System

Abstract

A system is provided.SOLUTION: A system comprising: means for a user uploading a video of an exercise taken by using a terminal to a system; means for a server dividing the received video data into frames and extracting important position information; means for the server evaluating a good point and an improvement point of a form based on the extracted position information; means for the server generating an ideal form based on a purpose of the exercise input by the user through the terminal; and means for the server comparing the ideal form generated by the server with an actual form of the user and providing specific advice.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the 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] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional exercise form analysis and coaching requires specialized trainers and coaches, which requires a great deal of time and cost. Furthermore, it is difficult to provide consistent form evaluation and feedback, leaving users unable to obtain specific improvements or practice methods to effectively improve their performance. To address these issues, a system is needed that allows users to easily analyze their own form and receive specific, practical feedback. [Means for solving the problem]

[0005] This invention solves the above-mentioned problems by combining a means for a user to upload an exercise video to a system using a terminal, a means for a server to divide the received video data into frames and extract important location information, a means for the server to evaluate good points and areas for improvement in form based on the extracted location information, a means for the server to generate an ideal form based on the exercise goal the user wants to achieve, and a means for comparing the ideal form generated by the server with the actual form and providing specific advice.This allows users to analyze and improve their own form without the need for a professional trainer or coach.

[0006] "User" refers to an individual who uses the system to improve their athletic form.

[0007] "Terminal" refers to an electronic device, such as a smartphone or computer, that a user uses to record and upload exercise videos to the system.

[0008] "Exercise video" refers to a digital file that records a continuous video of a user exercising.

[0009] The term "system" refers to the entire set of structures or programs that analyze and evaluate a user's exercise form, generate an ideal form, make comparisons, and provide advice based on the present invention.

[0010] "Server" refers to the central computer or computing resource of the system, a device or platform that analyzes and processes received video data, generates feedback, creates ideal forms, makes comparisons, and provides advice.

[0011] "Splitting by frames" refers to the process of breaking down a video into the individual still images (frames) that make up the video.

[0012] "Location information" refers to data related to the specific position coordinates, angles, and velocities of the user's joints and body parts detected in the video.

[0013] "Evaluation" refers to the process by which the server analyzes the strengths and areas for improvement of the user's exercise form based on location information and generates feedback.

[0014] "Purpose" refers to the specific outcome or goal that the user wants to achieve through improving their exercise form (for example, increasing pitching speed).

[0015] "Ideal form" refers to an exercise form optimized to achieve the goal entered by the user, and is a reference model generated by the server.

[0016] "Advice" refers to specific suggestions regarding training methods, areas to train, and injury prevention that the server derives from comparing the user's actual form with ideal form. [Brief explanation of the drawings]

[0017] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0018] 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.

[0019] First, the terms used in the following description will be explained.

[0020] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0021] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0022] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0023] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0024] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0025] [First embodiment]

[0026] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0027] 1, a 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.

[0028] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0029] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0030] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the 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.

[0031] 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 of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0032] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0033] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0034] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process 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.

[0035] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0036] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0037] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0038] The present invention relates to a system that allows a user to easily analyze his or her own exercise form and receive specific and practical feedback. Specific embodiments of the system of the present invention will be described below.

[0039] Recording and uploading videos

[0040] First, a user uses a device to record a video of their own exercise form, such as a baseball pitching form, a golf swing, or a tennis swing. The user takes the video with the right angle and lighting and uploads it to the system's application or website.

[0041] Video Analysis

[0042] The server then receives the video data, divides it into frames, and preprocesses it for input into the AI ​​model. The AI ​​model analyzes the position, angle, and speed of joints and body parts in the video to extract important positional information.

[0043] Form evaluation

[0044] The server evaluates the user's exercise form based on the extracted position information. For example, it analyzes the elbow angle, shoulder position, and hip rotation, and lists good points and areas for improvement. Specifically, it may evaluate the user's form as "the elbow angle is appropriate, but the release point is low."

[0045] Setting goals and generating ideal forms

[0046] The user inputs the goal they want to achieve into the system using a terminal. For example, "I want to increase the ball speed" or "I want to increase the distance I can hit the ball." The server generates an ideal form based on the user's goal. This ideal form includes the actions required to optimally achieve the goal entered by the user.

[0047] Compare forms and provide feedback

[0048] The server compares the generated ideal form with the user's actual form. Based on the comparison results, the server provides advice on specific training methods, areas to train, and injury prevention methods. For example, the server may provide specific advice to the user, such as "Use band exercises to strengthen the inner shoulder muscles."

[0049] Specific examples

[0050] An example of baseball pitching form

[0051] 1. Recording and uploading videos

[0052] The user takes a photo of their pitching form with their smartphone and uploads it to the system.

[0053] 2. Video Analysis

[0054] The server divides the video into frames, and AI detects important positions such as elbows, shoulders, and waists.

[0055] 3. Form Evaluation

[0056] The server identifies specific areas for improvement, such as "elbows are lower than the head" or "center of gravity is too far forward."

[0057] 4. Setting goals

[0058] The user inputs a goal such as "I want to increase my pitching speed."

[0059] 5. Generation of ideal form

[0060] The server generates the ideal form and creates a new video with the elbow height adjusted.

[0061] 6. Compare forms and provide feedback

[0062] The device displays the user's form and the ideal form side by side, and the server provides advice on "band exercises to strengthen the inner shoulder muscles."

[0063] In this way, users can receive consistent support throughout the system, from analyzing their form to specific ways to improve it.

[0064] The processing flow will be explained below.

[0065] Step 1:

[0066] The user uses a device to record a video of their exercise form. The user sets the appropriate angle and lighting and records their exercise form using, for example, the camera function of a smartphone.

[0067] Step 2:

[0068] The user uses the device to open a dedicated interface to upload the captured video to the system's application or website. The device then sends the video data to the specified API endpoint.

[0069] Step 3:

[0070] The server receives the video data sent from the device and temporarily stores it in storage. The server checks the format and quality of the video data.

[0071] Step 4:

[0072] The server splits the video data it receives into frames, and each frame is extracted and saved individually.

[0073] Step 5:

[0074] The server sequentially inputs the divided frames into the AI ​​model and detects the position information (coordinates, angles, speed, etc.) of the user's joints and body parts. It then selects specific key frames and extracts their position information.

[0075] Step 6:

[0076] The server evaluates the user's form based on the extracted position information, using indicators such as elbow angle, shoulder position, and hip rotation to analyze good points and areas for improvement.

[0077] Step 7:

[0078] The server generates feedback on the form based on the evaluation results, specifically providing specific points for improvement in text format, such as "your elbows are lower than your head" or "your center of gravity is too far forward."

[0079] Step 8:

[0080] The user uses a terminal to input the exercise goal they want to achieve on the system interface, for example, setting a specific goal such as "I want to increase my pitching speed."

[0081] Step 9:

[0082] The server generates an ideal form based on the purpose entered by the user. It generates ideal form data and calculates the optimal keyframes.

[0083] Step 10:

[0084] The server generates a new video to visualize the ideal form, which can be compared to the user's actual form.

[0085] Step 11:

[0086] The device provides an interface that displays the ideal form and the user's actual form side by side, allowing users to directly compare the two forms using a video player or slider.

[0087] Step 12:

[0088] The server compares the user's form and generates advice on specific training methods, areas to train, and injury prevention. The device then notifies the user of this advice and provides detailed feedback.

[0089] These steps allow users to go through a series of processes through the system, from analyzing, evaluating, and improving their exercise form, in order to improve their performance.

[0090] Example 1

[0091] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0092] The present invention relates to a system that allows users to analyze their own exercise form and receive specific and practical feedback. Conventional technologies have made it difficult to provide methods for evaluating and improving exercise form, and require specialized knowledge and equipment. Therefore, the objective of the present invention is to provide technology that allows users to easily and effectively analyze and improve their own form.

[0093] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0094] In this invention, the server includes means for uploading video of movements taken by a user using an information processing device to the system, means for dividing the video data received by the server into video units and extracting important location information, means for evaluating good points and areas for improvement of the movements based on the extracted location information, means for generating ideal movements based on exercise goals input by the user through the information processing device, and means for comparing the ideal movements generated by the server with the user's actual movements and providing specific instructions, thereby enabling users to easily and effectively analyze and improve their own form.

[0095] An "information processing device" is an electronic device that processes digital data and can connect to the Internet, such as a smartphone, tablet, or personal computer used by a user.

[0096] "Video data" refers to video files of movements or exercises that a user captures using an information processing device, and is digital data that is uploaded to the system.

[0097] "Splitting into video units" means that the video data received by the server is divided into frames, and each frame is treated as a still image that can be analyzed individually.

[0098] "Important positional information" refers to data necessary for analyzing athletic form, such as the position, angle, speed, and movement trajectory of joints and body parts that the AI ​​model extracts from video data.

[0099] "Movement evaluation" is a process in which the server identifies the good points and areas for improvement of the user's exercise form based on the extracted location information and determines which parts are ideal.

[0100] An "exercise goal" is a specific exercise objective that the user wants to achieve and inputs into the system, and includes specific examples such as "I want to increase my pitching speed" or "I want to improve the accuracy of my swing."

[0101] "Ideal movements" are optimal exercise forms generated by the server based on the exercise goals entered by the user, and include ideal movement patterns and postures.

[0102] "Specific instructions" are feedback provided by the server to the user, such as specific practice methods for improving movement, areas that need strengthening, and methods for preventing injuries.

[0103] The "system" is a technical mechanism that consists of multiple components such as an information processing device, a server, and an AI model, and performs a series of processes to allow users to analyze and improve their exercise form.

[0104] The present invention relates to a system that enables a user to effectively analyze his or her own exercise form and receive specific and practical feedback. Specific embodiments of the system of the present invention will be described below.

[0105] Recording and uploading videos

[0106] First, a user uses an information processing device (such as a smartphone, tablet, or personal computer) to record a video of their own exercise form. Exercise forms include a wide variety of sports movements, such as a baseball pitching form, a golf swing, or a tennis swing. The user shoots the video with the appropriate angle and lighting, and then uploads the video through the system's application or website.

[0107] Video Analysis

[0108] Next, the server receives the video data uploaded by the user. The server divides this video data into frames and treats each individual frame as the target for analysis. The main hardware used here is a high-performance server computer, and the main software is a deep learning framework such as TENSORFLOW (registered trademark) or PyTorch. The server inputs each divided frame into an AI model and extracts important positional information such as the position, angle, and speed of joints and body parts.

[0109] Form evaluation

[0110] The server evaluates the user's exercise form based on the extracted position information. Specifically, it analyzes the elbow angle, shoulder position, and hip rotation, and lists good points and areas for improvement. For example, it may evaluate the form as "elbows are lower than the head" or "center of gravity is too far forward." This allows the user to understand specific areas for improvement.

[0111] Setting goals and generating ideal forms

[0112] The user inputs the exercise goal they want to achieve into the system on their information processing device. For example, specific goals such as "I want to increase the ball speed" or "I want to increase the distance I can hit the ball." The server generates an ideal exercise form based on the user's goal. This ideal form includes movements that will optimally achieve the goal input by the user.

[0113] Compare forms and provide feedback

[0114] The server compares the generated ideal form with the user's actual form and provides specific instructions based on the comparison results. It generates feedback to the user on specific training methods, areas to strengthen, and methods for preventing injuries. For example, the application displays a video of "band exercises to strengthen the inner shoulder muscles."

[0115] In this way, users can receive consistent support from the system, from analyzing their form to providing specific ways to improve. It is also possible to utilize a generative AI model to provide effective feedback based on prompts, allowing users to improve their exercise form without specialized knowledge.

[0116] Prompt Sentence Examples

[0117] Here we provide specific examples of prompt sentences to input to the generative AI model.

[0118] "Analyze a video of a user's exercise form and extract data on specific movements (e.g., elbow angle or shoulder position). Also, use this data to evaluate the user's form and provide specific suggestions for improvement."

[0119] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0120] Step 1:

[0121] Recording and uploading videos

[0122] The user records their own exercise form as a video using an information processing device. The appropriate angle and lighting are selected to record the exercise form (e.g., baseball pitching form, golf swing) in detail. The video is then uploaded to the system's application or website.

[0123] Input: Video file of exercise form

[0124] Output: Uploaded video data

[0125] Step 2:

[0126] Video data reception and preprocessing

[0127] The server receives video data uploaded by users. The server divides this video data into frames and treats each frame as an object for analysis. Specifically, if the video is 30 fps (frames per second), it is divided into 30 frames per second.

[0128] Input: Uploaded video data

[0129] Output: Split frame data

[0130] Step 3:

[0131] Extracting important location information

[0132] The server inputs each frame of images into a generative AI model, which extracts important positional information such as the position, angle, and velocity of joints and body parts. Deep learning frameworks such as TensorFlow and PyTorch are used as the generative AI model. Specifically, a convolutional neural network (CNN) is used to detect the joint positions in each frame.

[0133] Input: Split frame data

[0134] Output: Extracted location data

[0135] Step 4:

[0136] Exercise form evaluation

[0137] The server evaluates the user's exercise form based on the extracted position information. It analyzes the elbow angle, shoulder position, and hip rotation, and lists good points and areas for improvement. Specifically, it may evaluate the user's form as "the elbow position is appropriate, but the shoulder position is low."

[0138] Input: Location data

[0139] Output: Form evaluation results (list of good points and areas for improvement)

[0140] Step 5:

[0141] Setting goals

[0142] The user inputs the exercise goal they want to achieve into the system through an information processing device, such as "I want to increase my pitching speed" or "I want to increase my driving distance."

[0143] Input: User-entered exercise goals

[0144] Output: Set target data

[0145] Step 6:

[0146] Generating ideal forms

[0147] The server generates an ideal exercise form based on the user's goals. This ideal form includes movements that will optimally achieve the user's goals. The generated data is output as a video or model of the new ideal form.

[0148] Input: Set goal data, form evaluation result data

[0149] Output: Data on ideal exercise form

[0150] Step 7:

[0151] Compare forms and provide feedback

[0152] The server compares the generated ideal form with the user's actual form. Based on the comparison results, feedback including specific practice methods and areas for improvement is generated and provided to the user's device. Specific exercises are shown in the form of diagrams and videos.

[0153] Input: Ideal exercise form data, actual exercise form data

[0154] Output: Specific feedback data (practice methods, list of areas for improvement)

[0155] In this way, users can receive consistent and effective support through the system, from analyzing their own form to specific ways to improve. In addition, because the analysis is processed using a generative AI model, users can receive advanced feedback even without specialized knowledge.

[0156] (Application example 1)

[0157] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0158] In logistics centers, it is essential to provide accurate evaluations of employee movements and feedback in real time to improve the efficiency and safety of their work. However, conventional methods require the time-consuming task of filming and evaluating movements, making it difficult to provide immediate feedback. Rapid and effective improvements to movements are particularly required in large-scale centers, but current technology may not be able to provide satisfactory results.

[0159] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0160] In this invention, the server includes a means for uploading exercise videos taken by users using their terminals to the system, a means for dividing the received video data into frames and extracting important location information, and a means for evaluating good points and areas for improvement in form based on the extracted location information. This makes it possible to capture and analyze work movements in a logistics center in real time using smart glasses and provide immediate feedback.

[0161] "User" means an individual or corporation that uses this system.

[0162] "Device" refers to any electronic device used for recording and uploading videos.

[0163] "Video data" refers to video information of exercise captured by a user using a terminal.

[0164] A "server" is a computer system that receives, processes, stores, and analyzes data.

[0165] "Splitting by frame" refers to the process of splitting the video data into individual images.

[0166] "Important positional information" refers to data necessary for motion analysis, such as the position, angle, and speed of joints and body parts in the video.

[0167] "Form" refers to the way a user moves or exercises.

[0168] "Good points" refer to the characteristics of the form that were judged appropriate in the evaluation.

[0169] "Areas for improvement" refers to features of the form that were judged to be inappropriate during the evaluation.

[0170] An "ideal form" refers to a form generated to optimally achieve the goals set by the user.

[0171] "Comparison" refers to the process of contrasting a user's actual form with an ideal form.

[0172] "Specific advice" refers to detailed improvement methods provided based on the evaluation results.

[0173] "Logistics center" refers to a facility that stores, manages, and ships goods.

[0174] "Smart glasses" refers to a wearable device that can capture video and display information.

[0175] "Real-time" refers to processing and information provision occurring immediately.

[0176] "Feedback" refers to the notification of evaluation results and areas for improvement provided to the user.

[0177] A "generative AI model" refers to artificial intelligence that uses machine learning technology to generate ideal forms.

[0178] A "prompt sentence" refers to text information that instructs the AI ​​model to input.

[0179] This invention is a system that allows users to easily analyze their own exercise form and receive specific and practical feedback. This system allows logistics center employees to use smart glasses to capture and analyze videos of their work movements in real time and receive instant feedback.

[0180] Video recording and uploading

[0181] First, a user wears a device, specifically a pair of smart glasses, and records video of their daily work activities. The smart glasses have the function of uploading the video to a cloud server in real time.

[0182] Video Analysis

[0183] The server splits the received video data into frames and uses OpenCV to extract important positional information, detecting the position, angle, and velocity of joints and body parts in each frame, and inputs this data into a generative AI model.

[0184] Form evaluation

[0185] Using a generative AI model, the server evaluates the employee's motion form based on the extracted location information, identifies strengths and areas for improvement, and stores the evaluation results in a database.

[0186] Setting goals and generating ideal forms

[0187] The user inputs the goal of the movement they want to improve through the smart glasses or another device. For example, if they input a goal such as "I want to improve the efficiency of lifting," the server will generate an ideal movement form based on this goal.

[0188] Compare forms and provide feedback

[0189] The server compares the generated ideal form with the user's actual form and generates specific advice, including training methods suitable for a distribution center, areas to train, and advice on injury prevention. This feedback is provided to the user in real time via visual and audio feedback through the smart glasses.

[0190] Hardware and software used

[0191] Hardware: Smart glasses (e.g., Google® Glass®)

[0192] Software: Python, OpenCV, TensorFlow (generative AI model)

[0193] The system allows logistics center employees to receive immediate, specific feedback on how to improve their operations, thereby increasing efficiency and safety.

[0194] Specific examples

[0195] For example, an employee lifting a box can be filmed with smart glasses and uploaded to a cloud server. The server analyzes the video data to detect the position of the joints and the angle of the movement. If the employee's goal is to "improve the efficiency of lifting movements," the server generates an ideal form, compares it, and provides feedback such as "advice to improve the angle of the hips."

[0196] Example prompt sentence:

[0197] The user performed the following actions while working at the distribution center:

[0198] Type of Action: Lifting a Box

[0199] Key joint location: {joint detection result}

[0200] Purpose: Efficient lifting movements

[0201] Using this information, please evaluate what aspects of this behavior could be improved and provide specific feedback.

[0202] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0203] Step 1:

[0204] Users wear smart glasses and record video of their work activities at a logistics center. The video data is then uploaded to a server in real time.

[0205] Input: Video data (user's work actions)

[0206] Output: Video data uploaded to the server

[0207] Step 2:

[0208] The server splits the received video data into frames using OpenCV, and each frame is used in the subsequent analysis steps.

[0209] Input: Uploaded video data

[0210] Output: Split frame data

[0211] Step 3:

[0212] The server analyzes the split frame data and extracts important positional information, specifically using OpenCV to detect the positions, angles, and velocities of joints and body parts.

[0213] Input: Split frame data

[0214] Output: Positional data (position, angle, and velocity of joints and body parts)

[0215] Step 4:

[0216] The server inputs location data into a generative AI model and evaluates the employee's behavior, including good points and areas for improvement.

[0217] Input: Location data

[0218] Output: Form evaluation data (good points and areas for improvement)

[0219] Step 5:

[0220] Using the smart glasses or another device, the user inputs the goal of the movement they want to improve, for example, "I want to improve the efficiency of my lifting movements."

[0221] Input: Improvement Objective (User Input)

[0222] Output: Data for improvement purposes

[0223] Step 6:

[0224] The server generates an ideal action form based on the user's goal, and outputs an improved action form using a generative AI model.

[0225] Input: Form evaluation data, improvement data

[0226] Output: Ideal form data

[0227] Step 7:

[0228] The server compares your actual form with your ideal form and generates specific feedback, including advice on areas for improvement, training methods, and injury prevention.

[0229] Input: Actual form data, Ideal form data

[0230] Output: Feedback data (specific advice)

[0231] Step 8:

[0232] The server sends the feedback data to the smart glasses, and the user receives the feedback in real time, which is notified visually and audibly.

[0233] Input: Feedback data

[0234] Output: The visual and audio feedback the user receives

[0235] Specific prompt examples:

[0236] The user performed the following actions while working at the distribution center:

[0237] Type of Action: Lifting a Box

[0238] Key joint location: {joint detection result}

[0239] Purpose: Efficient lifting movements

[0240] Using this information, please evaluate what aspects of this behavior could be improved and provide specific feedback.

[0241] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0242] The present invention relates to a system that allows users to easily analyze their own exercise form and receive specific and practical feedback. In particular, it is characterized by the fact that by combining it with an emotion engine, it provides optimal feedback and advice based on the user's emotional state. Specific embodiments of the system of the present invention are described below.

[0243] Recording and uploading videos

[0244] First, a user uses a device to record a video of their own exercise form, such as a baseball pitching form, a golf swing, or a tennis swing. The user takes the video with the right angle and lighting and uploads it to the system's application or website.

