system

Abstract

We provide the system. [Solution] A data analysis method for determining an individual's growth stage, A motion analysis means for analyzing acquired motion data to identify technical challenges, A training generation means that generates individual training plans based on the identified technical challenges, An injury prediction method for evaluating the risk of injury in response to variable loads, The generated training plan and injury risk are evaluated and provided as an information provision means, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In modern times, it is difficult to provide appropriate sports training according to the growth of children, and the risk of injury during the growth period is also a major concern for parents and coaches. In addition, it is difficult to provide effective guidance that suits the characteristics and growth rate of individual children with uniform training. Therefore, it is necessary to improve these problems and provide an environment where children can grow safely while enjoying sports.

Means for Solving the Problems

[0005] This invention includes motion analysis means that determine an individual's growth stage through data analysis and identify technical challenges by analyzing acquired motor data. Furthermore, by combining means for generating individualized training plans based on the identified challenges with prediction means for evaluating the risk of injury in response to changes in load, the invention provides a system that balances individually optimized training with safety. This system provides accurate feedback based on the generated training plan and risk assessment, and also provides information including suggestions for form correction in a visual format, thereby enabling improved performance and safety for children.

[0006] "Data analysis means" refers to means for analyzing sensor data and video recording data collected to determine individual growth stages.

[0007] "Motion analysis means" refers to a method for identifying an individual's movement form and technical challenges using acquired movement data.

[0008] A "training generation means" is a means for generating individually optimized training plans based on identified technical challenges.

[0009] An "injury prediction method" is a means of predicting and evaluating an individual's risk of injury in response to fluctuations in training load.

[0010] "Information provision means" refers to means of providing users with feedback based on the generated training plan and injury risk assessment, which includes form correction suggestions in the form of visual feedback. [Brief explanation of the drawing]

[0011] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0012] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0013] First, let's explain the terminology used in the following explanation.

[0014] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0015] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0016] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0017] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor and 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), or Bluetooth (registered trademark), and the like.

[0018] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0019] [First Embodiment]

[0020] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0021] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0022] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0023] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0024] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0025] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0026] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0027] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0028] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0029] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0030] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0031] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0032] This invention is a system for providing an efficient and safe training environment to each individual undergoing sports training. This system includes data analysis means, motion analysis means, training generation means, injury prediction means, and information provision means as its main components.

[0033] First, users (children receiving training or instructors) collect exercise data using wearable sensors and cameras. This data includes detailed information about posture and movement during exercise. The data is converted to an appropriate format by the device and sent to the server.

[0034] The server processes the received data using data analysis tools to determine each individual's developmental stage. This determination of developmental stage makes it possible to identify the optimal training content for each child.

[0035] Next, the motion analysis system analyzes the motion data to identify technical challenges and form problems. For example, it can analyze things like shoulder position during a swing or the balance of a running form.

[0036] Subsequently, the training generation system automatically creates an individually optimized training plan based on the identified technical challenges. This provides each user with a training menu tailored to their needs.

[0037] Furthermore, injury prediction tools are used to assess the risk of injury that may occur during training. This checks for improper movements or excessive load, and suggestions for rest or form correction are made as needed.

[0038] Finally, the information provider provides the user with feedback on the generated training plan and injury prediction results. The feedback displayed on the device includes visual suggestions for form correction, and the user can use this feedback to guide their training as needed.

[0039] For example, if an analysis reveals that a child is experiencing excessive stress on their knees while running, the training plan will include exercises to strengthen muscles and improve running form, along with feedback on the need for rest based on injury predictions. Through this entire cycle, we support the effective and safe implementation of children's sports training.

[0040] The following describes the processing flow.

[0041] Step 1:

[0042] Users collect data from each exercise session using wearable sensors and cameras. This includes information such as movement trajectory, acceleration, and heart rate.

[0043] Step 2:

[0044] The terminal processes the collected data and converts it into a structured data format. This data is then sent to the server in a format suitable for subsequent analysis.

[0045] Step 3:

[0046] The server receives the data and uses data analysis tools to determine each individual's growth stage. This establishes the basic data based on age and developmental stage.

[0047] Step 4:

[0048] The server uses motion analysis tools to analyze motion data in detail. Based on the analysis results, problems and technical issues with the form are identified.

[0049] Step 5:

[0050] The server generates individually optimized training plans based on identified technical challenges using the training generation mechanism. These plans are tailored to the user's stage of development.

[0051] Step 6:

[0052] The server uses injury prediction tools to assess the risk of injury from the analysis data and detect signs of overload or improper movement. Rest and plan modifications are incorporated into the training plan as needed.

[0053] Step 7:

[0054] The device provides the user with a generated training plan and injury prediction feedback. The user can then perform the training based on the visually presented instructions and form correction suggestions.

[0055] (Example 1)

[0056] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0057] The challenge lies in providing training plans optimized for individual abilities and characteristics, as well as systems to reduce the risk of injury during exercise. Conventional, general-purpose training menus often lacked adaptability to individual users, making effective growth and injury prevention difficult. Furthermore, there was a lack of concrete indicators to properly identify and resolve technical challenges.

[0058] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0059] In this invention, the server includes a data analysis device that determines an individual's growth stage; a motion analysis device that analyzes acquired motion information to identify technical challenges; a training generation device that generates individual training plans based on the identified technical challenges; an injury prediction device that evaluates the risk of injury for variable loads; an information supply device that evaluates the generated training plan and the risk of injury and provides feedback; a device that predicts potential injuries based on received motion information using a central device for receiving and analyzing digital information; and a device that automatically generates customized training plans based on the analysis results using an artificial intelligence model. This enables effective and safe training tailored to each user's characteristics and condition.

[0060] A "data analysis device" is a device that analyzes acquired motor information to evaluate an individual's developmental stage and provides specific evaluation results.

[0061] A "motion analysis device" is a device that analyzes acquired motion information to identify technical challenges and problems with movement.

[0062] A "training generation device" is a device that automatically generates individualized training plans based on identified technical challenges.

[0063] A "injury prediction device" is a device that assesses the risk of injury that may occur during exercise and provides information to enable appropriate preventive measures to be taken.

[0064] An "information supply device" is a device that provides feedback to the user based on the generated training plan and injury risk assessment, in order to improve exercise efficiency.

[0065] A "central system" is a central computer system that receives digital information and performs centralized analysis and processing.

[0066] An "artificial intelligence model" is a system that uses machine learning algorithms to generate data-driven training plans based on analysis results and supports performance improvement.

[0067] The system according to the present invention provides exercise training optimized for individual users and maximizes the effectiveness of exercise in a safe environment. This system utilizes multiple hardware components, including wearable devices, cameras, terminals, and servers, to collect and analyze data.

[0068] First, the user puts on a wearable device. This device is equipped with an accelerometer and a gyroscope, allowing it to collect the user's movement information in real time. It also uses a camera to capture the user's movements. The data obtained from this hardware is then sent to the terminal.

[0069] The device processes this data into an appropriate digital format. For example, sensor data containing detailed exercise information needs to be converted into CSV or JSON format. The converted data is then sent to the server using a secure and reliable communication protocol.

[0070] The server receives this data and activates algorithms for data analysis and behavioral analysis. The server's central system employs various data analysis techniques to determine an individual's stage of development and identify technical challenges. It also utilizes AI models to automatically generate personalized training plans. Based on the input data, the AI ​​models evaluate each user's behavior and identify forms that need improvement.

[0071] Next, the server can use an injury prediction device to assess potential injury risks based on the collected data. The AI ​​analyzes the user's movement patterns and predicts and warns about the risk of injury due to improper movements or excessive strain. Based on this analysis, it provides feedback such as suggestions for appropriate rest and form correction.

[0072] Feedback is sent from the server to the user via the device. The device provides visual or audio feedback, which the user can use to improve the quality of their training. For example, they might receive specific advice such as, "Bend your knees more flexibly while running."

[0073] A concrete example of a prompt message would be, "Generate a training plan to improve the running form of a 10-year-old child. Assume there is excessive stress on the knees." This allows the AI ​​model to propose the optimal plan.

[0074] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0075] Step 1:

[0076] The user puts on a wearable device, sets up the camera, and begins exercising. The wearable device has an accelerometer and a gyroscope, which collect data on movement and posture during exercise in real time. As the user exercises, the sensors acquire each data point and send the results to the terminal. The input is raw exercise data, and the output is in a digital data format that can be processed by the terminal.

[0077] Step 2:

[0078] The device converts the raw motion data received from the user into a different format. Specifically, this involves organizing sensor data into CSV or JSON format and decomposing camera footage into a digital format that can be analyzed frame by frame. In this process, raw sensor data and video data are received as input, and data prepared for analysis is generated as output.

[0079] Step 3:

[0080] The terminal sends the generated digital data to the server. A secure and efficient communication protocol is used for this transmission to ensure processing speed and data protection. The input contains digital data, and the output is processed as data successfully transmitted to the server.

[0081] Step 4:

[0082] The server initiates the process of analyzing the received data. A data analysis device assesses the individual's developmental stage, and a motion analysis device analyzes the movement data to identify technical challenges. The input is the movement data transmitted from the terminal, and the output is the evaluation results of the developmental stage and a list of identified technical challenges.

[0083] Step 5:

[0084] The server uses a generative AI model to automatically generate a customized training plan based on the user's technical challenges. This involves executing algorithms based on prompts, and the AI ​​constructs the most effective training menu. The technical challenge is given as input, and the output is a specific training plan.

[0085] Step 6:

[0086] The server uses an injury prediction device to perform data-driven injury risk analysis. An AI model utilizes motion data to predict potential injuries, assessing risks arising from improper form or excessive movement load. The input is motion data, and the output is an injury risk assessment and preventative advice.

[0087] Step 7:

[0088] The training plan and injury prediction results generated by the server are sent to the terminal. The terminal provides visual and audible feedback on this information to the user. The user adjusts the training based on this feedback. The input is the feedback data from the server, and the output is the improvement suggestions and exercise guidelines provided to the user.

[0089] (Application Example 1)

[0090] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0091] In individual sports training, providing efficient and safe instruction requires customized training plans tailored to each person's progress and individual technical challenges. However, conventional systems have struggled to monitor individual movement in real time and provide timely, appropriate feedback. Furthermore, predicting the risk of injury during training and dynamically adjusting advice has been difficult. Effective solutions addressing these challenges are needed.

[0092] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0093] In this invention, the server includes data analysis means for determining an individual's growth stage; motion analysis means for analyzing acquired movement data and identifying technical challenges; training generation means for generating individual training plans based on the identified technical challenges; injury prediction means for evaluating the risk of injury under variable load; information provision means for evaluating the generated training plan and injury risk and providing feedback; and communication means for monitoring movement status through communication with a robot and adjusting training content in real time. This makes it possible to provide an optimal training plan and safety measures while monitoring individual movement status in real time.

[0094] "Data analysis means" refers to a device or technology that analyzes sensor data or video data in order to determine an individual's growth stage.

[0095] "Motion analysis means" refers to a device or technology used to identify individual technical challenges using acquired motion data.

[0096] "Training generation means" refers to a device or technology that creates individually optimized training plans based on identified technical challenges.

[0097] "Injury prediction means" refers to a device or technology that evaluates the risk of injury in response to variable loads associated with exercise.

[0098] "Information provision means" refers to a device or technology that provides feedback to the user based on the generated training plan and injury risk.

[0099] "Communication means" refers to a device or technology for monitoring the robot's movement state and adjusting training content in real time through information exchange with the robot.

[0100] The system that realizes this invention combines data analysis means, motion analysis means, training generation means, injury prediction means, information provision means, and communication means.

[0101] The server first acquires motion data from sensors and cameras worn by the user. This data contains detailed information about posture and movement and is transmitted to the terminal in real time. The data analysis means processes the sensor data and video data to evaluate the individual's progress. Based on this evaluation, the motion analysis means identifies individual technical challenges and clearly indicates problems and areas for improvement in form.

[0102] Next, the training generation means automatically creates an individualized training plan according to the identified technical challenges. The injury prediction means assesses the risk of injury based on the load applied during exercise and suggests rest or form modifications to the user as needed.

[0103] The system provides users with visual feedback on generated training plans and injury prediction results. Furthermore, a robot monitors the user's movement state via communication and adjusts the training content on the spot. This allows users to train efficiently and safely.

[0104] For example, the server analyzes the user's running form, and if it detects excessive stress on the knees, it incorporates strength training exercises and form-improvement exercises into the training plan. Furthermore, if the risk of injury is deemed high, feedback recommending rest is provided through the information system. An example of a prompt might be, "Assess whether your current running form is putting too much stress on your knees, and suggest exercises as needed."

[0105] This embodiment enables real-time monitoring and the provision of dynamic training plans, allowing users to perform optimal training.

[0106] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0107] Step 1:

[0108] When a user begins exercising, the device collects data in real time through wearable sensors and cameras. This data includes detailed information such as the user's posture, movement, and speed. The device takes this data as input, converts the format, and sends it to the server.

[0109] Step 2:

[0110] The server processes the received data using data analysis tools to evaluate the individual's growth stage. Input includes not only exercise data but also historical data. This allows for a time-series data-based evaluation of growth stages, and the evaluation results are output.

[0111] Step 3:

[0112] The server identifies the user's technical challenges using motion analysis tools. Specifically, it analyzes form problems and areas for improvement based on the input motion data. This analysis process clearly defines the technical challenges and provides them as output for the next processing step.

