system

Abstract

Provide a system. 【Solution means】 Collection means for acquiring operation information, Processing means for standardizing the acquired operation information, Comparison means for comparing the standardized operation information with the operation information of excellent athletes, Analysis means for identifying improvement areas based on the comparison results, Generation means for generating feedback based on the identified improvement areas, Presentation means for presenting the generated feedback to the user, Simulation means for imitating the user's actions, Voice means for providing improvement guidelines through voice guidance A system including.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method 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 in 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 order to improve skills in sports, it is essential to practice with correct form. However, it is difficult for individual users to appropriately compare their own form with the ideal form of top athletes and identify areas for improvement. Therefore, there is a problem that subjective judgment has to be relied on, which hinders efficient skill improvement.

Means for Solving the Problems

[0005] This invention provides a device equipped with recording means for recording user movements, and further provides a comparison means for accurately comparing the movements recorded by the user with the movement data of top athletes by using processing means for standardizing the movement data. The device also includes analysis means for identifying areas for improvement by analyzing the data obtained by the comparison means, and generation means for generating feedback based on the identified areas for improvement. Finally, it provides display means for visually presenting the generated feedback to the user, thereby supporting the improvement of the user's skills.

[0006] "Motion data" refers to digital data representing information about a user's physical movements, acquired via a motion capture device.

[0007] "Recording means" refers to hardware or software components for continuously collecting user actions along a time axis.

[0008] A "processing means" is a functional module within the system used to standardize or filter acquired operational data.

[0009] A "comparison means" is a device or program that has the function of comparing a user's standardized motion data with the motion data of a top-level athlete and quantifying the difference.

[0010] An "analysis tool" is a module that analyzes data based on the differences obtained through comparison tools and identifies points necessary for improving user behavior.

[0011] A "generation means" is a unit equipped with the function to automatically create specific feedback based on the improvement points identified by the analysis means.

[0012] "Display means" refers to an interface or display used to visually show the generated feedback to the user. [Brief explanation of the drawing]

[0013] [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. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0014] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0015] First, the terms used in the following description will be explained.

[0016] In the following embodiments, a tagged processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0017] In the following embodiments, a tagged RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0018] In the following embodiments, a tagged storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0019] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. 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).

[0020] 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 A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0021] [First Embodiment]

[0022] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0023] As shown in Figure 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.

[0024] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0025] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0026] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input 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 device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0027] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (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.

[0028] 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.

[0029] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0030] 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.

[0031] The 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.

[0032] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0033] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0034] This invention is a system for supporting the improvement of a user's form in sports. It utilizes motion capture technology to acquire the user's movement data, and by comparing this data with the movement data of top athletes, it generates specific areas for improvement as feedback.

[0035] To record their movements, users must wear a motion capture system and capture their actions. Once the recording is complete, the device sends the data to a server, which receives the data and standardizes its format. The standardized data is then compared to an existing database of top athletes.

[0036] The server detects differences between user motion data and athlete data for specific metrics such as joint angles and movement speed. Based on these differences, an analysis algorithm identifies areas in the user's form that need improvement. Based on the identified areas for improvement, the server generates feedback indicating which parts of the movement should be modified and how.

[0037] The generated feedback is presented visually to the user through the device. The device uses skeletal animations and video comparisons to help the user intuitively understand which actions need correction. This system allows users to specifically grasp how to improve their training and improve their skills by putting those improvements into practice.

[0038] As a concrete example, let's consider a scenario where a user wants to improve their baseball pitching form and uses this system. The user receives feedback on aspects such as shoulder rotation and release point height, allowing them to consciously focus on and correct these points during actual practice. By receiving this feedback, the user can develop concrete improvement directions based on actual data, rather than just theoretical concepts.

[0039] The following describes the processing flow.

[0040] Step 1:

[0041] The user wears a motion capture device and selects a specific sports movement. They then perform this movement, recording the movement data through the motion capture device. The terminal captures this data in real time and saves it locally.

[0042] Step 2:

[0043] The terminal sends recorded operational data to the server. During this process, necessary conversion is performed to ensure the data format is suitable for analysis on the server. The data is transferred using a secure communication protocol.

[0044] Step 3:

[0045] The server stores the received operational data and performs standardization processing. This includes data normalization and noise reduction. As a result, all data becomes comparable using the same criteria.

[0046] Step 4:

[0047] The server retrieves motion data of top athletes from the database. This data includes ideal form data corresponding to the sports motion selected by the user.

[0048] Step 5:

[0049] The server compares standardized user motion data with data from top-level athletes. This comparison utilizes vectorized data of joint movements and posture to quantitatively analyze the differences.

[0050] Step 6:

[0051] The server uses the analysis results to identify areas where the user's movements need improvement. This includes identifying which joint movements deviate from the ideal and which parts have different speeds or timings.

[0052] Step 7:

[0053] The server automatically generates user feedback based on identified areas for improvement. This feedback indicates which behaviors should be improved and how.

[0054] Step 8:

[0055] The device receives the generated feedback and presents it to the user. This presentation uses a graphical user interface, combining skeleton animations and visual comparisons to show details.

[0056] Step 9:

[0057] The user reviews the feedback provided and plans to modify their actions based on it. In the next practice session, they utilize the feedback to make specific improvements to their actions.

[0058] (Example 1)

[0059] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0060] In sports activities, it is crucial to efficiently and effectively support the improvement of participants' skills. However, traditional coaching methods inevitably rely on subjective advice and lack specific and visual feedback. As a result, participants find it difficult to identify areas for improvement in their form and movements, leading to challenges in improving their skills over time.

[0061] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0062] In this invention, the server includes recording means for acquiring motion information, processing means for standardizing the acquired motion information, and comparison means for comparing the standardized motion information with the motion information of top athletes. This allows users to compare their own motion information with that of top athletes, identify specific areas for improvement based on the analysis results, and obtain effective feedback.

[0063] "Motion information" is a term that refers to data related to the user's physical movements and posture, and this data is acquired using motion capture technology, etc.

[0064] "Recording means" refers to devices or groups of devices used to capture user actions and record them as digital data.

[0065] "Processing means" refers to methods or devices that convert acquired operational information into an appropriate format and perform standardization to maintain data consistency.

[0066] "Comparison means" refers to methods or devices for comparing standardized operational information with data from top performers to identify differences.

[0067] "Analysis means" refers to methods and devices for analyzing differences obtained through comparison means and identifying areas in the user's actions that require improvement.

[0068] "Generation means" refers to methods or devices for creating feedback to be presented to the user based on the analyzed results.

[0069] "Display means" refers to methods or devices that visually represent the generated feedback to the user, allowing them to intuitively understand the areas for improvement.

[0070] A "posture acquisition device" refers to the sensor devices and the entire system used to acquire user movement information in real time.

[0071] "Directionalization" refers to the process of converting joint angle data in motion information into a vector format, enabling accurate comparison.

[0072] This invention is a system that assists in improving a user's motor skills and includes data collection, analysis, and feedback generation utilizing motion capture technology. The user records their movements using a motion capture device. An example of a device used here is a general posture acquisition device.

[0073] The terminal sends recorded behavioral information to the server. The server standardizes the received data and compares it with the behavioral data of top performers. This can utilize data analysis libraries (e.g., Python's NumPy and Pandas) and machine learning libraries (e.g., TENSORFLOW® and PyTorch).

[0074] The server analyzes the user's actions based on the comparison results to identify areas for improvement. Then, using a generative AI model, it generates natural language feedback based on the analysis results. This feedback provides specific ways to improve actions and is output in a format that the user can easily understand.

[0075] The generated feedback is presented to the user through the device. The device uses visual tools such as skeleton animations and video comparisons to clearly communicate areas for improvement. This visual presentation allows users to intuitively understand how to specifically correct their motor skills.

[0076] For example, if a user wants to improve their baseball pitching motion, the system will generate feedback on things like shoulder rotation and arm movement during pitching. The user can then use this feedback to continue practicing. Furthermore, an example of a prompt message could be: "Please tell me how to improve my baseball pitching form. I would like specific feedback based on comparative data with top players."

[0077] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0078] Step 1:

[0079] The user records their movements using a motion capture device. The input here is the user's physical movements, and the output is digital motion information. Specifically, the user wears a motion capture suit or markers, and joint position data is collected in real time during movement.

[0080] Step 2:

[0081] The terminal transmits the recorded operation information to the server. The input is the operation information obtained in step 1, and the output is the operation information received by the server. Communication protocols such as Wi-Fi and Bluetooth can be used for this transmission operation.

[0082] Step 3:

[0083] The server standardizes the received operational information. The input is raw operational data transmitted from the terminal, and the output is standardized operational data. Specifically, the process involves unifying data formats from different sensor systems and preparing the data to a comparable state.

[0084] Step 4:

[0085] The server compares standardized motion data with the motion data of top athletes. The input consists of standardized user motion data and data from top athletes in a database. The output is the difference information between the two. Specific calculations include methods for calculating joint angles and vectorizing the differences on a timeline for comparison.

[0086] Step 5:

[0087] The server performs analysis based on the comparison results and identifies areas for improvement. The input is difference information, and the output is a list of analyzed areas for improvement. The server uses an analysis algorithm to calculate which parts should be improved and to what extent.

[0088] Step 6:

[0089] The server generates feedback based on the analysis results. The input is a list of areas for improvement, and the output is specific feedback provided to the user. This includes using a generative AI model to output the feedback in natural language.

[0090] Step 7:

[0091] The terminal presents the generated feedback to the user. The input is feedback information sent from the server, and the output is the feedback displayed on the terminal. The terminal uses skeleton animations and video clips to visually indicate areas for improvement to the user.

[0092] (Application Example 1)

[0093] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0094] Traditional form improvement systems made it difficult for users to understand specifically which parts of their movements needed improvement and how. This resulted in users being unable to efficiently correct their form and achieve optimal performance improvements. Furthermore, immediate feedback was often unavailable, limiting the effectiveness of the training.

[0095] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0096] In this invention, the server includes a collection means for acquiring motion information, a processing means for standardizing the acquired motion information, and a comparison means for comparing the standardized motion information with the motion information of a superior athlete. This allows the user to efficiently analyze their own movements and immediately understand specific areas for improvement. They can intuitively grasp how to improve and maximize the effectiveness of their training.

[0097] "Means for collecting motion information" refers to devices or methods for measuring user actions in real time and collecting them as data.

[0098] "Processing means for standardizing acquired operational information" refers to a processing function that converts collected operational information into an analyzable format.

[0099] "A comparison method for comparing standardized motion information with the motion information of superior athletes" refers to a method that compares a user's standardized motion data with existing motion data of superior athletes based on a standard.

[0100] An "analysis method for identifying areas for improvement" is a method for clarifying which parts of a user's behavior should be improved based on the comparison results.

[0101] "Means for generating feedback" refers to the procedure of creating guidelines for users based on improvement information obtained through analysis.

[0102] "Means of presenting generated feedback to the user" refers to a function that communicates the generated feedback to the user visually or audibly.

[0103] A "simulation method that imitates user actions" is a simulation technique that faithfully reproduces user actions and visualizes areas that need improvement.

[0104] "Audio guidance that provides improvement instructions" refers to a means of communicating areas for improvement and training guidelines to users via audio.

[0105] This invention relates to a system that collects and analyzes user motion information using motion capture technology. The user wears a motion capture device on their body, thereby collecting motion information in real time. This information is immediately transmitted to a server, which performs processing to standardize the collected information. The standardized data is compared with motion information of top athletes stored in the cloud. The server uses a comparison means to identify the differences between the user's movements and those of top athletes, and an analysis means identifies areas that require improvement.

[0106] Based on identified areas for improvement, the server generates feedback and provides visual and auditory feedback to the user using simulated and audio means to mimic the user's actions. This system allows users to intuitively understand how to improve their actions, maximizing the effectiveness of their training.

[0107] As a concrete example, consider improving yoga form. When a user performs a specific yoga pose, their form information is captured and compared by a server to data from a professional instructor. Once areas for improvement are identified, the user receives visual animations and audio guidance on how to correct which parts of the pose and how.

[0108] An example of a prompt for the generating AI model is: "Use the Smart Form Coach Robot to generate feedback on how to improve the cobra pose in yoga. Present specific areas for improvement to the user using voice and animation." This prompt ensures that the feedback is specific and practical.

[0109] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0110] Step 1:

[0111] The user wears a motion capture device and begins a specific movement. The terminal captures this movement in real time and acquires motion information. The input is the user's physical movement, and the output is motion information in digital format. This data is measured by the motion capture device and transmitted to the terminal.