[0245] Video Analysis

[0246] The server then receives the video data, divides it into frames, and preprocesses it for input into the AI ​​model. The AI ​​model analyzes the position, angle, and speed of joints and body parts in the video to extract important positional information.

[0247] Form evaluation

[0248] The server evaluates the user's exercise form based on the extracted position information. For example, it analyzes the elbow angle, shoulder position, and hip rotation, and lists good points and areas for improvement. Specifically, it may evaluate the user's form as "the elbow angle is appropriate, but the release point is low."

[0249] Setting goals and generating ideal forms

[0250] The user inputs the goal they want to achieve into the system using a terminal. For example, "I want to increase the ball speed" or "I want to increase the distance I can hit the ball." The server generates an ideal form based on the user's goal. This ideal form includes the actions required to optimally achieve the goal entered by the user.

[0251] Compare forms and provide feedback

[0252] The server compares the generated ideal form with the user's actual form. Based on the comparison results, the server provides advice on specific training methods, areas to train, and injury prevention methods. For example, the server may provide specific advice to the user, such as "Use band exercises to strengthen the inner shoulder muscles."

[0253] Emotion recognition and feedback adjustment by emotion engine

[0254] The emotion engine uses facial recognition and voice analysis to recognize the user's emotions. While the user is recording their exercise form, the device's camera and microphone are used to collect emotion data in real time. The emotion engine analyzes the collected data to identify the user's emotional state.

[0255] Optimizing Emotional Feedback

[0256] The server tailors the feedback based on data from the emotion engine: for example, if it recognizes that the user is tired, it offers motivational messages and stretching suggestions, or, conversely, if the user is focused, it offers more detailed technical advice.

[0257] Specific examples

[0258] An example of baseball pitching form

[0259] 1. Recording and uploading videos

[0260] The user takes a photo of their pitching form with their smartphone and uploads it to the system.

[0261] 2. Video Analysis

[0262] The server divides the video into frames, and AI detects important positions such as elbows, shoulders, and waists.

[0263] 3. Form Evaluation

[0264] The server identifies specific areas for improvement, such as "your elbows are lower than your head" or "your center of gravity is too far forward."

[0265] 4. Setting goals

[0266] The user inputs a goal such as "I want to increase my pitching speed."

[0267] 5. Generation of ideal form

[0268] The server generates the ideal form and creates a new video with the elbow height adjusted.

[0269] 6. Compare forms and provide feedback

[0270] The device displays the user's form and the ideal form side by side, and the server provides advice on "band exercises to strengthen the inner shoulder muscles."

[0271] 7. Emotional Recognition

[0272] The emotion engine recognizes the user's face and voice and determines that the user is tired.

[0273] 8. Optimizing Emotional Feedback

[0274] Taking into consideration the user's sense of fatigue, the server suggests taking a moderate break or doing some light stretching.

[0275] In this way, users can not only analyze and improve their form, but also receive optimal feedback based on their emotional state. The entire system helps users improve their performance and promotes more effective practice.

[0276] The processing flow will be explained below.

[0277] Step 1:

[0278] The user uses a device to record a video of their exercise form. The user sets the appropriate angle and lighting and records their exercise form using, for example, the camera function of a smartphone.

[0279] Step 2:

[0280] The user uses the device to open a dedicated interface to upload the captured video to the system's application or website. The device sends the video data to the specified API endpoint.

[0281] Step 3:

[0282] The server receives the video data sent from the device and temporarily stores it in storage. The server checks the format and quality of the video data.

[0283] Step 4:

[0284] The server splits the video data it receives into frames, and each frame is extracted and saved individually.

[0285] Step 5:

[0286] The server sequentially inputs the divided frames into the AI ​​model and detects the position information (coordinates, angles, speed, etc.) of the user's joints and body parts. It then selects specific key frames and extracts their position information.

[0287] Step 6:

[0288] The server evaluates the user's form based on the extracted position information, using indicators such as elbow angle, shoulder position, and hip rotation to analyze good points and areas for improvement.

[0289] Step 7:

[0290] The server generates feedback on the form based on the evaluation results, specifically providing specific points for improvement in text format, such as "your elbows are lower than your head" or "your center of gravity is too far forward."

[0291] Step 8:

[0292] The user uses a terminal to input the exercise goal they want to achieve on the system interface, for example, setting a specific goal such as "I want to increase my pitching speed."

[0293] Step 9:

[0294] The server generates an ideal form based on the purpose entered by the user. It generates ideal form data and calculates the optimal keyframes.

[0295] Step 10:

[0296] The server generates a new video to visualize the ideal form, which is then compared to the user's actual form.

[0297] Step 11:

[0298] The device provides an interface that displays the ideal form and the user's actual form side by side, allowing users to directly compare the two forms using a video player or slider.

[0299] Step 12:

[0300] The server compares the user's form and generates advice on specific training methods, areas to train, and injury prevention. The device then notifies the user of this advice and provides detailed feedback.

[0301] Step 13:

[0302] The device runs an emotion engine that performs real-time facial recognition and voice analysis of the user, collecting emotion data using the device's camera and microphone.

[0303] Step 14:

[0304] The emotion engine analyzes the collected data and identifies the user's emotional state, identifying emotions such as fatigue, concentration, frustration, and joy.

[0305] Step 15:

[0306] The server receives data from the emotion engine and adjusts the feedback, for example suggesting light stretching or a break if it detects that the user is tired.

[0307] Step 16:

[0308] The server generates motivational messages and additional advice according to the user's emotional state, and the device notifies the user of these messages and advice.

[0309] In this way, by combining the emotion engine, it is possible to not only analyze and improve the user's exercise form, but also to provide optimal feedback and advice adapted to the user's emotional state.

[0310] Example 2

[0311] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0312] While conventional exercise form analysis systems can provide feedback on the exercise itself, they are unable to provide feedback that takes into account the user's emotional state. Furthermore, conventional systems have difficulty automatically comparing the user's actual form with their ideal form, and providing detailed advice requires a great deal of time and effort. Therefore, there is a need for comprehensive exercise form analysis and improvement support that includes feedback based on the user's emotional state.

[0313] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0314] In this invention, the server includes means for uploading exercise videos taken by a user using a terminal to the system, means for dividing the received video data into frames and extracting important location information, means for evaluating good points and areas for improvement of the form based on the extracted location information, means for generating an ideal form based on the exercise purpose input by the user through the terminal, means for comparing the ideal form generated by the server with the user's actual form and providing specific advice, means for recognizing the user's emotional state using the camera and microphone of the terminal, and means for adjusting feedback based on the emotional state by the server, thereby enabling optimal feedback based on the user's emotional state as well as analysis of the user's exercise form.

[0315] "Terminal" refers to a device used by a user to record a video of their exercise form and upload it to the system, and refers to a portable electronic device such as a smartphone or tablet.

[0316] "Server" refers to a central processing unit that analyzes video data uploaded to the system and evaluates, generates, and provides feedback on exercise form.

[0317] "Video data" refers to video data of a user's exercise form, which is uploaded to the system and analyzed by the server.

[0318] "Splitting by frame" refers to the process of dividing video data into frames, which are time units, in order to improve the accuracy of analysis.

[0319] "Positional information" refers to data such as the position, angle, and speed of the user's joints and body parts in the video, and is used to evaluate exercise form.

[0320] "Extraction" refers to the process of obtaining the necessary location information from video data.

[0321] "Evaluation" refers to the process of analyzing and determining the strengths and areas for improvement of the user's exercise form based on the extracted location information.

[0322] "Purpose of exercise" refers to the goal or purpose of exercise that the user inputs into the system, such as "I want to increase the ball speed" or "I want to increase the distance I can hit the ball."

[0323] "Ideal form" refers to a model of exercise form generated by the server to optimally achieve the user's goals.

[0324] "Comparison" refers to the process of evaluating the server-generated ideal form side-by-side with the user's actual form.

[0325] "Feedback" refers to specific advice or guidance provided by the server based on the comparison results, such as training methods or injury prevention methods.

[0326] "Emotional state" refers to the psychological state determined from the user's facial expression and tone of voice, and includes fatigue, concentration, joy, etc.

[0327] "Camera and microphone" refers to the video and audio input devices built into the device, which are used to recognize emotional states.

[0328] "Emotion Engine" refers to software or algorithms that analyze data captured through the camera and microphone to identify the user's emotional state.

[0329] "Adjusting feedback" refers to the process in which the server changes the feedback content based on the analysis results of the emotion engine.

[0330] The present invention relates to a system that allows users to easily analyze their own exercise form and receive specific and practical feedback, and is characterized by the fact that it combines an emotion engine to provide optimal feedback and advice based on the user's emotional state. The system of the present invention is implemented in the following specific manner.

[0331] Recording and uploading videos

[0332] The user uses a device (such as a smartphone or tablet) to record a video of their own athletic form. Using the device's camera, they can capture athletic form, such as a baseball pitch, golf swing, or tennis swing, at the appropriate angle and lighting. Once the video is complete, it can be uploaded to the system's application or website.

[0333] Video Analysis

[0334] The server receives video data uploaded by users. The server divides the video into frames and performs preprocessing. Preprocessing includes adjusting the video resolution and removing noise. The server then inputs the preprocessed video data into an AI model (e.g., OpenPose) and analyzes the position, angle, and velocity of joints and body parts from the video to extract important positional information.

[0335] Form evaluation

[0336] The server evaluates the user's exercise form based on the extracted position information. It analyzes the elbow angle, shoulder position, and hip rotation, and lists good points and areas for improvement. A specific example would be an evaluation such as "The elbow angle is appropriate, but the release point is low."

[0337] Setting goals and generating ideal forms

[0338] The user inputs the exercise goal they want to achieve into the system using their device. For example, they might say, "I want to increase my pitching speed" or "I want to increase my driving distance." Based on the goal entered by the user, the server uses a generative AI model (e.g., StyleGAN) to generate an ideal exercise form. This ideal form includes the movements that will optimally achieve the user's goal and provides a detailed description of that form.

[0339] Compare forms and provide feedback

[0340] The server compares the ideal form it has generated with the user's actual form. Evaluation criteria for the comparison include the degree of alignment of posture and the accuracy of movement. Based on the comparison results, the server provides advice on specific training methods, areas to train, and how to prevent injury. For example, it generates specific advice such as "To train the inner muscles of your shoulders, you should use band exercises." The device notifies the user of this feedback and displays detailed advice.

[0341] Emotion recognition and feedback adjustment by emotion engine

[0342] While the emotion engine captures the user's exercise form, it uses the device's camera and microphone to collect emotional data in real time. The emotion engine then uses facial recognition and voice analysis technologies to identify the user's emotional state from their facial expressions and tone of voice. For example, fatigue, concentration, joy, etc. can be analyzed.

[0343] Optimizing Emotional Feedback

[0344] The server receives emotional data from the emotion engine and adjusts the user's feedback. If the server detects that the user is tired, it will provide motivational messages and suggestions for light stretching. Conversely, if the user is concentrating, it will provide more detailed technical advice. The device will notify the user of the emotional feedback and encourage appropriate action.

[0345] Example: Analysis of baseball pitching form

[0346] 1. Recording and uploading videos

[0347] The user takes a photo of their pitching form from a diagonal forward angle on their smartphone and uploads it to the system.

[0348] 2. Video Analysis

[0349] The server divides the video into frames, and AI detects important positions such as elbows, shoulders, and waists.

[0350] 3. Form Evaluation

[0351] The server identifies specific areas for improvement, such as "your elbows are lower than your head" or "your center of gravity is too far forward."

[0352] 4. Setting goals

[0353] The user inputs a goal such as "I want to increase my pitching speed."

[0354] 5. Generation of ideal form

[0355] The server generates the ideal form and creates a new video with the elbow height adjusted.

[0356] 6. Compare forms and provide feedback

[0357] The device displays the user's form and the ideal form side by side, and the server provides advice on "band exercises to strengthen the inner shoulder muscles."

[0358] 7. Emotional Recognition

[0359] The emotion engine recognizes the user's face and voice and determines that the user is tired.

[0360] 8. Optimizing Emotional Feedback

[0361] The server considers the user's level of fatigue and suggests taking a moderate break or doing some light stretching.

[0362] Example of input prompt for generative AI model

[0363] "Analyze videos of baseball pitching form and provide user feedback. Also, use an emotion engine to suggest resting and stretching when fatigue occurs."

[0364] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0365] Detailed flow of system program processing

[0366] Step 1: Record and upload your video

[0367] The user uses a device (smartphone or tablet) to record a video of their exercise form.

[0368] Input: Video data such as baseball pitching form, golf swing, and tennis swing captured by the device's camera.

[0369] Specific operation: The user uploads the captured video to an application on the device or to a website.

[0370] Output: The uploaded video file will be saved on the system's server.

[0371] Step 2: Receive and split the video

[0372] The server receives the uploaded video data.

[0373] Input: User uploaded video file.

[0374] Specific operation: The server divides the video data into frames and generates still images that are consecutive in time.

[0375] Output: A set of still images separated by frames.

[0376] Step 3: Preprocessing the video

[0377] The server preprocesses the received video frames.

[0378] Input: A set of segmented still images.

[0379] Specific operation: The server performs pre-processing such as adjusting the video resolution, removing noise, and correcting color tone.

[0380] Output: A set of pre-processed still images.

[0381] Step 4: Extracting important location information

[0382] The server uses the pre-processed video frames to extract important location information.

[0383] Input: A set of preprocessed still images.

[0384] How it works: The server uses an AI model (e.g., OpenPose) to analyze the position, angle, and velocity of joints and body parts in each frame.

[0385] Output: A dataset related to joint and body part positions and their movements.

[0386] Step 5: Evaluate your form

[0387] The server evaluates the exercise form based on the extracted position information.

[0388] Input: A dataset related to important location and movement information.

[0389] Specific movements: The server analyzes the angle of each joint, shoulder position, hip rotation, etc. to identify good points and areas for improvement in form.

[0390] Output: A list of form evaluation results (e.g. "The elbow angle is appropriate, but the release point is low").

[0391] Step 6: Set your objectives

[0392] The user uses a terminal to input the purpose of the exercise into the system.

[0393] Input: The purpose of the exercise that the user inputs into the device (e.g., "I want to increase my pitching speed" or "I want to increase my driving distance").

[0394] Specific operation: When the user enters their purpose, the data is sent to the server and stored.

[0395] Output: The user's exercise goal data is stored on the server.

[0396] Step 7: Generate the ideal form

[0397] The server generates the ideal form based on the user's goals.

[0398] Input: User's exercise goal data.

[0399] Specific behavior: The server uses a generative AI model (e.g., StyleGAN) to generate an ideal exercise form to optimally achieve the user's goal.

[0400] Output: Generated ideal exercise form data.

[0401] Step 8: Compare forms and provide feedback

[0402] The server compares the ideal form generated by the server with the user's actual form and provides specific advice.

[0403] Input: Ideal exercise form data and the user's actual form data.

[0404] Specific operation: The server compares the two and evaluates the degree of correspondence between postures and the accuracy of movements. The device displays the results to the user.

[0405] Output: Comparison results and specific feedback (e.g., "You should do band exercises to strengthen your shoulder muscles").

[0406] Step 9: Emotion Recognition with the Emotion Engine

[0407] The device collects data to recognize the user's emotional state.

[0408] Input: User's facial expression data and voice data.

[0409] Specific operation: Using the device's camera and microphone, real-time emotional data is collected while the user is recording their exercise form.

[0410] Output: Collected emotion data.

[0411] Step 10: Adjusting Emotional Feedback

[0412] The server adjusts the feedback based on data from the emotion engine.

[0413] Input: User emotion data parsed by the emotion engine.

[0414] Specific behavior: If the server recognizes that the user is tired, it will provide motivational messages and suggestions for light stretching. If the user is concentrating, it will provide detailed technical advice.

[0415] Output: The adjusted feedback is displayed on the terminal.

[0416] (Application example 2)

[0417] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0418] Modern factories lack systems that can accurately analyze robot movements and efficiently provide improvement instructions. Furthermore, the lack of feedback based on workers' emotional states makes it difficult to improve production efficiency and worker safety. To solve this problem, a comprehensive system that integrates robot movement analysis and workers' emotional states is needed.

[0419] The specification processing by the specification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for uploading a video of exercise taken by a user using a terminal to the system, means for dividing the video data received by the server into frames and extracting important location information, means for evaluating good points and areas for improvement of the form based on the extracted location information, means for generating an ideal form based on the exercise purpose input by the user through the terminal, means for comparing the ideal form generated by the server with the user's actual form and providing specific advice, and means for recognizing the user's emotional state using an emotion engine and adjusting feedback based on the emotion data. This enables robot motion analysis and optimal feedback based on the worker's emotional state.

[0420] A "terminal" is an electronic device that a user uses to record and upload exercise videos to the system.

[0421] The "server" is a computer system that analyzes the received video data, generates the ideal form, and provides feedback.

[0422] "Video data" refers to video files of exercises taken by a user using a terminal.

[0423] "Splitting by frame" refers to the process of dividing video data into individual still images and analyzing them.

[0424] "Important positional information" refers to information about the positions and angles of joints and body parts that is necessary for analyzing movement.

[0425] "Form" refers to the user's body position and movement posture during exercise.

[0426] A "good point" is a part of the form being evaluated that is judged to be correct in relation to the intended action or goal.

[0427] "Areas for improvement" refers to any part of the form being evaluated that is judged to be inappropriate or inefficient.

[0428] "Ideal form" is the optimal movement pattern generated by the server to most effectively achieve the exercise goal entered by the user.

[0429] An "emotion engine" is software that analyzes a user's emotional state and adjusts feedback based on that.

[0430] "Feedback" refers to specific advice or instructions provided to users based on their form evaluation and areas for improvement.

[0431] System Overview

[0432] This invention is a system that analyzes videos of exercise form taken by the user using a device and provides specific and practical feedback. In particular, by combining it with an emotion engine, optimal feedback and advice based on the user's emotional state is provided, promoting more effective practice.

[0433] Initial Setup and Video Upload

[0434] Users use a device (such as a smartphone or tablet) to record a video of their athletic form. This video can cover a wide range of topics, such as the movement of a robot arm in a factory or a pitching form in a sports game. The video is then uploaded to the system's application or website. The device should have a support function that displays guidelines to help users record the video at the appropriate angle and with the appropriate lighting.

[0435] Video analysis and English extraction processing on the server

[0436] The server receives the uploaded video data and splits it into frames using a video processing library such as OpenCV. The frame-by-frame data is then fed into a pre-trained AI model (using Keras) to extract key positional information. This information includes the position and angle of joints and body parts. This processing reveals details about the movements of the user and the robot arm.

[0437] Evaluating forms and generating ideal forms

[0438] Based on the extracted positional information, the server evaluates the good points and areas for improvement in the form. For example, it may evaluate whether "the elbow position is correct" or "the shoulder movement is inappropriate." The user inputs their exercise goals (such as "I want to increase the speed of the ball" or "I want to improve the accuracy of the product") through the terminal, and the server generates an ideal form. This ideal form is a movement pattern that will optimally achieve the goal set by the user.

[0439] Compare and provide feedback

[0440] The server compares the generated ideal form with the user's actual form. Based on the comparison results, it provides specific practice methods and areas for improvement. There is also a function that displays the ideal form and the actual form side by side. Specifically, feedback such as "You should adjust your shoulder position like this" or "You should perform this exercise" is sent to the device.

[0441] Emotional state recognition and feedback optimization

[0442] The emotion engine uses facial recognition and voice analysis to identify the user's emotional state while recording video. Based on the emotion data, the server adjusts the feedback. For example, it analyzes the user's fatigue and concentration level. If the user is tired, it suggests a message of encouragement or a break, and if the user is concentrating, it provides detailed technical advice.

[0443] Example

[0444] For example, in analyzing the operation of a robot arm in a factory, a user takes a video of the robot arm and uploads it. The server analyzes the video and provides specific improvements and ideal operation patterns for the robot. The emotion engine recognizes the worker's level of fatigue and provides appropriate feedback.

[0445] Example prompt sentence:

[0446] "Please record video and analyze the behavior of factory robots."

[0447] "Provide feedback according to the worker's emotional state."

[0448] This allows users to effectively improve their own form and the robot's movements, and also allows them to optimally practice and adjust according to their emotional state.

[0449] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0450] Step 1:

[0451] The user uses a terminal to take a video of their exercise form and uploads it to the system.

[0452] Input: A video file of the exercise form taken by the user.

[0453] Processing: Capture video using the device camera and upload the video file to a system application or website.

[0454] Output: Uploaded video data is sent to the server.

[0455] Step 2:

[0456] The video data received by the server is divided into frames and important location information is extracted.

[0457] Input: Uploaded video data.

[0458] Processing: Using a video processing library such as OpenCV, the video is split into frames. The frame data is then input into an AI model (using Keras) to extract position and angle information for joints and body parts.

[0459] Output: Extracted geolocation data.

[0460] Step 3:

[0461] The server evaluates the form's strengths and areas for improvement based on the extracted location information.

[0462] Input: Extracted geolocation data.

[0463] Processing: Evaluate form based on position information, for example checking whether elbow position is correct or shoulder movement is appropriate.

[0464] Output: A list of good points and areas for improvement for the form.

[0465] Step 4:

[0466] The server generates an ideal form based on the exercise purpose entered by the user through the terminal.

[0467] Input: The purpose of the exercise entered by the user through the device (e.g., "I want to increase my pitching speed," "I want to improve the accuracy of my product," etc.).

[0468] Processing: Generate ideal form with optimal movement patterns based on the goal of the exercise.

[0469] Output: The generated data in ideal form.