[0113] Step 4:

[0114] The server uses training generation methods to create personalized training plans based on identified technical challenges. Leveraging a generation AI model, it generates an optimal exercise plan for the user's current state. This plan is then sent to the terminal as output.

[0115] Step 5:

[0116] The server uses injury prediction tools to assess the risk of injury based on the exercise load. It analyzes the input exercise data and training plan to calculate the risk of injury. Based on this assessment, it suggests rest periods or form modifications if necessary.

[0117] Step 6:

[0118] The device provides users with visual feedback on generated training plans and injury predictions through information delivery mechanisms. Users can then adjust their training content based on this feedback.

[0119] Step 7:

[0120] The terminal uses communication to allow the robot to monitor the user's exercise state and adjust the training content in real time as needed. Processing is performed based on prompts, and optimized instructions are output to the user. For example, specific advice such as, "Assess whether your current running form is putting too much stress on your knees, and suggest exercises if necessary," is provided in real time.

[0121] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0122] This invention provides a sports training system that takes into account not only the physical but also the emotional aspects of the individual receiving training. This system combines data analysis means, motion analysis means, training generation means, injury prediction means, information provision means, and an emotion engine that recognizes emotions.

[0123] First, the user collects exercise data using wearable sensors and cameras. This data includes not only information about normal movements, but also facial expressions and voice data. The data is then transmitted to a server via the device.

[0124] The server uses data analysis tools to analyze collected sensor data and video data to determine the individual's growth stage. Furthermore, motion analysis tools identify form and technical challenges from movement data. In addition, an emotion engine analyzes facial expression data and voice data to recognize the user's emotional state. This analysis allows for an understanding of the user's emotional condition.

[0125] Next, the training generation means generates an individualized training plan, taking into account the tasks identified by the motion analysis means as well as data from the emotion engine. For example, if the user is tense, exercises to promote relaxation may be included, and if motivation is low, goal setting to increase self-efficacy may be incorporated.

[0126] By combining physical data with stress assessments based on emotional data, injury prediction methods can perform more precise risk assessments. As a result, the risk of accidents and injuries caused by overwork or inappropriate mental states can be reduced.

[0127] Finally, the information delivery method provides users with a training plan along with feedback based on emotional data. In addition to suggesting form corrections through visual feedback, it improves the quality and safety of training by providing advice tailored to the emotional state.

[0128] For example, if a user is perceived as feeling anxious about a new training session, relaxation techniques are suggested using the results of the emotional engine, and a technical training plan is provided simultaneously. This allows the user to train in an optimal mental and physical state. This system enables comprehensive sports training tailored to the user's physical and emotional needs.

[0129] The following describes the processing flow.

[0130] Step 1:

[0131] The user prepares a wearable sensor and camera at the start of an exercise session to collect exercise data. For emotion recognition, facial expressions are captured by the camera, and audio data is recorded as needed.

[0132] Step 2:

[0133] The device processes the collected motor and emotional data and sends it to the server. The data is highly compressed and converted into a format suitable for analysis.

[0134] Step 3:

[0135] The server uses data analysis tools to determine each individual's growth stage from their exercise data. This determination is based on physical characteristics and past data.

[0136] Step 4:

[0137] The server uses motion analysis tools to identify form and operational issues from the data. For example, if the smash speed is slow, the cause can be investigated.

[0138] Step 5:

[0139] The server activates an emotion engine, analyzing the user's facial expression and voice data to assess their emotional state. This assessment allows for a clear understanding of their stress levels and motivation levels.

[0140] Step 6:

[0141] The server utilizes training generation methods to create an optimized training plan based on operational challenges and emotional assessments. For example, if relaxation is deemed necessary, stretching will be incorporated into the plan.

[0142] Step 7:

[0143] The server performs risk assessments based on exercise and emotional data through injury prediction mechanisms. In particular, it detects risks arising from the combination of emotional stress and physical fatigue.

[0144] Step 8:

[0145] The device provides the user with a server-generated training plan and injury prediction feedback. The visual feedback specifically indicates form corrections and also includes specific advice tailored to the user's emotional state.

[0146] Step 9:

[0147] Users conduct training based on the provided feedback and plan. They can receive supplementary guidance and additional instructions via their device as needed.

[0148] (Example 2)

[0149] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0150] In modern sports training, there is a growing demand for effective training plans that consider not only individual athletic ability but also emotional state. However, conventional training systems have struggled to adequately recognize and reflect an individual's emotional state, making it difficult to provide optimized training plans. Against this backdrop, realizing a system that simultaneously considers athletic ability and emotional state to provide training tailored to individual needs has become a crucial challenge.

[0151] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0152] In this invention, the server includes information analysis means for evaluating an individual's stage of development, motion evaluation means for analyzing acquired motion information to identify technical challenges, and training creation means for generating an individualized training plan based on the identified technical challenges and emotional data. This makes it possible to provide an integrated training system that takes both motion and emotion into consideration.

[0153] "Information analysis means for evaluating an individual's stage of growth" refers to a system component that analyzes collected sensor data and video data to determine the stage of growth for each individual user.

[0154] A "motion evaluation means" is a system element that has the function of analyzing acquired motion information and identifying the user's technical challenges.

[0155] A "training creation method" is a component of a system that has the function of generating a training plan optimized for each user based on identified technical challenges and emotional data.

[0156] A "risk prediction tool" is a system element that has the function of evaluating the possibility of injury with high accuracy, taking into account variable loads and the user's emotional state.

[0157] "Information provision means" refers to a component of a system that has the function of providing feedback to the user, including visual and emotional advice, based on the generated training plan and emotional data.

[0158] This system combines data collection using wearable devices and cameras with advanced data analysis techniques to enhance users' sports training. Users wear wearable sensors during their daily training, and the camera captures their movements, collecting exercise data, facial expressions, and audio data. This data is transmitted to a server via the terminal.

[0159] The server first uses information analysis tools to analyze sensor data and video data to evaluate the user's developmental stage. Simultaneously, it uses an emotion recognition engine to analyze facial expressions and voice data to determine the user's emotional state. Subsequently, it uses motion evaluation tools to analyze motion data and identify technical challenges.

[0160] The training creation mechanism combines identified technical challenges with perceived emotional states to generate individually customized training plans. For example, if a user is experiencing anxiety, exercises that promote relaxation may be included. For injury prevention, the risk prediction mechanism uses exercise and emotional data to perform a stress assessment and evaluate the likelihood of injury in advance.

[0161] Finally, users receive visual and audio feedback through information delivery methods. The device presents a training plan tailored to the user and advice based on their emotional state via screen and audio. This feedback allows users to continue training with better form.

[0162] For example, if a user starts a new workout and is feeling nervous, the server will provide a workout plan that incorporates relaxation techniques. An example of a prompt for the generating AI model would be, "Integrate user's emotional and physical data to generate a customized workout plan." This system enables comprehensive workouts that address the user's physical and emotional needs.

[0163] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0164] Step 1:

[0165] The user wears wearable sensors and a camera and begins training. This collects data on body movements (acceleration, position, etc.), facial expressions, and voice data. The input data captures the user's physical activity and emotional responses, and this serves as the starting point for individually optimizing the training.

[0166] Step 2:

[0167] The device temporarily stores the collected motor and emotional data and transmits it to the server via a secure communication protocol. The transmitted data serves as foundational data that the server needs for subsequent analysis. The data may be transmitted in real time.

[0168] Step 3:

[0169] The server uses information analysis tools to analyze received data and evaluate the user's growth stage. Sensor data and video data are provided as input, and the output is an evaluation of the degree of growth and current status. The analysis compares current performance data with past performance data.

[0170] Step 4:

[0171] The server uses an emotion engine to analyze facial expression and voice data to recognize the user's emotional state. The input is the acquired emotion-related data, and the output is numerical or categorical information representing the emotional state. For example, emotions can be inferred from the frequency of smiles or the tone of voice.

[0172] Step 5:

[0173] The server uses motion evaluation tools to analyze movement data and identify technical issues. Inputs include information on specific movement patterns and forms, while output is a list of technical elements that need improvement. This allows for checking muscle usage and form precision.

[0174] Step 6:

[0175] The server uses training creation tools to generate personalized training plans, taking into account technical challenges and emotional states. Inputs include evaluation data and emotional data, while outputs consist of specific exercises and recommendations. This is how the training curriculum is constructed.

[0176] Step 7:

[0177] The server analyzes exercise and emotional data using risk prediction tools to assess the likelihood of injury. The input is a combination of physical and emotional data, and the output is the injury risk assessment result. This provides a safe training environment.

[0178] Step 8:

[0179] The device receives training plans generated from the server and feedback based on sentiment data, and presents it to the user. Input is feedback data from the server, and output is provided to the user as visual and auditory information. This allows the user to prepare for training.

[0180] (Application Example 2)

[0181] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0182] Traditional training systems, while focusing on physical aspects, have a drawback in that they fail to address changes in psychological state. Despite the significant impact of psychological state on the effectiveness and sustainability of training, general systems often fail to consider this, potentially hindering the user's optimal progress. Furthermore, receiving real-time guidance from a professional is difficult when training at home.

[0183] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0184] In this invention, the server includes information analysis means for determining an individual's progress stage, action analysis means for analyzing collected activity data to identify technical challenges, and training plan generation means for generating individualized training plans based on the identified technical challenges and psychological states detected by emotion analysis means. This makes it possible to provide personalized training plans that take psychological aspects into consideration. Furthermore, by utilizing automated machines, it is possible to promote user growth through real-time instructional feedback in a home environment.

[0185] "Information analysis tools" are technical means used to perform analysis in order to determine an individual's stage of progress.

[0186] A "motion analysis means" is a technical means that identifies technical problems based on collected activity data.

[0187] A "training plan generation means" is a technical means that generates individual training plans based on identified technical challenges and psychological states detected through emotion analysis.

[0188] A "risk assessment tool" is a technical means for evaluating the risk of injury based on variable load and psychological state.

[0189] "Information provision means" refers to technical means that provide feedback to users based on the generated training plan and risk assessment results.

[0190] An "automated machine" is a device that monitors physical activity and emotional state in a home environment and provides real-time guidance and feedback.

[0191] This invention provides a system using automated machinery as an application example for optimizing individual training. The server uses information analysis means to determine the individual's progress stage. Specifically, wearable sensors and built-in cameras are used to analyze collected activity data and emotions. The server uses motion analysis means to identify technical challenges from this data. Meanwhile, emotion analysis technology is used to evaluate the psychological state based on facial recognition and voice analysis.

[0192] The training plan generation means generates an individualized training plan based on identified technical challenges and psychological state. In this process, a generation AI model is used to design training content that fits the user's condition. The risk assessment means assesses the risk of injury based on variable load and psychological state. This ensures the user's physical and psychological safety.

[0193] The information delivery system provides real-time feedback via an automated machine in a home environment, based on the generated training plan and risk assessment results. This process utilizes speech synthesis technology to present instructions and advice to the user via voice.

[0194] As a concrete example, when a user begins training at home, an automated machine monitors the user's form and provides suggestions for improvement as needed. At the same time, if it detects tension in the user, it provides voice guidance on relaxation techniques.

[0195] An example of a generated AI model prompt is as follows: "Design an AI algorithm that generates personalized training suggestions aimed at improving athletic performance and reducing stress, based on the user's real-time movement and facial expression data."

[0196] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0197] Step 1:

[0198] The user attaches wearable sensors and a camera and begins training. This method collects motion data and facial expression data in real time. The input consists of the user's body motion data and facial expression data, which are sent to the server via the sensors and camera. The output is a data stream that is available on the server.

[0199] Step 2:

[0200] The server processes the collected data stream through an information analysis tool to determine the stage of progress. The input consists of user motion data and facial expression data. A data analysis algorithm evaluates physical progress and emotional changes, determining the individual's progress level as output. During this process, the system compares the facial expressions and motions with a standard database and analyzes the differences from the baseline.

[0201] Step 3:

[0202] The server uses motion analysis tools to analyze the motion data provided as input. This identifies technical issues and clarifies which parts of the form require improvement. The output is recorded as the identified technical issues. Specifically, processing is performed to identify abnormalities and asymmetries in body movement.

[0203] Step 4:

[0204] The server utilizes an emotion analysis engine to evaluate the user's psychological state. Inputs include facial expression data and voice data. This processing step applies an emotion analysis model to recognize the user's mental state and outputs a classification of the current emotional state. Specifically, it analyzes the user's facial muscle movements and voice tone.

[0205] Step 5:

[0206] The server uses a training plan generation mechanism to generate an individualized training plan based on identified technical challenges and emotional states. The inputs are technical challenge data and emotional state data. The output is a user-specific training plan, including recommendations for exercise types, repetitions, and relaxation techniques. Here, the optimal solution is selected by comparing it with historical data.

[0207] Step 6:

[0208] The server uses risk assessment tools to evaluate the risk of injury based on the planned load and mental state as inputs. The output is the risk assessment and proposed mitigation measures. Specifically, it quantifies the risk of injury and provides load adjustments and stress reduction measures based on that quantification.

[0209] Step 7:

[0210] Through the terminal, the home-based automated machine provides the user with feedback on the generated training plan and risk assessment results using speech synthesis technology. The input is the plan and risk assessment provided by the server. The output is voice guidance and motivational feedback for the user. Specific actions in this process include providing voice guidance in a tone appropriate to the user and specific exercise suggestions in real time.