[0112] Step 2:

[0113] The terminal transmits the acquired digital operational information to the server. The server denoises and normalizes the received data to standardize it. The input is raw operational information, and the output is standardized data after denoising. This standardization converts it into a comparable format.

[0114] Step 3:

[0115] The server compares standardized data with existing motion information from top athletes. It vectorizes the specific data and calculates the differences between each data point. The input is standardized user data and athlete data, and the output is difference information for each joint angle and movement speed.

[0116] Step 4:

[0117] The server analyzes the obtained difference information to identify areas that need improvement. Using analytical tools, it quantitatively evaluates which areas can be improved and to what extent. The input is difference information, and the output is information identifying areas for improvement.

[0118] Step 5:

[0119] The server generates feedback based on the identified areas for improvement and provides visual and auditory presentations. It uses presentation methods to send animations and audio guides to the terminal. The input is information identifying the areas for improvement, and the output is feedback information presented to the user. This information helps the user understand specific correction methods.

[0120] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0121] This invention provides a system that, in addition to conventional systems that support the improvement of a user's form in sports, recognizes the user's emotional state in real time and reflects it in the generation of feedback, thereby providing more personalized training support.

[0122] Users wear a dedicated motion capture device and record their sports movements through a terminal equipped with an emotion engine. While performing the movements, the terminal uses cameras and sensors to analyze the user's facial expressions and physiological changes in their skin, collecting emotion data in real time. This data is also recorded and transmitted to a server along with the movement data.

[0123] The server compares the received motion data with that of top athletes and analyzes the differences. Furthermore, it incorporates emotional data obtained from the emotion engine to determine the psychological state in which the user is performing the actions. The analysis method evaluates not only the technical differences in the actions but also how the psychological state influences the actions.

[0124] Based on this information, the server generates feedback. This feedback includes not only technical improvement points but also emotionally conscious encouragement and advice. For example, if a user is feeling tense during operation, relaxation techniques and breathing exercises to help them calm down and concentrate may be recommended. If a user is experiencing heightened emotions, the feedback may include advice on how to channel that positive energy into more effective performance.

[0125] The device presents this feedback to the user. In addition to visual comparison data, text and voice guidance based on emotional data is provided. This allows users not only to hone their skills but also to learn how to manage their mental state and perform at their best.

[0126] For example, suppose a baseball player is practicing and feels anxious, causing their form to deteriorate. This system identifies that emotional state and provides feedback on specific techniques to help them manage their anxiety. In this way, the user can improve their overall performance.

[0127] The following describes the processing flow.

[0128] Step 1:

[0129] The user activates the emotion engine built into the motion capture device and terminal and begins exercising. The terminal records motion data and also collects the user's facial expressions and skin changes through cameras and biosensors, recording emotion data in real time.

[0130] Step 2:

[0131] The device sends emotional data along with operational data to the server. This data transfer is performed using a secure communication protocol, and the data is output in a way that maintains data integrity.

[0132] Step 3:

[0133] The server compares the received performance data with a predefined database of top-tier athletes. The server then performs data standardization, normalizing the performance and removing noise to enable effective comparison.

[0134] Step 4:

[0135] The server analyzes emotional data obtained from the emotion engine to identify the user's psychological state. During the analysis, it considers the impact of specific behavioral patterns and emotional changes on performance.

[0136] Step 5:

[0137] The server integrates the results of a technical analysis of motion data with the results of user emotion data to generate areas for improvement in the user's form and psychological advice. For example, if the motion is accurate but tension is observed, guidance for relaxation will be included.

[0138] Step 6:

[0139] The generated feedback is sent to the device in a form that combines technical elements with emotional support. The device presents this information to the user, providing guidance in text or voice, especially for emotionally-based advice.

[0140] Step 7:

[0141] Based on the feedback received, users identify areas for improvement in their training and practice the relaxation and concentration-enhancing techniques provided by the emotional engine. This is expected to lead to more effective results in subsequent practice sessions.

[0142] (Example 2)

[0143] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0144] Traditional sports training support systems have focused on improving technical movements, while failing to adequately consider the impact of the user's psychological state on training outcomes. Therefore, there is a need for methods that appropriately manage the user's mental motivation and concentration while simultaneously improving their technical level. To provide more personalized training support, a system is needed that comprehensively analyzes movement and emotional data and provides real-time feedback based on that analysis.

[0145] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0146] In this invention, the server includes recording means for acquiring motion data and emotional data, processing means for integrating and standardizing the acquired motion data and emotional data, and comparison means for comparing the integrated data with the motion and ideal psychological state of a top athlete. This makes it possible to provide personalized training feedback that integrates the user's technical improvement and psychological state management.

[0147] "Motion data" refers to information about the user's physical body movements, including detailed data such as joint angles and trajectories acquired by motion capture equipment.

[0148] "Emotional data" refers to information about the user's psychological state and includes data based on facial expressions and physiological changes acquired using emotion recognition functions.

[0149] "Recording means" refers to means for acquiring motion data and emotion data using motion capture devices and emotion recognition sensors.

[0150] "Standardization" refers to a series of processes performed to transform acquired data into a comparable format, including the unification of data formats and scaling based on standards.

[0151] "Comparison methods" refer to methods for analyzing technical and psychological differences by comparing integrated data with baseline data of top-level athletes.

[0152] "Analysis means" refers to a method for identifying areas for improvement in the user's technical and emotional aspects based on the differences extracted by comparison means.

[0153] "Generative means" refers to the process of formalizing technical and emotional feedback to users based on improvement points identified through analysis.

[0154] "Display means" refers to means of conveying generated feedback to the user, and includes visual displays and audio guides.

[0155] This invention functions as an integrated system that supports the technical improvement of a user's sports movements and the management of their mental state. During system implementation, the user wears a motion capture device, and motion data is collected in real time. Simultaneously, the terminal uses cameras and emotion sensors to analyze the user's facial expressions and physiological changes in their skin, collecting emotional data. All of this data is transmitted to a server.

[0156] The server integrates behavioral and emotional data and standardizes it using advanced data processing software. This converts the data into a comparable format. Next, the server analyzes technical and psychological differences by comparing the integrated data with baseline data and ideal emotional states of top athletes. This comparison identifies specific areas for improvement regarding the user's behavior and emotional state.

[0157] The generative AI model generates feedback based on the analysis results. This feedback includes specific advice to improve the user's motor skills, as well as suggestions for maintaining an optimal psychological state. For example, if the user is feeling nervous, it might recommend deep breathing. The device presents this feedback to the user as visual and audio guidance, allowing the user to immediately utilize it during training.

[0158] As a concrete example, for users who feel anxious during baseball training, this system identifies the feeling of anxiety and provides feedback, including ways to alleviate it. In this way, users can improve not only their physical skills but also their mental flexibility.

[0159] An example of a prompt message would be, "Based on user behavior and emotional data in a specific sports scene, please provide feedback including appropriate areas for improvement and advice on emotional management." Following this prompt, the generating AI model provides optimal feedback.

[0160] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0161] Step 1:

[0162] The user wears a motion capture device on their body, which allows for real-time acquisition of motion data. The device simultaneously monitors facial expressions and skin temperature changes using cameras and emotion sensors, collecting emotional data. Inputs are the user's body movements and facial expression data, while outputs are motion data and emotional data.

[0163] Step 2:

[0164] The terminal sends acquired behavioral and emotional data to the server. The server receives this data and stores both in a database. The data is standardized through programmatic preprocessing and converted into a format suitable for comparison. The input is the transmitted raw data, and the output is the standardized behavioral and emotional data.

[0165] Step 3:

[0166] The server compares standardized data to baseline data and ideal psychological states of top athletes. Machine learning algorithms are used as comparison tools to comprehensively analyze performance accuracy and emotional stability. Input is standardized user data, and output is points for technical and psychological improvement.

[0167] Step 4:

[0168] The server generates feedback using an AI model based on the analyzed improvement points. This feedback includes specific advice about the user's technical and emotional state. Inputs are technical and psychological improvement points, and output is text or audio data as feedback.

[0169] Step 5:

[0170] The device presents the generated feedback to the user visually and audibly. The device uses video playback and audio guidance to help the user understand the feedback. The input is the feedback data, and the output is the information presented to the user.

[0171] (Application Example 2)

[0172] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0173] In traditional factory environments, it was difficult to monitor workers' operational efficiency and psychological stress in real time and provide optimal feedback. This meant that workers' performance could not be maximized, leading to a risk of decreased labor efficiency. Furthermore, inadequate stress management could potentially impact workers' health and safety. To improve the quality and efficiency of work in factory environments, there was a need to provide advice and technical feedback that considered the psychological state of workers.

[0174] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0175] In this invention, the server includes recording means for acquiring operational data, processing means for standardizing the acquired operational data, and analysis means for analyzing the user's psychological state. This makes it possible to acquire and analyze the worker's operational data and psychological state in real time and provide feedback including optimal improvement points and psychological advice.

[0176] "Recording means for acquiring motion data" refers to devices or methods that record the user's actions and movements as digital information in real time.

[0177] "Processing means for standardizing acquired motion data" refers to a system that adjusts recorded motion data based on consistent criteria and converts it into a format that can be compared with other reference data.

[0178] "Comparison means for comparing standardized operating data with standard operating data" refers to a method or apparatus for comparing adjusted operating data with reference operating data to confirm differences and degrees of agreement.

[0179] "Analysis means for identifying areas for improvement based on comparison results" refers to a process or apparatus that analyzes the differences in compared data and clarifies which parts should be improved.

[0180] A "generation method for generating feedback based on identified areas for improvement" is a system that creates specific advice on how users should modify their behavior based on the analyzed areas for improvement.

[0181] "Display means for presenting generated feedback to the user" means a device or method that provides generated feedback information to the user visually or audibly.

[0182] "Analytical means for analyzing the psychological state of a user" refers to a technology or device that digitizes the user's emotions and mental state and evaluates them in real time.

[0183] "Generating means for incorporating psychological advice into feedback based on psychological state analysis results" refers to a technology or method that includes advice that takes the user's mental health into consideration, based on analyzed psychological state data.

[0184] The system for realizing this invention is configured to generate and provide feedback to the user based on motion data and psychological state. This system operates in conjunction with smart glasses or similar devices. The server acquires motion data in real time and processes the acquired data to standardize it. In this process, a motion tracking device (motion capture device) is used to record the motion data, and the software used is an image processing library such as OpenCV.

[0185] Furthermore, the server analyzes the acquired psychological state data to evaluate the user's emotions and stress levels. AI models such as TensorFlow are used for this analysis. The results of the analysis form the basis for the feedback presented to the user. This feedback combines technical improvement points with psychological advice to help the user work more efficiently and stress-free.

[0186] The device plays the role of presenting this generated feedback to the user. Visual data may be provided through the smart glasses' display, and auditory data may be provided through the headset speaker. Based on the server's processing results, the information is conveyed in the most effective way for the user.

[0187] For example, if the system identifies a user performing monotonous tasks for extended periods as having a high stress level, the terminal will display a relaxation message recommending that the user take a short break. An example of an input prompt to the generating AI model might be: "The following data indicates the worker's stress level: [data]. Please generate appropriate feedback based on this."

[0188] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0189] Step 1:

[0190] When a user begins to move, the device acquires motion data in real time via a motion tracking device. The input is raw data from sensors, and the output is recorded motion data. This data includes position and orientation information over time.

[0191] Step 2:

[0192] The server receives the acquired motion data and performs a standardization process. The input is the motion data acquired in step 1, and the output is standardized motion data transformed into a comparable standard. This process involves data manipulation to align the motion characteristics to a unified scale.

[0193] Step 3:

[0194] The server compares standardized operation data with standard operation data. The input is the standardized operation data from step 2 and predefined standard operation data, and the output is comparison data showing the differences between the operations. This comparison calculates differences in operation angle and velocity.

[0195] Step 4:

[0196] The server analyzes the comparison data and identifies areas for improvement. The input is the comparison data from step 3, and the output is the detailed analysis results of the areas for improvement. Here, parts where specific operations are not performing as expected become clear.

[0197] Step 5:

[0198] The server analyzes psychological state data independently of the user's actions. Input is psychological state data from sensors and cameras, and output is the analysis results indicating the user's emotions and stress levels. This process includes facial expression analysis and biosignal analysis.

[0199] Step 6:

[0200] The server generates feedback based on the improvement points and the results of the psychological state analysis. The inputs are the improvement points from Step 4 and the results of the psychological state analysis from Step 5, and the output is the feedback information presented to the user. The feedback includes not only technical advice but also psychological support.