[0470] Step 5:

[0471] The server compares the ideal form generated by the server with the user's actual form and provides specific advice.

[0472] Input: The user's actual form data and the generated ideal form data.

[0473] Processing: Compare the two and generate advice to the user on specific practice methods and areas for improvement.

[0474] Output: A specific advice message.

[0475] Step 6:

[0476] The server uses an emotion engine to recognize the user's emotional state and adjusts the feedback based on that emotional data.

[0477] Input: User's facial recognition and voice analysis data.

[0478] Processing: The emotion engine analyzes the user's emotional state (fatigue, concentration, etc.) and adjusts the feedback based on the results.

[0479] Output: Providing optimal feedback based on emotional state.

[0480] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0481] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0482] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0483] [Second embodiment]

[0484] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0485] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0486] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0487] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0488] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0489] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0490] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0491] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0492] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[0493] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0494] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0495] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0496] The present invention relates to a system that allows a user to easily analyze his or her own exercise form and receive specific and practical feedback. Specific embodiments of the system of the present invention will be described below.

[0497] Recording and uploading videos

[0498] First, a user uses a device to record a video of their own exercise form, such as a baseball pitching form, a golf swing, or a tennis swing. The user takes the video with the right angle and lighting and uploads it to the system's application or website.

[0499] Video Analysis

[0500] The server then receives the video data, divides it into frames, and preprocesses it for input into the AI ​​model. The AI ​​model analyzes the position, angle, and speed of joints and body parts in the video to extract important positional information.

[0501] Form evaluation

[0502] The server evaluates the user's exercise form based on the extracted position information. For example, it analyzes the elbow angle, shoulder position, and hip rotation, and lists good points and areas for improvement. Specifically, it may evaluate the user's form as "the elbow angle is appropriate, but the release point is low."

[0503] Setting goals and generating ideal forms

[0504] The user inputs the goal they want to achieve into the system using a terminal. For example, "I want to increase the ball speed" or "I want to increase the distance I can hit the ball." The server generates an ideal form based on the user's goal. This ideal form includes the actions required to optimally achieve the goal entered by the user.

[0505] Compare forms and provide feedback

[0506] The server compares the generated ideal form with the user's actual form. Based on the comparison results, the server provides advice on specific training methods, areas to train, and injury prevention methods. For example, the server may provide specific advice to the user, such as "Use band exercises to strengthen the inner shoulder muscles."

[0507] Specific examples

[0508] An example of baseball pitching form

[0509] 1. Recording and uploading videos

[0510] The user takes a photo of their pitching form with their smartphone and uploads it to the system.

[0511] 2. Video Analysis

[0512] The server divides the video into frames, and AI detects important positions such as elbows, shoulders, and waists.

[0513] 3. Form Evaluation

[0514] The server identifies specific areas for improvement, such as "elbows are lower than the head" or "center of gravity is too far forward."

[0515] 4. Setting goals

[0516] The user inputs a goal such as "I want to increase my pitching speed."

[0517] 5. Generation of ideal form

[0518] The server generates the ideal form and creates a new video with the elbow height adjusted.

[0519] 6. Compare forms and provide feedback

[0520] The device displays the user's form and the ideal form side by side, and the server provides advice on "band exercises to strengthen the inner shoulder muscles."

[0521] In this way, users can receive consistent support throughout the system, from analyzing their form to specific ways to improve it.

[0522] The processing flow will be explained below.

[0523] Step 1:

[0524] The user uses a device to record a video of their exercise form. The user sets the appropriate angle and lighting and records their exercise form using, for example, the camera function of a smartphone.

[0525] Step 2:

[0526] The user uses the device to open a dedicated interface to upload the captured video to the system's application or website. The device then sends the video data to the specified API endpoint.

[0527] Step 3:

[0528] The server receives the video data sent from the device and temporarily stores it in storage. The server checks the format and quality of the video data.

[0529] Step 4:

[0530] The server splits the video data it receives into frames, and each frame is extracted and saved individually.

[0531] Step 5:

[0532] The server sequentially inputs the divided frames into the AI ​​model and detects the position information (coordinates, angles, speed, etc.) of the user's joints and body parts. It then selects specific key frames and extracts their position information.

[0533] Step 6:

[0534] The server evaluates the user's form based on the extracted position information, using indicators such as elbow angle, shoulder position, and hip rotation to analyze good points and areas for improvement.

[0535] Step 7:

[0536] The server generates feedback on the form based on the evaluation results, specifically providing specific points for improvement in text format, such as "your elbows are lower than your head" or "your center of gravity is too far forward."

[0537] Step 8:

[0538] The user uses a terminal to input the exercise goal they want to achieve on the system interface, for example, setting a specific goal such as "I want to increase my pitching speed."

[0539] Step 9:

[0540] The server generates an ideal form based on the purpose entered by the user. It generates ideal form data and calculates the optimal keyframes.

[0541] Step 10:

[0542] The server generates a new video to visualize the ideal form, which can be compared to the user's actual form.

[0543] Step 11:

[0544] The device provides an interface that displays the ideal form and the user's actual form side by side, allowing users to directly compare the two forms using a video player or slider.

[0545] Step 12:

[0546] The server compares the user's form and generates advice on specific training methods, areas to train, and injury prevention. The device then notifies the user of this advice and provides detailed feedback.

[0547] These steps allow users to go through a series of processes through the system, from analyzing, evaluating, and improving their exercise form, in order to improve their performance.

[0548] Example 1

[0549] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0550] The present invention relates to a system that allows users to analyze their own exercise form and receive specific and practical feedback. Conventional technologies have made it difficult to provide methods for evaluating and improving exercise form, and require specialized knowledge and equipment. Therefore, the objective of the present invention is to provide technology that allows users to easily and effectively analyze and improve their own form.

[0551] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0552] In this invention, the server includes means for uploading video of movements taken by a user using an information processing device to the system, means for dividing the video data received by the server into video units and extracting important location information, means for evaluating good points and areas for improvement of the movements based on the extracted location information, means for generating ideal movements based on exercise goals input by the user through the information processing device, and means for comparing the ideal movements generated by the server with the user's actual movements and providing specific instructions, thereby enabling users to easily and effectively analyze and improve their own form.

[0553] An "information processing device" is an electronic device that processes digital data and can connect to the Internet, such as a smartphone, tablet, or personal computer used by a user.

[0554] "Video data" refers to video files of movements or exercises that a user captures using an information processing device, and is digital data that is uploaded to the system.

[0555] "Splitting into video units" means that the video data received by the server is divided into frames, and each frame is treated as a still image that can be analyzed individually.

[0556] "Important positional information" refers to data necessary for analyzing athletic form, such as the position, angle, speed, and movement trajectory of joints and body parts that the AI ​​model extracts from video data.

[0557] "Movement evaluation" is a process in which the server identifies the good points and areas for improvement of the user's exercise form based on the extracted location information and determines which parts are ideal.

[0558] An "exercise goal" is a specific exercise objective that the user wants to achieve and inputs into the system, and includes specific examples such as "I want to increase my pitching speed" or "I want to improve the accuracy of my swing."

[0559] "Ideal movements" are optimal exercise forms generated by the server based on the exercise goals entered by the user, and include ideal movement patterns and postures.

[0560] "Specific instructions" are feedback provided by the server to the user, such as specific practice methods for improving movement, areas that need strengthening, and methods for preventing injuries.

[0561] The "system" is a technical mechanism that consists of multiple components such as an information processing device, a server, and an AI model, and performs a series of processes to allow users to analyze and improve their exercise form.

[0562] The present invention relates to a system that enables a user to effectively analyze his or her own exercise form and receive specific and practical feedback. Specific embodiments of the system of the present invention will be described below.

[0563] Recording and uploading videos

[0564] First, a user uses an information processing device (such as a smartphone, tablet, or personal computer) to record a video of their own exercise form. Exercise forms include a wide variety of sports movements, such as a baseball pitching form, a golf swing, or a tennis swing. The user shoots the video with the appropriate angle and lighting, and then uploads the video through the system's application or website.

[0565] Video Analysis

[0566] Next, the server receives the video data uploaded by the user. The server divides this video data into frames and treats each individual frame as the target for analysis. The main hardware used here is a high-performance server computer, and the main software is a deep learning framework such as TensorFlow or PyTorch. The server inputs each divided frame into an AI model and extracts important positional information such as the position, angle, and velocity of joints and body parts.

[0567] Form evaluation

[0568] The server evaluates the user's exercise form based on the extracted position information. Specifically, it analyzes the elbow angle, shoulder position, and hip rotation, and lists good points and areas for improvement. For example, it may evaluate the form as "elbows are lower than the head" or "center of gravity is too far forward." This allows the user to understand specific areas for improvement.

[0569] Setting goals and generating ideal forms

[0570] The user inputs the exercise goal they want to achieve into the system on their information processing device. For example, specific goals such as "I want to increase the ball speed" or "I want to increase the distance I can hit the ball." The server generates an ideal exercise form based on the user's goal. This ideal form includes movements that will optimally achieve the goal input by the user.

[0571] Compare forms and provide feedback

[0572] The server compares the generated ideal form with the user's actual form and provides specific instructions based on the comparison results. It generates feedback to the user on specific training methods, areas to strengthen, and methods for preventing injuries. For example, the application displays a video of "band exercises to strengthen the inner shoulder muscles."

[0573] In this way, users can receive consistent support from the system, from analyzing their form to providing specific ways to improve. It is also possible to utilize a generative AI model to provide effective feedback based on prompts, allowing users to improve their exercise form without specialized knowledge.

[0574] Prompt Sentence Examples

[0575] Here we provide specific examples of prompt sentences to input to the generative AI model.

[0576] "Analyze a video of a user's exercise form and extract data on specific movements (e.g., elbow angle or shoulder position). Also, use this data to evaluate the user's form and provide specific suggestions for improvement."

[0577] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0578] Step 1:

[0579] Recording and uploading videos

[0580] The user records their own exercise form as a video using an information processing device. The appropriate angle and lighting are selected to record the exercise form (e.g., baseball pitching form, golf swing) in detail. The video is then uploaded to the system's application or website.

[0581] Input: Video file of exercise form

[0582] Output: Uploaded video data

[0583] Step 2:

[0584] Video data reception and preprocessing

[0585] The server receives video data uploaded by users. The server divides this video data into frames and treats each frame as an object for analysis. Specifically, if the video is 30 fps (frames per second), it is divided into 30 frames per second.

[0586] Input: Uploaded video data

[0587] Output: Split frame data

[0588] Step 3:

[0589] Extracting important location information

[0590] The server inputs each frame of images into a generative AI model, which extracts important positional information such as the position, angle, and velocity of joints and body parts. Deep learning frameworks such as TensorFlow and PyTorch are used as the generative AI model. Specifically, a convolutional neural network (CNN) is used to detect the joint positions in each frame.

[0591] Input: Split frame data

[0592] Output: Extracted location data

[0593] Step 4:

[0594] Exercise form evaluation

[0595] The server evaluates the user's exercise form based on the extracted position information. It analyzes the elbow angle, shoulder position, and hip rotation, and lists good points and areas for improvement. Specifically, it may evaluate the user's form as "the elbow position is appropriate, but the shoulder position is low."

[0596] Input: Location data

[0597] Output: Form evaluation results (list of good points and areas for improvement)

[0598] Step 5:

[0599] Setting goals

[0600] The user inputs the exercise goal they want to achieve into the system through an information processing device, such as "I want to increase my pitching speed" or "I want to increase my driving distance."

[0601] Input: User-entered exercise goals

[0602] Output: Set target data

[0603] Step 6:

[0604] Generating ideal forms

[0605] The server generates an ideal exercise form based on the user's goals. This ideal form includes movements that will optimally achieve the user's goals. The generated data is output as a video or model of the new ideal form.

[0606] Input: Set goal data, form evaluation result data

[0607] Output: Data on ideal exercise form

[0608] Step 7:

[0609] Compare forms and provide feedback

[0610] The server compares the generated ideal form with the user's actual form. Based on the comparison results, feedback including specific practice methods and areas for improvement is generated and provided to the user's device. Specific exercises are shown in the form of diagrams and videos.

[0611] Input: Ideal exercise form data, actual exercise form data

[0612] Output: Specific feedback data (practice methods, list of areas for improvement)

[0613] In this way, users can receive consistent and effective support through the system, from analyzing their own form to specific ways to improve. In addition, because the analysis is processed using a generative AI model, users can receive advanced feedback even without specialized knowledge.

[0614] (Application example 1)

[0615] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0616] In logistics centers, it is essential to provide accurate evaluations of employee movements and feedback in real time to improve the efficiency and safety of their work. However, conventional methods require the time-consuming task of filming and evaluating movements, making it difficult to provide immediate feedback. Rapid and effective improvements to movements are particularly required in large-scale centers, but current technology may not be able to provide satisfactory results.

[0617] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0618] In this invention, the server includes a means for uploading exercise videos taken by users using their terminals to the system, a means for dividing the received video data into frames and extracting important location information, and a means for evaluating good points and areas for improvement in form based on the extracted location information. This makes it possible to capture and analyze work movements in a logistics center in real time using smart glasses and provide immediate feedback.

[0619] "User" means an individual or corporation that uses this system.

[0620] "Device" refers to any electronic device used for recording and uploading videos.

[0621] "Video data" refers to video information of exercise captured by a user using a terminal.

[0622] A "server" is a computer system that receives, processes, stores, and analyzes data.

[0623] "Splitting by frame" refers to the process of splitting the video data into individual images.

[0624] "Important positional information" refers to data necessary for motion analysis, such as the position, angle, and speed of joints and body parts in the video.

[0625] "Form" refers to the way a user moves or exercises.

[0626] "Good points" refer to the characteristics of the form that were judged appropriate in the evaluation.

[0627] "Areas for improvement" refers to features of the form that were judged to be inappropriate during the evaluation.

[0628] An "ideal form" refers to a form generated to optimally achieve the goals set by the user.

[0629] "Comparison" refers to the process of contrasting a user's actual form with an ideal form.

[0630] "Specific advice" refers to detailed improvement methods provided based on the evaluation results.

[0631] "Logistics center" refers to a facility that stores, manages, and ships goods.

[0632] "Smart glasses" refers to a wearable device that can capture video and display information.

[0633] "Real-time" refers to processing and information provision occurring immediately.

[0634] "Feedback" refers to the notification of evaluation results and areas for improvement provided to the user.

[0635] A "generative AI model" refers to artificial intelligence that uses machine learning technology to generate ideal forms.

[0636] A "prompt sentence" refers to text information that instructs the AI ​​model to input.

[0637] This invention is a system that allows users to easily analyze their own exercise form and receive specific and practical feedback. This system allows logistics center employees to use smart glasses to capture and analyze videos of their work movements in real time and receive instant feedback.

[0638] Video recording and uploading

[0639] First, a user wears a device, specifically a pair of smart glasses, and records video of their daily work activities. The smart glasses have the function of uploading the video to a cloud server in real time.

[0640] Video Analysis

[0641] The server splits the received video data into frames and uses OpenCV to extract important positional information, detecting the position, angle, and velocity of joints and body parts in each frame, and inputs this data into a generative AI model.

[0642] Form evaluation

[0643] Using a generative AI model, the server evaluates the employee's motion form based on the extracted location information, identifies strengths and areas for improvement, and stores the evaluation results in a database.

[0644] Setting goals and generating ideal forms

[0645] The user inputs the goal of the movement they want to improve through the smart glasses or another device. For example, if they input a goal such as "I want to improve the efficiency of lifting," the server will generate an ideal movement form based on this goal.

[0646] Compare forms and provide feedback

[0647] The server compares the generated ideal form with the user's actual form and generates specific advice, including training methods suitable for a distribution center, areas to train, and advice on injury prevention. This feedback is provided to the user in real time via visual and audio feedback through the smart glasses.

[0648] Hardware and software used

[0649] Hardware: Smart glasses (e.g., Google Glass)

[0650] Software: Python, OpenCV, TensorFlow (generative AI model)

[0651] The system allows logistics center employees to receive immediate, specific feedback on how to improve their operations, thereby increasing efficiency and safety.

[0652] Specific examples

[0653] For example, an employee lifting a box can be filmed with smart glasses and uploaded to a cloud server. The server analyzes the video data to detect the position of the joints and the angle of the movement. If the employee's goal is to "improve the efficiency of lifting movements," the server generates an ideal form, compares it, and provides feedback such as "advice to improve the angle of the hips."

[0654] Example prompt sentence:

[0655] The user performed the following actions while working at the distribution center:

[0656] Type of Action: Lifting a Box

[0657] Key joint location: {joint detection result}

[0658] Purpose: Efficient lifting movements

[0659] Using this information, please evaluate what aspects of this behavior could be improved and provide specific feedback.

[0660] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0661] Step 1:

[0662] Users wear smart glasses and record video of their work activities at a logistics center. The video data is then uploaded to a server in real time.

[0663] Input: Video data (user's work actions)

[0664] Output: Video data uploaded to the server

[0665] Step 2:

[0666] The server splits the received video data into frames using OpenCV, and each frame is used in the subsequent analysis steps.

[0667] Input: Uploaded video data

[0668] Output: Split frame data

[0669] Step 3:

[0670] The server analyzes the split frame data and extracts important positional information, specifically using OpenCV to detect the positions, angles, and velocities of joints and body parts.

[0671] Input: Split frame data

[0672] Output: Positional data (position, angle, and velocity of joints and body parts)

[0673] Step 4:

[0674] The server inputs location data into a generative AI model and evaluates the employee's behavior, including good points and areas for improvement.

[0675] Input: Location data

[0676] Output: Form evaluation data (good points and areas for improvement)

[0677] Step 5:

[0678] Using the smart glasses or another device, the user inputs the goal of the movement they want to improve, for example, "I want to improve the efficiency of my lifting movements."

[0679] Input: Improvement Objective (User Input)

[0680] Output: Data for improvement purposes

[0681] Step 6:

[0682] The server generates an ideal action form based on the user's goal, and outputs an improved action form using a generative AI model.

[0683] Input: Form evaluation data, improvement data

[0684] Output: Ideal form data

[0685] Step 7:

[0686] The server compares your actual form with your ideal form and generates specific feedback, including advice on areas for improvement, training methods, and injury prevention.

[0687] Input: Actual form data, Ideal form data

[0688] Output: Feedback data (specific advice)

[0689] Step 8:

[0690] The server sends the feedback data to the smart glasses, and the user receives the feedback in real time, which is notified visually and audibly.

[0691] Input: Feedback data

[0692] Output: The visual and audio feedback the user receives

[0693] Specific prompt examples:

[0694] The user performed the following actions while working at the distribution center:

[0695] Type of Action: Lifting a Box

[0696] Key joint location: {joint detection result}

[0697] Purpose: Efficient lifting movements

[0698] Using this information, please evaluate what aspects of this behavior could be improved and provide specific feedback.

[0699] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0700] The present invention relates to a system that allows users to easily analyze their own exercise form and receive specific and practical feedback. In particular, it is characterized by the fact that by combining it with an emotion engine, it provides optimal feedback and advice based on the user's emotional state. Specific embodiments of the system of the present invention are described below.

[0701] Recording and uploading videos

[0702] First, a user uses a device to record a video of their own exercise form, such as a baseball pitching form, a golf swing, or a tennis swing. The user takes the video with the right angle and lighting and uploads it to the system's application or website.

[0703] Video Analysis

[0704] The server then receives the video data, divides it into frames, and preprocesses it for input into the AI ​​model. The AI ​​model analyzes the position, angle, and speed of joints and body parts in the video to extract important positional information.

[0705] Form evaluation

[0706] The server evaluates the user's exercise form based on the extracted position information. For example, it analyzes the elbow angle, shoulder position, and hip rotation, and lists good points and areas for improvement. Specifically, it may evaluate the user's form as "the elbow angle is appropriate, but the release point is low."

[0707] Setting goals and generating ideal forms

[0708] The user inputs the goal they want to achieve into the system using a terminal. For example, "I want to increase the ball speed" or "I want to increase the distance I can hit the ball." The server generates an ideal form based on the user's goal. This ideal form includes the actions required to optimally achieve the goal entered by the user.

[0709] Compare forms and provide feedback

[0710] The server compares the generated ideal form with the user's actual form. Based on the comparison results, the server provides advice on specific training methods, areas to train, and injury prevention methods. For example, the server may provide specific advice to the user, such as "Use band exercises to strengthen the inner shoulder muscles."

[0711] Emotion recognition and feedback adjustment by emotion engine

[0712] The emotion engine uses facial recognition and voice analysis to recognize the user's emotions. While the user is recording their exercise form, the device's camera and microphone are used to collect emotion data in real time. The emotion engine analyzes the collected data to identify the user's emotional state.

[0713] Optimizing Emotional Feedback

[0714] The server tailors the feedback based on data from the emotion engine: for example, if it recognizes that the user is tired, it offers motivational messages and stretching suggestions, or, conversely, if the user is focused, it offers more detailed technical advice.

[0715] Specific examples

[0716] An example of baseball pitching form

[0717] 1. Recording and uploading videos

[0718] The user takes a photo of their pitching form with their smartphone and uploads it to the system.

[0719] 2. Video Analysis

[0720] The server divides the video into frames, and AI detects important positions such as elbows, shoulders, and waists.

[0721] 3. Form Evaluation

[0722] The server identifies specific areas for improvement, such as "your elbows are lower than your head" or "your center of gravity is too far forward."

[0723] 4. Setting goals

[0724] The user inputs a goal such as "I want to increase my pitching speed."