[0211] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0212] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0213] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0214] [Second Embodiment]

[0215] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0216] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0217] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0218] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0219] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0220] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0221] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0222] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0223] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0224] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0225] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0226] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0227] This invention is a system for providing an efficient and safe training environment to each individual undergoing sports training. This system includes data analysis means, motion analysis means, training generation means, injury prediction means, and information provision means as its main components.

[0228] First, users (children receiving training or instructors) collect exercise data using wearable sensors and cameras. This data includes detailed information about posture and movement during exercise. The data is converted to an appropriate format by the device and sent to the server.

[0229] The server processes the received data using data analysis tools to determine each individual's developmental stage. This determination of developmental stage makes it possible to identify the optimal training content for each child.

[0230] Next, the motion analysis system analyzes the motion data to identify technical challenges and form problems. For example, it can analyze things like shoulder position during a swing or the balance of a running form.

[0231] Subsequently, the training generation system automatically creates an individually optimized training plan based on the identified technical challenges. This provides each user with a training menu tailored to their needs.

[0232] Furthermore, injury prediction tools are used to assess the risk of injury that may occur during training. This checks for improper movements or excessive load, and suggestions for rest or form correction are made as needed.

[0233] Finally, the information provider provides the user with feedback on the generated training plan and injury prediction results. The feedback displayed on the device includes visual suggestions for form correction, and the user can use this feedback to guide their training as needed.

[0234] For example, if an analysis reveals that a child is experiencing excessive stress on their knees while running, the training plan will include exercises to strengthen muscles and improve running form, along with feedback on the need for rest based on injury predictions. Through this entire cycle, we support the effective and safe implementation of children's sports training.

[0235] The following describes the processing flow.

[0236] Step 1:

[0237] Users collect data from each exercise session using wearable sensors and cameras. This includes information such as movement trajectory, acceleration, and heart rate.

[0238] Step 2:

[0239] The terminal processes the collected data and converts it into a structured data format. This data is then sent to the server in a format suitable for subsequent analysis.

[0240] Step 3:

[0241] The server receives the data and uses data analysis tools to determine each individual's growth stage. This establishes the basic data based on age and developmental stage.

[0242] Step 4:

[0243] The server uses motion analysis tools to analyze motion data in detail. Based on the analysis results, problems and technical issues with the form are identified.

[0244] Step 5:

[0245] The server generates individually optimized training plans based on identified technical challenges using the training generation mechanism. These plans are tailored to the user's stage of development.

[0246] Step 6:

[0247] The server uses injury prediction tools to assess the risk of injury from the analysis data and detect signs of overload or improper movement. Rest and plan modifications are incorporated into the training plan as needed.

[0248] Step 7:

[0249] The device provides the user with a generated training plan and injury prediction feedback. The user can then perform the training based on the visually presented instructions and form correction suggestions.

[0250] (Example 1)

[0251] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0252] The challenge lies in providing training plans optimized for individual abilities and characteristics, as well as systems to reduce the risk of injury during exercise. Conventional, general-purpose training menus often lacked adaptability to individual users, making effective growth and injury prevention difficult. Furthermore, there was a lack of concrete indicators to properly identify and resolve technical challenges.

[0253] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0254] In this invention, the server includes a data analysis device that determines an individual's growth stage; a motion analysis device that analyzes acquired motion information to identify technical challenges; a training generation device that generates individual training plans based on the identified technical challenges; an injury prediction device that evaluates the risk of injury for variable loads; an information supply device that evaluates the generated training plan and the risk of injury and provides feedback; a device that predicts potential injuries based on received motion information using a central device for receiving and analyzing digital information; and a device that automatically generates customized training plans based on the analysis results using an artificial intelligence model. This enables effective and safe training tailored to each user's characteristics and condition.

[0255] A "data analysis device" is a device that analyzes acquired motor information to evaluate an individual's developmental stage and provides specific evaluation results.

[0256] A "motion analysis device" is a device that analyzes acquired motion information to identify technical challenges and problems with movement.

[0257] A "training generation device" is a device that automatically generates individualized training plans based on identified technical challenges.

[0258] A "injury prediction device" is a device that assesses the risk of injury that may occur during exercise and provides information to enable appropriate preventive measures to be taken.

[0259] An "information supply device" is a device that provides feedback to the user based on the generated training plan and injury risk assessment, in order to improve exercise efficiency.

[0260] A "central system" is a central computer system that receives digital information and performs centralized analysis and processing.

[0261] An "artificial intelligence model" is a system that uses machine learning algorithms to generate data-driven training plans based on analysis results and supports performance improvement.

[0262] The system according to the present invention provides exercise training optimized for individual users and maximizes the effectiveness of exercise in a safe environment. This system utilizes multiple hardware components, including wearable devices, cameras, terminals, and servers, to collect and analyze data.

[0263] First, the user puts on a wearable device. This device is equipped with an accelerometer and a gyroscope, allowing it to collect the user's movement information in real time. It also uses a camera to capture the user's movements. The data obtained from this hardware is then sent to the terminal.

[0264] The device processes this data into an appropriate digital format. For example, sensor data containing detailed exercise information needs to be converted into CSV or JSON format. The converted data is then sent to the server using a secure and reliable communication protocol.

[0265] The server receives this data and activates algorithms for data analysis and behavioral analysis. The server's central system employs various data analysis techniques to determine an individual's stage of development and identify technical challenges. It also utilizes AI models to automatically generate personalized training plans. Based on the input data, the AI ​​models evaluate each user's behavior and identify forms that need improvement.

[0266] Next, the server can use an injury prediction device to assess potential injury risks based on the collected data. The AI ​​analyzes the user's movement patterns and predicts and warns about the risk of injury due to improper movements or excessive strain. Based on this analysis, it provides feedback such as suggestions for appropriate rest and form correction.

[0267] Feedback is sent from the server to the user via the device. The device provides visual or audio feedback, which the user can use to improve the quality of their training. For example, they might receive specific advice such as, "Bend your knees more flexibly while running."

[0268] A concrete example of a prompt message would be, "Generate a training plan to improve the running form of a 10-year-old child. Assume there is excessive stress on the knees." This allows the AI ​​model to propose the optimal plan.

[0269] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0270] Step 1:

[0271] The user puts on a wearable device, sets up the camera, and begins exercising. The wearable device has an accelerometer and a gyroscope, which collect data on movement and posture during exercise in real time. As the user exercises, the sensors acquire each data point and send the results to the terminal. The input is raw exercise data, and the output is in a digital data format that can be processed by the terminal.

[0272] Step 2:

[0273] The device converts the raw motion data received from the user into a different format. Specifically, this involves organizing sensor data into CSV or JSON format and decomposing camera footage into a digital format that can be analyzed frame by frame. In this process, raw sensor data and video data are received as input, and data prepared for analysis is generated as output.

[0274] Step 3:

[0275] The terminal sends the generated digital data to the server. A secure and efficient communication protocol is used for this transmission to ensure processing speed and data protection. The input contains digital data, and the output is processed as data successfully transmitted to the server.

[0276] Step 4:

[0277] The server initiates the process of analyzing the received data. A data analysis device assesses the individual's developmental stage, and a motion analysis device analyzes the movement data to identify technical challenges. The input is the movement data transmitted from the terminal, and the output is the evaluation results of the developmental stage and a list of identified technical challenges.

[0278] Step 5:

[0279] The server automatically generates a customized training plan based on the user's technical problems by utilizing a generative AI model. This includes executing algorithms based on prompt texts, and the AI constructs the most effective training menu. The technical problem is given as the input, and the output is a specific training plan.

[0280] Step 6:

[0281] The server uses an injury prediction device to perform an analysis of injury risks based on data. To evaluate risks arising from inappropriate forms and excessive operation loads, the AI model utilizes operation data to predict potential injuries. The input is operation data, and the output is an evaluation of injury risks and preventive advice.

[0282] Step 7:

[0283] The training plan generated by the server and the results of injury prediction are sent to the terminal. The terminal provides visual and audio feedback of this information to the user. The user adjusts the training based on this feedback. The input is feedback data from the server, and the output is improvement measures and exercise guidelines provided to the user.

[0284] (Application Example 1)

[0285] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0286] In personal sports training, in order to provide specialized guidance efficiently and safely, a customized training plan corresponding to that person's growth state and individual technical problems is required. However, in conventional systems, it has been difficult to grasp individual exercise states in real time and provide appropriate feedback quickly. Also, it has not been easy to predict the risk of injury during training and dynamically adjust advice. An effective solution corresponding to such problems is demanded.

[0287] The specific processing by the specific processing unit 290 of the data processing apparatus 12 in Application Example 1 is realized by the following respective means.

[0288] In this invention, the server includes: data analysis means for determining an individual's growth stage; operation analysis means for analyzing the acquired exercise data to identify technical problems; training generation means for individually generating a training plan based on the identified technical problems; injury prediction means for evaluating the risk of injury against variable loads; information providing means for evaluating the generated training plan and the risk of injury and providing feedback; and communication means for monitoring the exercise state through communication with the robot and adjusting the training content in real time. Thereby, it becomes possible to provide an optimal training plan and safety measures while monitoring individual exercise states in real time.

[0289] The "data analysis means" is a device or technology for analyzing sensor data or recording data in order to determine an individual's growth stage.

[0290] The "operation analysis means" is a device or technology for identifying individual technical problems using the acquired exercise data.

[0291] The "training generation means" is a device or technology for creating an individually optimized training plan based on the identified technical problems.

[0292] The "injury prediction means" is a device or technology for evaluating the risk of injury against variable loads associated with exercise.

[0293] The "information providing means" is a device or technology for providing feedback to the user based on the generated training plan and the risk of injury.

[0294] "Communication means" refers to a device or technology for monitoring the robot's movement state and adjusting training content in real time through information exchange with the robot.

[0295] The system that realizes this invention combines data analysis means, motion analysis means, training generation means, injury prediction means, information provision means, and communication means.

[0296] The server first acquires motion data from sensors and cameras worn by the user. This data contains detailed information about posture and movement and is transmitted to the terminal in real time. The data analysis means processes the sensor data and video data to evaluate the individual's progress. Based on this evaluation, the motion analysis means identifies individual technical challenges and clearly indicates problems and areas for improvement in form.

[0297] Next, the training generation means automatically creates an individualized training plan according to the identified technical challenges. The injury prediction means assesses the risk of injury based on the load applied during exercise and suggests rest or form modifications to the user as needed.

[0298] The system provides users with visual feedback on generated training plans and injury prediction results. Furthermore, a robot monitors the user's movement state via communication and adjusts the training content on the spot. This allows users to train efficiently and safely.

[0299] For example, the server analyzes the user's running form, and if it detects excessive stress on the knees, it incorporates strength training exercises and form-improvement exercises into the training plan. Furthermore, if the risk of injury is deemed high, feedback recommending rest is provided through the information system. An example of a prompt might be, "Assess whether your current running form is putting too much stress on your knees, and suggest exercises as needed."

[0300] According to this embodiment, real-time monitoring and the provision of a dynamic training plan are enabled, and the user can perform optimal training.

[0301] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0302] Step 1:

[0303] When the user starts exercising, the terminal collects data in real time through wearable sensors and cameras. This data includes detailed information such as the user's posture, movement, and speed. The terminal uses these data as input, performs format conversion, and transmits it to the server.

[0304] Step 2:

[0305] The server processes the received data using data analysis means and evaluates the individual's growth stage. As input, not only the exercise data but also the past history data is used. Thereby, an evaluation of the growth stage using time-series data is executed, and an evaluation result is output.

[0306] Step 3:

[0307] The server identifies the user's technical problems using motion analysis means. Specifically, based on the input exercise data, the problems with the form and areas for improvement are analyzed. Through this analysis process, the technical problems are clearly defined and provided as output to the next processing step.

[0308] Step 4:

[0309] The server creates an individualized training plan based on the identified technical problems by making full use of training generation means. Utilizing the generation AI model, an exercise plan optimal for the user's current state is generated. This plan is transmitted to the terminal as output.

[0310] Step 5:

[0311] The server uses injury prediction tools to assess the risk of injury based on the exercise load. It analyzes the input exercise data and training plan to calculate the risk of injury. Based on this assessment, it suggests rest periods or form modifications if necessary.

[0312] Step 6:

[0313] The device provides users with visual feedback on generated training plans and injury predictions through information delivery mechanisms. Users can then adjust their training content based on this feedback.

[0314] Step 7:

[0315] The terminal uses communication to allow the robot to monitor the user's exercise state and adjust the training content in real time as needed. Processing is performed based on prompts, and optimized instructions are output to the user. For example, specific advice such as, "Assess whether your current running form is putting too much stress on your knees, and suggest exercises if necessary," is provided in real time.

[0316] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0317] This invention provides a sports training system that takes into account not only the physical but also the emotional aspects of the individual receiving training. This system combines data analysis means, motion analysis means, training generation means, injury prediction means, information provision means, and an emotion engine that recognizes emotions.

[0318] First, the user collects exercise data using wearable sensors and cameras. This data includes not only information about normal movements, but also facial expressions and voice data. The data is then transmitted to a server via the device.