[0201] Step 7:

[0202] The device presents the generated feedback to the user. The input is the feedback information from step 6, and the output is the presentation of visual or auditory information to the user. Messages and guidance are provided using smart glasses displays or headsets.

[0203] 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.

[0204] Data generation model 58 is a 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> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0205] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0206] [Second Embodiment]

[0207] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0208] 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.

[0209] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0210] 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.

[0211] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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.

[0212] 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0213] 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.

[0214] 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 using the processor 28. The storage 32 stores the specific processing program 56.

[0215] The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0216] The 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.

[0217] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0218] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0219] This invention is a system for supporting the improvement of a user's form in sports. It utilizes motion capture technology to acquire the user's movement data, and by comparing this data with the movement data of top athletes, it generates specific areas for improvement as feedback.

[0220] To record their movements, users must wear a motion capture system and capture their actions. Once the recording is complete, the device sends the data to a server, which receives the data and standardizes its format. The standardized data is then compared to an existing database of top athletes.

[0221] The server detects differences between user motion data and athlete data for specific metrics such as joint angles and movement speed. Based on these differences, an analysis algorithm identifies areas in the user's form that need improvement. Based on the identified areas for improvement, the server generates feedback indicating which parts of the movement should be modified and how.

[0222] The generated feedback is presented visually to the user through the device. The device uses skeletal animations and video comparisons to help the user intuitively understand which actions need correction. This system allows users to specifically grasp how to improve their training and improve their skills by putting those improvements into practice.

[0223] As a concrete example, let's consider a scenario where a user wants to improve their baseball pitching form and uses this system. The user receives feedback on aspects such as shoulder rotation and release point height, allowing them to consciously focus on and correct these points during actual practice. By receiving this feedback, the user can develop concrete improvement directions based on actual data, rather than just theoretical concepts.

[0224] The following describes the processing flow.

[0225] Step 1:

[0226] The user wears a motion capture device and selects a specific sports movement. They then perform this movement, recording the movement data through the motion capture device. The terminal captures this data in real time and saves it locally.

[0227] Step 2:

[0228] The terminal sends recorded operational data to the server. During this process, necessary conversion is performed to ensure the data format is suitable for analysis on the server. The data is transferred using a secure communication protocol.

[0229] Step 3:

[0230] The server stores the received operational data and performs standardization processing. This includes data normalization and noise reduction. As a result, all data becomes comparable using the same criteria.

[0231] Step 4:

[0232] The server retrieves motion data of top athletes from the database. This data includes ideal form data corresponding to the sports motion selected by the user.

[0233] Step 5:

[0234] The server compares standardized user motion data with data from top-level athletes. This comparison utilizes vectorized data of joint movements and posture to quantitatively analyze the differences.

[0235] Step 6:

[0236] The server uses the analysis results to identify areas where the user's movements need improvement. This includes identifying which joint movements deviate from the ideal and which parts have different speeds or timings.

[0237] Step 7:

[0238] The server automatically generates user feedback based on identified areas for improvement. This feedback indicates which behaviors should be improved and how.

[0239] Step 8:

[0240] The device receives the generated feedback and presents it to the user. This presentation uses a graphical user interface, combining skeleton animations and visual comparisons to show details.

[0241] Step 9:

[0242] The user reviews the feedback provided and plans to modify their actions based on it. In the next practice session, they utilize the feedback to make specific improvements to their actions.

[0243] (Example 1)

[0244] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0245] In sports activities, it is crucial to efficiently and effectively support the improvement of participants' skills. However, traditional coaching methods inevitably rely on subjective advice and lack specific and visual feedback. As a result, participants find it difficult to identify areas for improvement in their form and movements, leading to challenges in improving their skills over time.

[0246] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0247] In this invention, the server includes recording means for acquiring motion information, processing means for standardizing the acquired motion information, and comparison means for comparing the standardized motion information with the motion information of top athletes. This allows users to compare their own motion information with that of top athletes, identify specific areas for improvement based on the analysis results, and obtain effective feedback.

[0248] "Motion information" is a term that refers to data related to the user's physical movements and posture, and this data is acquired using motion capture technology, etc.

[0249] "Recording means" refers to devices or groups of devices used to capture user actions and record them as digital data.

[0250] "Processing means" refers to methods or devices that convert acquired operational information into an appropriate format and perform standardization to maintain data consistency.

[0251] "Comparison means" refers to methods or devices for comparing standardized operational information with data from top performers to identify differences.

[0252] "Analysis means" refers to methods and devices for analyzing differences obtained through comparison means and identifying areas in the user's actions that require improvement.

[0253] "Generation means" refers to methods or devices for creating feedback to be presented to the user based on the analyzed results.

[0254] "Display means" refers to methods or devices that visually represent the generated feedback to the user, allowing them to intuitively understand the areas for improvement.

[0255] A "posture acquisition device" refers to the sensor devices and the entire system used to acquire user movement information in real time.

[0256] "Directionalization" refers to the process of converting joint angle data in motion information into a vector format, enabling accurate comparison.

[0257] This invention is a system that assists in improving a user's motor skills and includes data collection, analysis, and feedback generation utilizing motion capture technology. The user records their movements using a motion capture device. An example of a device used here is a general posture acquisition device.

[0258] The terminal sends recorded behavioral information to the server. The server standardizes the received data and compares it with the behavioral data of top performers. This can utilize data analysis libraries (e.g., Python's NumPy and Pandas) and machine learning libraries (e.g., TensorFlow and PyTorch).

[0259] The server analyzes the user's actions based on the comparison results to identify areas for improvement. Then, using a generative AI model, it generates natural language feedback based on the analysis results. This feedback provides specific ways to improve actions and is output in a format that the user can easily understand.

[0260] The generated feedback is presented to the user through the device. The device uses visual tools such as skeleton animations and video comparisons to clearly communicate areas for improvement. This visual presentation allows users to intuitively understand how to specifically correct their motor skills.

[0261] For example, if a user wants to improve their baseball pitching motion, the system will generate feedback on things like shoulder rotation and arm movement during pitching. The user can then use this feedback to continue practicing. Furthermore, an example of a prompt message could be: "Please tell me how to improve my baseball pitching form. I would like specific feedback based on comparative data with top players."

[0262] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0263] Step 1:

[0264] The user records their movements using a motion capture device. The input here is the user's physical movements, and the output is digital motion information. Specifically, the user wears a motion capture suit or markers, and joint position data is collected in real time during movement.

[0265] Step 2:

[0266] The terminal transmits the recorded operation information to the server. The input is the operation information obtained in step 1, and the output is the operation information received by the server. Communication protocols such as Wi-Fi and Bluetooth can be used for this transmission operation.

[0267] Step 3:

[0268] The server standardizes the received operational information. The input is raw operational data transmitted from the terminal, and the output is standardized operational data. Specifically, the process involves unifying data formats from different sensor systems and preparing the data to a comparable state.

[0269] Step 4:

[0270] The server compares standardized motion data with the motion data of top athletes. The input consists of standardized user motion data and data from top athletes in a database. The output is the difference information between the two. Specific calculations include methods for calculating joint angles and vectorizing the differences on a timeline for comparison.

[0271] Step 5:

[0272] The server performs analysis based on the comparison results and identifies areas for improvement. The input is difference information, and the output is a list of analyzed areas for improvement. The server uses an analysis algorithm to calculate which parts should be improved and to what extent.

[0273] Step 6:

[0274] The server generates feedback based on the analysis results. The input is a list of areas for improvement, and the output is specific feedback provided to the user. This includes using a generative AI model to output the feedback in natural language.

[0275] Step 7:

[0276] The terminal presents the generated feedback to the user. The input is feedback information sent from the server, and the output is the feedback displayed on the terminal. The terminal uses skeleton animations and video clips to visually indicate areas for improvement to the user.

[0277] (Application Example 1)

[0278] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0279] Traditional form improvement systems made it difficult for users to understand specifically which parts of their movements needed improvement and how. This resulted in users being unable to efficiently correct their form and achieve optimal performance improvements. Furthermore, immediate feedback was often unavailable, limiting the effectiveness of the training.

[0280] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0281] In this invention, the server includes a collection means for acquiring operation information, a processing means for standardizing the acquired operation information, and a comparison means for comparing the standardized operation information with the operation information of excellent athletes. As a result, the user can efficiently analyze his or her own actions and immediately understand specific areas for improvement. The user can intuitively grasp how to improve, and can maximize the effect of training.

[0282] The "collection means for acquiring operation information" is a device or method for measuring the user's actions in real time and collecting them as data.

[0283] The "processing means for standardizing the acquired operation information" is a processing function for converting the collected operation information into an analyzable format.

[0284] The "comparison means for comparing the standardized operation information with the operation information of excellent athletes" is a method for comparing the standardized operation data of the user with the operation data of existing excellent athletes based on a standard.

[0285] The "analysis means for identifying improvement areas" is a method for clarifying which parts of the user's actions should be improved based on the comparison results.

[0286] The "generation means for generating feedback" is a procedure for creating guidelines for the user based on the improvement information obtained through analysis.

[0287] The "presentation means for presenting the generated feedback to the user" is a function for visually or auditorily transmitting the generated feedback to the user.

[0288] The "simulation means for imitating the user's actions" is a simulation method for faithfully reproducing the user's actions and visualizing areas that need improvement.

[0289] "Audio guidance that provides improvement instructions" refers to a means of communicating areas for improvement and training guidelines to users via audio.

[0290] This invention relates to a system that collects and analyzes user motion information using motion capture technology. The user wears a motion capture device on their body, thereby collecting motion information in real time. This information is immediately transmitted to a server, which performs processing to standardize the collected information. The standardized data is compared with motion information of top athletes stored in the cloud. The server uses a comparison means to identify the differences between the user's movements and those of top athletes, and an analysis means identifies areas that require improvement.

[0291] Based on identified areas for improvement, the server generates feedback and provides visual and auditory feedback to the user using simulated and audio means to mimic the user's actions. This system allows users to intuitively understand how to improve their actions, maximizing the effectiveness of their training.

[0292] As a concrete example, consider improving yoga form. When a user performs a specific yoga pose, their form information is captured and compared by a server to data from a professional instructor. Once areas for improvement are identified, the user receives visual animations and audio guidance on how to correct which parts of the pose and how.

[0293] An example of a prompt for the generating AI model is: "Use the Smart Form Coach Robot to generate feedback on how to improve the cobra pose in yoga. Present specific areas for improvement to the user using voice and animation." This prompt ensures that the feedback is specific and practical.

[0294] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0295] Step 1:

[0296] The user wears a motion capture device and begins a specific movement. The terminal captures this movement in real time and acquires motion information. The input is the user's physical movement, and the output is motion information in digital format. This data is measured by the motion capture device and transmitted to the terminal.

[0297] Step 2:

[0298] The terminal transmits the acquired digital operational information to the server. The server denoises and normalizes the received data to standardize it. The input is raw operational information, and the output is standardized data after denoising. This standardization converts it into a comparable format.

[0299] Step 3:

[0300] The server compares standardized data with existing motion information from top athletes. It vectorizes the specific data and calculates the differences between each data point. The input is standardized user data and athlete data, and the output is difference information for each joint angle and movement speed.

[0301] Step 4:

[0302] The server analyzes the obtained difference information to identify areas that need improvement. Using analytical tools, it quantitatively evaluates which areas can be improved and to what extent. The input is difference information, and the output is information identifying areas for improvement.

[0303] Step 5:

[0304] The server generates feedback based on the identified improvement areas and provides visual and auditory presentations. Using the presentation means, animations and voice guides are transmitted to the terminal. The input is the identification information of the improvement area, and the output is the feedback information presented to the user. Based on this information, the user understands the specific correction methods.

[0305] Furthermore, an emotion engine for estimating the user's emotion may be combined. That is, the specific processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform specific processing using the user's emotion.

[0306] In addition to the conventional system that supports improving the user's form in sports, the present invention is a system that provides more individualized training support by recognizing the user's emotional state in real time and reflecting it in feedback generation.

[0307] The user wears a dedicated motion capture device and records sports actions through a terminal incorporating an emotion engine. While performing the actions, the terminal analyzes the user's facial expressions and physiological changes in the skin using a camera and sensors, and collects emotion data in real time. This data is recorded together and transmitted to the server along with the action data.

[0308] The server compares the received action data with the action data of top athletes and analyzes the differences. Furthermore, it incorporates the emotion data obtained from the emotion engine and determines in what psychological state the user is performing the actions. The analysis means evaluates how the psychological state affects the actions in addition to the technical differences in the actions.