[0725] 5. Generation of ideal form

[0726] The server generates the ideal form and creates a new video with the elbow height adjusted.

[0727] 6. Compare forms and provide feedback

[0728] The device displays the user's form and the ideal form side by side, and the server provides advice on "band exercises to strengthen the inner shoulder muscles."

[0729] 7. Emotional Recognition

[0730] The emotion engine recognizes the user's face and voice and determines that the user is tired.

[0731] 8. Optimizing Emotional Feedback

[0732] Taking into consideration the user's sense of fatigue, the server suggests taking a moderate break or doing some light stretching.

[0733] In this way, users can not only analyze and improve their form, but also receive optimal feedback based on their emotional state. The entire system helps users improve their performance and promotes more effective practice.

[0734] The processing flow will be explained below.

[0735] Step 1:

[0736] The user uses a device to record a video of their exercise form. The user sets the appropriate angle and lighting and records their exercise form using, for example, the camera function of a smartphone.

[0737] Step 2:

[0738] The user uses the device to open a dedicated interface to upload the captured video to the system's application or website. The device sends the video data to the specified API endpoint.

[0739] Step 3:

[0740] The server receives the video data sent from the device and temporarily stores it in storage. The server checks the format and quality of the video data.

[0741] Step 4:

[0742] The server splits the video data it receives into frames, and each frame is extracted and saved individually.

[0743] Step 5:

[0744] The server sequentially inputs the divided frames into the AI ​​model and detects the position information (coordinates, angles, speed, etc.) of the user's joints and body parts. It then selects specific key frames and extracts their position information.

[0745] Step 6:

[0746] The server evaluates the user's form based on the extracted position information, using indicators such as elbow angle, shoulder position, and hip rotation to analyze good points and areas for improvement.

[0747] Step 7:

[0748] The server generates feedback on the form based on the evaluation results, specifically providing specific points for improvement in text format, such as "your elbows are lower than your head" or "your center of gravity is too far forward."

[0749] Step 8:

[0750] The user uses a terminal to input the exercise goal they want to achieve on the system interface, for example, setting a specific goal such as "I want to increase my pitching speed."

[0751] Step 9:

[0752] The server generates an ideal form based on the purpose entered by the user. It generates ideal form data and calculates the optimal keyframes.

[0753] Step 10:

[0754] The server generates a new video to visualize the ideal form, which is then compared to the user's actual form.

[0755] Step 11:

[0756] The device provides an interface that displays the ideal form and the user's actual form side by side, allowing users to directly compare the two forms using a video player or slider.

[0757] Step 12:

[0758] The server compares the user's form and generates advice on specific training methods, areas to train, and injury prevention. The device then notifies the user of this advice and provides detailed feedback.

[0759] Step 13:

[0760] The device runs an emotion engine that performs real-time facial recognition and voice analysis of the user, collecting emotion data using the device's camera and microphone.

[0761] Step 14:

[0762] The emotion engine analyzes the collected data and identifies the user's emotional state, identifying emotions such as fatigue, concentration, frustration, and joy.

[0763] Step 15:

[0764] The server receives data from the emotion engine and adjusts the feedback, for example suggesting light stretching or a break if it detects that the user is tired.

[0765] Step 16:

[0766] The server generates motivational messages and additional advice according to the user's emotional state, and the device notifies the user of these messages and advice.

[0767] In this way, by combining the emotion engine, it is possible to not only analyze and improve the user's exercise form, but also to provide optimal feedback and advice adapted to the user's emotional state.

[0768] Example 2

[0769] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0770] While conventional exercise form analysis systems can provide feedback on the exercise itself, they are unable to provide feedback that takes into account the user's emotional state. Furthermore, conventional systems have difficulty automatically comparing the user's actual form with their ideal form, and providing detailed advice requires a great deal of time and effort. Therefore, there is a need for comprehensive exercise form analysis and improvement support that includes feedback based on the user's emotional state.

[0771] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0772] In this invention, the server includes means for uploading exercise videos taken by a user using a terminal to the system, means for dividing the received video data into frames and extracting important location information, means for evaluating good points and areas for improvement of the form based on the extracted location information, means for generating an ideal form based on the exercise purpose input by the user through the terminal, means for comparing the ideal form generated by the server with the user's actual form and providing specific advice, means for recognizing the user's emotional state using the camera and microphone of the terminal, and means for adjusting feedback based on the emotional state by the server, thereby enabling optimal feedback based on the user's emotional state as well as analysis of the user's exercise form.

[0773] "Terminal" refers to a device used by a user to record a video of their exercise form and upload it to the system, and refers to a portable electronic device such as a smartphone or tablet.

[0774] "Server" refers to a central processing unit that analyzes video data uploaded to the system and evaluates, generates, and provides feedback on exercise form.

[0775] "Video data" refers to video data of a user's exercise form, which is uploaded to the system and analyzed by the server.

[0776] "Splitting by frame" refers to the process of dividing video data into frames, which are time units, in order to improve the accuracy of analysis.

[0777] "Positional information" refers to data such as the position, angle, and speed of the user's joints and body parts in the video, and is used to evaluate exercise form.

[0778] "Extraction" refers to the process of obtaining the necessary location information from video data.

[0779] "Evaluation" refers to the process of analyzing and determining the strengths and areas for improvement of the user's exercise form based on the extracted location information.

[0780] "Purpose of exercise" refers to the goal or purpose of exercise that the user inputs into the system, such as "I want to increase the ball speed" or "I want to increase the distance I can hit the ball."

[0781] "Ideal form" refers to a model of exercise form generated by the server to optimally achieve the user's goals.

[0782] "Comparison" refers to the process of evaluating the server-generated ideal form side-by-side with the user's actual form.

[0783] "Feedback" refers to specific advice or guidance provided by the server based on the comparison results, such as training methods or injury prevention methods.

[0784] "Emotional state" refers to the psychological state determined from the user's facial expression and tone of voice, and includes fatigue, concentration, joy, etc.

[0785] "Camera and microphone" refers to the video and audio input devices built into the device, which are used to recognize emotional states.

[0786] "Emotion Engine" refers to software or algorithms that analyze data captured through the camera and microphone to identify the user's emotional state.

[0787] "Adjusting feedback" refers to the process in which the server changes the feedback content based on the analysis results of the emotion engine.

[0788] The present invention relates to a system that allows users to easily analyze their own exercise form and receive specific and practical feedback, and is characterized by the fact that it combines an emotion engine to provide optimal feedback and advice based on the user's emotional state. The system of the present invention is implemented in the following specific manner.

[0789] Recording and uploading videos

[0790] The user uses a device (such as a smartphone or tablet) to record a video of their own athletic form. Using the device's camera, they can capture athletic form, such as a baseball pitch, golf swing, or tennis swing, at the appropriate angle and lighting. Once the video is complete, it can be uploaded to the system's application or website.

[0791] Video Analysis

[0792] The server receives video data uploaded by users. The server divides the video into frames and performs preprocessing. Preprocessing includes adjusting the video resolution and removing noise. The server then inputs the preprocessed video data into an AI model (e.g., OpenPose) and analyzes the position, angle, and velocity of joints and body parts from the video to extract important positional information.

[0793] Form evaluation

[0794] The server evaluates the user's exercise form based on the extracted position information. It analyzes the elbow angle, shoulder position, and hip rotation, and lists good points and areas for improvement. A specific example would be an evaluation such as "The elbow angle is appropriate, but the release point is low."

[0795] Setting goals and generating ideal forms

[0796] The user inputs the exercise goal they want to achieve into the system using their device. For example, they might say, "I want to increase my pitching speed" or "I want to increase my driving distance." Based on the goal entered by the user, the server uses a generative AI model (e.g., StyleGAN) to generate an ideal exercise form. This ideal form includes the movements that will optimally achieve the user's goal and provides a detailed description of that form.

[0797] Compare forms and provide feedback

[0798] The server compares the ideal form it has generated with the user's actual form. Evaluation criteria for the comparison include the degree of alignment of posture and the accuracy of movement. Based on the comparison results, the server provides advice on specific training methods, areas to train, and how to prevent injury. For example, it generates specific advice such as "To train the inner muscles of your shoulders, you should use band exercises." The device notifies the user of this feedback and displays detailed advice.

[0799] Emotion recognition and feedback adjustment by emotion engine

[0800] While the emotion engine captures the user's exercise form, it uses the device's camera and microphone to collect emotional data in real time. The emotion engine then uses facial recognition and voice analysis technologies to identify the user's emotional state from their facial expressions and tone of voice. For example, fatigue, concentration, joy, etc. can be analyzed.

[0801] Optimizing Emotional Feedback

[0802] The server receives emotional data from the emotion engine and adjusts the user's feedback. If the server detects that the user is tired, it will provide motivational messages and suggestions for light stretching. Conversely, if the user is concentrating, it will provide more detailed technical advice. The device will notify the user of the emotional feedback and encourage appropriate action.

[0803] Example: Analysis of baseball pitching form

[0804] 1. Recording and uploading videos

[0805] The user takes a photo of their pitching form from a diagonal forward angle on their smartphone and uploads it to the system.

[0806] 2. Video Analysis

[0807] The server divides the video into frames, and AI detects important positions such as elbows, shoulders, and waists.

[0808] 3. Form Evaluation

[0809] The server identifies specific areas for improvement, such as "your elbows are lower than your head" or "your center of gravity is too far forward."

[0810] 4. Setting goals

[0811] The user inputs a goal such as "I want to increase my pitching speed."

[0812] 5. Generation of ideal form

[0813] The server generates the ideal form and creates a new video with the elbow height adjusted.

[0814] 6. Compare forms and provide feedback

[0815] The device displays the user's form and the ideal form side by side, and the server provides advice on "band exercises to strengthen the inner shoulder muscles."

[0816] 7. Emotional Recognition

[0817] The emotion engine recognizes the user's face and voice and determines that the user is tired.

[0818] 8. Optimizing Emotional Feedback

[0819] The server considers the user's level of fatigue and suggests taking a moderate break or doing some light stretching.

[0820] Example of input prompt for generative AI model

[0821] "Analyze videos of baseball pitching form and provide user feedback. Also, use an emotion engine to suggest resting and stretching when fatigue occurs."

[0822] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0823] Detailed flow of system program processing

[0824] Step 1: Record and upload your video

[0825] The user uses a device (smartphone or tablet) to record a video of their exercise form.

[0826] Input: Video data such as baseball pitching form, golf swing, and tennis swing captured by the device's camera.

[0827] Specific operation: The user uploads the captured video to an application on the device or to a website.

[0828] Output: The uploaded video file will be saved on the system's server.

[0829] Step 2: Receive and split the video

[0830] The server receives the uploaded video data.

[0831] Input: User uploaded video file.

[0832] Specific operation: The server divides the video data into frames and generates still images that are consecutive in time.

[0833] Output: A set of still images separated by frames.

[0834] Step 3: Preprocessing the video

[0835] The server preprocesses the received video frames.

[0836] Input: A set of segmented still images.

[0837] Specific operation: The server performs pre-processing such as adjusting the video resolution, removing noise, and correcting color tone.

[0838] Output: A set of pre-processed still images.

[0839] Step 4: Extracting important location information

[0840] The server uses the pre-processed video frames to extract important location information.

[0841] Input: A set of preprocessed still images.

[0842] How it works: The server uses an AI model (e.g., OpenPose) to analyze the position, angle, and velocity of joints and body parts in each frame.

[0843] Output: A dataset related to joint and body part positions and their movements.

[0844] Step 5: Evaluate your form

[0845] The server evaluates the exercise form based on the extracted position information.

[0846] Input: A dataset related to important location and movement information.

[0847] Specific movements: The server analyzes the angle of each joint, shoulder position, hip rotation, etc. to identify good points and areas for improvement in form.

[0848] Output: A list of form evaluation results (e.g. "The elbow angle is appropriate, but the release point is low").

[0849] Step 6: Set your objectives

[0850] The user uses a terminal to input the purpose of the exercise into the system.

[0851] Input: The purpose of the exercise that the user inputs into the device (e.g., "I want to increase my pitching speed" or "I want to increase my driving distance").

[0852] Specific operation: When the user enters their purpose, the data is sent to the server and stored.

[0853] Output: The user's exercise goal data is stored on the server.

[0854] Step 7: Generate the ideal form

[0855] The server generates the ideal form based on the user's goals.

[0856] Input: User's exercise goal data.

[0857] Specific behavior: The server uses a generative AI model (e.g., StyleGAN) to generate an ideal exercise form to optimally achieve the user's goal.

[0858] Output: Generated ideal exercise form data.

[0859] Step 8: Compare forms and provide feedback

[0860] The server compares the ideal form generated by the server with the user's actual form and provides specific advice.

[0861] Input: Ideal exercise form data and the user's actual form data.

[0862] Specific operation: The server compares the two and evaluates the degree of correspondence between postures and the accuracy of movements. The device displays the results to the user.

[0863] Output: Comparison results and specific feedback (e.g., "You should do band exercises to strengthen your shoulder muscles").

[0864] Step 9: Emotion Recognition with the Emotion Engine

[0865] The device collects data to recognize the user's emotional state.

[0866] Input: User's facial expression data and voice data.

[0867] Specific operation: Using the device's camera and microphone, real-time emotional data is collected while the user is recording their exercise form.

[0868] Output: Collected emotion data.

[0869] Step 10: Adjusting Emotional Feedback

[0870] The server adjusts the feedback based on data from the emotion engine.

[0871] Input: User emotion data parsed by the emotion engine.

[0872] Specific behavior: If the server recognizes that the user is tired, it will provide motivational messages and suggestions for light stretching. If the user is concentrating, it will provide detailed technical advice.

[0873] Output: The adjusted feedback is displayed on the terminal.

[0874] (Application example 2)

[0875] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0876] Modern factories lack systems that can accurately analyze robot movements and efficiently provide improvement instructions. Furthermore, the lack of feedback based on workers' emotional states makes it difficult to improve production efficiency and worker safety. To solve this problem, a comprehensive system that integrates robot movement analysis and workers' emotional states is needed.

[0877] The specification processing by the specification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for uploading a video of exercise taken by a user using a terminal to the system, means for dividing the video data received by the server into frames and extracting important location information, means for evaluating good points and areas for improvement of the form based on the extracted location information, means for generating an ideal form based on the exercise purpose input by the user through the terminal, means for comparing the ideal form generated by the server with the user's actual form and providing specific advice, and means for recognizing the user's emotional state using an emotion engine and adjusting feedback based on the emotion data. This enables robot motion analysis and optimal feedback based on the worker's emotional state.

[0878] A "terminal" is an electronic device that a user uses to record and upload exercise videos to the system.

[0879] The "server" is a computer system that analyzes the received video data, generates the ideal form, and provides feedback.

[0880] "Video data" refers to video files of exercises taken by a user using a terminal.

[0881] "Splitting by frame" refers to the process of dividing video data into individual still images and analyzing them.

[0882] "Important positional information" refers to information about the positions and angles of joints and body parts that is necessary for analyzing movement.

[0883] "Form" refers to the user's body position and movement posture during exercise.

[0884] A "good point" is a part of the form being evaluated that is judged to be correct in relation to the intended action or goal.

[0885] "Areas for improvement" refers to any part of the form being evaluated that is judged to be inappropriate or inefficient.

[0886] "Ideal form" is the optimal movement pattern generated by the server to most effectively achieve the exercise goal entered by the user.

[0887] An "emotion engine" is software that analyzes a user's emotional state and adjusts feedback based on that.

[0888] "Feedback" refers to specific advice or instructions provided to users based on their form evaluation and areas for improvement.

[0889] System Overview

[0890] This invention is a system that analyzes videos of exercise form taken by the user using a device and provides specific and practical feedback. In particular, by combining it with an emotion engine, optimal feedback and advice based on the user's emotional state is provided, promoting more effective practice.

[0891] Initial Setup and Video Upload

[0892] Users use a device (such as a smartphone or tablet) to record a video of their athletic form. This video can cover a wide range of topics, such as the movement of a robot arm in a factory or a pitching form in a sports game. The video is then uploaded to the system's application or website. The device should have a support function that displays guidelines to help users record the video at the appropriate angle and with the appropriate lighting.

[0893] Video analysis and English extraction processing on the server

[0894] The server receives the uploaded video data and splits it into frames using a video processing library such as OpenCV. The frame-by-frame data is then fed into a pre-trained AI model (using Keras) to extract key positional information. This information includes the position and angle of joints and body parts. This processing reveals details about the movements of the user and the robot arm.

[0895] Evaluating forms and generating ideal forms

[0896] Based on the extracted positional information, the server evaluates the good points and areas for improvement in the form. For example, it may evaluate whether "the elbow position is correct" or "the shoulder movement is inappropriate." The user inputs their exercise goals (such as "I want to increase the speed of the ball" or "I want to improve the accuracy of the product") through the terminal, and the server generates an ideal form. This ideal form is a movement pattern that will optimally achieve the goal set by the user.

[0897] Compare and provide feedback

[0898] The server compares the generated ideal form with the user's actual form. Based on the comparison results, it provides specific practice methods and areas for improvement. There is also a function that displays the ideal form and the actual form side by side. Specifically, feedback such as "You should adjust your shoulder position like this" or "You should perform this exercise" is sent to the device.

[0899] Emotional state recognition and feedback optimization

[0900] The emotion engine uses facial recognition and voice analysis to identify the user's emotional state while recording video. Based on the emotion data, the server adjusts the feedback. For example, it analyzes the user's fatigue and concentration level. If the user is tired, it suggests a message of encouragement or a break, and if the user is concentrating, it provides detailed technical advice.

[0901] Example

[0902] For example, in analyzing the operation of a robot arm in a factory, a user takes a video of the robot arm and uploads it. The server analyzes the video and provides specific improvements and ideal operation patterns for the robot. The emotion engine recognizes the worker's level of fatigue and provides appropriate feedback.

[0903] Example prompt sentence:

[0904] "Please record video and analyze the behavior of factory robots."

[0905] "Provide feedback according to the worker's emotional state."

[0906] This allows users to effectively improve their own form and the robot's movements, and also allows them to optimally practice and adjust according to their emotional state.

[0907] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0908] Step 1:

[0909] The user uses a terminal to take a video of their exercise form and uploads it to the system.

[0910] Input: A video file of the exercise form taken by the user.

[0911] Processing: Capture video using the device camera and upload the video file to a system application or website.

[0912] Output: Uploaded video data is sent to the server.

[0913] Step 2:

[0914] The video data received by the server is divided into frames and important location information is extracted.

[0915] Input: Uploaded video data.

[0916] Processing: Using a video processing library such as OpenCV, the video is split into frames. The frame data is then input into an AI model (using Keras) to extract position and angle information for joints and body parts.

[0917] Output: Extracted geolocation data.

[0918] Step 3:

[0919] The server evaluates the form's strengths and areas for improvement based on the extracted location information.

[0920] Input: Extracted geolocation data.

[0921] Processing: Evaluate form based on position information, for example checking whether elbow position is correct or shoulder movement is appropriate.

[0922] Output: A list of good points and areas for improvement for the form.

[0923] Step 4:

[0924] The server generates an ideal form based on the exercise purpose entered by the user through the terminal.

[0925] Input: The purpose of the exercise entered by the user through the device (e.g., "I want to increase my pitching speed," "I want to improve the accuracy of my product," etc.).

[0926] Processing: Generate ideal form with optimal movement patterns based on the goal of the exercise.

[0927] Output: The generated data in ideal form.

[0928] Step 5:

[0929] The server compares the ideal form generated by the server with the user's actual form and provides specific advice.

[0930] Input: The user's actual form data and the generated ideal form data.

[0931] Processing: Compare the two and generate advice to the user on specific practice methods and areas for improvement.

[0932] Output: A specific advice message.

[0933] Step 6:

[0934] The server uses an emotion engine to recognize the user's emotional state and adjusts the feedback based on that emotional data.

[0935] Input: User's facial recognition and voice analysis data.

[0936] Processing: The emotion engine analyzes the user's emotional state (fatigue, concentration, etc.) and adjusts the feedback based on the results.

[0937] Output: Providing optimal feedback based on emotional state.

[0938] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0939] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0940] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0941] [Third embodiment]

[0942] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0943] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0944] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0945] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0946] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0947] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0948] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0949] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0950] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[0951] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0952] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0953] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0954] The present invention relates to a system that allows a user to easily analyze his or her own exercise form and receive specific and practical feedback. Specific embodiments of the system of the present invention will be described below.

[0955] Recording and uploading videos

[0956] First, a user uses a device to record a video of their own exercise form, such as a baseball pitching form, a golf swing, or a tennis swing. The user takes the video with the right angle and lighting and uploads it to the system's application or website.

[0957] Video Analysis

[0958] The server then receives the video data, divides it into frames, and preprocesses it for input into the AI ​​model. The AI ​​model analyzes the position, angle, and speed of joints and body parts in the video to extract important positional information.

[0959] Form evaluation

[0960] The server evaluates the user's exercise form based on the extracted position information. For example, it analyzes the elbow angle, shoulder position, and hip rotation, and lists good points and areas for improvement. Specifically, it may evaluate the user's form as "the elbow angle is appropriate, but the release point is low."

[0961] Setting goals and generating ideal forms

[0962] The user inputs the goal they want to achieve into the system using a terminal. For example, "I want to increase the ball speed" or "I want to increase the distance I can hit the ball." The server generates an ideal form based on the user's goal. This ideal form includes the actions required to optimally achieve the goal entered by the user.