[0319] The server uses data analysis tools to analyze collected sensor data and video data to determine the individual's growth stage. Furthermore, motion analysis tools identify form and technical challenges from movement data. In addition, an emotion engine analyzes facial expression data and voice data to recognize the user's emotional state. This analysis allows for an understanding of the user's emotional condition.

[0320] Next, the training generation means generates an individualized training plan, taking into account the tasks identified by the motion analysis means as well as data from the emotion engine. For example, if the user is tense, exercises to promote relaxation may be included, and if motivation is low, goal setting to increase self-efficacy may be incorporated.

[0321] By combining physical data with stress assessments based on emotional data, injury prediction methods can perform more precise risk assessments. As a result, the risk of accidents and injuries caused by overwork or inappropriate mental states can be reduced.

[0322] Finally, the information delivery method provides users with a training plan along with feedback based on emotional data. In addition to suggesting form corrections through visual feedback, it improves the quality and safety of training by providing advice tailored to the emotional state.

[0323] For example, if a user is perceived as feeling anxious about a new training session, relaxation techniques are suggested using the results of the emotional engine, and a technical training plan is provided simultaneously. This allows the user to train in an optimal mental and physical state. This system enables comprehensive sports training tailored to the user's physical and emotional needs.

[0324] The following describes the processing flow.

[0325] Step 1:

[0326] The user prepares a wearable sensor and camera at the start of an exercise session to collect exercise data. For emotion recognition, facial expressions are captured by the camera, and audio data is recorded as needed.

[0327] Step 2:

[0328] The device processes the collected motor and emotional data and sends it to the server. The data is highly compressed and converted into a format suitable for analysis.

[0329] Step 3:

[0330] The server uses data analysis tools to determine each individual's growth stage from their exercise data. This determination is based on physical characteristics and past data.

[0331] Step 4:

[0332] The server uses motion analysis tools to identify form and operational issues from the data. For example, if the smash speed is slow, the cause can be investigated.

[0333] Step 5:

[0334] The server activates an emotion engine, analyzing the user's facial expression and voice data to assess their emotional state. This assessment allows for a clear understanding of their stress levels and motivation levels.

[0335] Step 6:

[0336] The server utilizes training generation methods to create an optimized training plan based on operational challenges and emotional assessments. For example, if relaxation is deemed necessary, stretching will be incorporated into the plan.

[0337] Step 7:

[0338] The server performs risk assessments based on exercise and emotional data through injury prediction mechanisms. In particular, it detects risks arising from the combination of emotional stress and physical fatigue.

[0339] Step 8:

[0340] The device provides the user with a server-generated training plan and injury prediction feedback. The visual feedback specifically indicates form corrections and also includes specific advice tailored to the user's emotional state.

[0341] Step 9:

[0342] Users conduct training based on the provided feedback and plan. They can receive supplementary guidance and additional instructions via their device as needed.

[0343] (Example 2)

[0344] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0345] In modern sports training, there is a growing demand for effective training plans that consider not only individual athletic ability but also emotional state. However, conventional training systems have struggled to adequately recognize and reflect an individual's emotional state, making it difficult to provide optimized training plans. Against this backdrop, realizing a system that simultaneously considers athletic ability and emotional state to provide training tailored to individual needs has become a crucial challenge.

[0346] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0347] In this invention, the server includes information analysis means for evaluating an individual's stage of development, motion evaluation means for analyzing acquired motion information to identify technical challenges, and training creation means for generating an individualized training plan based on the identified technical challenges and emotional data. This makes it possible to provide an integrated training system that takes both motion and emotion into consideration.

[0348] "Information analysis means for evaluating an individual's stage of growth" refers to a system component that analyzes collected sensor data and video data to determine the stage of growth for each individual user.

[0349] A "motion evaluation means" is a system element that has the function of analyzing acquired motion information and identifying the user's technical challenges.

[0350] A "training creation method" is a component of a system that has the function of generating a training plan optimized for each user based on identified technical challenges and emotional data.

[0351] A "risk prediction tool" is a system element that has the function of evaluating the possibility of injury with high accuracy, taking into account variable loads and the user's emotional state.

[0352] "Information provision means" refers to a component of a system that has the function of providing feedback to the user, including visual and emotional advice, based on the generated training plan and emotional data.

[0353] This system combines data collection using wearable devices and cameras with advanced data analysis techniques to enhance users' sports training. Users wear wearable sensors during their daily training, and the camera captures their movements, collecting exercise data, facial expressions, and audio data. This data is transmitted to a server via the terminal.

[0354] The server first uses information analysis tools to analyze sensor data and video data to evaluate the user's developmental stage. Simultaneously, it uses an emotion recognition engine to analyze facial expressions and voice data to determine the user's emotional state. Subsequently, it uses motion evaluation tools to analyze motion data and identify technical challenges.

[0355] The training creation mechanism combines identified technical challenges with perceived emotional states to generate individually customized training plans. For example, if a user is experiencing anxiety, exercises that promote relaxation may be included. For injury prevention, the risk prediction mechanism uses exercise and emotional data to perform a stress assessment and evaluate the likelihood of injury in advance.

[0356] Finally, users receive visual and audio feedback through information delivery methods. The device presents a training plan tailored to the user and advice based on their emotional state via screen and audio. This feedback allows users to continue training with better form.

[0357] For example, if a user starts a new workout and is feeling nervous, the server will provide a workout plan that incorporates relaxation techniques. An example of a prompt for the generating AI model would be, "Integrate user's emotional and physical data to generate a customized workout plan." This system enables comprehensive workouts that address the user's physical and emotional needs.

[0358] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0359] Step 1:

[0360] The user wears wearable sensors and a camera and begins training. This collects data on body movements (acceleration, position, etc.), facial expressions, and voice data. The input data captures the user's physical activity and emotional responses, and this serves as the starting point for individually optimizing the training.

[0361] Step 2:

[0362] The device temporarily stores the collected motor and emotional data and transmits it to the server via a secure communication protocol. The transmitted data serves as foundational data that the server needs for subsequent analysis. The data may be transmitted in real time.

[0363] Step 3:

[0364] The server uses information analysis tools to analyze received data and evaluate the user's growth stage. Sensor data and video data are provided as input, and the output is an evaluation of the degree of growth and current status. The analysis compares current performance data with past performance data.

[0365] Step 4:

[0366] The server uses an emotion engine to analyze facial expression and voice data to recognize the user's emotional state. The input is the acquired emotion-related data, and the output is numerical or categorical information representing the emotional state. For example, emotions can be inferred from the frequency of smiles or the tone of voice.

[0367] Step 5:

[0368] The server uses motion evaluation tools to analyze movement data and identify technical issues. Inputs include information on specific movement patterns and forms, while output is a list of technical elements that need improvement. This allows for checking muscle usage and form precision.

[0369] Step 6:

[0370] The server uses training creation tools to generate personalized training plans, taking into account technical challenges and emotional states. Inputs include evaluation data and emotional data, while outputs consist of specific exercises and recommendations. This is how the training curriculum is constructed.

[0371] Step 7:

[0372] The server analyzes exercise and emotional data using risk prediction tools to assess the likelihood of injury. The input is a combination of physical and emotional data, and the output is the injury risk assessment result. This provides a safe training environment.

[0373] Step 8:

[0374] The device receives training plans generated from the server and feedback based on sentiment data, and presents it to the user. Input is feedback data from the server, and output is provided to the user as visual and auditory information. This allows the user to prepare for training.

[0375] (Application Example 2)

[0376] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0377] Traditional training systems, while focusing on physical aspects, have a drawback in that they fail to address changes in psychological state. Despite the significant impact of psychological state on the effectiveness and sustainability of training, general systems often fail to consider this, potentially hindering the user's optimal progress. Furthermore, receiving real-time guidance from a professional is difficult when training at home.

[0378] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0379] In this invention, the server includes information analysis means for determining an individual's progress stage, action analysis means for analyzing collected activity data to identify technical challenges, and training plan generation means for generating individualized training plans based on the identified technical challenges and psychological states detected by emotion analysis means. This makes it possible to provide personalized training plans that take psychological aspects into consideration. Furthermore, by utilizing automated machines, it is possible to promote user growth through real-time instructional feedback in a home environment.

[0380] "Information analysis tools" are technical means used to perform analysis in order to determine an individual's stage of progress.

[0381] A "motion analysis means" is a technical means that identifies technical problems based on collected activity data.

[0382] A "training plan generation means" is a technical means that generates individual training plans based on identified technical challenges and psychological states detected through emotion analysis.

[0383] A "risk assessment tool" is a technical means for evaluating the risk of injury based on variable load and psychological state.

[0384] "Information provision means" refers to technical means that provide feedback to users based on the generated training plan and risk assessment results.

[0385] An "automated machine" is a device that monitors physical activity and emotional state in a home environment and provides real-time guidance and feedback.

[0386] This invention provides a system using automated machinery as an application example for optimizing individual training. The server uses information analysis means to determine the individual's progress stage. Specifically, wearable sensors and built-in cameras are used to analyze collected activity data and emotions. The server uses motion analysis means to identify technical challenges from this data. Meanwhile, emotion analysis technology is used to evaluate the psychological state based on facial recognition and voice analysis.

[0387] The training plan generation means generates an individualized training plan based on identified technical challenges and psychological state. In this process, a generation AI model is used to design training content that fits the user's condition. The risk assessment means assesses the risk of injury based on variable load and psychological state. This ensures the user's physical and psychological safety.

[0388] The information delivery system provides real-time feedback via an automated machine in a home environment, based on the generated training plan and risk assessment results. This process utilizes speech synthesis technology to present instructions and advice to the user via voice.

[0389] As a concrete example, when a user begins training at home, an automated machine monitors the user's form and provides suggestions for improvement as needed. At the same time, if it detects tension in the user, it provides voice guidance on relaxation techniques.

[0390] An example of a generated AI model prompt is as follows: "Design an AI algorithm that generates personalized training suggestions aimed at improving athletic performance and reducing stress, based on the user's real-time movement and facial expression data."

[0391] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0392] Step 1:

[0393] The user attaches wearable sensors and a camera and begins training. This method collects motion data and facial expression data in real time. The input consists of the user's body motion data and facial expression data, which are sent to the server via the sensors and camera. The output is a data stream that is available on the server.

[0394] Step 2:

[0395] The server processes the collected data stream through an information analysis tool to determine the stage of progress. The input consists of user motion data and facial expression data. A data analysis algorithm evaluates physical progress and emotional changes, determining the individual's progress level as output. During this process, the system compares the facial expressions and motions with a standard database and analyzes the differences from the baseline.

[0396] Step 3:

[0397] The server uses motion analysis tools to analyze the motion data provided as input. This identifies technical issues and clarifies which parts of the form require improvement. The output is recorded as the identified technical issues. Specifically, processing is performed to identify abnormalities and asymmetries in body movement.

[0398] Step 4:

[0399] The server utilizes an emotion analysis engine to evaluate the user's psychological state. Inputs include facial expression data and voice data. This processing step applies an emotion analysis model to recognize the user's mental state and outputs a classification of the current emotional state. Specifically, it analyzes the user's facial muscle movements and voice tone.

[0400] Step 5:

[0401] The server uses a training plan generation mechanism to generate an individualized training plan based on identified technical challenges and emotional states. The inputs are technical challenge data and emotional state data. The output is a user-specific training plan, including recommendations for exercise types, repetitions, and relaxation techniques. Here, the optimal solution is selected by comparing it with historical data.

[0402] Step 6:

[0403] The server uses risk assessment tools to evaluate the risk of injury based on the planned load and mental state as inputs. The output is the risk assessment and proposed mitigation measures. Specifically, it quantifies the risk of injury and provides load adjustments and stress reduction measures based on that quantification.

[0404] Step 7:

[0405] Through the terminal, the home-based automated machine provides the user with feedback on the generated training plan and risk assessment results using speech synthesis technology. The input is the plan and risk assessment provided by the server. The output is voice guidance and motivational feedback for the user. Specific actions in this process include providing voice guidance in a tone appropriate to the user and specific exercise suggestions in real time.

[0406] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0407] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0408] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0409] [Third Embodiment]

[0410] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0411] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0412] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0413] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0414] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0415] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0416] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0417] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0418] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0419] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0420] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0421] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0422] This invention is a system for providing an efficient and safe training environment to each individual undergoing sports training. This system includes data analysis means, motion analysis means, training generation means, injury prediction means, and information provision means as its main components.

[0423] First, users (children receiving training or instructors) collect exercise data using wearable sensors and cameras. This data includes detailed information about posture and movement during exercise. The data is converted to an appropriate format by the device and sent to the server.

[0424] The server processes the received data using data analysis tools to determine each individual's developmental stage. This determination of developmental stage makes it possible to identify the optimal training content for each child.

[0425] Next, the motion analysis system analyzes the motion data to identify technical challenges and form problems. For example, it can analyze things like shoulder position during a swing or the balance of a running form.

[0426] Subsequently, the training generation system automatically creates an individually optimized training plan based on the identified technical challenges. This provides each user with a training menu tailored to their needs.

[0427] Furthermore, injury prediction tools are used to assess the risk of injury that may occur during training. This checks for improper movements or excessive load, and suggestions for rest or form correction are made as needed.

[0428] Finally, the information provider provides the user with feedback on the generated training plan and injury prediction results. The feedback displayed on the device includes visual suggestions for form correction, and the user can use this feedback to guide their training as needed.