[0309] Based on this information, the server generates feedback. This feedback includes not only technical improvement points but also emotionally conscious encouragement and advice. For example, if a user is feeling tense during operation, relaxation techniques and breathing exercises to help them calm down and concentrate may be recommended. If a user is experiencing heightened emotions, the feedback may include advice on how to channel that positive energy into more effective performance.

[0310] The device presents this feedback to the user. In addition to visual comparison data, text and voice guidance based on emotional data is provided. This allows users not only to hone their skills but also to learn how to manage their mental state and perform at their best.

[0311] For example, suppose a baseball player is practicing and feels anxious, causing their form to deteriorate. This system identifies that emotional state and provides feedback on specific techniques to help them manage their anxiety. In this way, the user can improve their overall performance.

[0312] The following describes the processing flow.

[0313] Step 1:

[0314] The user activates the emotion engine built into the motion capture device and terminal and begins exercising. The terminal records motion data and also collects the user's facial expressions and skin changes through cameras and biosensors, recording emotion data in real time.

[0315] Step 2:

[0316] The device sends emotional data along with operational data to the server. This data transfer is performed using a secure communication protocol, and the data is output in a way that maintains data integrity.

[0317] Step 3:

[0318] The server compares the received performance data with a predefined database of top-tier athletes. The server then performs data standardization, normalizing the performance and removing noise to enable effective comparison.

[0319] Step 4:

[0320] The server analyzes emotional data obtained from the emotion engine to identify the user's psychological state. During the analysis, it considers the impact of specific behavioral patterns and emotional changes on performance.

[0321] Step 5:

[0322] The server integrates the results of a technical analysis of motion data with the results of user emotion data to generate areas for improvement in the user's form and psychological advice. For example, if the motion is accurate but tension is observed, guidance for relaxation will be included.

[0323] Step 6:

[0324] The generated feedback is sent to the device in a form that combines technical elements with emotional support. The device presents this information to the user, providing guidance in text or voice, especially for emotionally-based advice.

[0325] Step 7:

[0326] Based on the feedback received, users identify areas for improvement in their training and practice the relaxation and concentration-enhancing techniques provided by the emotional engine. This is expected to lead to more effective results in subsequent practice sessions.

[0327] (Example 2)

[0328] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0329] Traditional sports training support systems have focused on improving technical movements, while failing to adequately consider the impact of the user's psychological state on training outcomes. Therefore, there is a need for methods that appropriately manage the user's mental motivation and concentration while simultaneously improving their technical level. To provide more personalized training support, a system is needed that comprehensively analyzes movement and emotional data and provides real-time feedback based on that analysis.

[0330] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0331] In this invention, the server includes recording means for acquiring motion data and emotional data, processing means for integrating and standardizing the acquired motion data and emotional data, and comparison means for comparing the integrated data with the motion and ideal psychological state of a top athlete. This makes it possible to provide personalized training feedback that integrates the user's technical improvement and psychological state management.

[0332] "Motion data" refers to information about the user's physical body movements, including detailed data such as joint angles and trajectories acquired by motion capture equipment.

[0333] "Emotional data" refers to information about the user's psychological state and includes data based on facial expressions and physiological changes acquired using emotion recognition functions.

[0334] "Recording means" refers to means for acquiring motion data and emotion data using motion capture devices and emotion recognition sensors.

[0335] "Standardization" refers to a series of processes performed to transform acquired data into a comparable format, including the unification of data formats and scaling based on standards.

[0336] "Comparison methods" refer to methods for analyzing technical and psychological differences by comparing integrated data with baseline data of top-level athletes.

[0337] "Analysis means" refers to a method for identifying areas for improvement in the user's technical and emotional aspects based on the differences extracted by comparison means.

[0338] "Generative means" refers to the process of formalizing technical and emotional feedback to users based on improvement points identified through analysis.

[0339] "Display means" refers to means of conveying generated feedback to the user, and includes visual displays and audio guides.

[0340] This invention functions as an integrated system that supports the technical improvement of a user's sports movements and the management of their mental state. During system implementation, the user wears a motion capture device, and motion data is collected in real time. Simultaneously, the terminal uses cameras and emotion sensors to analyze the user's facial expressions and physiological changes in their skin, collecting emotional data. All of this data is transmitted to a server.

[0341] The server integrates behavioral and emotional data and standardizes it using advanced data processing software. This converts the data into a comparable format. Next, the server analyzes technical and psychological differences by comparing the integrated data with baseline data and ideal emotional states of top athletes. This comparison identifies specific areas for improvement regarding the user's behavior and emotional state.

[0342] The generative AI model generates feedback based on the analysis results. This feedback includes specific advice to improve the user's motor skills, as well as suggestions for maintaining an optimal psychological state. For example, if the user is feeling nervous, it might recommend deep breathing. The device presents this feedback to the user as visual and audio guidance, allowing the user to immediately utilize it during training.

[0343] As a concrete example, for users who feel anxious during baseball training, this system identifies the feeling of anxiety and provides feedback, including ways to alleviate it. In this way, users can improve not only their physical skills but also their mental flexibility.

[0344] An example of a prompt message would be, "Based on user behavior and emotional data in a specific sports scene, please provide feedback including appropriate areas for improvement and advice on emotional management." Following this prompt, the generating AI model provides optimal feedback.

[0345] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0346] Step 1:

[0347] The user wears a motion capture device on their body, which allows for real-time acquisition of motion data. The device simultaneously monitors facial expressions and skin temperature changes using cameras and emotion sensors, collecting emotional data. Inputs are the user's body movements and facial expression data, while outputs are motion data and emotional data.

[0348] Step 2:

[0349] The terminal sends acquired behavioral and emotional data to the server. The server receives this data and stores both in a database. The data is standardized through programmatic preprocessing and converted into a format suitable for comparison. The input is the transmitted raw data, and the output is the standardized behavioral and emotional data.

[0350] Step 3:

[0351] The server compares standardized data to baseline data and ideal psychological states of top athletes. Machine learning algorithms are used as comparison tools to comprehensively analyze performance accuracy and emotional stability. Input is standardized user data, and output is points for technical and psychological improvement.

[0352] Step 4:

[0353] The server generates feedback using an AI model based on the analyzed improvement points. This feedback includes specific advice about the user's technical and emotional state. Inputs are technical and psychological improvement points, and output is text or audio data as feedback.

[0354] Step 5:

[0355] The device presents the generated feedback to the user visually and audibly. The device uses video playback and audio guidance to help the user understand the feedback. The input is the feedback data, and the output is the information presented to the user.

[0356] (Application Example 2)

[0357] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the smart glasses 214 as the "terminal".

[0358] In traditional factory environments, it was difficult to monitor workers' operational efficiency and psychological stress in real time and provide optimal feedback. This meant that workers' performance could not be maximized, leading to a risk of decreased labor efficiency. Furthermore, inadequate stress management could potentially impact workers' health and safety. To improve the quality and efficiency of work in factory environments, there was a need to provide advice and technical feedback that considered the psychological state of workers.

[0359] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0360] In this invention, the server includes recording means for acquiring operational data, processing means for standardizing the acquired operational data, and analysis means for analyzing the user's psychological state. This makes it possible to acquire and analyze the worker's operational data and psychological state in real time and provide feedback including optimal improvement points and psychological advice.

[0361] "Recording means for acquiring motion data" refers to devices or methods that record the user's actions and movements as digital information in real time.

[0362] "Processing means for standardizing acquired motion data" refers to a system that adjusts recorded motion data based on consistent criteria and converts it into a format that can be compared with other reference data.

[0363] "Comparison means for comparing standardized operating data with standard operating data" refers to a method or apparatus for comparing adjusted operating data with reference operating data to confirm differences and degrees of agreement.

[0364] "Analysis means for identifying areas for improvement based on comparison results" refers to a process or apparatus that analyzes the differences in compared data and clarifies which parts should be improved.

[0365] A "generation method for generating feedback based on identified areas for improvement" is a system that creates specific advice on how users should modify their behavior based on the analyzed areas for improvement.

[0366] "Display means for presenting generated feedback to the user" means a device or method that provides generated feedback information to the user visually or audibly.

[0367] "Analytical means for analyzing the psychological state of a user" refers to a technology or device that digitizes the user's emotions and mental state and evaluates them in real time.

[0368] "Generating means for incorporating psychological advice into feedback based on psychological state analysis results" refers to a technology or method that includes advice that takes the user's mental health into consideration, based on analyzed psychological state data.

[0369] The system for realizing this invention is configured to generate and provide feedback to the user based on motion data and psychological state. This system operates in conjunction with smart glasses or similar devices. The server acquires motion data in real time and processes the acquired data to standardize it. In this process, a motion tracking device (motion capture device) is used to record the motion data, and the software used is an image processing library such as OpenCV.

[0370] Furthermore, the server analyzes the acquired psychological state data to evaluate the user's emotions and stress levels. AI models such as TensorFlow are used for this analysis. The results of the analysis form the basis for the feedback presented to the user. This feedback combines technical improvement points with psychological advice to help the user work more efficiently and stress-free.

[0371] The device plays the role of presenting this generated feedback to the user. Visual data may be provided through the smart glasses' display, and auditory data may be provided through the headset speaker. Based on the server's processing results, the information is conveyed in the most effective way for the user.

[0372] For example, if the system identifies a user performing monotonous tasks for extended periods as having a high stress level, the terminal will display a relaxation message recommending that the user take a short break. An example of an input prompt to the generating AI model might be: "The following data indicates the worker's stress level: [data]. Please generate appropriate feedback based on this."

[0373] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0374] Step 1:

[0375] When a user begins to move, the device acquires motion data in real time via a motion tracking device. The input is raw data from sensors, and the output is recorded motion data. This data includes position and orientation information over time.

[0376] Step 2:

[0377] The server receives the acquired motion data and performs a standardization process. The input is the motion data acquired in step 1, and the output is standardized motion data transformed into a comparable standard. This process involves data manipulation to align the motion characteristics to a unified scale.

[0378] Step 3:

[0379] The server compares standardized operation data with standard operation data. The input is the standardized operation data from step 2 and predefined standard operation data, and the output is comparison data showing the differences between the operations. This comparison calculates differences in operation angle and velocity.

[0380] Step 4:

[0381] The server analyzes the comparison data and identifies areas for improvement. The input is the comparison data from step 3, and the output is the detailed analysis results of the areas for improvement. Here, parts where specific operations are not performing as expected become clear.

[0382] Step 5:

[0383] The server analyzes psychological state data independently of the user's actions. Input is psychological state data from sensors and cameras, and output is the analysis results indicating the user's emotions and stress levels. This process includes facial expression analysis and biosignal analysis.

[0384] Step 6:

[0385] The server generates feedback based on the improvement points and the results of the psychological state analysis. The inputs are the improvement points from Step 4 and the results of the psychological state analysis from Step 5, and the output is the feedback information presented to the user. The feedback includes not only technical advice but also psychological support.

[0386] Step 7:

[0387] The device presents the generated feedback to the user. The input is the feedback information from step 6, and the output is the presentation of visual or auditory information to the user. Messages and guidance are provided using smart glasses displays or headsets.

[0388] 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.

[0389] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0390] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0391] [Third Embodiment]

[0392] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0393] 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.

[0394] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0395] 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.

[0396] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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.

[0397] 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0398] 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.

[0399] 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.

[0400] The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0401] The 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.

[0402] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0403] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0404] This invention is a system for supporting the improvement of a user's form in sports. It utilizes motion capture technology to acquire the user's movement data, and by comparing this data with the movement data of top athletes, it generates specific areas for improvement as feedback.

[0405] To record their movements, users must wear a motion capture system and capture their actions. Once the recording is complete, the device sends the data to a server, which receives the data and standardizes its format. The standardized data is then compared to an existing database of top athletes.

[0406] The server detects differences between user motion data and athlete data for specific metrics such as joint angles and movement speed. Based on these differences, an analysis algorithm identifies areas in the user's form that need improvement. Based on the identified areas for improvement, the server generates feedback indicating which parts of the movement should be modified and how.

[0407] The generated feedback is presented visually to the user through the device. The device uses skeletal animations and video comparisons to help the user intuitively understand which actions need correction. This system allows users to specifically grasp how to improve their training and improve their skills by putting those improvements into practice.

[0408] As a concrete example, let's consider a scenario where a user wants to improve their baseball pitching form and uses this system. The user receives feedback on aspects such as shoulder rotation and release point height, allowing them to consciously focus on and correct these points during actual practice. By receiving this feedback, the user can develop concrete improvement directions based on actual data, rather than just theoretical concepts.