[0963] Compare forms and provide feedback

[0964] The server compares the generated ideal form with the user's actual form. Based on the comparison results, the server provides advice on specific training methods, areas to train, and injury prevention methods. For example, the server may provide specific advice to the user, such as "Use band exercises to strengthen the inner shoulder muscles."

[0965] Specific examples

[0966] An example of baseball pitching form

[0967] 1. Recording and uploading videos

[0968] The user takes a photo of their pitching form with their smartphone and uploads it to the system.

[0969] 2. Video Analysis

[0970] The server divides the video into frames, and AI detects important positions such as elbows, shoulders, and waists.

[0971] 3. Form Evaluation

[0972] The server identifies specific areas for improvement, such as "elbows are lower than the head" or "center of gravity is too far forward."

[0973] 4. Setting goals

[0974] The user inputs a goal such as "I want to increase my pitching speed."

[0975] 5. Generation of ideal form

[0976] The server generates the ideal form and creates a new video with the elbow height adjusted.

[0977] 6. Compare forms and provide feedback

[0978] The device displays the user's form and the ideal form side by side, and the server provides advice on "band exercises to strengthen the inner shoulder muscles."

[0979] In this way, users can receive consistent support throughout the system, from analyzing their form to specific ways to improve it.

[0980] The processing flow will be explained below.

[0981] Step 1:

[0982] The user uses a device to record a video of their exercise form. The user sets the appropriate angle and lighting and records their exercise form using, for example, the camera function of a smartphone.

[0983] Step 2:

[0984] The user uses the device to open a dedicated interface to upload the captured video to the system's application or website. The device then sends the video data to the specified API endpoint.

[0985] Step 3:

[0986] The server receives the video data sent from the device and temporarily stores it in storage. The server checks the format and quality of the video data.

[0987] Step 4:

[0988] The server splits the video data it receives into frames, and each frame is extracted and saved individually.

[0989] Step 5:

[0990] The server sequentially inputs the divided frames into the AI ​​model and detects the position information (coordinates, angles, speed, etc.) of the user's joints and body parts. It then selects specific key frames and extracts their position information.

[0991] Step 6:

[0992] The server evaluates the user's form based on the extracted position information, using indicators such as elbow angle, shoulder position, and hip rotation to analyze good points and areas for improvement.

[0993] Step 7:

[0994] The server generates feedback on the form based on the evaluation results, specifically providing specific points for improvement in text format, such as "your elbows are lower than your head" or "your center of gravity is too far forward."

[0995] Step 8:

[0996] The user uses a terminal to input the exercise goal they want to achieve on the system interface, for example, setting a specific goal such as "I want to increase my pitching speed."

[0997] Step 9:

[0998] The server generates an ideal form based on the purpose entered by the user. It generates ideal form data and calculates the optimal keyframes.

[0999] Step 10:

[1000] The server generates a new video to visualize the ideal form, which can be compared to the user's actual form.

[1001] Step 11:

[1002] The device provides an interface that displays the ideal form and the user's actual form side by side, allowing users to directly compare the two forms using a video player or slider.

[1003] Step 12:

[1004] The server compares the user's form and generates advice on specific training methods, areas to train, and injury prevention. The device then notifies the user of this advice and provides detailed feedback.

[1005] These steps allow users to go through a series of processes through the system, from analyzing, evaluating, and improving their exercise form, in order to improve their performance.

[1006] Example 1

[1007] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1008] The present invention relates to a system that allows users to analyze their own exercise form and receive specific and practical feedback. Conventional technologies have made it difficult to provide methods for evaluating and improving exercise form, and require specialized knowledge and equipment. Therefore, the objective of the present invention is to provide technology that allows users to easily and effectively analyze and improve their own form.

[1009] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1010] In this invention, the server includes means for uploading video of movements taken by a user using an information processing device to the system, means for dividing the video data received by the server into video units and extracting important location information, means for evaluating good points and areas for improvement of the movements based on the extracted location information, means for generating ideal movements based on exercise goals input by the user through the information processing device, and means for comparing the ideal movements generated by the server with the user's actual movements and providing specific instructions, thereby enabling users to easily and effectively analyze and improve their own form.

[1011] An "information processing device" is an electronic device that processes digital data and can connect to the Internet, such as a smartphone, tablet, or personal computer used by a user.

[1012] "Video data" refers to video files of movements or exercises that a user captures using an information processing device, and is digital data that is uploaded to the system.

[1013] "Splitting into video units" means that the video data received by the server is divided into frames, and each frame is treated as a still image that can be analyzed individually.

[1014] "Important positional information" refers to data necessary for analyzing athletic form, such as the position, angle, speed, and movement trajectory of joints and body parts that the AI ​​model extracts from video data.

[1015] "Movement evaluation" is a process in which the server identifies the good points and areas for improvement of the user's exercise form based on the extracted location information and determines which parts are ideal.

[1016] An "exercise goal" is a specific exercise objective that the user wants to achieve and inputs into the system, and includes specific examples such as "I want to increase my pitching speed" or "I want to improve the accuracy of my swing."

[1017] "Ideal movements" are optimal exercise forms generated by the server based on the exercise goals entered by the user, and include ideal movement patterns and postures.

[1018] "Specific instructions" are feedback provided by the server to the user, such as specific practice methods for improving movement, areas that need strengthening, and methods for preventing injuries.

[1019] The "system" is a technical mechanism that consists of multiple components such as an information processing device, a server, and an AI model, and performs a series of processes to allow users to analyze and improve their exercise form.

[1020] The present invention relates to a system that enables a user to effectively analyze his or her own exercise form and receive specific and practical feedback. Specific embodiments of the system of the present invention will be described below.

[1021] Recording and uploading videos

[1022] First, a user uses an information processing device (such as a smartphone, tablet, or personal computer) to record a video of their own exercise form. Exercise forms include a wide variety of sports movements, such as a baseball pitching form, a golf swing, or a tennis swing. The user shoots the video with the appropriate angle and lighting, and then uploads the video through the system's application or website.

[1023] Video Analysis

[1024] Next, the server receives the video data uploaded by the user. The server divides this video data into frames and treats each individual frame as the target for analysis. The main hardware used here is a high-performance server computer, and the main software is a deep learning framework such as TensorFlow or PyTorch. The server inputs each divided frame into an AI model and extracts important positional information such as the position, angle, and velocity of joints and body parts.

[1025] Form evaluation

[1026] The server evaluates the user's exercise form based on the extracted position information. Specifically, it analyzes the elbow angle, shoulder position, and hip rotation, and lists good points and areas for improvement. For example, it may evaluate the form as "elbows are lower than the head" or "center of gravity is too far forward." This allows the user to understand specific areas for improvement.

[1027] Setting goals and generating ideal forms

[1028] The user inputs the exercise goal they want to achieve into the system on their information processing device. For example, specific goals such as "I want to increase the ball speed" or "I want to increase the distance I can hit the ball." The server generates an ideal exercise form based on the user's goal. This ideal form includes movements that will optimally achieve the goal input by the user.

[1029] Compare forms and provide feedback

[1030] The server compares the generated ideal form with the user's actual form and provides specific instructions based on the comparison results. It generates feedback to the user on specific training methods, areas to strengthen, and methods for preventing injuries. For example, the application displays a video of "band exercises to strengthen the inner shoulder muscles."

[1031] In this way, users can receive consistent support from the system, from analyzing their form to providing specific ways to improve. It is also possible to utilize a generative AI model to provide effective feedback based on prompts, allowing users to improve their exercise form without specialized knowledge.

[1032] Prompt Sentence Examples

[1033] Here we provide specific examples of prompt sentences to input to the generative AI model.

[1034] "Analyze a video of a user's exercise form and extract data on specific movements (e.g., elbow angle or shoulder position). Also, use this data to evaluate the user's form and provide specific suggestions for improvement."

[1035] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1036] Step 1:

[1037] Recording and uploading videos

[1038] The user records their own exercise form as a video using an information processing device. The appropriate angle and lighting are selected to record the exercise form (e.g., baseball pitching form, golf swing) in detail. The video is then uploaded to the system's application or website.

[1039] Input: Video file of exercise form

[1040] Output: Uploaded video data

[1041] Step 2:

[1042] Video data reception and preprocessing

[1043] The server receives video data uploaded by users. The server divides this video data into frames and treats each frame as an object for analysis. Specifically, if the video is 30 fps (frames per second), it is divided into 30 frames per second.

[1044] Input: Uploaded video data

[1045] Output: Split frame data

[1046] Step 3:

[1047] Extracting important location information

[1048] The server inputs each frame of images into a generative AI model, which extracts important positional information such as the position, angle, and velocity of joints and body parts. Deep learning frameworks such as TensorFlow and PyTorch are used as the generative AI model. Specifically, a convolutional neural network (CNN) is used to detect the joint positions in each frame.

[1049] Input: Split frame data

[1050] Output: Extracted location data

[1051] Step 4:

[1052] Exercise form evaluation

[1053] The server evaluates the user's exercise form based on the extracted position information. It analyzes the elbow angle, shoulder position, and hip rotation, and lists good points and areas for improvement. Specifically, it may evaluate the user's form as "the elbow position is appropriate, but the shoulder position is low."

[1054] Input: Location data

[1055] Output: Form evaluation results (list of good points and areas for improvement)

[1056] Step 5:

[1057] Setting goals

[1058] The user inputs the exercise goal they want to achieve into the system through an information processing device, such as "I want to increase my pitching speed" or "I want to increase my driving distance."

[1059] Input: User-entered exercise goals

[1060] Output: Set target data

[1061] Step 6:

[1062] Generating ideal forms

[1063] The server generates an ideal exercise form based on the user's goals. This ideal form includes movements that will optimally achieve the user's goals. The generated data is output as a video or model of the new ideal form.

[1064] Input: Set goal data, form evaluation result data

[1065] Output: Data on ideal exercise form

[1066] Step 7:

[1067] Compare forms and provide feedback

[1068] The server compares the generated ideal form with the user's actual form. Based on the comparison results, feedback including specific practice methods and areas for improvement is generated and provided to the user's device. Specific exercises are shown in the form of diagrams and videos.

[1069] Input: Ideal exercise form data, actual exercise form data

[1070] Output: Specific feedback data (practice methods, list of areas for improvement)

[1071] In this way, users can receive consistent and effective support through the system, from analyzing their own form to specific ways to improve. In addition, because the analysis is processed using a generative AI model, users can receive advanced feedback even without specialized knowledge.

[1072] (Application example 1)

[1073] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1074] In logistics centers, it is essential to provide accurate evaluations of employee movements and feedback in real time to improve the efficiency and safety of their work. However, conventional methods require the time-consuming task of filming and evaluating movements, making it difficult to provide immediate feedback. Rapid and effective improvements to movements are particularly required in large-scale centers, but current technology may not be able to provide satisfactory results.

[1075] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1076] In this invention, the server includes a means for uploading exercise videos taken by users using their terminals to the system, a means for dividing the received video data into frames and extracting important location information, and a means for evaluating good points and areas for improvement in form based on the extracted location information. This makes it possible to capture and analyze work movements in a logistics center in real time using smart glasses and provide immediate feedback.

[1077] "User" means an individual or corporation that uses this system.

[1078] "Device" refers to any electronic device used for recording and uploading videos.

[1079] "Video data" refers to video information of exercise captured by a user using a terminal.

[1080] A "server" is a computer system that receives, processes, stores, and analyzes data.

[1081] "Splitting by frame" refers to the process of splitting the video data into individual images.

[1082] "Important positional information" refers to data necessary for motion analysis, such as the position, angle, and speed of joints and body parts in the video.

[1083] "Form" refers to the way a user moves or exercises.

[1084] "Good points" refer to the characteristics of the form that were judged appropriate in the evaluation.

[1085] "Areas for improvement" refers to features of the form that were judged to be inappropriate during the evaluation.

[1086] An "ideal form" refers to a form generated to optimally achieve the goals set by the user.

[1087] "Comparison" refers to the process of contrasting a user's actual form with an ideal form.

[1088] "Specific advice" refers to detailed improvement methods provided based on the evaluation results.

[1089] "Logistics center" refers to a facility that stores, manages, and ships goods.

[1090] "Smart glasses" refers to a wearable device that can capture video and display information.

[1091] "Real-time" refers to processing and information provision occurring immediately.

[1092] "Feedback" refers to the notification of evaluation results and areas for improvement provided to the user.

[1093] A "generative AI model" refers to artificial intelligence that uses machine learning technology to generate ideal forms.

[1094] A "prompt sentence" refers to text information that instructs the AI ​​model to input.

[1095] This invention is a system that allows users to easily analyze their own exercise form and receive specific and practical feedback. This system allows logistics center employees to use smart glasses to capture and analyze videos of their work movements in real time and receive instant feedback.

[1096] Video recording and uploading

[1097] First, a user wears a device, specifically a pair of smart glasses, and records video of their daily work activities. The smart glasses have the function of uploading the video to a cloud server in real time.

[1098] Video Analysis

[1099] The server splits the received video data into frames and uses OpenCV to extract important positional information, detecting the position, angle, and velocity of joints and body parts in each frame, and inputs this data into a generative AI model.

[1100] Form evaluation

[1101] Using a generative AI model, the server evaluates the employee's motion form based on the extracted location information, identifies strengths and areas for improvement, and stores the evaluation results in a database.

[1102] Setting goals and generating ideal forms

[1103] The user inputs the goal of the movement they want to improve through the smart glasses or another device. For example, if they input a goal such as "I want to improve the efficiency of lifting," the server will generate an ideal movement form based on this goal.

[1104] Compare forms and provide feedback

[1105] The server compares the generated ideal form with the user's actual form and generates specific advice, including training methods suitable for a distribution center, areas to train, and advice on injury prevention. This feedback is provided to the user in real time via visual and audio feedback through the smart glasses.

[1106] Hardware and software used

[1107] Hardware: Smart glasses (e.g., Google Glass)

[1108] Software: Python, OpenCV, TensorFlow (generative AI model)

[1109] The system allows logistics center employees to receive immediate, specific feedback on how to improve their operations, thereby increasing efficiency and safety.

[1110] Specific examples

[1111] For example, an employee lifting a box can be filmed with smart glasses and uploaded to a cloud server. The server analyzes the video data to detect the position of the joints and the angle of the movement. If the employee's goal is to "improve the efficiency of lifting movements," the server generates an ideal form, compares it, and provides feedback such as "advice to improve the angle of the hips."

[1112] Example prompt sentence:

[1113] The user performed the following actions while working at the distribution center:

[1114] Type of Action: Lifting a Box

[1115] Key joint location: {joint detection result}

[1116] Purpose: Efficient lifting movements

[1117] Using this information, please evaluate what aspects of this behavior could be improved and provide specific feedback.

[1118] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1119] Step 1:

[1120] Users wear smart glasses and record video of their work activities at a logistics center. The video data is then uploaded to a server in real time.

[1121] Input: Video data (user's work actions)

[1122] Output: Video data uploaded to the server

[1123] Step 2:

[1124] The server splits the received video data into frames using OpenCV, and each frame is used in the subsequent analysis steps.

[1125] Input: Uploaded video data

[1126] Output: Split frame data

[1127] Step 3:

[1128] The server analyzes the split frame data and extracts important positional information, specifically using OpenCV to detect the positions, angles, and velocities of joints and body parts.

[1129] Input: Split frame data

[1130] Output: Positional data (position, angle, and velocity of joints and body parts)

[1131] Step 4:

[1132] The server inputs location data into a generative AI model and evaluates the employee's behavior, including good points and areas for improvement.

[1133] Input: Location data

[1134] Output: Form evaluation data (good points and areas for improvement)

[1135] Step 5:

[1136] Using the smart glasses or another device, the user inputs the goal of the movement they want to improve, for example, "I want to improve the efficiency of my lifting movements."

[1137] Input: Improvement Objective (User Input)

[1138] Output: Data for improvement purposes

[1139] Step 6:

[1140] The server generates an ideal action form based on the user's goal, and outputs an improved action form using a generative AI model.

[1141] Input: Form evaluation data, improvement data

[1142] Output: Ideal form data

[1143] Step 7:

[1144] The server compares your actual form with your ideal form and generates specific feedback, including advice on areas for improvement, training methods, and injury prevention.

[1145] Input: Actual form data, Ideal form data

[1146] Output: Feedback data (specific advice)

[1147] Step 8:

[1148] The server sends the feedback data to the smart glasses, and the user receives the feedback in real time, which is notified visually and audibly.

[1149] Input: Feedback data

[1150] Output: The visual and audio feedback the user receives

[1151] Specific prompt examples:

[1152] The user performed the following actions while working at the distribution center:

[1153] Type of Action: Lifting a Box

[1154] Key joint location: {joint detection result}

[1155] Purpose: Efficient lifting movements

[1156] Using this information, please evaluate what aspects of this behavior could be improved and provide specific feedback.

[1157] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1158] The present invention relates to a system that allows users to easily analyze their own exercise form and receive specific and practical feedback. In particular, it is characterized by the fact that by combining it with an emotion engine, it provides optimal feedback and advice based on the user's emotional state. Specific embodiments of the system of the present invention are described below.

[1159] Recording and uploading videos

[1160] First, a user uses a device to record a video of their own exercise form, such as a baseball pitching form, a golf swing, or a tennis swing. The user takes the video with the right angle and lighting and uploads it to the system's application or website.

[1161] Video Analysis

[1162] The server then receives the video data, divides it into frames, and preprocesses it for input into the AI ​​model. The AI ​​model analyzes the position, angle, and speed of joints and body parts in the video to extract important positional information.

[1163] Form evaluation

[1164] The server evaluates the user's exercise form based on the extracted position information. For example, it analyzes the elbow angle, shoulder position, and hip rotation, and lists good points and areas for improvement. Specifically, it may evaluate the user's form as "the elbow angle is appropriate, but the release point is low."

[1165] Setting goals and generating ideal forms

[1166] The user inputs the goal they want to achieve into the system using a terminal. For example, "I want to increase the ball speed" or "I want to increase the distance I can hit the ball." The server generates an ideal form based on the user's goal. This ideal form includes the actions required to optimally achieve the goal entered by the user.

[1167] Compare forms and provide feedback

[1168] The server compares the generated ideal form with the user's actual form. Based on the comparison results, the server provides advice on specific training methods, areas to train, and injury prevention methods. For example, the server may provide specific advice to the user, such as "Use band exercises to strengthen the inner shoulder muscles."

[1169] Emotion recognition and feedback adjustment by emotion engine

[1170] The emotion engine uses facial recognition and voice analysis to recognize the user's emotions. While the user is recording their exercise form, the device's camera and microphone are used to collect emotion data in real time. The emotion engine analyzes the collected data to identify the user's emotional state.

[1171] Optimizing Emotional Feedback

[1172] The server tailors the feedback based on data from the emotion engine: for example, if it recognizes that the user is tired, it offers motivational messages and stretching suggestions, or, conversely, if the user is focused, it offers more detailed technical advice.

[1173] Specific examples

[1174] An example of baseball pitching form

[1175] 1. Recording and uploading videos

[1176] The user takes a photo of their pitching form with their smartphone and uploads it to the system.

[1177] 2. Video Analysis

[1178] The server divides the video into frames, and AI detects important positions such as elbows, shoulders, and waists.

[1179] 3. Form Evaluation

[1180] The server identifies specific areas for improvement, such as "your elbows are lower than your head" or "your center of gravity is too far forward."

[1181] 4. Setting goals

[1182] The user inputs a goal such as "I want to increase my pitching speed."

[1183] 5. Generation of ideal form

[1184] The server generates the ideal form and creates a new video with the elbow height adjusted.

[1185] 6. Compare forms and provide feedback

[1186] The device displays the user's form and the ideal form side by side, and the server provides advice on "band exercises to strengthen the inner shoulder muscles."

[1187] 7. Emotional Recognition

[1188] The emotion engine recognizes the user's face and voice and determines that the user is tired.

[1189] 8. Optimizing Emotional Feedback

[1190] Taking into consideration the user's sense of fatigue, the server suggests taking a moderate break or doing some light stretching.

[1191] In this way, users can not only analyze and improve their form, but also receive optimal feedback based on their emotional state. The entire system helps users improve their performance and promotes more effective practice.

[1192] The processing flow will be explained below.

[1193] Step 1:

[1194] The user uses a device to record a video of their exercise form. The user sets the appropriate angle and lighting and records their exercise form using, for example, the camera function of a smartphone.

[1195] Step 2:

[1196] The user uses the device to open a dedicated interface to upload the captured video to the system's application or website. The device sends the video data to the specified API endpoint.

[1197] Step 3:

[1198] The server receives the video data sent from the device and temporarily stores it in storage. The server checks the format and quality of the video data.

[1199] Step 4:

[1200] The server splits the video data it receives into frames, and each frame is extracted and saved individually.

[1201] Step 5:

[1202] The server sequentially inputs the divided frames into the AI ​​model and detects the position information (coordinates, angles, speed, etc.) of the user's joints and body parts. It then selects specific key frames and extracts their position information.

[1203] Step 6:

[1204] The server evaluates the user's form based on the extracted position information, using indicators such as elbow angle, shoulder position, and hip rotation to analyze good points and areas for improvement.

[1205] Step 7:

[1206] The server generates feedback on the form based on the evaluation results, specifically providing specific points for improvement in text format, such as "your elbows are lower than your head" or "your center of gravity is too far forward."

[1207] Step 8:

[1208] The user uses a terminal to input the exercise goal they want to achieve on the system interface, for example, setting a specific goal such as "I want to increase my pitching speed."

[1209] Step 9:

[1210] The server generates an ideal form based on the purpose entered by the user. It generates ideal form data and calculates the optimal keyframes.