[0429] For example, if an analysis reveals that a child is experiencing excessive stress on their knees while running, the training plan will include exercises to strengthen muscles and improve running form, along with feedback on the need for rest based on injury predictions. Through this entire cycle, we support the effective and safe implementation of children's sports training.

[0430] The following describes the processing flow.

[0431] Step 1:

[0432] Users collect data from each exercise session using wearable sensors and cameras. This includes information such as movement trajectory, acceleration, and heart rate.

[0433] Step 2:

[0434] The terminal processes the collected data and converts it into a structured data format. This data is then sent to the server in a format suitable for subsequent analysis.

[0435] Step 3:

[0436] The server receives the data and uses data analysis tools to determine each individual's growth stage. This establishes the basic data based on age and developmental stage.

[0437] Step 4:

[0438] The server uses motion analysis tools to analyze motion data in detail. Based on the analysis results, problems and technical issues with the form are identified.

[0439] Step 5:

[0440] The server generates individually optimized training plans based on identified technical challenges using the training generation mechanism. These plans are tailored to the user's stage of development.

[0441] Step 6:

[0442] The server uses injury prediction tools to assess the risk of injury from the analysis data and detect signs of overload or improper movement. Rest and plan modifications are incorporated into the training plan as needed.

[0443] Step 7:

[0444] The device provides the user with a generated training plan and injury prediction feedback. The user can then perform the training based on the visually presented instructions and form correction suggestions.

[0445] (Example 1)

[0446] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0447] The challenge lies in providing training plans optimized for individual abilities and characteristics, as well as systems to reduce the risk of injury during exercise. Conventional, general-purpose training menus often lacked adaptability to individual users, making effective growth and injury prevention difficult. Furthermore, there was a lack of concrete indicators to properly identify and resolve technical challenges.

[0448] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0449] In this invention, the server includes a data analysis device that determines an individual's growth stage; a motion analysis device that analyzes acquired motion information to identify technical challenges; a training generation device that generates individual training plans based on the identified technical challenges; an injury prediction device that evaluates the risk of injury for variable loads; an information supply device that evaluates the generated training plan and the risk of injury and provides feedback; a device that predicts potential injuries based on received motion information using a central device for receiving and analyzing digital information; and a device that automatically generates customized training plans based on the analysis results using an artificial intelligence model. This enables effective and safe training tailored to each user's characteristics and condition.

[0450] A "data analysis device" is a device that analyzes acquired motor information to evaluate an individual's developmental stage and provides specific evaluation results.

[0451] A "motion analysis device" is a device that analyzes acquired motion information to identify technical challenges and problems with movement.

[0452] A "training generation device" is a device that automatically generates individualized training plans based on identified technical challenges.

[0453] A "injury prediction device" is a device that assesses the risk of injury that may occur during exercise and provides information to enable appropriate preventive measures to be taken.

[0454] An "information supply device" is a device that provides feedback to the user based on the generated training plan and injury risk assessment, in order to improve exercise efficiency.

[0455] A "central system" is a central computer system that receives digital information and performs centralized analysis and processing.

[0456] An "artificial intelligence model" is a system that uses machine learning algorithms to generate data-driven training plans based on analysis results and supports performance improvement.

[0457] The system according to the present invention provides exercise training optimized for individual users and maximizes the effectiveness of exercise in a safe environment. This system utilizes multiple hardware components, including wearable devices, cameras, terminals, and servers, to collect and analyze data.

[0458] First, the user puts on a wearable device. This device is equipped with an accelerometer and a gyroscope, allowing it to collect the user's movement information in real time. It also uses a camera to capture the user's movements. The data obtained from this hardware is then sent to the terminal.

[0459] The device processes this data into an appropriate digital format. For example, sensor data containing detailed exercise information needs to be converted into CSV or JSON format. The converted data is then sent to the server using a secure and reliable communication protocol.

[0460] The server receives this data and activates algorithms for data analysis and behavioral analysis. The server's central system employs various data analysis techniques to determine an individual's stage of development and identify technical challenges. It also utilizes AI models to automatically generate personalized training plans. Based on the input data, the AI ​​models evaluate each user's behavior and identify forms that need improvement.

[0461] Next, the server can use an injury prediction device to assess potential injury risks based on the collected data. The AI ​​analyzes the user's movement patterns and predicts and warns about the risk of injury due to improper movements or excessive strain. Based on this analysis, it provides feedback such as suggestions for appropriate rest and form correction.

[0462] Feedback is sent from the server to the user via the device. The device provides visual or audio feedback, which the user can use to improve the quality of their training. For example, they might receive specific advice such as, "Bend your knees more flexibly while running."

[0463] A concrete example of a prompt message would be, "Generate a training plan to improve the running form of a 10-year-old child. Assume there is excessive stress on the knees." This allows the AI ​​model to propose the optimal plan.

[0464] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0465] Step 1:

[0466] The user puts on a wearable device, sets up the camera, and begins exercising. The wearable device has an accelerometer and a gyroscope, which collect data on movement and posture during exercise in real time. As the user exercises, the sensors acquire each data point and send the results to the terminal. The input is raw exercise data, and the output is in a digital data format that can be processed by the terminal.

[0467] Step 2:

[0468] The device converts the raw motion data received from the user into a different format. Specifically, this involves organizing sensor data into CSV or JSON format and decomposing camera footage into a digital format that can be analyzed frame by frame. In this process, raw sensor data and video data are received as input, and data prepared for analysis is generated as output.

[0469] Step 3:

[0470] The terminal sends the generated digital data to the server. A secure and efficient communication protocol is used for this transmission to ensure processing speed and data protection. The input contains digital data, and the output is processed as data successfully transmitted to the server.

[0471] Step 4:

[0472] The server initiates the process of analyzing the received data. A data analysis device assesses the individual's developmental stage, and a motion analysis device analyzes the movement data to identify technical challenges. The input is the movement data transmitted from the terminal, and the output is the evaluation results of the developmental stage and a list of identified technical challenges.

[0473] Step 5:

[0474] The server uses a generative AI model to automatically generate a customized training plan based on the user's technical challenges. This involves executing algorithms based on prompts, and the AI ​​constructs the most effective training menu. The technical challenge is given as input, and the output is a specific training plan.

[0475] Step 6:

[0476] The server uses an injury prediction device to perform data-driven injury risk analysis. An AI model utilizes motion data to predict potential injuries, assessing risks arising from improper form or excessive movement load. The input is motion data, and the output is an injury risk assessment and preventative advice.

[0477] Step 7:

[0478] The training plan and injury prediction results generated by the server are sent to the terminal. The terminal provides visual and audible feedback on this information to the user. The user adjusts the training based on this feedback. The input is the feedback data from the server, and the output is the improvement suggestions and exercise guidelines provided to the user.

[0479] (Application Example 1)

[0480] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0481] In individual sports training, providing efficient and safe instruction requires customized training plans tailored to each person's progress and individual technical challenges. However, conventional systems have struggled to monitor individual movement in real time and provide timely, appropriate feedback. Furthermore, predicting the risk of injury during training and dynamically adjusting advice has been difficult. Effective solutions addressing these challenges are needed.

[0482] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0483] In this invention, the server includes data analysis means for determining an individual's growth stage; motion analysis means for analyzing acquired movement data and identifying technical challenges; training generation means for generating individual training plans based on the identified technical challenges; injury prediction means for evaluating the risk of injury under variable load; information provision means for evaluating the generated training plan and injury risk and providing feedback; and communication means for monitoring movement status through communication with a robot and adjusting training content in real time. This makes it possible to provide an optimal training plan and safety measures while monitoring individual movement status in real time.

[0484] "Data analysis means" refers to a device or technology that analyzes sensor data or video data in order to determine an individual's growth stage.

[0485] "Motion analysis means" refers to a device or technology used to identify individual technical challenges using acquired motion data.

[0486] "Training generation means" refers to a device or technology that creates individually optimized training plans based on identified technical challenges.

[0487] "Injury prediction means" refers to a device or technology that evaluates the risk of injury in response to variable loads associated with exercise.

[0488] "Information provision means" refers to a device or technology that provides feedback to the user based on the generated training plan and injury risk.

[0489] "Communication means" refers to a device or technology for monitoring the robot's movement state and adjusting training content in real time through information exchange with the robot.

[0490] The system that realizes this invention combines data analysis means, motion analysis means, training generation means, injury prediction means, information provision means, and communication means.

[0491] The server first acquires motion data from sensors and cameras worn by the user. This data contains detailed information about posture and movement and is transmitted to the terminal in real time. The data analysis means processes the sensor data and video data to evaluate the individual's progress. Based on this evaluation, the motion analysis means identifies individual technical challenges and clearly indicates problems and areas for improvement in form.

[0492] Next, the training generation means automatically creates an individualized training plan according to the identified technical challenges. The injury prediction means assesses the risk of injury based on the load applied during exercise and suggests rest or form modifications to the user as needed.

[0493] The system provides users with visual feedback on generated training plans and injury prediction results. Furthermore, a robot monitors the user's movement state via communication and adjusts the training content on the spot. This allows users to train efficiently and safely.

[0494] For example, the server analyzes the user's running form, and if it detects excessive stress on the knees, it incorporates strength training exercises and form-improvement exercises into the training plan. Furthermore, if the risk of injury is deemed high, feedback recommending rest is provided through the information system. An example of a prompt might be, "Assess whether your current running form is putting too much stress on your knees, and suggest exercises as needed."

[0495] This embodiment enables real-time monitoring and the provision of dynamic training plans, allowing users to perform optimal training.

[0496] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0497] Step 1:

[0498] When a user begins exercising, the device collects data in real time through wearable sensors and cameras. This data includes detailed information such as the user's posture, movement, and speed. The device takes this data as input, converts the format, and sends it to the server.

[0499] Step 2:

[0500] The server processes the received data using data analysis tools to evaluate the individual's growth stage. Input includes not only exercise data but also historical data. This allows for a time-series data-based evaluation of growth stages, and the evaluation results are output.

[0501] Step 3:

[0502] The server identifies the user's technical challenges using motion analysis tools. Specifically, it analyzes form problems and areas for improvement based on the input motion data. This analysis process clearly defines the technical challenges and provides them as output for the next processing step.

[0503] Step 4:

[0504] The server uses training generation methods to create personalized training plans based on identified technical challenges. Leveraging a generation AI model, it generates an optimal exercise plan for the user's current state. This plan is then sent to the terminal as output.

[0505] Step 5:

[0506] The server uses injury prediction tools to assess the risk of injury based on the exercise load. It analyzes the input exercise data and training plan to calculate the risk of injury. Based on this assessment, it suggests rest periods or form modifications if necessary.

[0507] Step 6:

[0508] The device provides users with visual feedback on generated training plans and injury predictions through information delivery mechanisms. Users can then adjust their training content based on this feedback.

[0509] Step 7:

[0510] The terminal uses communication to allow the robot to monitor the user's exercise state and adjust the training content in real time as needed. Processing is performed based on prompts, and optimized instructions are output to the user. For example, specific advice such as, "Assess whether your current running form is putting too much stress on your knees, and suggest exercises if necessary," is provided in real time.

[0511] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0512] This invention provides a sports training system that takes into account not only the physical but also the emotional aspects of the individual receiving training. This system combines data analysis means, motion analysis means, training generation means, injury prediction means, information provision means, and an emotion engine that recognizes emotions.

[0513] First, the user collects exercise data using wearable sensors and cameras. This data includes not only information about normal movements, but also facial expressions and voice data. The data is then transmitted to a server via the device.

[0514] The server uses data analysis tools to analyze collected sensor data and video data to determine the individual's growth stage. Furthermore, motion analysis tools identify form and technical challenges from movement data. In addition, an emotion engine analyzes facial expression data and voice data to recognize the user's emotional state. This analysis allows for an understanding of the user's emotional condition.

[0515] Next, the training generation means generates an individualized training plan, taking into account the tasks identified by the motion analysis means as well as data from the emotion engine. For example, if the user is tense, exercises to promote relaxation may be included, and if motivation is low, goal setting to increase self-efficacy may be incorporated.

[0516] By combining physical data with stress assessments based on emotional data, injury prediction methods can perform more precise risk assessments. As a result, the risk of accidents and injuries caused by overwork or inappropriate mental states can be reduced.

[0517] Finally, the information delivery method provides users with a training plan along with feedback based on emotional data. In addition to suggesting form corrections through visual feedback, it improves the quality and safety of training by providing advice tailored to the emotional state.

[0518] For example, if a user is perceived as feeling anxious about a new training session, relaxation techniques are suggested using the results of the emotional engine, and a technical training plan is provided simultaneously. This allows the user to train in an optimal mental and physical state. This system enables comprehensive sports training tailored to the user's physical and emotional needs.

[0519] The following describes the processing flow.

[0520] Step 1:

[0521] The user prepares a wearable sensor and camera at the start of an exercise session to collect exercise data. For emotion recognition, facial expressions are captured by the camera, and audio data is recorded as needed.

[0522] Step 2:

[0523] The device processes the collected motor and emotional data and sends it to the server. The data is highly compressed and converted into a format suitable for analysis.

[0524] Step 3:

[0525] The server uses data analysis tools to determine each individual's growth stage from their exercise data. This determination is based on physical characteristics and past data.

[0526] Step 4:

[0527] The server uses motion analysis tools to identify form and operational issues from the data. For example, if the smash speed is slow, the cause can be investigated.