[0409] The following describes the processing flow.

[0410] Step 1:

[0411] The user wears a motion capture device and selects a specific sports movement. They then perform this movement, recording the movement data through the motion capture device. The terminal captures this data in real time and saves it locally.

[0412] Step 2:

[0413] The terminal sends recorded operational data to the server. During this process, necessary conversion is performed to ensure the data format is suitable for analysis on the server. The data is transferred using a secure communication protocol.

[0414] Step 3:

[0415] The server stores the received operational data and performs standardization processing. This includes data normalization and noise reduction. As a result, all data becomes comparable using the same criteria.

[0416] Step 4:

[0417] The server retrieves motion data of top athletes from the database. This data includes ideal form data corresponding to the sports motion selected by the user.

[0418] Step 5:

[0419] The server compares standardized user motion data with data from top-level athletes. This comparison utilizes vectorized data of joint movements and posture to quantitatively analyze the differences.

[0420] Step 6:

[0421] The server uses the analysis results to identify areas where the user's movements need improvement. This includes identifying which joint movements deviate from the ideal and which parts have different speeds or timings.

[0422] Step 7:

[0423] The server automatically generates user feedback based on identified areas for improvement. This feedback indicates which behaviors should be improved and how.

[0424] Step 8:

[0425] The device receives the generated feedback and presents it to the user. This presentation uses a graphical user interface, combining skeleton animations and visual comparisons to show details.

[0426] Step 9:

[0427] The user reviews the feedback provided and plans to modify their actions based on it. In the next practice session, they utilize the feedback to make specific improvements to their actions.

[0428] (Example 1)

[0429] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0430] In sports activities, it is crucial to efficiently and effectively support the improvement of participants' skills. However, traditional coaching methods inevitably rely on subjective advice and lack specific and visual feedback. As a result, participants find it difficult to identify areas for improvement in their form and movements, leading to challenges in improving their skills over time.

[0431] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0432] In this invention, the server includes recording means for acquiring motion information, processing means for standardizing the acquired motion information, and comparison means for comparing the standardized motion information with the motion information of top athletes. This allows users to compare their own motion information with that of top athletes, identify specific areas for improvement based on the analysis results, and obtain effective feedback.

[0433] "Motion information" is a term that refers to data related to the user's physical movements and posture, and this data is acquired using motion capture technology, etc.

[0434] "Recording means" refers to devices or groups of devices used to capture user actions and record them as digital data.

[0435] "Processing means" refers to methods or devices that convert acquired operational information into an appropriate format and perform standardization to maintain data consistency.

[0436] "Comparison means" refers to methods or devices for comparing standardized operational information with data from top performers to identify differences.

[0437] "Analysis means" refers to methods and devices for analyzing differences obtained through comparison means and identifying areas in the user's actions that require improvement.

[0438] "Generation means" refers to methods or devices for creating feedback to be presented to the user based on the analyzed results.

[0439] "Display means" refers to methods or devices that visually represent the generated feedback to the user, allowing them to intuitively understand the areas for improvement.

[0440] A "posture acquisition device" refers to the sensor devices and the entire system used to acquire user movement information in real time.

[0441] "Directionalization" refers to the process of converting joint angle data in motion information into a vector format, enabling accurate comparison.

[0442] This invention is a system that assists in improving a user's motor skills and includes data collection, analysis, and feedback generation utilizing motion capture technology. The user records their movements using a motion capture device. An example of a device used here is a general posture acquisition device.

[0443] The terminal sends recorded behavioral information to the server. The server standardizes the received data and compares it with the behavioral data of top performers. This can utilize data analysis libraries (e.g., Python's NumPy and Pandas) and machine learning libraries (e.g., TensorFlow and PyTorch).

[0444] The server analyzes the user's actions based on the comparison results to identify areas for improvement. Then, using a generative AI model, it generates natural language feedback based on the analysis results. This feedback provides specific ways to improve actions and is output in a format that the user can easily understand.

[0445] The generated feedback is presented to the user through the device. The device uses visual tools such as skeleton animations and video comparisons to clearly communicate areas for improvement. This visual presentation allows users to intuitively understand how to specifically correct their motor skills.

[0446] For example, if a user wants to improve their baseball pitching motion, the system will generate feedback on things like shoulder rotation and arm movement during pitching. The user can then use this feedback to continue practicing. Furthermore, an example of a prompt message could be: "Please tell me how to improve my baseball pitching form. I would like specific feedback based on comparative data with top players."

[0447] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0448] Step 1:

[0449] The user records their movements using a motion capture device. The input here is the user's physical movements, and the output is digital motion information. Specifically, the user wears a motion capture suit or markers, and joint position data is collected in real time during movement.

[0450] Step 2:

[0451] The terminal transmits the recorded operation information to the server. The input is the operation information obtained in step 1, and the output is the operation information received by the server. Communication protocols such as Wi-Fi and Bluetooth can be used for this transmission operation.

[0452] Step 3:

[0453] The server standardizes the received operational information. The input is raw operational data transmitted from the terminal, and the output is standardized operational data. Specifically, the process involves unifying data formats from different sensor systems and preparing the data to a comparable state.

[0454] Step 4:

[0455] The server compares standardized motion data with the motion data of top athletes. The input consists of standardized user motion data and data from top athletes in a database. The output is the difference information between the two. Specific calculations include methods for calculating joint angles and vectorizing the differences on a timeline for comparison.

[0456] Step 5:

[0457] The server performs analysis based on the comparison results and identifies areas for improvement. The input is difference information, and the output is a list of analyzed areas for improvement. The server uses an analysis algorithm to calculate which parts should be improved and to what extent.

[0458] Step 6:

[0459] The server generates feedback based on the analysis results. The input is a list of areas for improvement, and the output is specific feedback provided to the user. This includes using a generative AI model to output the feedback in natural language.

[0460] Step 7:

[0461] The terminal presents the generated feedback to the user. The input is feedback information sent from the server, and the output is the feedback displayed on the terminal. The terminal uses skeleton animations and video clips to visually indicate areas for improvement to the user.

[0462] (Application Example 1)

[0463] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0464] Traditional form improvement systems made it difficult for users to understand specifically which parts of their movements needed improvement and how. This resulted in users being unable to efficiently correct their form and achieve optimal performance improvements. Furthermore, immediate feedback was often unavailable, limiting the effectiveness of the training.

[0465] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0466] In this invention, the server includes a collection means for acquiring motion information, a processing means for standardizing the acquired motion information, and a comparison means for comparing the standardized motion information with the motion information of a superior athlete. This allows the user to efficiently analyze their own movements and immediately understand specific areas for improvement. They can intuitively grasp how to improve and maximize the effectiveness of their training.

[0467] "Means for collecting motion information" refers to devices or methods for measuring user actions in real time and collecting them as data.

[0468] "Processing means for standardizing acquired operational information" refers to a processing function that converts collected operational information into an analyzable format.

[0469] "A comparison method for comparing standardized motion information with the motion information of superior athletes" refers to a method that compares a user's standardized motion data with existing motion data of superior athletes based on a standard.

[0470] An "analysis method for identifying areas for improvement" is a method for clarifying which parts of a user's behavior should be improved based on the comparison results.

[0471] "Means for generating feedback" refers to the procedure of creating guidelines for users based on improvement information obtained through analysis.

[0472] "Means of presenting generated feedback to the user" refers to a function that communicates the generated feedback to the user visually or audibly.

[0473] A "simulation method that imitates user actions" is a simulation technique that faithfully reproduces user actions and visualizes areas that need improvement.

[0474] "Audio guidance that provides improvement instructions" refers to a means of communicating areas for improvement and training guidelines to users via audio.

[0475] This invention relates to a system that collects and analyzes user motion information using motion capture technology. The user wears a motion capture device on their body, thereby collecting motion information in real time. This information is immediately transmitted to a server, which performs processing to standardize the collected information. The standardized data is compared with motion information of top athletes stored in the cloud. The server uses a comparison means to identify the differences between the user's movements and those of top athletes, and an analysis means identifies areas that require improvement.

[0476] Based on identified areas for improvement, the server generates feedback and provides visual and auditory feedback to the user using simulated and audio means to mimic the user's actions. This system allows users to intuitively understand how to improve their actions, maximizing the effectiveness of their training.

[0477] As a concrete example, consider improving yoga form. When a user performs a specific yoga pose, their form information is captured and compared by a server to data from a professional instructor. Once areas for improvement are identified, the user receives visual animations and audio guidance on how to correct which parts of the pose and how.

[0478] An example of a prompt for the generating AI model is: "Use the Smart Form Coach Robot to generate feedback on how to improve the cobra pose in yoga. Present specific areas for improvement to the user using voice and animation." This prompt ensures that the feedback is specific and practical.

[0479] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0480] Step 1:

[0481] The user wears a motion capture device and begins a specific movement. The terminal captures this movement in real time and acquires motion information. The input is the user's physical movement, and the output is motion information in digital format. This data is measured by the motion capture device and transmitted to the terminal.

[0482] Step 2:

[0483] The terminal transmits the acquired digital operational information to the server. The server denoises and normalizes the received data to standardize it. The input is raw operational information, and the output is standardized data after denoising. This standardization converts it into a comparable format.

[0484] Step 3:

[0485] The server compares standardized data with existing motion information from top athletes. It vectorizes the specific data and calculates the differences between each data point. The input is standardized user data and athlete data, and the output is difference information for each joint angle and movement speed.

[0486] Step 4:

[0487] The server analyzes the obtained difference information to identify areas that need improvement. Using analytical tools, it quantitatively evaluates which areas can be improved and to what extent. The input is difference information, and the output is information identifying areas for improvement.

[0488] Step 5:

[0489] The server generates feedback based on the identified areas for improvement and provides visual and auditory presentations. It uses presentation methods to send animations and audio guides to the terminal. The input is information identifying the areas for improvement, and the output is feedback information presented to the user. This information helps the user understand specific correction methods.

[0490] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0491] This invention provides a system that, in addition to conventional systems that support the improvement of a user's form in sports, recognizes the user's emotional state in real time and reflects it in the generation of feedback, thereby providing more personalized training support.

[0492] Users wear a dedicated motion capture device and record their sports movements through a terminal equipped with an emotion engine. While performing the movements, the terminal uses cameras and sensors to analyze the user's facial expressions and physiological changes in their skin, collecting emotion data in real time. This data is also recorded and transmitted to a server along with the movement data.

[0493] The server compares the received motion data with that of top athletes and analyzes the differences. Furthermore, it incorporates emotional data obtained from the emotion engine to determine the psychological state in which the user is performing the actions. The analysis method evaluates not only the technical differences in the actions but also how the psychological state influences the actions.

[0494] Based on this information, the server generates feedback. This feedback includes not only technical improvement points but also emotionally conscious encouragement and advice. For example, if a user is feeling tense during operation, relaxation techniques and breathing exercises to help them calm down and concentrate may be recommended. If a user is experiencing heightened emotions, the feedback may include advice on how to channel that positive energy into more effective performance.

[0495] The device presents this feedback to the user. In addition to visual comparison data, text and voice guidance based on emotional data is provided. This allows users not only to hone their skills but also to learn how to manage their mental state and perform at their best.

[0496] For example, suppose a baseball player is practicing and feels anxious, causing their form to deteriorate. This system identifies that emotional state and provides feedback on specific techniques to help them manage their anxiety. In this way, the user can improve their overall performance.

[0497] The following describes the processing flow.

[0498] Step 1:

[0499] The user activates the emotion engine built into the motion capture device and terminal and begins exercising. The terminal records motion data and also collects the user's facial expressions and skin changes through cameras and biosensors, recording emotion data in real time.

[0500] Step 2:

[0501] The device sends emotional data along with operational data to the server. This data transfer is performed using a secure communication protocol, and the data is output in a way that maintains data integrity.

[0502] Step 3:

[0503] The server compares the received performance data with a predefined database of top-tier athletes. The server then performs data standardization, normalizing the performance and removing noise to enable effective comparison.

[0504] Step 4:

[0505] The server analyzes emotional data obtained from the emotion engine to identify the user's psychological state. During the analysis, it considers the impact of specific behavioral patterns and emotional changes on performance.

[0506] Step 5:

[0507] The server integrates the results of a technical analysis of motion data with the results of user emotion data to generate areas for improvement in the user's form and psychological advice. For example, if the motion is accurate but tension is observed, guidance for relaxation will be included.

[0508] Step 6:

[0509] The generated feedback is sent to the device in a form that combines technical elements with emotional support. The device presents this information to the user, providing guidance in text or voice, especially for emotionally-based advice.