[1211] Step 10:

[1212] The server generates a new video to visualize the ideal form, which is then compared to the user's actual form.

[1213] Step 11:

[1214] The device provides an interface that displays the ideal form and the user's actual form side by side, allowing users to directly compare the two forms using a video player or slider.

[1215] Step 12:

[1216] The server compares the user's form and generates advice on specific training methods, areas to train, and injury prevention. The device then notifies the user of this advice and provides detailed feedback.

[1217] Step 13:

[1218] The device runs an emotion engine that performs real-time facial recognition and voice analysis of the user, collecting emotion data using the device's camera and microphone.

[1219] Step 14:

[1220] The emotion engine analyzes the collected data and identifies the user's emotional state, identifying emotions such as fatigue, concentration, frustration, and joy.

[1221] Step 15:

[1222] The server receives data from the emotion engine and adjusts the feedback, for example suggesting light stretching or a break if it detects that the user is tired.

[1223] Step 16:

[1224] The server generates motivational messages and additional advice according to the user's emotional state, and the device notifies the user of these messages and advice.

[1225] In this way, by combining the emotion engine, it is possible to not only analyze and improve the user's exercise form, but also to provide optimal feedback and advice adapted to the user's emotional state.

[1226] Example 2

[1227] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1228] While conventional exercise form analysis systems can provide feedback on the exercise itself, they are unable to provide feedback that takes into account the user's emotional state. Furthermore, conventional systems have difficulty automatically comparing the user's actual form with their ideal form, and providing detailed advice requires a great deal of time and effort. Therefore, there is a need for comprehensive exercise form analysis and improvement support that includes feedback based on the user's emotional state.

[1229] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1230] In this invention, the server includes means for uploading exercise videos taken by a user using a terminal to the system, means for dividing the received video data into frames and extracting important location information, means for evaluating good points and areas for improvement of the form based on the extracted location information, means for generating an ideal form based on the exercise purpose input by the user through the terminal, means for comparing the ideal form generated by the server with the user's actual form and providing specific advice, means for recognizing the user's emotional state using the camera and microphone of the terminal, and means for adjusting feedback based on the emotional state by the server, thereby enabling optimal feedback based on the user's emotional state as well as analysis of the user's exercise form.

[1231] "Terminal" refers to a device used by a user to record a video of their exercise form and upload it to the system, and refers to a portable electronic device such as a smartphone or tablet.

[1232] "Server" refers to a central processing unit that analyzes video data uploaded to the system and evaluates, generates, and provides feedback on exercise form.

[1233] "Video data" refers to video data of a user's exercise form, which is uploaded to the system and analyzed by the server.

[1234] "Splitting by frame" refers to the process of dividing video data into frames, which are time units, in order to improve the accuracy of analysis.

[1235] "Positional information" refers to data such as the position, angle, and speed of the user's joints and body parts in the video, and is used to evaluate exercise form.

[1236] "Extraction" refers to the process of obtaining the necessary location information from video data.

[1237] "Evaluation" refers to the process of analyzing and determining the strengths and areas for improvement of the user's exercise form based on the extracted location information.

[1238] "Purpose of exercise" refers to the goal or purpose of exercise that the user inputs into the system, such as "I want to increase the ball speed" or "I want to increase the distance I can hit the ball."

[1239] "Ideal form" refers to a model of exercise form generated by the server to optimally achieve the user's goals.

[1240] "Comparison" refers to the process of evaluating the server-generated ideal form side-by-side with the user's actual form.

[1241] "Feedback" refers to specific advice or guidance provided by the server based on the comparison results, such as training methods or injury prevention methods.

[1242] "Emotional state" refers to the psychological state determined from the user's facial expression and tone of voice, and includes fatigue, concentration, joy, etc.

[1243] "Camera and microphone" refers to the video and audio input devices built into the device, which are used to recognize emotional states.

[1244] "Emotion Engine" refers to software or algorithms that analyze data captured through the camera and microphone to identify the user's emotional state.

[1245] "Adjusting feedback" refers to the process in which the server changes the feedback content based on the analysis results of the emotion engine.

[1246] The present invention relates to a system that allows users to easily analyze their own exercise form and receive specific and practical feedback, and is characterized by the fact that it combines an emotion engine to provide optimal feedback and advice based on the user's emotional state. The system of the present invention is implemented in the following specific manner.

[1247] Recording and uploading videos

[1248] The user uses a device (such as a smartphone or tablet) to record a video of their own athletic form. Using the device's camera, they can capture athletic form, such as a baseball pitch, golf swing, or tennis swing, at the appropriate angle and lighting. Once the video is complete, it can be uploaded to the system's application or website.

[1249] Video Analysis

[1250] The server receives video data uploaded by users. The server divides the video into frames and performs preprocessing. Preprocessing includes adjusting the video resolution and removing noise. The server then inputs the preprocessed video data into an AI model (e.g., OpenPose) and analyzes the position, angle, and velocity of joints and body parts from the video to extract important positional information.

[1251] Form evaluation

[1252] The server evaluates the user's exercise form based on the extracted position information. It analyzes the elbow angle, shoulder position, and hip rotation, and lists good points and areas for improvement. A specific example would be an evaluation such as "The elbow angle is appropriate, but the release point is low."

[1253] Setting goals and generating ideal forms

[1254] The user inputs the exercise goal they want to achieve into the system using their device. For example, they might say, "I want to increase my pitching speed" or "I want to increase my driving distance." Based on the goal entered by the user, the server uses a generative AI model (e.g., StyleGAN) to generate an ideal exercise form. This ideal form includes the movements that will optimally achieve the user's goal and provides a detailed description of that form.

[1255] Compare forms and provide feedback

[1256] The server compares the ideal form it has generated with the user's actual form. Evaluation criteria for the comparison include the degree of alignment of posture and the accuracy of movement. Based on the comparison results, the server provides advice on specific training methods, areas to train, and how to prevent injury. For example, it generates specific advice such as "To train the inner muscles of your shoulders, you should use band exercises." The device notifies the user of this feedback and displays detailed advice.

[1257] Emotion recognition and feedback adjustment by emotion engine

[1258] While the emotion engine captures the user's exercise form, it uses the device's camera and microphone to collect emotional data in real time. The emotion engine then uses facial recognition and voice analysis technologies to identify the user's emotional state from their facial expressions and tone of voice. For example, fatigue, concentration, joy, etc. can be analyzed.

[1259] Optimizing Emotional Feedback

[1260] The server receives emotional data from the emotion engine and adjusts the user's feedback. If the server detects that the user is tired, it will provide motivational messages and suggestions for light stretching. Conversely, if the user is concentrating, it will provide more detailed technical advice. The device will notify the user of the emotional feedback and encourage appropriate action.

[1261] Example: Analysis of baseball pitching form

[1262] 1. Recording and uploading videos

[1263] The user takes a photo of their pitching form from a diagonal forward angle on their smartphone and uploads it to the system.

[1264] 2. Video Analysis

[1265] The server divides the video into frames, and AI detects important positions such as elbows, shoulders, and waists.

[1266] 3. Form Evaluation

[1267] The server identifies specific areas for improvement, such as "your elbows are lower than your head" or "your center of gravity is too far forward."

[1268] 4. Setting goals

[1269] The user inputs a goal such as "I want to increase my pitching speed."

[1270] 5. Generation of ideal form

[1271] The server generates the ideal form and creates a new video with the elbow height adjusted.

[1272] 6. Compare forms and provide feedback

[1273] The device displays the user's form and the ideal form side by side, and the server provides advice on "band exercises to strengthen the inner shoulder muscles."

[1274] 7. Emotional Recognition

[1275] The emotion engine recognizes the user's face and voice and determines that the user is tired.

[1276] 8. Optimizing Emotional Feedback

[1277] The server considers the user's level of fatigue and suggests taking a moderate break or doing some light stretching.

[1278] Example of input prompt for generative AI model

[1279] "Analyze videos of baseball pitching form and provide user feedback. Also, use an emotion engine to suggest resting and stretching when fatigue occurs."

[1280] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1281] Detailed flow of system program processing

[1282] Step 1: Record and upload your video

[1283] The user uses a device (smartphone or tablet) to record a video of their exercise form.

[1284] Input: Video data such as baseball pitching form, golf swing, and tennis swing captured by the device's camera.

[1285] Specific operation: The user uploads the captured video to an application on the device or to a website.

[1286] Output: The uploaded video file will be saved on the system's server.

[1287] Step 2: Receive and split the video

[1288] The server receives the uploaded video data.

[1289] Input: User uploaded video file.

[1290] Specific operation: The server divides the video data into frames and generates still images that are consecutive in time.

[1291] Output: A set of still images separated by frames.

[1292] Step 3: Preprocessing the video

[1293] The server preprocesses the received video frames.

[1294] Input: A set of segmented still images.

[1295] Specific operation: The server performs pre-processing such as adjusting the video resolution, removing noise, and correcting color tone.

[1296] Output: A set of pre-processed still images.

[1297] Step 4: Extracting important location information

[1298] The server uses the pre-processed video frames to extract important location information.

[1299] Input: A set of preprocessed still images.

[1300] How it works: The server uses an AI model (e.g., OpenPose) to analyze the position, angle, and velocity of joints and body parts in each frame.

[1301] Output: A dataset related to joint and body part positions and their movements.

[1302] Step 5: Evaluate your form

[1303] The server evaluates the exercise form based on the extracted position information.

[1304] Input: A dataset related to important location and movement information.

[1305] Specific movements: The server analyzes the angle of each joint, shoulder position, hip rotation, etc. to identify good points and areas for improvement in form.

[1306] Output: A list of form evaluation results (e.g. "The elbow angle is appropriate, but the release point is low").

[1307] Step 6: Set your objectives

[1308] The user uses a terminal to input the purpose of the exercise into the system.

[1309] Input: The purpose of the exercise that the user inputs into the device (e.g., "I want to increase my pitching speed" or "I want to increase my driving distance").

[1310] Specific operation: When the user enters their purpose, the data is sent to the server and stored.

[1311] Output: The user's exercise goal data is stored on the server.

[1312] Step 7: Generate the ideal form

[1313] The server generates the ideal form based on the user's goals.

[1314] Input: User's exercise goal data.

[1315] Specific behavior: The server uses a generative AI model (e.g., StyleGAN) to generate an ideal exercise form to optimally achieve the user's goal.

[1316] Output: Generated ideal exercise form data.

[1317] Step 8: Compare forms and provide feedback

[1318] The server compares the ideal form generated by the server with the user's actual form and provides specific advice.

[1319] Input: Ideal exercise form data and the user's actual form data.

[1320] Specific operation: The server compares the two and evaluates the degree of correspondence between postures and the accuracy of movements. The device displays the results to the user.

[1321] Output: Comparison results and specific feedback (e.g., "You should do band exercises to strengthen your shoulder muscles").

[1322] Step 9: Emotion Recognition with the Emotion Engine

[1323] The device collects data to recognize the user's emotional state.

[1324] Input: User's facial expression data and voice data.

[1325] Specific operation: Using the device's camera and microphone, real-time emotional data is collected while the user is recording their exercise form.

[1326] Output: Collected emotion data.

[1327] Step 10: Adjusting Emotional Feedback

[1328] The server adjusts the feedback based on data from the emotion engine.

[1329] Input: User emotion data parsed by the emotion engine.

[1330] Specific behavior: If the server recognizes that the user is tired, it will provide motivational messages and suggestions for light stretching. If the user is concentrating, it will provide detailed technical advice.

[1331] Output: The adjusted feedback is displayed on the terminal.

[1332] (Application example 2)

[1333] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1334] Modern factories lack systems that can accurately analyze robot movements and efficiently provide improvement instructions. Furthermore, the lack of feedback based on workers' emotional states makes it difficult to improve production efficiency and worker safety. To solve this problem, a comprehensive system that integrates robot movement analysis and workers' emotional states is needed.

[1335] The specification processing by the specification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for uploading a video of exercise taken by a user using a terminal to the system, means for dividing the video data received by the server into frames and extracting important location information, means for evaluating good points and areas for improvement of the form based on the extracted location information, means for generating an ideal form based on the exercise purpose input by the user through the terminal, means for comparing the ideal form generated by the server with the user's actual form and providing specific advice, and means for recognizing the user's emotional state using an emotion engine and adjusting feedback based on the emotion data. This enables robot motion analysis and optimal feedback based on the worker's emotional state.

[1336] A "terminal" is an electronic device that a user uses to record and upload exercise videos to the system.

[1337] The "server" is a computer system that analyzes the received video data, generates the ideal form, and provides feedback.

[1338] "Video data" refers to video files of exercises taken by a user using a terminal.

[1339] "Splitting by frame" refers to the process of dividing video data into individual still images and analyzing them.

[1340] "Important positional information" refers to information about the positions and angles of joints and body parts that is necessary for analyzing movement.

[1341] "Form" refers to the user's body position and movement posture during exercise.

[1342] A "good point" is a part of the form being evaluated that is judged to be correct in relation to the intended action or goal.

[1343] "Areas for improvement" refers to any part of the form being evaluated that is judged to be inappropriate or inefficient.

[1344] "Ideal form" is the optimal movement pattern generated by the server to most effectively achieve the exercise goal entered by the user.

[1345] An "emotion engine" is software that analyzes a user's emotional state and adjusts feedback based on that.

[1346] "Feedback" refers to specific advice or instructions provided to users based on their form evaluation and areas for improvement.

[1347] System Overview

[1348] This invention is a system that analyzes videos of exercise form taken by the user using a device and provides specific and practical feedback. In particular, by combining it with an emotion engine, optimal feedback and advice based on the user's emotional state is provided, promoting more effective practice.

[1349] Initial Setup and Video Upload

[1350] Users use a device (such as a smartphone or tablet) to record a video of their athletic form. This video can cover a wide range of topics, such as the movement of a robot arm in a factory or a pitching form in a sports game. The video is then uploaded to the system's application or website. The device should have a support function that displays guidelines to help users record the video at the appropriate angle and with the appropriate lighting.

[1351] Video analysis and English extraction processing on the server

[1352] The server receives the uploaded video data and splits it into frames using a video processing library such as OpenCV. The frame-by-frame data is then fed into a pre-trained AI model (using Keras) to extract key positional information. This information includes the position and angle of joints and body parts. This processing reveals details about the movements of the user and the robot arm.

[1353] Evaluating forms and generating ideal forms

[1354] Based on the extracted positional information, the server evaluates the good points and areas for improvement in the form. For example, it may evaluate whether "the elbow position is correct" or "the shoulder movement is inappropriate." The user inputs their exercise goals (such as "I want to increase the speed of the ball" or "I want to improve the accuracy of the product") through the terminal, and the server generates an ideal form. This ideal form is a movement pattern that will optimally achieve the goal set by the user.

[1355] Compare and provide feedback

[1356] The server compares the generated ideal form with the user's actual form. Based on the comparison results, it provides specific practice methods and areas for improvement. There is also a function that displays the ideal form and the actual form side by side. Specifically, feedback such as "You should adjust your shoulder position like this" or "You should perform this exercise" is sent to the device.

[1357] Emotional state recognition and feedback optimization

[1358] The emotion engine uses facial recognition and voice analysis to identify the user's emotional state while recording video. Based on the emotion data, the server adjusts the feedback. For example, it analyzes the user's fatigue and concentration level. If the user is tired, it suggests a message of encouragement or a break, and if the user is concentrating, it provides detailed technical advice.

[1359] Example

[1360] For example, in analyzing the operation of a robot arm in a factory, a user takes a video of the robot arm and uploads it. The server analyzes the video and provides specific improvements and ideal operation patterns for the robot. The emotion engine recognizes the worker's level of fatigue and provides appropriate feedback.

[1361] Example prompt sentence:

[1362] "Please record video and analyze the behavior of factory robots."

[1363] "Provide feedback according to the worker's emotional state."

[1364] This allows users to effectively improve their own form and the robot's movements, and also allows them to optimally practice and adjust according to their emotional state.

[1365] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1366] Step 1:

[1367] The user uses a terminal to take a video of their exercise form and uploads it to the system.

[1368] Input: A video file of the exercise form taken by the user.

[1369] Processing: Capture video using the device camera and upload the video file to a system application or website.

[1370] Output: Uploaded video data is sent to the server.

[1371] Step 2:

[1372] The video data received by the server is divided into frames and important location information is extracted.

[1373] Input: Uploaded video data.

[1374] Processing: Using a video processing library such as OpenCV, the video is split into frames. The frame data is then input into an AI model (using Keras) to extract position and angle information for joints and body parts.

[1375] Output: Extracted geolocation data.

[1376] Step 3:

[1377] The server evaluates the form's strengths and areas for improvement based on the extracted location information.

[1378] Input: Extracted geolocation data.

[1379] Processing: Evaluate form based on position information, for example checking whether elbow position is correct or shoulder movement is appropriate.

[1380] Output: A list of good points and areas for improvement for the form.

[1381] Step 4:

[1382] The server generates an ideal form based on the exercise purpose entered by the user through the terminal.

[1383] Input: The purpose of the exercise entered by the user through the device (e.g., "I want to increase my pitching speed," "I want to improve the accuracy of my product," etc.).

[1384] Processing: Generate ideal form with optimal movement patterns based on the goal of the exercise.

[1385] Output: The generated data in ideal form.

[1386] Step 5:

[1387] The server compares the ideal form generated by the server with the user's actual form and provides specific advice.

[1388] Input: The user's actual form data and the generated ideal form data.

[1389] Processing: Compare the two and generate advice to the user on specific practice methods and areas for improvement.

[1390] Output: A specific advice message.

[1391] Step 6:

[1392] The server uses an emotion engine to recognize the user's emotional state and adjusts the feedback based on that emotional data.

[1393] Input: User's facial recognition and voice analysis data.

[1394] Processing: The emotion engine analyzes the user's emotional state (fatigue, concentration, etc.) and adjusts the feedback based on the results.

[1395] Output: Providing optimal feedback based on emotional state.

[1396] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1397] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1398] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1399] [Fourth embodiment]

[1400] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1401] 7, a 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.

[1402] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1403] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1404] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1405] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1406] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1407] The control object 443 includes a display device, LEDs in the eyes, and motors for driving 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1408] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1409] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[1410] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1411] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1412] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1413] The present invention relates to a system that allows a user to easily analyze his or her own exercise form and receive specific and practical feedback. Specific embodiments of the system of the present invention will be described below.

[1414] Recording and uploading videos

[1415] First, a user uses a device to record a video of their own exercise form, such as a baseball pitching form, a golf swing, or a tennis swing. The user takes the video with the right angle and lighting and uploads it to the system's application or website.

[1416] Video Analysis

[1417] The server then receives the video data, divides it into frames, and preprocesses it for input into the AI ​​model. The AI ​​model analyzes the position, angle, and speed of joints and body parts in the video to extract important positional information.

[1418] Form evaluation

[1419] The server evaluates the user's exercise form based on the extracted position information. For example, it analyzes the elbow angle, shoulder position, and hip rotation, and lists good points and areas for improvement. Specifically, it may evaluate the user's form as "the elbow angle is appropriate, but the release point is low."

[1420] Setting goals and generating ideal forms

[1421] The user inputs the goal they want to achieve into the system using a terminal. For example, "I want to increase the ball speed" or "I want to increase the distance I can hit the ball." The server generates an ideal form based on the user's goal. This ideal form includes the actions required to optimally achieve the goal entered by the user.

[1422] Compare forms and provide feedback

[1423] The server compares the generated ideal form with the user's actual form. Based on the comparison results, the server provides advice on specific training methods, areas to train, and injury prevention methods. For example, the server may provide specific advice to the user, such as "Use band exercises to strengthen the inner shoulder muscles."

[1424] Specific examples

[1425] An example of baseball pitching form

[1426] 1. Recording and uploading videos

[1427] The user takes a photo of their pitching form with their smartphone and uploads it to the system.

[1428] 2. Video Analysis

[1429] The server divides the video into frames, and AI detects important positions such as elbows, shoulders, and waists.

[1430] 3. Form Evaluation

[1431] The server identifies specific areas for improvement, such as "elbows are lower than the head" or "center of gravity is too far forward."

[1432] 4. Setting goals

[1433] The user inputs a goal such as "I want to increase my pitching speed."

[1434] 5. Generation of ideal form

[1435] The server generates the ideal form and creates a new video with the elbow height adjusted.

[1436] 6. Compare forms and provide feedback

[1437] The device displays the user's form and the ideal form side by side, and the server provides advice on "band exercises to strengthen the inner shoulder muscles."

[1438] In this way, users can receive consistent support throughout the system, from analyzing their form to specific ways to improve it.

[1439] The processing flow will be explained below.

[1440] Step 1:

[1441] The user uses a device to record a video of their exercise form. The user sets the appropriate angle and lighting and records their exercise form using, for example, the camera function of a smartphone.

[1442] Step 2:

[1443] The user uses the device to open a dedicated interface to upload the captured video to the system's application or website. The device then sends the video data to the specified API endpoint.

[1444] Step 3:

[1445] The server receives the video data sent from the device and temporarily stores it in storage. The server checks the format and quality of the video data.

[1446] Step 4:

[1447] The server splits the video data it receives into frames, and each frame is extracted and saved individually.

[1448] Step 5:

[1449] The server sequentially inputs the divided frames into the AI ​​model and detects the position information (coordinates, angles, speed, etc.) of the user's joints and body parts. It then selects specific key frames and extracts their position information.