[0528] Step 5:

[0529] The server activates an emotion engine, analyzing the user's facial expression and voice data to assess their emotional state. This assessment allows for a clear understanding of their stress levels and motivation levels.

[0530] Step 6:

[0531] The server utilizes training generation methods to create an optimized training plan based on operational challenges and emotional assessments. For example, if relaxation is deemed necessary, stretching will be incorporated into the plan.

[0532] Step 7:

[0533] The server performs risk assessments based on exercise and emotional data through injury prediction mechanisms. In particular, it detects risks arising from the combination of emotional stress and physical fatigue.

[0534] Step 8:

[0535] The device provides the user with a server-generated training plan and injury prediction feedback. The visual feedback specifically indicates form corrections and also includes specific advice tailored to the user's emotional state.

[0536] Step 9:

[0537] Users conduct training based on the provided feedback and plan. They can receive supplementary guidance and additional instructions via their device as needed.

[0538] (Example 2)

[0539] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0540] In modern sports training, there is a growing demand for effective training plans that consider not only individual athletic ability but also emotional state. However, conventional training systems have struggled to adequately recognize and reflect an individual's emotional state, making it difficult to provide optimized training plans. Against this backdrop, realizing a system that simultaneously considers athletic ability and emotional state to provide training tailored to individual needs has become a crucial challenge.

[0541] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0542] In this invention, the server includes information analysis means for evaluating an individual's stage of development, motion evaluation means for analyzing acquired motion information to identify technical challenges, and training creation means for generating an individualized training plan based on the identified technical challenges and emotional data. This makes it possible to provide an integrated training system that takes both motion and emotion into consideration.

[0543] "Information analysis means for evaluating an individual's stage of growth" refers to a system component that analyzes collected sensor data and video data to determine the stage of growth for each individual user.

[0544] A "motion evaluation means" is a system element that has the function of analyzing acquired motion information and identifying the user's technical challenges.

[0545] A "training creation method" is a component of a system that has the function of generating a training plan optimized for each user based on identified technical challenges and emotional data.

[0546] A "risk prediction tool" is a system element that has the function of evaluating the possibility of injury with high accuracy, taking into account variable loads and the user's emotional state.

[0547] "Information provision means" refers to a component of a system that has the function of providing feedback to the user, including visual and emotional advice, based on the generated training plan and emotional data.

[0548] This system combines data collection using wearable devices and cameras with advanced data analysis techniques to enhance users' sports training. Users wear wearable sensors during their daily training, and the camera captures their movements, collecting exercise data, facial expressions, and audio data. This data is transmitted to a server via the terminal.

[0549] The server first uses information analysis tools to analyze sensor data and video data to evaluate the user's developmental stage. Simultaneously, it uses an emotion recognition engine to analyze facial expressions and voice data to determine the user's emotional state. Subsequently, it uses motion evaluation tools to analyze motion data and identify technical challenges.

[0550] The training creation mechanism combines identified technical challenges with perceived emotional states to generate individually customized training plans. For example, if a user is experiencing anxiety, exercises that promote relaxation may be included. For injury prevention, the risk prediction mechanism uses exercise and emotional data to perform a stress assessment and evaluate the likelihood of injury in advance.

[0551] Finally, users receive visual and audio feedback through information delivery methods. The device presents a training plan tailored to the user and advice based on their emotional state via screen and audio. This feedback allows users to continue training with better form.

[0552] For example, if a user starts a new workout and is feeling nervous, the server will provide a workout plan that incorporates relaxation techniques. An example of a prompt for the generating AI model would be, "Integrate user's emotional and physical data to generate a customized workout plan." This system enables comprehensive workouts that address the user's physical and emotional needs.

[0553] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0554] Step 1:

[0555] The user wears wearable sensors and a camera and begins training. This collects data on body movements (acceleration, position, etc.), facial expressions, and voice data. The input data captures the user's physical activity and emotional responses, and this serves as the starting point for individually optimizing the training.

[0556] Step 2:

[0557] The device temporarily stores the collected motor and emotional data and transmits it to the server via a secure communication protocol. The transmitted data serves as foundational data that the server needs for subsequent analysis. The data may be transmitted in real time.

[0558] Step 3:

[0559] The server uses information analysis tools to analyze received data and evaluate the user's growth stage. Sensor data and video data are provided as input, and the output is an evaluation of the degree of growth and current status. The analysis compares current performance data with past performance data.

[0560] Step 4:

[0561] The server uses an emotion engine to analyze facial expression and voice data to recognize the user's emotional state. The input is the acquired emotion-related data, and the output is numerical or categorical information representing the emotional state. For example, emotions can be inferred from the frequency of smiles or the tone of voice.

[0562] Step 5:

[0563] The server uses motion evaluation tools to analyze movement data and identify technical issues. Inputs include information on specific movement patterns and forms, while output is a list of technical elements that need improvement. This allows for checking muscle usage and form precision.

[0564] Step 6:

[0565] The server uses training creation tools to generate personalized training plans, taking into account technical challenges and emotional states. Inputs include evaluation data and emotional data, while outputs consist of specific exercises and recommendations. This is how the training curriculum is constructed.

[0566] Step 7:

[0567] The server analyzes exercise and emotional data using risk prediction tools to assess the likelihood of injury. The input is a combination of physical and emotional data, and the output is the injury risk assessment result. This provides a safe training environment.

[0568] Step 8:

[0569] The device receives training plans generated from the server and feedback based on sentiment data, and presents it to the user. Input is feedback data from the server, and output is provided to the user as visual and auditory information. This allows the user to prepare for training.

[0570] (Application Example 2)

[0571] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0572] Traditional training systems, while focusing on physical aspects, have a drawback in that they fail to address changes in psychological state. Despite the significant impact of psychological state on the effectiveness and sustainability of training, general systems often fail to consider this, potentially hindering the user's optimal progress. Furthermore, receiving real-time guidance from a professional is difficult when training at home.

[0573] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0574] In this invention, the server includes information analysis means for determining an individual's progress stage, action analysis means for analyzing collected activity data to identify technical challenges, and training plan generation means for generating individualized training plans based on the identified technical challenges and psychological states detected by emotion analysis means. This makes it possible to provide personalized training plans that take psychological aspects into consideration. Furthermore, by utilizing automated machines, it is possible to promote user growth through real-time instructional feedback in a home environment.

[0575] "Information analysis tools" are technical means used to perform analysis in order to determine an individual's stage of progress.

[0576] A "motion analysis means" is a technical means that identifies technical problems based on collected activity data.

[0577] A "training plan generation means" is a technical means that generates individual training plans based on identified technical challenges and psychological states detected through emotion analysis.

[0578] A "risk assessment tool" is a technical means for evaluating the risk of injury based on variable load and psychological state.

[0579] "Information provision means" refers to technical means that provide feedback to users based on the generated training plan and risk assessment results.

[0580] An "automated machine" is a device that monitors physical activity and emotional state in a home environment and provides real-time guidance and feedback.

[0581] This invention provides a system using automated machinery as an application example for optimizing individual training. The server uses information analysis means to determine the individual's progress stage. Specifically, wearable sensors and built-in cameras are used to analyze collected activity data and emotions. The server uses motion analysis means to identify technical challenges from this data. Meanwhile, emotion analysis technology is used to evaluate the psychological state based on facial recognition and voice analysis.

[0582] The training plan generation means generates an individualized training plan based on identified technical challenges and psychological state. In this process, a generation AI model is used to design training content that fits the user's condition. The risk assessment means assesses the risk of injury based on variable load and psychological state. This ensures the user's physical and psychological safety.

[0583] The information delivery system provides real-time feedback via an automated machine in a home environment, based on the generated training plan and risk assessment results. This process utilizes speech synthesis technology to present instructions and advice to the user via voice.

[0584] As a concrete example, when a user begins training at home, an automated machine monitors the user's form and provides suggestions for improvement as needed. At the same time, if it detects tension in the user, it provides voice guidance on relaxation techniques.

[0585] An example of a generated AI model prompt is as follows: "Design an AI algorithm that generates personalized training suggestions aimed at improving athletic performance and reducing stress, based on the user's real-time movement and facial expression data."

[0586] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0587] Step 1:

[0588] The user attaches wearable sensors and a camera and begins training. This method collects motion data and facial expression data in real time. The input consists of the user's body motion data and facial expression data, which are sent to the server via the sensors and camera. The output is a data stream that is available on the server.

[0589] Step 2:

[0590] The server processes the collected data stream through an information analysis tool to determine the stage of progress. The input consists of user motion data and facial expression data. A data analysis algorithm evaluates physical progress and emotional changes, determining the individual's progress level as output. During this process, the system compares the facial expressions and motions with a standard database and analyzes the differences from the baseline.

[0591] Step 3:

[0592] The server uses motion analysis tools to analyze the motion data provided as input. This identifies technical issues and clarifies which parts of the form require improvement. The output is recorded as the identified technical issues. Specifically, processing is performed to identify abnormalities and asymmetries in body movement.

[0593] Step 4:

[0594] The server utilizes an emotion analysis engine to evaluate the user's psychological state. Inputs include facial expression data and voice data. This processing step applies an emotion analysis model to recognize the user's mental state and outputs a classification of the current emotional state. Specifically, it analyzes the user's facial muscle movements and voice tone.

[0595] Step 5:

[0596] The server uses a training plan generation mechanism to generate an individualized training plan based on identified technical challenges and emotional states. The inputs are technical challenge data and emotional state data. The output is a user-specific training plan, including recommendations for exercise types, repetitions, and relaxation techniques. Here, the optimal solution is selected by comparing it with historical data.

[0597] Step 6:

[0598] The server uses risk assessment tools to evaluate the risk of injury based on the planned load and mental state as inputs. The output is the risk assessment and proposed mitigation measures. Specifically, it quantifies the risk of injury and provides load adjustments and stress reduction measures based on that quantification.

[0599] Step 7:

[0600] Through the terminal, the home-based automated machine provides the user with feedback on the generated training plan and risk assessment results using speech synthesis technology. The input is the plan and risk assessment provided by the server. The output is voice guidance and motivational feedback for the user. Specific actions in this process include providing voice guidance in a tone appropriate to the user and specific exercise suggestions in real time.

[0601] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0602] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0603] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0604] [Fourth Embodiment]

[0605] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0606] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0607] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0608] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0609] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0610] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0611] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0612] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0613] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0614] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0615] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0616] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0617] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0618] This invention is a system for providing an efficient and safe training environment to each individual undergoing sports training. This system includes data analysis means, motion analysis means, training generation means, injury prediction means, and information provision means as its main components.

[0619] First, users (children receiving training or instructors) collect exercise data using wearable sensors and cameras. This data includes detailed information about posture and movement during exercise. The data is converted to an appropriate format by the device and sent to the server.

[0620] The server processes the received data using data analysis tools to determine each individual's developmental stage. This determination of developmental stage makes it possible to identify the optimal training content for each child.

[0621] Next, the motion analysis system analyzes the motion data to identify technical challenges and form problems. For example, it can analyze things like shoulder position during a swing or the balance of a running form.

[0622] Subsequently, the training generation system automatically creates an individually optimized training plan based on the identified technical challenges. This provides each user with a training menu tailored to their needs.

[0623] Furthermore, injury prediction tools are used to assess the risk of injury that may occur during training. This checks for improper movements or excessive load, and suggestions for rest or form correction are made as needed.

[0624] Finally, the information provider provides the user with feedback on the generated training plan and injury prediction results. The feedback displayed on the device includes visual suggestions for form correction, and the user can use this feedback to guide their training as needed.

[0625] For example, if an analysis reveals that a child is experiencing excessive stress on their knees while running, the training plan will include exercises to strengthen muscles and improve running form, along with feedback on the need for rest based on injury predictions. Through this entire cycle, we support the effective and safe implementation of children's sports training.

[0626] The following describes the processing flow.

[0627] Step 1:

[0628] Users collect data from each exercise session using wearable sensors and cameras. This includes information such as movement trajectory, acceleration, and heart rate.

[0629] Step 2:

[0630] The terminal processes the collected data and converts it into a structured data format. This data is then sent to the server in a format suitable for subsequent analysis.

[0631] Step 3:

[0632] The server receives the data and uses data analysis tools to determine each individual's growth stage. This establishes the basic data based on age and developmental stage.

[0633] Step 4:

[0634] The server uses motion analysis tools to analyze motion data in detail. Based on the analysis results, problems and technical issues with the form are identified.

[0635] Step 5:

[0636] The server generates individually optimized training plans based on identified technical challenges using the training generation mechanism. These plans are tailored to the user's stage of development.

[0637] Step 6:

[0638] The server uses injury prediction tools to assess the risk of injury from the analysis data and detect signs of overload or improper movement. Rest and plan modifications are incorporated into the training plan as needed.

[0639] Step 7:

[0640] The device provides the user with a generated training plan and injury prediction feedback. The user can then perform the training based on the visually presented instructions and form correction suggestions.

[0641] (Example 1)

[0642] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0643] The challenge lies in providing training plans optimized for individual abilities and characteristics, as well as systems to reduce the risk of injury during exercise. Conventional, general-purpose training menus often lacked adaptability to individual users, making effective growth and injury prevention difficult. Furthermore, there was a lack of concrete indicators to properly identify and resolve technical challenges.