[0510] Step 7:

[0511] Based on the feedback received, users identify areas for improvement in their training and practice the relaxation and concentration-enhancing techniques provided by the emotional engine. This is expected to lead to more effective results in subsequent practice sessions.

[0512] (Example 2)

[0513] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0514] Traditional sports training support systems have focused on improving technical movements, while failing to adequately consider the impact of the user's psychological state on training outcomes. Therefore, there is a need for methods that appropriately manage the user's mental motivation and concentration while simultaneously improving their technical level. To provide more personalized training support, a system is needed that comprehensively analyzes movement and emotional data and provides real-time feedback based on that analysis.

[0515] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0516] In this invention, the server includes recording means for acquiring motion data and emotional data, processing means for integrating and standardizing the acquired motion data and emotional data, and comparison means for comparing the integrated data with the motion and ideal psychological state of a top athlete. This makes it possible to provide personalized training feedback that integrates the user's technical improvement and psychological state management.

[0517] "Motion data" refers to information about the user's physical body movements, including detailed data such as joint angles and trajectories acquired by motion capture equipment.

[0518] "Emotional data" refers to information about the user's psychological state and includes data based on facial expressions and physiological changes acquired using emotion recognition functions.

[0519] "Recording means" refers to means for acquiring motion data and emotion data using motion capture devices and emotion recognition sensors.

[0520] "Standardization" refers to a series of processes performed to transform acquired data into a comparable format, including the unification of data formats and scaling based on standards.

[0521] "Comparison methods" refer to methods for analyzing technical and psychological differences by comparing integrated data with baseline data of top-level athletes.

[0522] "Analysis means" refers to a method for identifying areas for improvement in the user's technical and emotional aspects based on the differences extracted by comparison means.

[0523] "Generative means" refers to the process of formalizing technical and emotional feedback to users based on improvement points identified through analysis.

[0524] "Display means" refers to means of conveying generated feedback to the user, and includes visual displays and audio guides.

[0525] This invention functions as an integrated system that supports the technical improvement of a user's sports movements and the management of their mental state. During system implementation, the user wears a motion capture device, and motion data is collected in real time. Simultaneously, the terminal uses cameras and emotion sensors to analyze the user's facial expressions and physiological changes in their skin, collecting emotional data. All of this data is transmitted to a server.

[0526] The server integrates behavioral and emotional data and standardizes it using advanced data processing software. This converts the data into a comparable format. Next, the server analyzes technical and psychological differences by comparing the integrated data with baseline data and ideal emotional states of top athletes. This comparison identifies specific areas for improvement regarding the user's behavior and emotional state.

[0527] The generative AI model generates feedback based on the analysis results. This feedback includes specific advice to improve the user's motor skills, as well as suggestions for maintaining an optimal psychological state. For example, if the user is feeling nervous, it might recommend deep breathing. The device presents this feedback to the user as visual and audio guidance, allowing the user to immediately utilize it during training.

[0528] As a concrete example, for users who feel anxious during baseball training, this system identifies the feeling of anxiety and provides feedback, including ways to alleviate it. In this way, users can improve not only their physical skills but also their mental flexibility.

[0529] An example of a prompt message would be, "Based on user behavior and emotional data in a specific sports scene, please provide feedback including appropriate areas for improvement and advice on emotional management." Following this prompt, the generating AI model provides optimal feedback.

[0530] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0531] Step 1:

[0532] The user wears a motion capture device on their body, which allows for real-time acquisition of motion data. The device simultaneously monitors facial expressions and skin temperature changes using cameras and emotion sensors, collecting emotional data. Inputs are the user's body movements and facial expression data, while outputs are motion data and emotional data.

[0533] Step 2:

[0534] The terminal sends acquired behavioral and emotional data to the server. The server receives this data and stores both in a database. The data is standardized through programmatic preprocessing and converted into a format suitable for comparison. The input is the transmitted raw data, and the output is the standardized behavioral and emotional data.

[0535] Step 3:

[0536] The server compares standardized data to baseline data and ideal psychological states of top athletes. Machine learning algorithms are used as comparison tools to comprehensively analyze performance accuracy and emotional stability. Input is standardized user data, and output is points for technical and psychological improvement.

[0537] Step 4:

[0538] The server generates feedback using an AI model based on the analyzed improvement points. This feedback includes specific advice about the user's technical and emotional state. Inputs are technical and psychological improvement points, and output is text or audio data as feedback.

[0539] Step 5:

[0540] The device presents the generated feedback to the user visually and audibly. The device uses video playback and audio guidance to help the user understand the feedback. The input is the feedback data, and the output is the information presented to the user.

[0541] (Application Example 2)

[0542] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0543] In traditional factory environments, it was difficult to monitor workers' operational efficiency and psychological stress in real time and provide optimal feedback. This meant that workers' performance could not be maximized, leading to a risk of decreased labor efficiency. Furthermore, inadequate stress management could potentially impact workers' health and safety. To improve the quality and efficiency of work in factory environments, there was a need to provide advice and technical feedback that considered the psychological state of workers.

[0544] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0545] In this invention, the server includes recording means for acquiring operational data, processing means for standardizing the acquired operational data, and analysis means for analyzing the user's psychological state. This makes it possible to acquire and analyze the worker's operational data and psychological state in real time and provide feedback including optimal improvement points and psychological advice.

[0546] "Recording means for acquiring motion data" refers to devices or methods that record the user's actions and movements as digital information in real time.

[0547] "Processing means for standardizing acquired motion data" refers to a system that adjusts recorded motion data based on consistent criteria and converts it into a format that can be compared with other reference data.

[0548] "Comparison means for comparing standardized operating data with standard operating data" refers to a method or apparatus for comparing adjusted operating data with reference operating data to confirm differences and degrees of agreement.

[0549] "Analysis means for identifying areas for improvement based on comparison results" refers to a process or apparatus that analyzes the differences in compared data and clarifies which parts should be improved.

[0550] A "generation method for generating feedback based on identified areas for improvement" is a system that creates specific advice on how users should modify their behavior based on the analyzed areas for improvement.

[0551] "Display means for presenting generated feedback to the user" means a device or method that provides generated feedback information to the user visually or audibly.

[0552] "Analytical means for analyzing the psychological state of a user" refers to a technology or device that digitizes the user's emotions and mental state and evaluates them in real time.

[0553] "Generating means for incorporating psychological advice into feedback based on psychological state analysis results" refers to a technology or method that includes advice that takes the user's mental health into consideration, based on analyzed psychological state data.

[0554] The system for realizing this invention is configured to generate and provide feedback to the user based on motion data and psychological state. This system operates in conjunction with smart glasses or similar devices. The server acquires motion data in real time and processes the acquired data to standardize it. In this process, a motion tracking device (motion capture device) is used to record the motion data, and the software used is an image processing library such as OpenCV.

[0555] Furthermore, the server analyzes the acquired psychological state data to evaluate the user's emotions and stress levels. AI models such as TensorFlow are used for this analysis. The results of the analysis form the basis for the feedback presented to the user. This feedback combines technical improvement points with psychological advice to help the user work more efficiently and stress-free.

[0556] The device plays the role of presenting this generated feedback to the user. Visual data may be provided through the smart glasses' display, and auditory data may be provided through the headset speaker. Based on the server's processing results, the information is conveyed in the most effective way for the user.

[0557] For example, if the system identifies a user performing monotonous tasks for extended periods as having a high stress level, the terminal will display a relaxation message recommending that the user take a short break. An example of an input prompt to the generating AI model might be: "The following data indicates the worker's stress level: [data]. Please generate appropriate feedback based on this."

[0558] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0559] Step 1:

[0560] When a user begins to move, the device acquires motion data in real time via a motion tracking device. The input is raw data from sensors, and the output is recorded motion data. This data includes position and orientation information over time.

[0561] Step 2:

[0562] The server receives the acquired motion data and performs a standardization process. The input is the motion data acquired in step 1, and the output is standardized motion data transformed into a comparable standard. This process involves data manipulation to align the motion characteristics to a unified scale.

[0563] Step 3:

[0564] The server compares standardized operation data with standard operation data. The input is the standardized operation data from step 2 and predefined standard operation data, and the output is comparison data showing the differences between the operations. This comparison calculates differences in operation angle and velocity.

[0565] Step 4:

[0566] The server analyzes the comparison data and identifies areas for improvement. The input is the comparison data from step 3, and the output is the detailed analysis results of the areas for improvement. Here, parts where specific operations are not performing as expected become clear.

[0567] Step 5:

[0568] The server analyzes psychological state data independently of the user's actions. Input is psychological state data from sensors and cameras, and output is the analysis results indicating the user's emotions and stress levels. This process includes facial expression analysis and biosignal analysis.

[0569] Step 6:

[0570] The server generates feedback based on the improvement points and the results of the psychological state analysis. The inputs are the improvement points from Step 4 and the results of the psychological state analysis from Step 5, and the output is the feedback information presented to the user. The feedback includes not only technical advice but also psychological support.

[0571] Step 7:

[0572] The device presents the generated feedback to the user. The input is the feedback information from step 6, and the output is the presentation of visual or auditory information to the user. Messages and guidance are provided using smart glasses displays or headsets.

[0573] 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.

[0574] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0575] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0576] [Fourth Embodiment]

[0577] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0578] 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.

[0579] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0580] 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.

[0581] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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.

[0582] 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0583] 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.

[0584] 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. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0585] 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.

[0586] The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0587] The 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.

[0588] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0589] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0590] This invention is a system for supporting the improvement of a user's form in sports. It utilizes motion capture technology to acquire the user's movement data, and by comparing this data with the movement data of top athletes, it generates specific areas for improvement as feedback.

[0591] To record their movements, users must wear a motion capture system and capture their actions. Once the recording is complete, the device sends the data to a server, which receives the data and standardizes its format. The standardized data is then compared to an existing database of top athletes.

[0592] The server detects differences between user motion data and athlete data for specific metrics such as joint angles and movement speed. Based on these differences, an analysis algorithm identifies areas in the user's form that need improvement. Based on the identified areas for improvement, the server generates feedback indicating which parts of the movement should be modified and how.

[0593] The generated feedback is presented visually to the user through the device. The device uses skeletal animations and video comparisons to help the user intuitively understand which actions need correction. This system allows users to specifically grasp how to improve their training and improve their skills by putting those improvements into practice.

[0594] As a concrete example, let's consider a scenario where a user wants to improve their baseball pitching form and uses this system. The user receives feedback on aspects such as shoulder rotation and release point height, allowing them to consciously focus on and correct these points during actual practice. By receiving this feedback, the user can develop concrete improvement directions based on actual data, rather than just theoretical concepts.

[0595] The following describes the processing flow.

[0596] Step 1:

[0597] The user wears a motion capture device and selects a specific sports movement. They then perform this movement, recording the movement data through the motion capture device. The terminal captures this data in real time and saves it locally.

[0598] Step 2:

[0599] The terminal sends recorded operational data to the server. During this process, necessary conversion is performed to ensure the data format is suitable for analysis on the server. The data is transferred using a secure communication protocol.

[0600] Step 3:

[0601] The server stores the received operational data and performs standardization processing. This includes data normalization and noise reduction. As a result, all data becomes comparable using the same criteria.

[0602] Step 4:

[0603] The server retrieves motion data of top athletes from the database. This data includes ideal form data corresponding to the sports motion selected by the user.

[0604] Step 5:

[0605] The server compares standardized user motion data with data from top-level athletes. This comparison utilizes vectorized data of joint movements and posture to quantitatively analyze the differences.

[0606] Step 6:

[0607] The server uses the analysis results to identify areas where the user's movements need improvement. This includes identifying which joint movements deviate from the ideal and which parts have different speeds or timings.

[0608] Step 7:

[0609] The server automatically generates user feedback based on identified areas for improvement. This feedback indicates which behaviors should be improved and how.

[0610] Step 8:

[0611] The device receives the generated feedback and presents it to the user. This presentation uses a graphical user interface, combining skeleton animations and visual comparisons to show details.

[0612] Step 9:

[0613] The user reviews the feedback provided and plans to modify their actions based on it. In the next practice session, they utilize the feedback to make specific improvements to their actions.

[0614] (Example 1)

[0615] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0616] In sports activities, it is crucial to efficiently and effectively support the improvement of participants' skills. However, traditional coaching methods inevitably rely on subjective advice and lack specific and visual feedback. As a result, participants find it difficult to identify areas for improvement in their form and movements, leading to challenges in improving their skills over time.