[1450] Step 6:

[1451] The server evaluates the user's form based on the extracted position information, using indicators such as elbow angle, shoulder position, and hip rotation to analyze good points and areas for improvement.

[1452] Step 7:

[1453] The server generates feedback on the form based on the evaluation results, specifically providing specific points for improvement in text format, such as "your elbows are lower than your head" or "your center of gravity is too far forward."

[1454] Step 8:

[1455] The user uses a terminal to input the exercise goal they want to achieve on the system interface, for example, setting a specific goal such as "I want to increase my pitching speed."

[1456] Step 9:

[1457] The server generates an ideal form based on the purpose entered by the user. It generates ideal form data and calculates the optimal keyframes.

[1458] Step 10:

[1459] The server generates a new video to visualize the ideal form, which can be compared to the user's actual form.

[1460] Step 11:

[1461] The device provides an interface that displays the ideal form and the user's actual form side by side, allowing users to directly compare the two forms using a video player or slider.

[1462] Step 12:

[1463] The server compares the user's form and generates advice on specific training methods, areas to train, and injury prevention. The device then notifies the user of this advice and provides detailed feedback.

[1464] These steps allow users to go through a series of processes through the system, from analyzing, evaluating, and improving their exercise form, in order to improve their performance.

[1465] Example 1

[1466] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1467] The present invention relates to a system that allows users to analyze their own exercise form and receive specific and practical feedback. Conventional technologies have made it difficult to provide methods for evaluating and improving exercise form, and require specialized knowledge and equipment. Therefore, the objective of the present invention is to provide technology that allows users to easily and effectively analyze and improve their own form.

[1468] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1469] In this invention, the server includes means for uploading video of movements taken by a user using an information processing device to the system, means for dividing the video data received by the server into video units and extracting important location information, means for evaluating good points and areas for improvement of the movements based on the extracted location information, means for generating ideal movements based on exercise goals input by the user through the information processing device, and means for comparing the ideal movements generated by the server with the user's actual movements and providing specific instructions, thereby enabling users to easily and effectively analyze and improve their own form.

[1470] An "information processing device" is an electronic device that processes digital data and can connect to the Internet, such as a smartphone, tablet, or personal computer used by a user.

[1471] "Video data" refers to video files of movements or exercises that a user captures using an information processing device, and is digital data that is uploaded to the system.

[1472] "Splitting into video units" means that the video data received by the server is divided into frames, and each frame is treated as a still image that can be analyzed individually.

[1473] "Important positional information" refers to data necessary for analyzing athletic form, such as the position, angle, speed, and movement trajectory of joints and body parts that the AI ​​model extracts from video data.

[1474] "Movement evaluation" is a process in which the server identifies the good points and areas for improvement of the user's exercise form based on the extracted location information and determines which parts are ideal.

[1475] An "exercise goal" is a specific exercise objective that the user wants to achieve and inputs into the system, and includes specific examples such as "I want to increase my pitching speed" or "I want to improve the accuracy of my swing."

[1476] "Ideal movements" are optimal exercise forms generated by the server based on the exercise goals entered by the user, and include ideal movement patterns and postures.

[1477] "Specific instructions" are feedback provided by the server to the user, such as specific practice methods for improving movement, areas that need strengthening, and methods for preventing injuries.

[1478] The "system" is a technical mechanism that consists of multiple components such as an information processing device, a server, and an AI model, and performs a series of processes to allow users to analyze and improve their exercise form.

[1479] The present invention relates to a system that enables a user to effectively analyze his or her own exercise form and receive specific and practical feedback. Specific embodiments of the system of the present invention will be described below.

[1480] Recording and uploading videos

[1481] First, a user uses an information processing device (such as a smartphone, tablet, or personal computer) to record a video of their own exercise form. Exercise forms include a wide variety of sports movements, such as a baseball pitching form, a golf swing, or a tennis swing. The user shoots the video with the appropriate angle and lighting, and then uploads the video through the system's application or website.

[1482] Video Analysis

[1483] Next, the server receives the video data uploaded by the user. The server divides this video data into frames and treats each individual frame as the target for analysis. The main hardware used here is a high-performance server computer, and the main software is a deep learning framework such as TensorFlow or PyTorch. The server inputs each divided frame into an AI model and extracts important positional information such as the position, angle, and velocity of joints and body parts.

[1484] Form evaluation

[1485] The server evaluates the user's exercise form based on the extracted position information. Specifically, it analyzes the elbow angle, shoulder position, and hip rotation, and lists good points and areas for improvement. For example, it may evaluate the form as "elbows are lower than the head" or "center of gravity is too far forward." This allows the user to understand specific areas for improvement.

[1486] Setting goals and generating ideal forms

[1487] The user inputs the exercise goal they want to achieve into the system on their information processing device. For example, specific goals such as "I want to increase the ball speed" or "I want to increase the distance I can hit the ball." The server generates an ideal exercise form based on the user's goal. This ideal form includes movements that will optimally achieve the goal input by the user.

[1488] Compare forms and provide feedback

[1489] The server compares the generated ideal form with the user's actual form and provides specific instructions based on the comparison results. It generates feedback to the user on specific training methods, areas to strengthen, and methods for preventing injuries. For example, the application displays a video of "band exercises to strengthen the inner shoulder muscles."

[1490] In this way, users can receive consistent support from the system, from analyzing their form to providing specific ways to improve. It is also possible to utilize a generative AI model to provide effective feedback based on prompts, allowing users to improve their exercise form without specialized knowledge.

[1491] Prompt Sentence Examples

[1492] Here we provide specific examples of prompt sentences to input to the generative AI model.

[1493] "Analyze a video of a user's exercise form and extract data on specific movements (e.g., elbow angle or shoulder position). Also, use this data to evaluate the user's form and provide specific suggestions for improvement."

[1494] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1495] Step 1:

[1496] Recording and uploading videos

[1497] The user records their own exercise form as a video using an information processing device. The appropriate angle and lighting are selected to record the exercise form (e.g., baseball pitching form, golf swing) in detail. The video is then uploaded to the system's application or website.

[1498] Input: Video file of exercise form

[1499] Output: Uploaded video data

[1500] Step 2:

[1501] Video data reception and preprocessing

[1502] The server receives video data uploaded by users. The server divides this video data into frames and treats each frame as an object for analysis. Specifically, if the video is 30 fps (frames per second), it is divided into 30 frames per second.

[1503] Input: Uploaded video data

[1504] Output: Split frame data

[1505] Step 3:

[1506] Extracting important location information

[1507] The server inputs each frame of images into a generative AI model, which extracts important positional information such as the position, angle, and velocity of joints and body parts. Deep learning frameworks such as TensorFlow and PyTorch are used as the generative AI model. Specifically, a convolutional neural network (CNN) is used to detect the joint positions in each frame.

[1508] Input: Split frame data

[1509] Output: Extracted location data

[1510] Step 4:

[1511] Exercise form evaluation

[1512] The server evaluates the user's exercise form based on the extracted position information. It analyzes the elbow angle, shoulder position, and hip rotation, and lists good points and areas for improvement. Specifically, it may evaluate the user's form as "the elbow position is appropriate, but the shoulder position is low."

[1513] Input: Location data

[1514] Output: Form evaluation results (list of good points and areas for improvement)

[1515] Step 5:

[1516] Setting goals

[1517] The user inputs the exercise goal they want to achieve into the system through an information processing device, such as "I want to increase my pitching speed" or "I want to increase my driving distance."

[1518] Input: User-entered exercise goals

[1519] Output: Set target data

[1520] Step 6:

[1521] Generating ideal forms

[1522] The server generates an ideal exercise form based on the user's goals. This ideal form includes movements that will optimally achieve the user's goals. The generated data is output as a video or model of the new ideal form.

[1523] Input: Set goal data, form evaluation result data

[1524] Output: Data on ideal exercise form

[1525] Step 7:

[1526] Compare forms and provide feedback

[1527] The server compares the generated ideal form with the user's actual form. Based on the comparison results, feedback including specific practice methods and areas for improvement is generated and provided to the user's device. Specific exercises are shown in the form of diagrams and videos.

[1528] Input: Ideal exercise form data, actual exercise form data

[1529] Output: Specific feedback data (practice methods, list of areas for improvement)

[1530] In this way, users can receive consistent and effective support through the system, from analyzing their own form to specific ways to improve. In addition, because the analysis is processed using a generative AI model, users can receive advanced feedback even without specialized knowledge.

[1531] (Application example 1)

[1532] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1533] In logistics centers, it is essential to provide accurate evaluations of employee movements and feedback in real time to improve the efficiency and safety of their work. However, conventional methods require the time-consuming task of filming and evaluating movements, making it difficult to provide immediate feedback. Rapid and effective improvements to movements are particularly required in large-scale centers, but current technology may not be able to provide satisfactory results.

[1534] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1535] In this invention, the server includes a means for uploading exercise videos taken by users using their terminals to the system, a means for dividing the received video data into frames and extracting important location information, and a means for evaluating good points and areas for improvement in form based on the extracted location information. This makes it possible to capture and analyze work movements in a logistics center in real time using smart glasses and provide immediate feedback.

[1536] "User" means an individual or corporation that uses this system.

[1537] "Device" refers to any electronic device used for recording and uploading videos.

[1538] "Video data" refers to video information of exercise captured by a user using a terminal.

[1539] A "server" is a computer system that receives, processes, stores, and analyzes data.

[1540] "Splitting by frame" refers to the process of splitting the video data into individual images.

[1541] "Important positional information" refers to data necessary for motion analysis, such as the position, angle, and speed of joints and body parts in the video.

[1542] "Form" refers to the way a user moves or exercises.

[1543] "Good points" refer to the characteristics of the form that were judged appropriate in the evaluation.

[1544] "Areas for improvement" refers to features of the form that were judged to be inappropriate during the evaluation.

[1545] An "ideal form" refers to a form generated to optimally achieve the goals set by the user.

[1546] "Comparison" refers to the process of contrasting a user's actual form with an ideal form.

[1547] "Specific advice" refers to detailed improvement methods provided based on the evaluation results.

[1548] "Logistics center" refers to a facility that stores, manages, and ships goods.

[1549] "Smart glasses" refers to a wearable device that can capture video and display information.

[1550] "Real-time" refers to processing and information provision occurring immediately.

[1551] "Feedback" refers to the notification of evaluation results and areas for improvement provided to the user.

[1552] A "generative AI model" refers to artificial intelligence that uses machine learning technology to generate ideal forms.

[1553] A "prompt sentence" refers to text information that instructs the AI ​​model to input.

[1554] This invention is a system that allows users to easily analyze their own exercise form and receive specific and practical feedback. This system allows logistics center employees to use smart glasses to capture and analyze videos of their work movements in real time and receive instant feedback.

[1555] Video recording and uploading

[1556] First, a user wears a device, specifically a pair of smart glasses, and records video of their daily work activities. The smart glasses have the function of uploading the video to a cloud server in real time.

[1557] Video Analysis

[1558] The server splits the received video data into frames and uses OpenCV to extract important positional information, detecting the position, angle, and velocity of joints and body parts in each frame, and inputs this data into a generative AI model.

[1559] Form evaluation

[1560] Using a generative AI model, the server evaluates the employee's motion form based on the extracted location information, identifies strengths and areas for improvement, and stores the evaluation results in a database.

[1561] Setting goals and generating ideal forms

[1562] The user inputs the goal of the movement they want to improve through the smart glasses or another device. For example, if they input a goal such as "I want to improve the efficiency of lifting," the server will generate an ideal movement form based on this goal.

[1563] Compare forms and provide feedback

[1564] The server compares the generated ideal form with the user's actual form and generates specific advice, including training methods suitable for a distribution center, areas to train, and advice on injury prevention. This feedback is provided to the user in real time via visual and audio feedback through the smart glasses.

[1565] Hardware and software used

[1566] Hardware: Smart glasses (e.g., Google Glass)

[1567] Software: Python, OpenCV, TensorFlow (generative AI model)

[1568] The system allows logistics center employees to receive immediate, specific feedback on how to improve their operations, thereby increasing efficiency and safety.

[1569] Specific examples

[1570] For example, an employee lifting a box can be filmed with smart glasses and uploaded to a cloud server. The server analyzes the video data to detect the position of the joints and the angle of the movement. If the employee's goal is to "improve the efficiency of lifting movements," the server generates an ideal form, compares it, and provides feedback such as "advice to improve the angle of the hips."

[1571] Example prompt sentence:

[1572] The user performed the following actions while working at the distribution center:

[1573] Type of Action: Lifting a Box

[1574] Key joint location: {joint detection result}

[1575] Purpose: Efficient lifting movements

[1576] Using this information, please evaluate what aspects of this behavior could be improved and provide specific feedback.

[1577] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1578] Step 1:

[1579] Users wear smart glasses and record video of their work activities at a logistics center. The video data is then uploaded to a server in real time.

[1580] Input: Video data (user's work actions)

[1581] Output: Video data uploaded to the server

[1582] Step 2:

[1583] The server splits the received video data into frames using OpenCV, and each frame is used in the subsequent analysis steps.

[1584] Input: Uploaded video data

[1585] Output: Split frame data

[1586] Step 3:

[1587] The server analyzes the split frame data and extracts important positional information, specifically using OpenCV to detect the positions, angles, and velocities of joints and body parts.

[1588] Input: Split frame data

[1589] Output: Positional data (position, angle, and velocity of joints and body parts)

[1590] Step 4:

[1591] The server inputs location data into a generative AI model and evaluates the employee's behavior, including good points and areas for improvement.

[1592] Input: Location data

[1593] Output: Form evaluation data (good points and areas for improvement)

[1594] Step 5:

[1595] Using the smart glasses or another device, the user inputs the goal of the movement they want to improve, for example, "I want to improve the efficiency of my lifting movements."

[1596] Input: Improvement Objective (User Input)

[1597] Output: Data for improvement purposes

[1598] Step 6:

[1599] The server generates an ideal action form based on the user's goal, and outputs an improved action form using a generative AI model.

[1600] Input: Form evaluation data, improvement data

[1601] Output: Ideal form data

[1602] Step 7:

[1603] The server compares your actual form with your ideal form and generates specific feedback, including advice on areas for improvement, training methods, and injury prevention.

[1604] Input: Actual form data, Ideal form data

[1605] Output: Feedback data (specific advice)

[1606] Step 8:

[1607] The server sends the feedback data to the smart glasses, and the user receives the feedback in real time, which is notified visually and audibly.

[1608] Input: Feedback data

[1609] Output: The visual and audio feedback the user receives

[1610] Specific prompt examples:

[1611] The user performed the following actions while working at the distribution center:

[1612] Type of Action: Lifting a Box

[1613] Key joint location: {joint detection result}

[1614] Purpose: Efficient lifting movements

[1615] Using this information, please evaluate what aspects of this behavior could be improved and provide specific feedback.

[1616] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1617] The present invention relates to a system that allows users to easily analyze their own exercise form and receive specific and practical feedback. In particular, it is characterized by the fact that by combining it with an emotion engine, it provides optimal feedback and advice based on the user's emotional state. Specific embodiments of the system of the present invention are described below.

[1618] Recording and uploading videos

[1619] First, a user uses a device to record a video of their own exercise form, such as a baseball pitching form, a golf swing, or a tennis swing. The user takes the video with the right angle and lighting and uploads it to the system's application or website.

[1620] Video Analysis

[1621] The server then receives the video data, divides it into frames, and preprocesses it for input into the AI ​​model. The AI ​​model analyzes the position, angle, and speed of joints and body parts in the video to extract important positional information.

[1622] Form evaluation

[1623] The server evaluates the user's exercise form based on the extracted position information. For example, it analyzes the elbow angle, shoulder position, and hip rotation, and lists good points and areas for improvement. Specifically, it may evaluate the user's form as "the elbow angle is appropriate, but the release point is low."

[1624] Setting goals and generating ideal forms

[1625] The user inputs the goal they want to achieve into the system using a terminal. For example, "I want to increase the ball speed" or "I want to increase the distance I can hit the ball." The server generates an ideal form based on the user's goal. This ideal form includes the actions required to optimally achieve the goal entered by the user.

[1626] Compare forms and provide feedback

[1627] The server compares the generated ideal form with the user's actual form. Based on the comparison results, the server provides advice on specific training methods, areas to train, and injury prevention methods. For example, the server may provide specific advice to the user, such as "Use band exercises to strengthen the inner shoulder muscles."

[1628] Emotion recognition and feedback adjustment by emotion engine

[1629] The emotion engine uses facial recognition and voice analysis to recognize the user's emotions. While the user is recording their exercise form, the device's camera and microphone are used to collect emotion data in real time. The emotion engine analyzes the collected data to identify the user's emotional state.

[1630] Optimizing Emotional Feedback

[1631] The server tailors the feedback based on data from the emotion engine: for example, if it recognizes that the user is tired, it offers motivational messages and stretching suggestions, or, conversely, if the user is focused, it offers more detailed technical advice.

[1632] Specific examples

[1633] An example of baseball pitching form

[1634] 1. Recording and uploading videos

[1635] The user takes a photo of their pitching form with their smartphone and uploads it to the system.

[1636] 2. Video Analysis

[1637] The server divides the video into frames, and AI detects important positions such as elbows, shoulders, and waists.

[1638] 3. Form Evaluation

[1639] The server identifies specific areas for improvement, such as "your elbows are lower than your head" or "your center of gravity is too far forward."

[1640] 4. Setting goals

[1641] The user inputs a goal such as "I want to increase my pitching speed."

[1642] 5. Generation of ideal form

[1643] The server generates the ideal form and creates a new video with the elbow height adjusted.

[1644] 6. Compare forms and provide feedback

[1645] The device displays the user's form and the ideal form side by side, and the server provides advice on "band exercises to strengthen the inner shoulder muscles."

[1646] 7. Emotional Recognition

[1647] The emotion engine recognizes the user's face and voice and determines that the user is tired.

[1648] 8. Optimizing Emotional Feedback

[1649] Taking into consideration the user's sense of fatigue, the server suggests taking a moderate break or doing some light stretching.

[1650] In this way, users can not only analyze and improve their form, but also receive optimal feedback based on their emotional state. The entire system helps users improve their performance and promotes more effective practice.

[1651] The processing flow will be explained below.

[1652] Step 1:

[1653] The user uses a device to record a video of their exercise form. The user sets the appropriate angle and lighting and records their exercise form using, for example, the camera function of a smartphone.

[1654] Step 2:

[1655] The user uses the device to open a dedicated interface to upload the captured video to the system's application or website. The device sends the video data to the specified API endpoint.

[1656] Step 3:

[1657] The server receives the video data sent from the device and temporarily stores it in storage. The server checks the format and quality of the video data.

[1658] Step 4:

[1659] The server splits the video data it receives into frames, and each frame is extracted and saved individually.

[1660] Step 5:

[1661] The server sequentially inputs the divided frames into the AI ​​model and detects the position information (coordinates, angles, speed, etc.) of the user's joints and body parts. It then selects specific key frames and extracts their position information.

[1662] Step 6:

[1663] The server evaluates the user's form based on the extracted position information, using indicators such as elbow angle, shoulder position, and hip rotation to analyze good points and areas for improvement.

[1664] Step 7:

[1665] The server generates feedback on the form based on the evaluation results, specifically providing specific points for improvement in text format, such as "your elbows are lower than your head" or "your center of gravity is too far forward."

[1666] Step 8:

[1667] The user uses a terminal to input the exercise goal they want to achieve on the system interface, for example, setting a specific goal such as "I want to increase my pitching speed."

[1668] Step 9:

[1669] The server generates an ideal form based on the purpose entered by the user. It generates ideal form data and calculates the optimal keyframes.

[1670] Step 10:

[1671] The server generates a new video to visualize the ideal form, which is then compared to the user's actual form.

[1672] Step 11:

[1673] The device provides an interface that displays the ideal form and the user's actual form side by side, allowing users to directly compare the two forms using a video player or slider.

[1674] Step 12:

[1675] The server compares the user's form and generates advice on specific training methods, areas to train, and injury prevention. The device then notifies the user of this advice and provides detailed feedback.

[1676] Step 13:

[1677] The device runs an emotion engine that performs real-time facial recognition and voice analysis of the user, collecting emotion data using the device's camera and microphone.

[1678] Step 14:

[1679] The emotion engine analyzes the collected data and identifies the user's emotional state, identifying emotions such as fatigue, concentration, frustration, and joy.

[1680] Step 15:

[1681] The server receives data from the emotion engine and adjusts the feedback, for example suggesting light stretching or a break if it detects that the user is tired.

[1682] Step 16:

[1683] The server generates motivational messages and additional advice according to the user's emotional state, and the device notifies the user of these messages and advice.

[1684] In this way, by combining the emotion engine, it is possible to not only analyze and improve the user's exercise form, but also to provide optimal feedback and advice adapted to the user's emotional state.

[1685] Example 2

[1686] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1687] While conventional exercise form analysis systems can provide feedback on the exercise itself, they are unable to provide feedback that takes into account the user's emotional state. Furthermore, conventional systems have difficulty automatically comparing the user's actual form with their ideal form, and providing detailed advice requires a great deal of time and effort. Therefore, there is a need for comprehensive exercise form analysis and improvement support that includes feedback based on the user's emotional state.

[1688] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1689] In this invention, the server includes means for uploading exercise videos taken by a user using a terminal to the system, means for dividing the received video data into frames and extracting important location information, means for evaluating good points and areas for improvement of the form based on the extracted location information, means for generating an ideal form based on the exercise purpose input by the user through the terminal, means for comparing the ideal form generated by the server with the user's actual form and providing specific advice, means for recognizing the user's emotional state using the camera and microphone of the terminal, and means for adjusting feedback based on the emotional state by the server, thereby enabling optimal feedback based on the user's emotional state as well as analysis of the user's exercise form.