[0644] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0645] In this invention, the server includes a data analysis device that determines an individual's growth stage; a motion analysis device that analyzes acquired motion information to identify technical challenges; a training generation device that generates individual training plans based on the identified technical challenges; an injury prediction device that evaluates the risk of injury for variable loads; an information supply device that evaluates the generated training plan and the risk of injury and provides feedback; a device that predicts potential injuries based on received motion information using a central device for receiving and analyzing digital information; and a device that automatically generates customized training plans based on the analysis results using an artificial intelligence model. This enables effective and safe training tailored to each user's characteristics and condition.

[0646] A "data analysis device" is a device that analyzes acquired motor information to evaluate an individual's developmental stage and provides specific evaluation results.

[0647] A "motion analysis device" is a device that analyzes acquired motion information to identify technical challenges and problems with movement.

[0648] A "training generation device" is a device that automatically generates individualized training plans based on identified technical challenges.

[0649] A "injury prediction device" is a device that assesses the risk of injury that may occur during exercise and provides information to enable appropriate preventive measures to be taken.

[0650] An "information supply device" is a device that provides feedback to the user based on the generated training plan and injury risk assessment, in order to improve exercise efficiency.

[0651] A "central system" is a central computer system that receives digital information and performs centralized analysis and processing.

[0652] An "artificial intelligence model" is a system that uses machine learning algorithms to generate data-driven training plans based on analysis results and supports performance improvement.

[0653] The system according to the present invention provides exercise training optimized for individual users and maximizes the effectiveness of exercise in a safe environment. This system utilizes multiple hardware components, including wearable devices, cameras, terminals, and servers, to collect and analyze data.

[0654] First, the user puts on a wearable device. This device is equipped with an accelerometer and a gyroscope, allowing it to collect the user's movement information in real time. It also uses a camera to capture the user's movements. The data obtained from this hardware is then sent to the terminal.

[0655] The device processes this data into an appropriate digital format. For example, sensor data containing detailed exercise information needs to be converted into CSV or JSON format. The converted data is then sent to the server using a secure and reliable communication protocol.

[0656] The server receives this data and activates algorithms for data analysis and behavioral analysis. The server's central system employs various data analysis techniques to determine an individual's stage of development and identify technical challenges. It also utilizes AI models to automatically generate personalized training plans. Based on the input data, the AI ​​models evaluate each user's behavior and identify forms that need improvement.

[0657] Next, the server can use an injury prediction device to assess potential injury risks based on the collected data. The AI ​​analyzes the user's movement patterns and predicts and warns about the risk of injury due to improper movements or excessive strain. Based on this analysis, it provides feedback such as suggestions for appropriate rest and form correction.

[0658] Feedback is sent from the server to the user via the device. The device provides visual or audio feedback, which the user can use to improve the quality of their training. For example, they might receive specific advice such as, "Bend your knees more flexibly while running."

[0659] A concrete example of a prompt message would be, "Generate a training plan to improve the running form of a 10-year-old child. Assume there is excessive stress on the knees." This allows the AI ​​model to propose the optimal plan.

[0660] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0661] Step 1:

[0662] The user puts on a wearable device, sets up the camera, and begins exercising. The wearable device has an accelerometer and a gyroscope, which collect data on movement and posture during exercise in real time. As the user exercises, the sensors acquire each data point and send the results to the terminal. The input is raw exercise data, and the output is in a digital data format that can be processed by the terminal.

[0663] Step 2:

[0664] The device converts the raw motion data received from the user into a different format. Specifically, this involves organizing sensor data into CSV or JSON format and decomposing camera footage into a digital format that can be analyzed frame by frame. In this process, raw sensor data and video data are received as input, and data prepared for analysis is generated as output.

[0665] Step 3:

[0666] The terminal sends the generated digital data to the server. A secure and efficient communication protocol is used for this transmission to ensure processing speed and data protection. The input contains digital data, and the output is processed as data successfully transmitted to the server.

[0667] Step 4:

[0668] The server initiates the process of analyzing the received data. A data analysis device assesses the individual's developmental stage, and a motion analysis device analyzes the movement data to identify technical challenges. The input is the movement data transmitted from the terminal, and the output is the evaluation results of the developmental stage and a list of identified technical challenges.

[0669] Step 5:

[0670] The server uses a generative AI model to automatically generate a customized training plan based on the user's technical challenges. This involves executing algorithms based on prompts, and the AI ​​constructs the most effective training menu. The technical challenge is given as input, and the output is a specific training plan.

[0671] Step 6:

[0672] The server uses an injury prediction device to perform data-driven injury risk analysis. An AI model utilizes motion data to predict potential injuries, assessing risks arising from improper form or excessive movement load. The input is motion data, and the output is an injury risk assessment and preventative advice.

[0673] Step 7:

[0674] The training plan and injury prediction results generated by the server are sent to the terminal. The terminal provides visual and audible feedback on this information to the user. The user adjusts the training based on this feedback. The input is the feedback data from the server, and the output is the improvement suggestions and exercise guidelines provided to the user.

[0675] (Application Example 1)

[0676] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0677] In individual sports training, providing efficient and safe instruction requires customized training plans tailored to each person's progress and individual technical challenges. However, conventional systems have struggled to monitor individual movement in real time and provide timely, appropriate feedback. Furthermore, predicting the risk of injury during training and dynamically adjusting advice has been difficult. Effective solutions addressing these challenges are needed.

[0678] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0679] In this invention, the server includes data analysis means for determining an individual's growth stage; motion analysis means for analyzing acquired movement data and identifying technical challenges; training generation means for generating individual training plans based on the identified technical challenges; injury prediction means for evaluating the risk of injury under variable load; information provision means for evaluating the generated training plan and injury risk and providing feedback; and communication means for monitoring movement status through communication with a robot and adjusting training content in real time. This makes it possible to provide an optimal training plan and safety measures while monitoring individual movement status in real time.

[0680] "Data analysis means" refers to a device or technology that analyzes sensor data or video data in order to determine an individual's growth stage.

[0681] "Motion analysis means" refers to a device or technology used to identify individual technical challenges using acquired motion data.

[0682] "Training generation means" refers to a device or technology that creates individually optimized training plans based on identified technical challenges.

[0683] "Injury prediction means" refers to a device or technology that evaluates the risk of injury in response to variable loads associated with exercise.

[0684] "Information provision means" refers to a device or technology that provides feedback to the user based on the generated training plan and injury risk.

[0685] "Communication means" refers to a device or technology for monitoring the robot's movement state and adjusting training content in real time through information exchange with the robot.

[0686] The system that realizes this invention combines data analysis means, motion analysis means, training generation means, injury prediction means, information provision means, and communication means.

[0687] The server first acquires motion data from sensors and cameras worn by the user. This data contains detailed information about posture and movement and is transmitted to the terminal in real time. The data analysis means processes the sensor data and video data to evaluate the individual's progress. Based on this evaluation, the motion analysis means identifies individual technical challenges and clearly indicates problems and areas for improvement in form.

[0688] Next, the training generation means automatically creates an individualized training plan according to the identified technical challenges. The injury prediction means assesses the risk of injury based on the load applied during exercise and suggests rest or form modifications to the user as needed.

[0689] The system provides users with visual feedback on generated training plans and injury prediction results. Furthermore, a robot monitors the user's movement state via communication and adjusts the training content on the spot. This allows users to train efficiently and safely.

[0690] For example, the server analyzes the user's running form, and if it detects excessive stress on the knees, it incorporates strength training exercises and form-improvement exercises into the training plan. Furthermore, if the risk of injury is deemed high, feedback recommending rest is provided through the information system. An example of a prompt might be, "Assess whether your current running form is putting too much stress on your knees, and suggest exercises as needed."

[0691] This embodiment enables real-time monitoring and the provision of dynamic training plans, allowing users to perform optimal training.

[0692] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0693] Step 1:

[0694] When a user begins exercising, the device collects data in real time through wearable sensors and cameras. This data includes detailed information such as the user's posture, movement, and speed. The device takes this data as input, converts the format, and sends it to the server.

[0695] Step 2:

[0696] The server processes the received data using data analysis tools to evaluate the individual's growth stage. Input includes not only exercise data but also historical data. This allows for a time-series data-based evaluation of growth stages, and the evaluation results are output.

[0697] Step 3:

[0698] The server identifies the user's technical challenges using motion analysis tools. Specifically, it analyzes form problems and areas for improvement based on the input motion data. This analysis process clearly defines the technical challenges and provides them as output for the next processing step.

[0699] Step 4:

[0700] The server uses training generation methods to create personalized training plans based on identified technical challenges. Leveraging a generation AI model, it generates an optimal exercise plan for the user's current state. This plan is then sent to the terminal as output.

[0701] Step 5:

[0702] The server uses injury prediction tools to assess the risk of injury based on the exercise load. It analyzes the input exercise data and training plan to calculate the risk of injury. Based on this assessment, it suggests rest periods or form modifications if necessary.

[0703] Step 6:

[0704] The device provides users with visual feedback on generated training plans and injury predictions through information delivery mechanisms. Users can then adjust their training content based on this feedback.

[0705] Step 7:

[0706] The terminal uses communication to allow the robot to monitor the user's exercise state and adjust the training content in real time as needed. Processing is performed based on prompts, and optimized instructions are output to the user. For example, specific advice such as, "Assess whether your current running form is putting too much stress on your knees, and suggest exercises if necessary," is provided in real time.

[0707] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0708] This invention provides a sports training system that takes into account not only the physical but also the emotional aspects of the individual receiving training. This system combines data analysis means, motion analysis means, training generation means, injury prediction means, information provision means, and an emotion engine that recognizes emotions.

[0709] First, the user collects exercise data using wearable sensors and cameras. This data includes not only information about normal movements, but also facial expressions and voice data. The data is then transmitted to a server via the device.

[0710] The server uses data analysis tools to analyze collected sensor data and video data to determine the individual's growth stage. Furthermore, motion analysis tools identify form and technical challenges from movement data. In addition, an emotion engine analyzes facial expression data and voice data to recognize the user's emotional state. This analysis allows for an understanding of the user's emotional condition.

[0711] Next, the training generation means generates an individualized training plan, taking into account the tasks identified by the motion analysis means as well as data from the emotion engine. For example, if the user is tense, exercises to promote relaxation may be included, and if motivation is low, goal setting to increase self-efficacy may be incorporated.

[0712] By combining physical data with stress assessments based on emotional data, injury prediction methods can perform more precise risk assessments. As a result, the risk of accidents and injuries caused by overwork or inappropriate mental states can be reduced.

[0713] Finally, the information delivery method provides users with a training plan along with feedback based on emotional data. In addition to suggesting form corrections through visual feedback, it improves the quality and safety of training by providing advice tailored to the emotional state.

[0714] For example, if a user is perceived as feeling anxious about a new training session, relaxation techniques are suggested using the results of the emotional engine, and a technical training plan is provided simultaneously. This allows the user to train in an optimal mental and physical state. This system enables comprehensive sports training tailored to the user's physical and emotional needs.

[0715] The following describes the processing flow.

[0716] Step 1:

[0717] The user prepares a wearable sensor and camera at the start of an exercise session to collect exercise data. For emotion recognition, facial expressions are captured by the camera, and audio data is recorded as needed.

[0718] Step 2:

[0719] The device processes the collected motor and emotional data and sends it to the server. The data is highly compressed and converted into a format suitable for analysis.

[0720] Step 3:

[0721] The server uses data analysis tools to determine each individual's growth stage from their exercise data. This determination is based on physical characteristics and past data.

[0722] Step 4:

[0723] The server uses motion analysis tools to identify form and operational issues from the data. For example, if the smash speed is slow, the cause can be investigated.

[0724] Step 5:

[0725] The server activates an emotion engine, analyzing the user's facial expression and voice data to assess their emotional state. This assessment allows for a clear understanding of their stress levels and motivation levels.

[0726] Step 6:

[0727] The server utilizes training generation methods to create an optimized training plan based on operational challenges and emotional assessments. For example, if relaxation is deemed necessary, stretching will be incorporated into the plan.

[0728] Step 7:

[0729] The server performs risk assessments based on exercise and emotional data through injury prediction mechanisms. In particular, it detects risks arising from the combination of emotional stress and physical fatigue.

[0730] Step 8:

[0731] The device provides the user with a server-generated training plan and injury prediction feedback. The visual feedback specifically indicates form corrections and also includes specific advice tailored to the user's emotional state.

[0732] Step 9:

[0733] Users conduct training based on the provided feedback and plan. They can receive supplementary guidance and additional instructions via their device as needed.

[0734] (Example 2)

[0735] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0736] In modern sports training, there is a growing demand for effective training plans that consider not only individual athletic ability but also emotional state. However, conventional training systems have struggled to adequately recognize and reflect an individual's emotional state, making it difficult to provide optimized training plans. Against this backdrop, realizing a system that simultaneously considers athletic ability and emotional state to provide training tailored to individual needs has become a crucial challenge.

[0737] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0738] In this invention, the server includes information analysis means for evaluating an individual's stage of development, motion evaluation means for analyzing acquired motion information to identify technical challenges, and training creation means for generating an individualized training plan based on the identified technical challenges and emotional data. This makes it possible to provide an integrated training system that takes both motion and emotion into consideration.

[0739] "Information analysis means for evaluating an individual's stage of growth" refers to a system component that analyzes collected sensor data and video data to determine the stage of growth for each individual user.

[0740] A "motion evaluation means" is a system element that has the function of analyzing acquired motion information and identifying the user's technical challenges.

