioned analysis unit, We provide customizable analysis tailored to each individual athlete. The system according to feature 1.

4. The aforementioned collection unit is Collect video feeds The system according to feature 1.

5. The aforementioned analysis unit, We propose form optimization using deep learning models. The system according to feature 1.

6. The aforementioned supply unit is, If a form error is detected, the athlete will be immediately informed of the necessary corrections. The system according to feature 1.

7. The aforementioned collection unit is It estimates the athlete's emotions and adjusts the timing of the collected video based on the estimated emotions of the athlete. The system according to feature 1.

8. The aforementioned collection unit is Analyze the athlete's past form data and select the optimal data collection method. The system according to feature 1.

9. The aforementioned collection unit is When collecting videos, filtering is performed based on the athlete's current training status and goals. The system according to feature 1.

10. The aforementioned collection unit is The system estimates the emotions of athletes and prioritizes which videos to collect based on those estimated emotions. The system according to feature 1.

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