[1690] "Terminal" refers to a device used by a user to record a video of their exercise form and upload it to the system, and refers to a portable electronic device such as a smartphone or tablet.

[1691] "Server" refers to a central processing unit that analyzes video data uploaded to the system and evaluates, generates, and provides feedback on exercise form.

[1692] "Video data" refers to video data of a user's exercise form, which is uploaded to the system and analyzed by the server.

[1693] "Splitting by frame" refers to the process of dividing video data into frames, which are time units, in order to improve the accuracy of analysis.

[1694] "Positional information" refers to data such as the position, angle, and speed of the user's joints and body parts in the video, and is used to evaluate exercise form.

[1695] "Extraction" refers to the process of obtaining the necessary location information from video data.

[1696] "Evaluation" refers to the process of analyzing and determining the strengths and areas for improvement of the user's exercise form based on the extracted location information.

[1697] "Purpose of exercise" refers to the goal or purpose of exercise that the user inputs into the system, such as "I want to increase the ball speed" or "I want to increase the distance I can hit the ball."

[1698] "Ideal form" refers to a model of exercise form generated by the server to optimally achieve the user's goals.

[1699] "Comparison" refers to the process of evaluating the server-generated ideal form side-by-side with the user's actual form.

[1700] "Feedback" refers to specific advice or guidance provided by the server based on the comparison results, such as training methods or injury prevention methods.

[1701] "Emotional state" refers to the psychological state determined from the user's facial expression and tone of voice, and includes fatigue, concentration, joy, etc.

[1702] "Camera and microphone" refers to the video and audio input devices built into the device, which are used to recognize emotional states.

[1703] "Emotion Engine" refers to software or algorithms that analyze data captured through the camera and microphone to identify the user's emotional state.

[1704] "Adjusting feedback" refers to the process in which the server changes the feedback content based on the analysis results of the emotion engine.

[1705] The present invention relates to a system that allows users to easily analyze their own exercise form and receive specific and practical feedback, and is characterized by the fact that it combines an emotion engine to provide optimal feedback and advice based on the user's emotional state. The system of the present invention is implemented in the following specific manner.

[1706] Recording and uploading videos

[1707] The user uses a device (such as a smartphone or tablet) to record a video of their own athletic form. Using the device's camera, they can capture athletic form, such as a baseball pitch, golf swing, or tennis swing, at the appropriate angle and lighting. Once the video is complete, it can be uploaded to the system's application or website.

[1708] Video Analysis

[1709] The server receives video data uploaded by users. The server divides the video into frames and performs preprocessing. Preprocessing includes adjusting the video resolution and removing noise. The server then inputs the preprocessed video data into an AI model (e.g., OpenPose) and analyzes the position, angle, and velocity of joints and body parts from the video to extract important positional information.

[1710] Form evaluation

[1711] The server evaluates the user's exercise form based on the extracted position information. It analyzes the elbow angle, shoulder position, and hip rotation, and lists good points and areas for improvement. A specific example would be an evaluation such as "The elbow angle is appropriate, but the release point is low."

[1712] Setting goals and generating ideal forms

[1713] The user inputs the exercise goal they want to achieve into the system using their device. For example, they might say, "I want to increase my pitching speed" or "I want to increase my driving distance." Based on the goal entered by the user, the server uses a generative AI model (e.g., StyleGAN) to generate an ideal exercise form. This ideal form includes the movements that will optimally achieve the user's goal and provides a detailed description of that form.

[1714] Compare forms and provide feedback

[1715] The server compares the ideal form it has generated with the user's actual form. Evaluation criteria for the comparison include the degree of alignment of posture and the accuracy of movement. Based on the comparison results, the server provides advice on specific training methods, areas to train, and how to prevent injury. For example, it generates specific advice such as "To train the inner muscles of your shoulders, you should use band exercises." The device notifies the user of this feedback and displays detailed advice.

[1716] Emotion recognition and feedback adjustment by emotion engine

[1717] While the emotion engine captures the user's exercise form, it uses the device's camera and microphone to collect emotional data in real time. The emotion engine then uses facial recognition and voice analysis technologies to identify the user's emotional state from their facial expressions and tone of voice. For example, fatigue, concentration, joy, etc. can be analyzed.

[1718] Optimizing Emotional Feedback

[1719] The server receives emotional data from the emotion engine and adjusts the user's feedback. If the server detects that the user is tired, it will provide motivational messages and suggestions for light stretching. Conversely, if the user is concentrating, it will provide more detailed technical advice. The device will notify the user of the emotional feedback and encourage appropriate action.

[1720] Example: Analysis of baseball pitching form

[1721] 1. Recording and uploading videos

[1722] The user takes a photo of their pitching form from a diagonal forward angle on their smartphone and uploads it to the system.

[1723] 2. Video Analysis

[1724] The server divides the video into frames, and AI detects important positions such as elbows, shoulders, and waists.

[1725] 3. Form Evaluation

[1726] The server identifies specific areas for improvement, such as "your elbows are lower than your head" or "your center of gravity is too far forward."

[1727] 4. Setting goals

[1728] The user inputs a goal such as "I want to increase my pitching speed."

[1729] 5. Generation of ideal form

[1730] The server generates the ideal form and creates a new video with the elbow height adjusted.

[1731] 6. Compare forms and provide feedback

[1732] The device displays the user's form and the ideal form side by side, and the server provides advice on "band exercises to strengthen the inner shoulder muscles."

[1733] 7. Emotional Recognition

[1734] The emotion engine recognizes the user's face and voice and determines that the user is tired.

[1735] 8. Optimizing Emotional Feedback

[1736] The server considers the user's level of fatigue and suggests taking a moderate break or doing some light stretching.

[1737] Example of input prompt for generative AI model

[1738] "Analyze videos of baseball pitching form and provide user feedback. Also, use an emotion engine to suggest resting and stretching when fatigue occurs."

[1739] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1740] Detailed flow of system program processing

[1741] Step 1: Record and upload your video

[1742] The user uses a device (smartphone or tablet) to record a video of their exercise form.

[1743] Input: Video data such as baseball pitching form, golf swing, and tennis swing captured by the device's camera.

[1744] Specific operation: The user uploads the captured video to an application on the device or to a website.

[1745] Output: The uploaded video file will be saved on the system's server.

[1746] Step 2: Receive and split the video

[1747] The server receives the uploaded video data.

[1748] Input: User uploaded video file.

[1749] Specific operation: The server divides the video data into frames and generates still images that are consecutive in time.

[1750] Output: A set of still images separated by frames.

[1751] Step 3: Preprocessing the video

[1752] The server preprocesses the received video frames.

[1753] Input: A set of segmented still images.

[1754] Specific operation: The server performs pre-processing such as adjusting the video resolution, removing noise, and correcting color tone.

[1755] Output: A set of pre-processed still images.

[1756] Step 4: Extracting important location information

[1757] The server uses the pre-processed video frames to extract important location information.

[1758] Input: A set of preprocessed still images.

[1759] How it works: The server uses an AI model (e.g., OpenPose) to analyze the position, angle, and velocity of joints and body parts in each frame.

[1760] Output: A dataset related to joint and body part positions and their movements.

[1761] Step 5: Evaluate your form

[1762] The server evaluates the exercise form based on the extracted position information.

[1763] Input: A dataset related to important location and movement information.

[1764] Specific movements: The server analyzes the angle of each joint, shoulder position, hip rotation, etc. to identify good points and areas for improvement in form.

[1765] Output: A list of form evaluation results (e.g. "The elbow angle is appropriate, but the release point is low").

[1766] Step 6: Set your objectives

[1767] The user uses a terminal to input the purpose of the exercise into the system.

[1768] Input: The purpose of the exercise that the user inputs into the device (e.g., "I want to increase my pitching speed" or "I want to increase my driving distance").

[1769] Specific operation: When the user enters their purpose, the data is sent to the server and stored.

[1770] Output: The user's exercise goal data is stored on the server.

[1771] Step 7: Generate the ideal form

[1772] The server generates the ideal form based on the user's goals.

[1773] Input: User's exercise goal data.

[1774] Specific behavior: The server uses a generative AI model (e.g., StyleGAN) to generate an ideal exercise form to optimally achieve the user's goal.

[1775] Output: Generated ideal exercise form data.

[1776] Step 8: Compare forms and provide feedback

[1777] The server compares the ideal form generated by the server with the user's actual form and provides specific advice.

[1778] Input: Ideal exercise form data and the user's actual form data.

[1779] Specific operation: The server compares the two and evaluates the degree of correspondence between postures and the accuracy of movements. The device displays the results to the user.

[1780] Output: Comparison results and specific feedback (e.g., "You should do band exercises to strengthen your shoulder muscles").

[1781] Step 9: Emotion Recognition with the Emotion Engine

[1782] The device collects data to recognize the user's emotional state.

[1783] Input: User's facial expression data and voice data.

[1784] Specific operation: Using the device's camera and microphone, real-time emotional data is collected while the user is recording their exercise form.

[1785] Output: Collected emotion data.

[1786] Step 10: Adjusting Emotional Feedback

[1787] The server adjusts the feedback based on data from the emotion engine.

[1788] Input: User emotion data parsed by the emotion engine.

[1789] Specific behavior: If the server recognizes that the user is tired, it will provide motivational messages and suggestions for light stretching. If the user is concentrating, it will provide detailed technical advice.

[1790] Output: The adjusted feedback is displayed on the terminal.

[1791] (Application example 2)

[1792] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1793] Modern factories lack systems that can accurately analyze robot movements and efficiently provide improvement instructions. Furthermore, the lack of feedback based on workers' emotional states makes it difficult to improve production efficiency and worker safety. To solve this problem, a comprehensive system that integrates robot movement analysis and workers' emotional states is needed.

[1794] The specification processing by the specification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for uploading a video of exercise taken by a user using a terminal to the system, means for dividing the video data received by the server into frames and extracting important location information, means for evaluating good points and areas for improvement of the form based on the extracted location information, means for generating an ideal form based on the exercise purpose input by the user through the terminal, means for comparing the ideal form generated by the server with the user's actual form and providing specific advice, and means for recognizing the user's emotional state using an emotion engine and adjusting feedback based on the emotion data. This enables robot motion analysis and optimal feedback based on the worker's emotional state.

[1795] A "terminal" is an electronic device that a user uses to record and upload exercise videos to the system.

[1796] The "server" is a computer system that analyzes the received video data, generates the ideal form, and provides feedback.

[1797] "Video data" refers to video files of exercises taken by a user using a terminal.

[1798] "Splitting by frame" refers to the process of dividing video data into individual still images and analyzing them.

[1799] "Important positional information" refers to information about the positions and angles of joints and body parts that is necessary for analyzing movement.

[1800] "Form" refers to the user's body position and movement posture during exercise.

[1801] A "good point" is a part of the form being evaluated that is judged to be correct in relation to the intended action or goal.

[1802] "Areas for improvement" refers to any part of the form being evaluated that is judged to be inappropriate or inefficient.

[1803] "Ideal form" is the optimal movement pattern generated by the server to most effectively achieve the exercise goal entered by the user.

[1804] An "emotion engine" is software that analyzes a user's emotional state and adjusts feedback based on that.

[1805] "Feedback" refers to specific advice or instructions provided to users based on their form evaluation and areas for improvement.

[1806] System Overview

[1807] This invention is a system that analyzes videos of exercise form taken by the user using a device and provides specific and practical feedback. In particular, by combining it with an emotion engine, optimal feedback and advice based on the user's emotional state is provided, promoting more effective practice.

[1808] Initial Setup and Video Upload

[1809] Users use a device (such as a smartphone or tablet) to record a video of their athletic form. This video can cover a wide range of topics, such as the movement of a robot arm in a factory or a pitching form in a sports game. The video is then uploaded to the system's application or website. The device should have a support function that displays guidelines to help users record the video at the appropriate angle and with the appropriate lighting.

[1810] Video analysis and English extraction processing on the server

[1811] The server receives the uploaded video data and splits it into frames using a video processing library such as OpenCV. The frame-by-frame data is then fed into a pre-trained AI model (using Keras) to extract key positional information. This information includes the position and angle of joints and body parts. This processing reveals details about the movements of the user and the robot arm.

[1812] Evaluating forms and generating ideal forms

[1813] Based on the extracted positional information, the server evaluates the good points and areas for improvement in the form. For example, it may evaluate whether "the elbow position is correct" or "the shoulder movement is inappropriate." The user inputs their exercise goals (such as "I want to increase the speed of the ball" or "I want to improve the accuracy of the product") through the terminal, and the server generates an ideal form. This ideal form is a movement pattern that will optimally achieve the goal set by the user.

[1814] Compare and provide feedback

[1815] The server compares the generated ideal form with the user's actual form. Based on the comparison results, it provides specific practice methods and areas for improvement. There is also a function that displays the ideal form and the actual form side by side. Specifically, feedback such as "You should adjust your shoulder position like this" or "You should perform this exercise" is sent to the device.

[1816] Emotional state recognition and feedback optimization

[1817] The emotion engine uses facial recognition and voice analysis to identify the user's emotional state while recording video. Based on the emotion data, the server adjusts the feedback. For example, it analyzes the user's fatigue and concentration level. If the user is tired, it suggests a message of encouragement or a break, and if the user is concentrating, it provides detailed technical advice.

[1818] Example

[1819] For example, in analyzing the operation of a robot arm in a factory, a user takes a video of the robot arm and uploads it. The server analyzes the video and provides specific improvements and ideal operation patterns for the robot. The emotion engine recognizes the worker's level of fatigue and provides appropriate feedback.

[1820] Example prompt sentence:

[1821] "Please record video and analyze the behavior of factory robots."

[1822] "Provide feedback according to the worker's emotional state."

[1823] This allows users to effectively improve their own form and the robot's movements, and also allows them to optimally practice and adjust according to their emotional state.

[1824] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1825] Step 1:

[1826] The user uses a terminal to take a video of their exercise form and uploads it to the system.

[1827] Input: A video file of the exercise form taken by the user.

[1828] Processing: Capture video using the device camera and upload the video file to a system application or website.

[1829] Output: Uploaded video data is sent to the server.

[1830] Step 2:

[1831] The video data received by the server is divided into frames and important location information is extracted.

[1832] Input: Uploaded video data.

[1833] Processing: Using a video processing library such as OpenCV, the video is split into frames. The frame data is then input into an AI model (using Keras) to extract position and angle information for joints and body parts.

[1834] Output: Extracted geolocation data.

[1835] Step 3:

[1836] The server evaluates the form's strengths and areas for improvement based on the extracted location information.

[1837] Input: Extracted geolocation data.

[1838] Processing: Evaluate form based on position information, for example checking whether elbow position is correct or shoulder movement is appropriate.

[1839] Output: A list of good points and areas for improvement for the form.

[1840] Step 4:

[1841] The server generates an ideal form based on the exercise purpose entered by the user through the terminal.

[1842] Input: The purpose of the exercise entered by the user through the device (e.g., "I want to increase my pitching speed," "I want to improve the accuracy of my product," etc.).

[1843] Processing: Generate ideal form with optimal movement patterns based on the goal of the exercise.

[1844] Output: The generated data in ideal form.

[1845] Step 5:

[1846] The server compares the ideal form generated by the server with the user's actual form and provides specific advice.

[1847] Input: The user's actual form data and the generated ideal form data.

[1848] Processing: Compare the two and generate advice to the user on specific practice methods and areas for improvement.

[1849] Output: A specific advice message.

[1850] Step 6:

[1851] The server uses an emotion engine to recognize the user's emotional state and adjusts the feedback based on that emotional data.

[1852] Input: User's facial recognition and voice analysis data.

[1853] Processing: The emotion engine analyzes the user's emotional state (fatigue, concentration, etc.) and adjusts the feedback based on the results.

[1854] Output: Providing optimal feedback based on emotional state.

[1855] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1856] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1857] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1858] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1859] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1860] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1861] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1862] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1863] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1864] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1865] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1866] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1867] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1868] 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.

[1869] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1870] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1871] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific processing may be a single processor.

[1872] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1873] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1874] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1875] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1876] The following is further disclosed regarding the above embodiment.

[1877] (Claim 1)

[1878] A means for uploading a video of an exercise taken by a user using a terminal to the system;

[1879] A means for dividing the video data received by the server into frames and extracting important location information;

[1880] A means for the server to evaluate the form's strengths and areas for improvement based on the extracted location information; and

[1881] A means for the server to generate an ideal form based on the purpose of exercise input by the user through the terminal;

[1882] A means of comparing the server-generated ideal form with the user's actual form and providing specific advice

[1883] A system including:

[1884] (Claim 2)

[1885] 2. The system of claim 1, wherein the server includes means for displaying the user's actual form and ideal form side by side.

[1886] (Claim 3)

[1887] 2. The system according to claim 1, wherein the server includes means for generating and providing advice on specific training methods, areas to train, and injury prevention.

[1888] "Example 1"

[1889] (Claim 1)

[1890] means for uploading to the system a video of an action taken by a user using an information processing device;

[1891] A means for dividing the video data received by the server into video units and extracting important location information;

[1892] a means for the server to evaluate performance merits and areas for improvement based on the extracted location information;

[1893] A means for generating ideal movements by a server based on an exercise goal input by a user through an information processing device;

[1894] A means of comparing the ideal behavior generated by the server with the user's actual behavior and providing specific instructions

[1895] A system including:

[1896] (Claim 2)

[1897] 2. The system according to claim 1, wherein the server includes means for displaying the user's actual behavior and ideal behavior side by side.

[1898] (Claim 3)

[1899] 2. The system according to claim 1, wherein the server includes means for generating and providing instructions regarding specific training methods, areas to be strengthened, and injury prevention.

[1900] "Application Example 1"

[1901] (Claim 1)

[1902] A means for uploading a video of an exercise taken by a user using a terminal to the system;

[1903] A means for dividing the video data received by the server into frames and extracting important location information;

[1904] A means for the server to evaluate the form's strengths and areas for improvement based on the extracted location information; and

[1905] A means for the server to generate an ideal form based on the purpose of exercise input by the user through the terminal;

[1906] A means of comparing the server-generated ideal form with the user's actual form and providing specific advice; and

[1907] A means of capturing and analyzing video in real time using smart glasses to optimize work operations in logistics centers;

[1908] A way for employees to receive feedback on areas for improvement through smart glasses

[1909] A system including:

[1910] (Claim 2)

[1911] 2. The system of claim 1, wherein the server includes means for displaying the user's actual form and ideal form side by side.

[1912] (Claim 3)

[1913] The server generates and provides advice on specific training methods, areas to train, and injury prevention.

[1914] A method to use generative AI models to evaluate the efficiency of employees' actions and generate prompts to suggest specific areas for improvement.

[1915] 10. The system of claim 1, comprising:

[1916] "Example 2: Combining Emotion Engines"

[1917] (Claim 1)

[1918] A means for uploading a video of an exercise taken by a user using a terminal to the system;

[1919] A means for dividing the video data received by the server into frames and extracting important location information;

[1920] A means for the server to evaluate the form's strengths and areas for improvement based on the extracted location information; and

[1921] A means for the server to generate an ideal form based on the purpose of exercise input by the user through the terminal;

[1922] A means of comparing the server-generated ideal form with the user's actual form and providing specific advice; and

[1923] means for recognizing the emotional state of a user using a camera and a microphone of the device;

[1924] A means for the server to adjust feedback based on emotional state

[1925] A system including:

[1926] (Claim 2)

[1927] 2. The system of claim 1, wherein the server includes means for displaying the user's actual form and ideal form side by side.

[1928] (Claim 3)

[1929] 2. The system according to claim 1, wherein the server includes means for generating and providing advice on specific training methods, areas to train, and injury prevention.

[1930] "Application example 2 when combining emotion engines"

[1931] (Claim 1)

[1932] A means for uploading a video of an exercise taken by a user using a terminal to the system;

[1933] A means for dividing the video data received by the server into frames and extracting important location information;

[1934] A means for the server to evaluate the form's strengths and areas for improvement based on the extracted location information; and

[1935] A means for the server to generate an ideal form based on the purpose of exercise input by the user through the terminal;

[1936] A means of comparing the server-generated ideal form with the user's actual form and providing specific advice; and

[1937] A means of recognizing the user's emotional state using an emotion engine and adjusting feedback based on that emotional data.

[1938] A system including:

[1939] (Claim 2)

[1940] 2. The system of claim 1, wherein the server includes means for displaying the user's actual form and ideal form side by side.

[1941] (Claim 3)

[1942] The server generates and provides advice on specific training methods, areas to train, and injury prevention.

[1943] 2. The system according to claim 1, further comprising means for analyzing the emotional state of a worker or user and providing optimal feedback according to the worker's level of concentration or fatigue. [Explanation of symbols]

[1944] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for uploading a video of an exercise taken by a user using a terminal to the system; A means for dividing the video data received by the server into frames and extracting important location information; A means for the server to evaluate the form's strengths and areas for improvement based on the extracted location information; and A means for the server to generate an ideal form based on the purpose of exercise input by the user through the terminal; A means of comparing the server-generated ideal form with the user's actual form and providing specific advice A system including:

2. 2. The system of claim 1, wherein the server includes means for displaying the user's actual form and ideal form side by side.

3. 2. The system according to claim 1, wherein the server includes means for generating and providing advice on specific training methods, areas to train, and injury prevention.

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