[0617] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0618] In this invention, the server includes recording means for acquiring motion information, processing means for standardizing the acquired motion information, and comparison means for comparing the standardized motion information with the motion information of top athletes. This allows users to compare their own motion information with that of top athletes, identify specific areas for improvement based on the analysis results, and obtain effective feedback.

[0619] "Motion information" is a term that refers to data related to the user's physical movements and posture, and this data is acquired using motion capture technology, etc.

[0620] "Recording means" refers to devices or groups of devices used to capture user actions and record them as digital data.

[0621] "Processing means" refers to methods or devices that convert acquired operational information into an appropriate format and perform standardization to maintain data consistency.

[0622] "Comparison means" refers to methods or devices for comparing standardized operational information with data from top performers to identify differences.

[0623] "Analysis means" refers to methods and devices for analyzing differences obtained through comparison means and identifying areas in the user's actions that require improvement.

[0624] "Generation means" refers to methods or devices for creating feedback to be presented to the user based on the analyzed results.

[0625] "Display means" refers to methods or devices that visually represent the generated feedback to the user, allowing them to intuitively understand the areas for improvement.

[0626] A "posture acquisition device" refers to the sensor devices and the entire system used to acquire user movement information in real time.

[0627] "Directionalization" refers to the process of converting joint angle data in motion information into a vector format, enabling accurate comparison.

[0628] This invention is a system that assists in improving a user's motor skills and includes data collection, analysis, and feedback generation utilizing motion capture technology. The user records their movements using a motion capture device. An example of a device used here is a general posture acquisition device.

[0629] The terminal sends recorded behavioral information to the server. The server standardizes the received data and compares it with the behavioral data of top performers. This can utilize data analysis libraries (e.g., Python's NumPy and Pandas) and machine learning libraries (e.g., TensorFlow and PyTorch).

[0630] The server analyzes the user's actions based on the comparison results to identify areas for improvement. Then, using a generative AI model, it generates natural language feedback based on the analysis results. This feedback provides specific ways to improve actions and is output in a format that the user can easily understand.

[0631] The generated feedback is presented to the user through the device. The device uses visual tools such as skeleton animations and video comparisons to clearly communicate areas for improvement. This visual presentation allows users to intuitively understand how to specifically correct their motor skills.

[0632] For example, if a user wants to improve their baseball pitching motion, the system will generate feedback on things like shoulder rotation and arm movement during pitching. The user can then use this feedback to continue practicing. Furthermore, an example of a prompt message could be: "Please tell me how to improve my baseball pitching form. I would like specific feedback based on comparative data with top players."

[0633] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0634] Step 1:

[0635] The user records their movements using a motion capture device. The input here is the user's physical movements, and the output is digital motion information. Specifically, the user wears a motion capture suit or markers, and joint position data is collected in real time during movement.

[0636] Step 2:

[0637] The terminal transmits the recorded operation information to the server. The input is the operation information obtained in step 1, and the output is the operation information received by the server. Communication protocols such as Wi-Fi and Bluetooth can be used for this transmission operation.

[0638] Step 3:

[0639] The server standardizes the received operational information. The input is raw operational data transmitted from the terminal, and the output is standardized operational data. Specifically, the process involves unifying data formats from different sensor systems and preparing the data to a comparable state.

[0640] Step 4:

[0641] The server compares standardized motion data with the motion data of top athletes. The input consists of standardized user motion data and data from top athletes in a database. The output is the difference information between the two. Specific calculations include methods for calculating joint angles and vectorizing the differences on a timeline for comparison.

[0642] Step 5:

[0643] The server performs analysis based on the comparison results and identifies areas for improvement. The input is difference information, and the output is a list of analyzed areas for improvement. The server uses an analysis algorithm to calculate which parts should be improved and to what extent.

[0644] Step 6:

[0645] The server generates feedback based on the analysis results. The input is a list of areas for improvement, and the output is specific feedback provided to the user. This includes using a generative AI model to output the feedback in natural language.

[0646] Step 7:

[0647] The terminal presents the generated feedback to the user. The input is feedback information sent from the server, and the output is the feedback displayed on the terminal. The terminal uses skeleton animations and video clips to visually indicate areas for improvement to the user.

[0648] (Application Example 1)

[0649] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0650] Traditional form improvement systems made it difficult for users to understand specifically which parts of their movements needed improvement and how. This resulted in users being unable to efficiently correct their form and achieve optimal performance improvements. Furthermore, immediate feedback was often unavailable, limiting the effectiveness of the training.

[0651] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0652] In this invention, the server includes a collection means for acquiring motion information, a processing means for standardizing the acquired motion information, and a comparison means for comparing the standardized motion information with the motion information of a superior athlete. This allows the user to efficiently analyze their own movements and immediately understand specific areas for improvement. They can intuitively grasp how to improve and maximize the effectiveness of their training.

[0653] "Means for collecting motion information" refers to devices or methods for measuring user actions in real time and collecting them as data.

[0654] "Processing means for standardizing acquired operational information" refers to a processing function that converts collected operational information into an analyzable format.

[0655] "A comparison method for comparing standardized motion information with the motion information of superior athletes" refers to a method that compares a user's standardized motion data with existing motion data of superior athletes based on a standard.

[0656] An "analysis method for identifying areas for improvement" is a method for clarifying which parts of a user's behavior should be improved based on the comparison results.

[0657] "Means for generating feedback" refers to the procedure of creating guidelines for users based on improvement information obtained through analysis.

[0658] "Means of presenting generated feedback to the user" refers to a function that communicates the generated feedback to the user visually or audibly.

[0659] A "simulation method that imitates user actions" is a simulation technique that faithfully reproduces user actions and visualizes areas that need improvement.

[0660] "Audio guidance that provides improvement instructions" refers to a means of communicating areas for improvement and training guidelines to users via audio.

[0661] This invention relates to a system that collects and analyzes user motion information using motion capture technology. The user wears a motion capture device on their body, thereby collecting motion information in real time. This information is immediately transmitted to a server, which performs processing to standardize the collected information. The standardized data is compared with motion information of top athletes stored in the cloud. The server uses a comparison means to identify the differences between the user's movements and those of top athletes, and an analysis means identifies areas that require improvement.

[0662] Based on identified areas for improvement, the server generates feedback and provides visual and auditory feedback to the user using simulated and audio means to mimic the user's actions. This system allows users to intuitively understand how to improve their actions, maximizing the effectiveness of their training.

[0663] As a concrete example, consider improving yoga form. When a user performs a specific yoga pose, their form information is captured and compared by a server to data from a professional instructor. Once areas for improvement are identified, the user receives visual animations and audio guidance on how to correct which parts of the pose and how.

[0664] An example of a prompt for the generating AI model is: "Use the Smart Form Coach Robot to generate feedback on how to improve the cobra pose in yoga. Present specific areas for improvement to the user using voice and animation." This prompt ensures that the feedback is specific and practical.

[0665] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0666] Step 1:

[0667] The user wears a motion capture device and begins a specific movement. The terminal captures this movement in real time and acquires motion information. The input is the user's physical movement, and the output is motion information in digital format. This data is measured by the motion capture device and transmitted to the terminal.

[0668] Step 2:

[0669] The terminal transmits the acquired digital operational information to the server. The server denoises and normalizes the received data to standardize it. The input is raw operational information, and the output is standardized data after denoising. This standardization converts it into a comparable format.

[0670] Step 3:

[0671] The server compares standardized data with existing motion information from top athletes. It vectorizes the specific data and calculates the differences between each data point. The input is standardized user data and athlete data, and the output is difference information for each joint angle and movement speed.

[0672] Step 4:

[0673] The server analyzes the obtained difference information to identify areas that need improvement. Using analytical tools, it quantitatively evaluates which areas can be improved and to what extent. The input is difference information, and the output is information identifying areas for improvement.

[0674] Step 5:

[0675] The server generates feedback based on the identified areas for improvement and provides visual and auditory presentations. It uses presentation methods to send animations and audio guides to the terminal. The input is information identifying the areas for improvement, and the output is feedback information presented to the user. This information helps the user understand specific correction methods.

[0676] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0677] This invention provides a system that, in addition to conventional systems that support the improvement of a user's form in sports, recognizes the user's emotional state in real time and reflects it in the generation of feedback, thereby providing more personalized training support.

[0678] Users wear a dedicated motion capture device and record their sports movements through a terminal equipped with an emotion engine. While performing the movements, the terminal uses cameras and sensors to analyze the user's facial expressions and physiological changes in their skin, collecting emotion data in real time. This data is also recorded and transmitted to a server along with the movement data.

[0679] The server compares the received motion data with that of top athletes and analyzes the differences. Furthermore, it incorporates emotional data obtained from the emotion engine to determine the psychological state in which the user is performing the actions. The analysis method evaluates not only the technical differences in the actions but also how the psychological state influences the actions.

[0680] Based on this information, the server generates feedback. This feedback includes not only technical improvement points but also emotionally conscious encouragement and advice. For example, if a user is feeling tense during operation, relaxation techniques and breathing exercises to help them calm down and concentrate may be recommended. If a user is experiencing heightened emotions, the feedback may include advice on how to channel that positive energy into more effective performance.

[0681] The device presents this feedback to the user. In addition to visual comparison data, text and voice guidance based on emotional data is provided. This allows users not only to hone their skills but also to learn how to manage their mental state and perform at their best.

[0682] For example, suppose a baseball player is practicing and feels anxious, causing their form to deteriorate. This system identifies that emotional state and provides feedback on specific techniques to help them manage their anxiety. In this way, the user can improve their overall performance.

[0683] The following describes the processing flow.

[0684] Step 1:

[0685] The user activates the emotion engine built into the motion capture device and terminal and begins exercising. The terminal records motion data and also collects the user's facial expressions and skin changes through cameras and biosensors, recording emotion data in real time.

[0686] Step 2:

[0687] The device sends emotional data along with operational data to the server. This data transfer is performed using a secure communication protocol, and the data is output in a way that maintains data integrity.

[0688] Step 3:

[0689] The server compares the received performance data with a predefined database of top-tier athletes. The server then performs data standardization, normalizing the performance and removing noise to enable effective comparison.

[0690] Step 4:

[0691] The server analyzes emotional data obtained from the emotion engine to identify the user's psychological state. During the analysis, it considers the impact of specific behavioral patterns and emotional changes on performance.

[0692] Step 5:

[0693] The server integrates the results of a technical analysis of motion data with the results of user emotion data to generate areas for improvement in the user's form and psychological advice. For example, if the motion is accurate but tension is observed, guidance for relaxation will be included.

[0694] Step 6:

[0695] The generated feedback is sent to the device in a form that combines technical elements with emotional support. The device presents this information to the user, providing guidance in text or voice, especially for emotionally-based advice.

[0696] Step 7:

[0697] Based on the feedback received, users identify areas for improvement in their training and practice the relaxation and concentration-enhancing techniques provided by the emotional engine. This is expected to lead to more effective results in subsequent practice sessions.

[0698] (Example 2)

[0699] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0700] Traditional sports training support systems have focused on improving technical movements, while failing to adequately consider the impact of the user's psychological state on training outcomes. Therefore, there is a need for methods that appropriately manage the user's mental motivation and concentration while simultaneously improving their technical level. To provide more personalized training support, a system is needed that comprehensively analyzes movement and emotional data and provides real-time feedback based on that analysis.

[0701] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0702] In this invention, the server includes recording means for acquiring motion data and emotional data, processing means for integrating and standardizing the acquired motion data and emotional data, and comparison means for comparing the integrated data with the motion and ideal psychological state of a top athlete. This makes it possible to provide personalized training feedback that integrates the user's technical improvement and psychological state management.

[0703] "Motion data" refers to information about the user's physical body movements, including detailed data such as joint angles and trajectories acquired by motion capture equipment.

[0704] "Emotional data" refers to information about the user's psychological state and includes data based on facial expressions and physiological changes acquired using emotion recognition functions.

[0705] "Recording means" refers to means for acquiring motion data and emotion data using motion capture devices and emotion recognition sensors.

[0706] "Standardization" refers to a series of processes performed to transform acquired data into a comparable format, including the unification of data formats and scaling based on standards.

[0707] "Comparison methods" refer to methods for analyzing technical and psychological differences by comparing integrated data with baseline data of top-level athletes.

[0708] "Analysis means" refers to a method for identifying areas for improvement in the user's technical and emotional aspects based on the differences extracted by comparison means.

[0709] "Generative means" refers to the process of formalizing technical and emotional feedback to users based on improvement points identified through analysis.

[0710] "Display means" refers to means of conveying generated feedback to the user, and includes visual displays and audio guides.