[0741] A "training creation method" is a component of a system that has the function of generating a training plan optimized for each user based on identified technical challenges and emotional data.

[0742] A "risk prediction tool" is a system element that has the function of evaluating the possibility of injury with high accuracy, taking into account variable loads and the user's emotional state.

[0743] "Information provision means" refers to a component of a system that has the function of providing feedback to the user, including visual and emotional advice, based on the generated training plan and emotional data.

[0744] This system combines data collection using wearable devices and cameras with advanced data analysis techniques to enhance users' sports training. Users wear wearable sensors during their daily training, and the camera captures their movements, collecting exercise data, facial expressions, and audio data. This data is transmitted to a server via the terminal.

[0745] The server first uses information analysis tools to analyze sensor data and video data to evaluate the user's developmental stage. Simultaneously, it uses an emotion recognition engine to analyze facial expressions and voice data to determine the user's emotional state. Subsequently, it uses motion evaluation tools to analyze motion data and identify technical challenges.

[0746] The training creation mechanism combines identified technical challenges with perceived emotional states to generate individually customized training plans. For example, if a user is experiencing anxiety, exercises that promote relaxation may be included. For injury prevention, the risk prediction mechanism uses exercise and emotional data to perform a stress assessment and evaluate the likelihood of injury in advance.

[0747] Finally, users receive visual and audio feedback through information delivery methods. The device presents a training plan tailored to the user and advice based on their emotional state via screen and audio. This feedback allows users to continue training with better form.

[0748] For example, if a user starts a new workout and is feeling nervous, the server will provide a workout plan that incorporates relaxation techniques. An example of a prompt for the generating AI model would be, "Integrate user's emotional and physical data to generate a customized workout plan." This system enables comprehensive workouts that address the user's physical and emotional needs.

[0749] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0750] Step 1:

[0751] The user wears wearable sensors and a camera and begins training. This collects data on body movements (acceleration, position, etc.), facial expressions, and voice data. The input data captures the user's physical activity and emotional responses, and this serves as the starting point for individually optimizing the training.

[0752] Step 2:

[0753] The device temporarily stores the collected motor and emotional data and transmits it to the server via a secure communication protocol. The transmitted data serves as foundational data that the server needs for subsequent analysis. The data may be transmitted in real time.

[0754] Step 3:

[0755] The server uses information analysis tools to analyze received data and evaluate the user's growth stage. Sensor data and video data are provided as input, and the output is an evaluation of the degree of growth and current status. The analysis compares current performance data with past performance data.

[0756] Step 4:

[0757] The server uses an emotion engine to analyze facial expression and voice data to recognize the user's emotional state. The input is the acquired emotion-related data, and the output is numerical or categorical information representing the emotional state. For example, emotions can be inferred from the frequency of smiles or the tone of voice.

[0758] Step 5:

[0759] The server uses motion evaluation tools to analyze movement data and identify technical issues. Inputs include information on specific movement patterns and forms, while output is a list of technical elements that need improvement. This allows for checking muscle usage and form precision.

[0760] Step 6:

[0761] The server uses training creation tools to generate personalized training plans, taking into account technical challenges and emotional states. Inputs include evaluation data and emotional data, while outputs consist of specific exercises and recommendations. This is how the training curriculum is constructed.

[0762] Step 7:

[0763] The server analyzes exercise and emotional data using risk prediction tools to assess the likelihood of injury. The input is a combination of physical and emotional data, and the output is the injury risk assessment result. This provides a safe training environment.

[0764] Step 8:

[0765] The device receives training plans generated from the server and feedback based on sentiment data, and presents it to the user. Input is feedback data from the server, and output is provided to the user as visual and auditory information. This allows the user to prepare for training.

[0766] (Application Example 2)

[0767] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0768] Traditional training systems, while focusing on physical aspects, have a drawback in that they fail to address changes in psychological state. Despite the significant impact of psychological state on the effectiveness and sustainability of training, general systems often fail to consider this, potentially hindering the user's optimal progress. Furthermore, receiving real-time guidance from a professional is difficult when training at home.

[0769] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0770] In this invention, the server includes information analysis means for determining an individual's progress stage, action analysis means for analyzing collected activity data to identify technical challenges, and training plan generation means for generating individualized training plans based on the identified technical challenges and psychological states detected by emotion analysis means. This makes it possible to provide personalized training plans that take psychological aspects into consideration. Furthermore, by utilizing automated machines, it is possible to promote user growth through real-time instructional feedback in a home environment.

[0771] "Information analysis tools" are technical means used to perform analysis in order to determine an individual's stage of progress.

[0772] A "motion analysis means" is a technical means that identifies technical problems based on collected activity data.

[0773] A "training plan generation means" is a technical means that generates individual training plans based on identified technical challenges and psychological states detected through emotion analysis.

[0774] A "risk assessment tool" is a technical means for evaluating the risk of injury based on variable load and psychological state.

[0775] "Information provision means" refers to technical means that provide feedback to users based on the generated training plan and risk assessment results.

[0776] An "automated machine" is a device that monitors physical activity and emotional state in a home environment and provides real-time guidance and feedback.

[0777] This invention provides a system using automated machinery as an application example for optimizing individual training. The server uses information analysis means to determine the individual's progress stage. Specifically, wearable sensors and built-in cameras are used to analyze collected activity data and emotions. The server uses motion analysis means to identify technical challenges from this data. Meanwhile, emotion analysis technology is used to evaluate the psychological state based on facial recognition and voice analysis.

[0778] The training plan generation means generates an individualized training plan based on identified technical challenges and psychological state. In this process, a generation AI model is used to design training content that fits the user's condition. The risk assessment means assesses the risk of injury based on variable load and psychological state. This ensures the user's physical and psychological safety.

[0779] The information delivery system provides real-time feedback via an automated machine in a home environment, based on the generated training plan and risk assessment results. This process utilizes speech synthesis technology to present instructions and advice to the user via voice.

[0780] As a concrete example, when a user begins training at home, an automated machine monitors the user's form and provides suggestions for improvement as needed. At the same time, if it detects tension in the user, it provides voice guidance on relaxation techniques.

[0781] An example of a generated AI model prompt is as follows: "Design an AI algorithm that generates personalized training suggestions aimed at improving athletic performance and reducing stress, based on the user's real-time movement and facial expression data."

[0782] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0783] Step 1:

[0784] The user attaches wearable sensors and a camera and begins training. This method collects motion data and facial expression data in real time. The input consists of the user's body motion data and facial expression data, which are sent to the server via the sensors and camera. The output is a data stream that is available on the server.

[0785] Step 2:

[0786] The server processes the collected data stream through an information analysis tool to determine the stage of progress. The input consists of user motion data and facial expression data. A data analysis algorithm evaluates physical progress and emotional changes, determining the individual's progress level as output. During this process, the system compares the facial expressions and motions with a standard database and analyzes the differences from the baseline.

[0787] Step 3:

[0788] The server uses motion analysis tools to analyze the motion data provided as input. This identifies technical issues and clarifies which parts of the form require improvement. The output is recorded as the identified technical issues. Specifically, processing is performed to identify abnormalities and asymmetries in body movement.

[0789] Step 4:

[0790] The server utilizes an emotion analysis engine to evaluate the user's psychological state. Inputs include facial expression data and voice data. This processing step applies an emotion analysis model to recognize the user's mental state and outputs a classification of the current emotional state. Specifically, it analyzes the user's facial muscle movements and voice tone.

[0791] Step 5:

[0792] The server uses a training plan generation mechanism to generate an individualized training plan based on identified technical challenges and emotional states. The inputs are technical challenge data and emotional state data. The output is a user-specific training plan, including recommendations for exercise types, repetitions, and relaxation techniques. Here, the optimal solution is selected by comparing it with historical data.

[0793] Step 6:

[0794] The server uses risk assessment tools to evaluate the risk of injury based on the planned load and mental state as inputs. The output is the risk assessment and proposed mitigation measures. Specifically, it quantifies the risk of injury and provides load adjustments and stress reduction measures based on that quantification.

[0795] Step 7:

[0796] Through the terminal, the home-based automated machine provides the user with feedback on the generated training plan and risk assessment results using speech synthesis technology. The input is the plan and risk assessment provided by the server. The output is voice guidance and motivational feedback for the user. Specific actions in this process include providing voice guidance in a tone appropriate to the user and specific exercise suggestions in real time.

[0797] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0798] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0799] 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 this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0800] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0801] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0802] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0803] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0804] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0805] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0806] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0807] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0808] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0809] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0810] 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.

[0811] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0812] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0813] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0814] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0815] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0816] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0817] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0818] The following is further disclosed regarding the embodiments described above.

[0819] (Claim 1)

[0820] A data analysis method for determining an individual's growth stage,

[0821] A motion analysis means for analyzing acquired motion data to identify technical challenges,

[0822] A training generation means that generates individual training plans based on the identified technical challenges,

[0823] An injury prediction method for evaluating the risk of injury in response to variable loads,

[0824] The generated training plan and injury risk are evaluated and provided as an information provision means,

[0825] A system that includes this.

[0826] (Claim 2)

[0827] The data analysis means determines the growth stage based on sensor data and recorded data, according to claim 1.

[0828] (Claim 3)

[0829] The system according to claim 1, wherein the information providing means includes suggestions for form modifications in the form of visual feedback.

[0830] "Example 1"

[0831] (Claim 1)

[0832] A data analysis device that determines an individual's stage of growth,

[0833] A motion analysis device that analyzes acquired motion information to identify technical challenges,

[0834] A training generation device that generates individual training plans based on the identified technical challenges,

[0835] An injury prediction device that evaluates the risk of injury in response to variable loads,

[0836] An information supply device that evaluates the generated training plan and the risk of injury and provides feedback,

[0837] A device that uses a central unit to receive and analyze digital information to predict potential injuries based on received motion information,

[0838] A device that automatically generates a customized training plan based on analysis results using an artificial intelligence model,

[0839] A system that includes this.

[0840] (Claim 2)

[0841] The data analysis device determines the growth stage based on measurement device data and video information, according to claim 1.

[0842] (Claim 3)

[0843] The information supply device includes suggestions for form correction in the form of visual feedback, according to claim 1.

[0844] "Application Example 1"

[0845] (Claim 1)

[0846] A data analysis method for determining an individual's growth stage,

[0847] A motion analysis means for analyzing acquired motion data to identify technical challenges,

[0848] A training generation means that generates a training plan individually based on the identified technical issues,

[0849] An injury prediction method for evaluating the risk of injury in response to variable loads,

[0850] The generated training plan and injury risk are evaluated and an information provision means is provided to provide feedback.

[0851] A communication method that monitors the exercise state through communication with the robot and adjusts the training content in real time,

[0852] A system that includes this.

[0853] (Claim 2)

[0854] The system according to claim 1, wherein the data analysis means determines the stage of growth based on sensor data and video recording data.

[0855] (Claim 3)

[0856] The system according to claim 1, wherein the information providing means includes suggestions for posture correction in the form of visual feedback.

[0857] "Example 2 of combining an emotion engine"

[0858] (Claim 1)

[0859] Information analysis tools for evaluating an individual's stage of growth,

[0860] A motion evaluation means that analyzes acquired motion information to identify technical challenges,

[0861] A training creation means for generating an individualized training plan based on the identified technical challenges and emotional data,

[0862] A risk prediction method for evaluating the likelihood of injury in response to variable loads,

[0863] Information provision means that provides feedback based on the generated training plan and emotional data,

[0864] A system that includes this.

[0865] (Claim 2)

[0866] The information analysis means evaluates the growth stage based on sensor data and video data, and recognizes the emotional state from facial expressions and voice data, according to claim 1.

[0867] (Claim 3)

[0868] The system according to claim 1, wherein the information provision means includes suggestions for form correction in the form of visual feedback and advice according to emotional state.

[0869] "Application example 2 when combining with an emotional engine"

[0870] (Claim 1)

[0871] Information analysis tools for determining an individual's stage of progress,

[0872] A motion analysis means for analyzing collected activity data to identify technical issues,

[0873] A training plan generation means that generates an individualized training plan based on identified technical challenges and psychological states detected by emotion analysis means,

[0874] A risk assessment means for evaluating the risk of injury in response to variable loads and psychological states,

[0875] A means of providing information that provides feedback based on the generated training plan and risk assessment results,

[0876] An automated machine that monitors physical activity and emotional state in the home environment and provides real-time guidance and feedback,

[0877] A system that includes this.

[0878] (Claim 2)

[0879] The system according to claim 1, wherein the data analysis means recognizes an emotional state based on facial expression data and voice data using emotion analysis technology and determines the stage of development.

[0880] (Claim 3)

[0881] The system according to claim 1, wherein the means of providing information includes suggesting form corrections and psychological advice in a real-time voice feedback format using speech synthesis technology. [Explanation of symbols]

[0882] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A data analysis method for determining an individual's growth stage, A motion analysis means for analyzing acquired motion data to identify technical challenges, A training generation means that generates individual training plans based on the identified technical challenges, An injury prediction method for evaluating the risk of injury in response to variable loads, The generated training plan and injury risk are evaluated and provided as an information provision means, A system that includes this.

2. The data analysis means determines the growth stage based on sensor data and recorded video data, according to claim 1.

3. The system according to claim 1, wherein the information providing means includes suggesting form modifications in the form of visual feedback.

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