[0711] This invention functions as an integrated system that supports the technical improvement of a user's sports movements and the management of their mental state. During system implementation, the user wears a motion capture device, and motion data is collected in real time. Simultaneously, the terminal uses cameras and emotion sensors to analyze the user's facial expressions and physiological changes in their skin, collecting emotional data. All of this data is transmitted to a server.

[0712] The server integrates behavioral and emotional data and standardizes it using advanced data processing software. This converts the data into a comparable format. Next, the server analyzes technical and psychological differences by comparing the integrated data with baseline data and ideal emotional states of top athletes. This comparison identifies specific areas for improvement regarding the user's behavior and emotional state.

[0713] The generative AI model generates feedback based on the analysis results. This feedback includes specific advice to improve the user's motor skills, as well as suggestions for maintaining an optimal psychological state. For example, if the user is feeling nervous, it might recommend deep breathing. The device presents this feedback to the user as visual and audio guidance, allowing the user to immediately utilize it during training.

[0714] As a concrete example, for users who feel anxious during baseball training, this system identifies the feeling of anxiety and provides feedback, including ways to alleviate it. In this way, users can improve not only their physical skills but also their mental flexibility.

[0715] An example of a prompt message would be, "Based on user behavior and emotional data in a specific sports scene, please provide feedback including appropriate areas for improvement and advice on emotional management." Following this prompt, the generating AI model provides optimal feedback.

[0716] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0717] Step 1:

[0718] The user wears a motion capture device on their body, which allows for real-time acquisition of motion data. The device simultaneously monitors facial expressions and skin temperature changes using cameras and emotion sensors, collecting emotional data. Inputs are the user's body movements and facial expression data, while outputs are motion data and emotional data.

[0719] Step 2:

[0720] The terminal sends acquired behavioral and emotional data to the server. The server receives this data and stores both in a database. The data is standardized through programmatic preprocessing and converted into a format suitable for comparison. The input is the transmitted raw data, and the output is the standardized behavioral and emotional data.

[0721] Step 3:

[0722] The server compares standardized data to baseline data and ideal psychological states of top athletes. Machine learning algorithms are used as comparison tools to comprehensively analyze performance accuracy and emotional stability. Input is standardized user data, and output is points for technical and psychological improvement.

[0723] Step 4:

[0724] The server generates feedback using an AI model based on the analyzed improvement points. This feedback includes specific advice about the user's technical and emotional state. Inputs are technical and psychological improvement points, and output is text or audio data as feedback.

[0725] Step 5:

[0726] The device presents the generated feedback to the user visually and audibly. The device uses video playback and audio guidance to help the user understand the feedback. The input is the feedback data, and the output is the information presented to the user.

[0727] (Application Example 2)

[0728] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0729] In traditional factory environments, it was difficult to monitor workers' operational efficiency and psychological stress in real time and provide optimal feedback. This meant that workers' performance could not be maximized, leading to a risk of decreased labor efficiency. Furthermore, inadequate stress management could potentially impact workers' health and safety. To improve the quality and efficiency of work in factory environments, there was a need to provide advice and technical feedback that considered the psychological state of workers.

[0730] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0731] In this invention, the server includes recording means for acquiring operational data, processing means for standardizing the acquired operational data, and analysis means for analyzing the user's psychological state. This makes it possible to acquire and analyze the worker's operational data and psychological state in real time and provide feedback including optimal improvement points and psychological advice.

[0732] "Recording means for acquiring motion data" refers to devices or methods that record the user's actions and movements as digital information in real time.

[0733] "Processing means for standardizing acquired motion data" refers to a system that adjusts recorded motion data based on consistent criteria and converts it into a format that can be compared with other reference data.

[0734] "Comparison means for comparing standardized operating data with standard operating data" refers to a method or apparatus for comparing adjusted operating data with reference operating data to confirm differences and degrees of agreement.

[0735] "Analysis means for identifying areas for improvement based on comparison results" refers to a process or apparatus that analyzes the differences in compared data and clarifies which parts should be improved.

[0736] A "generation method for generating feedback based on identified areas for improvement" is a system that creates specific advice on how users should modify their behavior based on the analyzed areas for improvement.

[0737] "Display means for presenting generated feedback to the user" means a device or method that provides generated feedback information to the user visually or audibly.

[0738] "Analytical means for analyzing the psychological state of a user" refers to a technology or device that digitizes the user's emotions and mental state and evaluates them in real time.

[0739] "Generating means for incorporating psychological advice into feedback based on psychological state analysis results" refers to a technology or method that includes advice that takes the user's mental health into consideration, based on analyzed psychological state data.

[0740] The system for realizing this invention is configured to generate and provide feedback to the user based on motion data and psychological state. This system operates in conjunction with smart glasses or similar devices. The server acquires motion data in real time and processes the acquired data to standardize it. In this process, a motion tracking device (motion capture device) is used to record the motion data, and the software used is an image processing library such as OpenCV.

[0741] Furthermore, the server analyzes the acquired psychological state data to evaluate the user's emotions and stress levels. AI models such as TensorFlow are used for this analysis. The results of the analysis form the basis for the feedback presented to the user. This feedback combines technical improvement points with psychological advice to help the user work more efficiently and stress-free.

[0742] The device plays the role of presenting this generated feedback to the user. Visual data may be provided through the smart glasses' display, and auditory data may be provided through the headset speaker. Based on the server's processing results, the information is conveyed in the most effective way for the user.

[0743] For example, if the system identifies a user performing monotonous tasks for extended periods as having a high stress level, the terminal will display a relaxation message recommending that the user take a short break. An example of an input prompt to the generating AI model might be: "The following data indicates the worker's stress level: [data]. Please generate appropriate feedback based on this."

[0744] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0745] Step 1:

[0746] When a user begins to move, the device acquires motion data in real time via a motion tracking device. The input is raw data from sensors, and the output is recorded motion data. This data includes position and orientation information over time.

[0747] Step 2:

[0748] The server receives the acquired motion data and performs a standardization process. The input is the motion data acquired in step 1, and the output is standardized motion data transformed into a comparable standard. This process involves data manipulation to align the motion characteristics to a unified scale.

[0749] Step 3:

[0750] The server compares standardized operation data with standard operation data. The input is the standardized operation data from step 2 and predefined standard operation data, and the output is comparison data showing the differences between the operations. This comparison calculates differences in operation angle and velocity.

[0751] Step 4:

[0752] The server analyzes the comparison data and identifies areas for improvement. The input is the comparison data from step 3, and the output is the detailed analysis results of the areas for improvement. Here, parts where specific operations are not performing as expected become clear.

[0753] Step 5:

[0754] The server analyzes psychological state data independently of the user's actions. Input is psychological state data from sensors and cameras, and output is the analysis results indicating the user's emotions and stress levels. This process includes facial expression analysis and biosignal analysis.

[0755] Step 6:

[0756] The server generates feedback based on the improvement points and the results of the psychological state analysis. The inputs are the improvement points from Step 4 and the results of the psychological state analysis from Step 5, and the output is the feedback information presented to the user. The feedback includes not only technical advice but also psychological support.

[0757] Step 7:

[0758] The device presents the generated feedback to the user. The input is the feedback information from step 6, and the output is the presentation of visual or auditory information to the user. Messages and guidance are provided using smart glasses displays or headsets.

[0759] 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.

[0760] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0761] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0762] 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.

[0763] Figure 9 shows an 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. In the upper and lower directions of the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. Also, the upper side of the concentric circles is where "pleasant" emotions are located, and the lower side is where "unpleasant" emotions are located. In this way, 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.

[0764] 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.

[0765] 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.

[0766] 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, motorcycles, etc., 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, for example, based 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.

[0767] 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."

[0768] 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.

[0769] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0770] 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 of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0771] 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.

[0772] 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.

[0773] 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.

[0774] 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.

[0775] 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.

[0776] 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.

[0777] 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.

[0778] 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 the like 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.

[0779] 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.

[0780] The following is further disclosed regarding the embodiments described above.

[0781] (Claim 1)

[0782] A recording means for acquiring operational data,

[0783] A processing means for standardizing acquired motion data,

[0784] A comparison method for comparing standardized motion data with the motion data of top athletes,

[0785] An analytical means for identifying areas for improvement based on comparison results,

[0786] A generation means for generating feedback based on identified improvement points,

[0787] A display means for presenting generated feedback to the user.

[0788] A system that includes this.

[0789] (Claim 2)

[0790] The system according to claim 1, wherein the recording means is connected to a motion capture device and has the function of acquiring motion data in real time.

[0791] (Claim 3)

[0792] The system according to claim 1, wherein the comparison means has the function of vectorizing and comparing joint angle data.

[0793] "Example 1"

[0794] (Claim 1)

[0795] A recording means for acquiring operational information,

[0796] A processing means for standardizing acquired operational information,

[0797] A comparison method for comparing standardized motion information with motion information of top athletes,

[0798] An analytical means for identifying areas for improvement based on comparison results,

[0799] A generation means for generating feedback based on identified areas for improvement,

[0800] A display means for presenting generated feedback to the user.

[0801] A system that includes this.

[0802] (Claim 2)

[0803] The system according to claim 1, wherein the recording means is connected to a posture acquisition device and has a function to acquire motion information immediately.

[0804] (Claim 3)

[0805] The system according to claim 1, wherein the comparison means has a function to directionalize and compare joint angle information.

[0806] "Application Example 1"

[0807] (Claim 1)

[0808] A means for collecting operational information,

[0809] A processing means for standardizing acquired operational information,

[0810] A comparison method for comparing standardized motion information with motion information of superior athletes,

[0811] An analytical means for identifying areas for improvement based on comparison results,

[0812] A generation means for generating feedback based on identified areas for improvement,

[0813] A means of presenting the generated feedback to the user,

[0814] A simulation method that imitates the user's actions,

[0815] Voice guidance provides improvement instructions.

[0816] A system that includes this.

[0817] (Claim 2)

[0818] The system according to claim 1, wherein the collection means is connected to a motion detection device and has the function of acquiring motion information immediately.

[0819] (Claim 3)

[0820] The system according to claim 1, wherein the comparison means has a function to quantify and compare the angular information of limb segments.

[0821] "Example 2 of combining an emotion engine"

[0822] (Claim 1)

[0823] Recording means for acquiring motion data and emotion data,

[0824] A processing method for integrating and standardizing acquired motion data and emotion data,

[0825] A comparative method that uses integrated data to compare the movements and ideal psychological state of top athletes,

[0826] An analytical means for identifying technical and emotional improvement points based on comparison results,

[0827] A generation means for generating feedback that takes into account technical and psychological aspects based on identified areas for improvement,

[0828] Display means for presenting generated feedback to the user visually and audibly.

[0829] A system that includes this.

[0830] (Claim 2)

[0831] The system according to claim 1, wherein the recording means is connected to a motion capture device having an emotion recognition function and has the function of acquiring motion and emotion data in real time.

[0832] (Claim 3)

[0833] The system according to claim 1, wherein the comparison means has a function to vectorize motion data and quantify emotion indicators, and then integrate and compare them.

[0834] "Application example 2 when combining with an emotional engine"

[0835] (Claim 1)

[0836] A recording means for acquiring operational data,

[0837] A processing means for standardizing acquired motion data,

[0838] A comparison means for comparing standardized operating data with standard operating data,

[0839] An analytical means for identifying areas for improvement based on comparison results,

[0840] A generation means for generating feedback based on identified areas for improvement,

[0841] A display means for presenting the generated feedback to the user,

[0842] Analytical means for analyzing the psychological state of the user,

[0843] A generation method that incorporates psychological advice into feedback based on the results of psychological state analysis.

[0844] A system that includes this.

[0845] (Claim 2)

[0846] The system according to claim 1, wherein the recording means is connected to a motion tracking device and has the function of acquiring motion data in real time.

[0847] (Claim 3)

[0848] The system according to claim 1, wherein the comparison means has a function to quantify and compare the angular data of the structure. [Explanation of symbols]

[0849] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for collecting operational information, A processing means for standardizing acquired operational information, A comparison method for comparing standardized motion information with motion information of superior athletes, An analytical means for identifying areas for improvement based on comparison results, A generation means for generating feedback based on identified areas for improvement, A means of presenting the generated feedback to the user, A simulation method that imitates the user's actions, Voice guidance provides improvement instructions. A system that includes this.

2. The system according to claim 1, wherein the collection means is connected to a motion detection device and has the function of acquiring motion information immediately.

3. The system according to claim 1, wherein the comparison means has a function to quantify and compare the angular information of limb segments.

Images