system

Abstract

We provide the system. [Solution] A detection device for measuring biological data and a communication means for communicating with it, Information processing means for processing biometric data received from the aforementioned communication terminal and evaluating the efficiency of exercise execution, Based on the aforementioned evaluation, a plan generation means for constructing an exercise plan suitable for the exerciser, A display means and an audio output means for communicating the exercise plan to the exerciser and providing dynamic feedback during the execution of the exercise, A mobile assistive device to support exercise for people within the home, 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 method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] Conventional exercise training often provides uniform guidance for a group, so there is a problem that it is difficult to provide optimal guidance according to the characteristics and goals of individual people. In addition, there is also a risk that the form during exercise is not sufficiently checked and injuries are not sufficiently prevented. In such a situation, a system for performing effective and safe training is required.

Means for Solving the Problems

[0005] This invention solves the above problem by generating an individualized exercise plan by collecting physiological data of a person in real time and analyzing it with an information processing device. This allows for receiving data from a terminal and providing appropriate feedback, enabling real-time form correction and training efficiency improvements during exercise. Furthermore, the information processing device has a function to provide more accurate analysis results by performing data cleansing, enabling it to understand the user's fitness level and provide a training plan that matches it. Through this, this invention realizes effective and safe training tailored to individual needs.

[0006] "Person" refers to the individual who will provide physiological data, and an exercise plan will be created based on that data.

[0007] A "detection device" is a device used to collect physiological data from a person, such as heart rate, activity level, and location information.

[0008] A "terminal" is a device that communicates data obtained from a detection device and transfers it to an information processing device.

[0009] An "information processing device" is a device that receives data transmitted from a terminal, analyzes it, and evaluates athletic performance.

[0010] "Exercise performance" is an evaluation index related to the efficiency and effectiveness of exercise performed by a person, and includes elements such as heart rate and pace.

[0011] A "generation device" is a device that creates an individualized motion plan based on evaluation by an information processing device.

[0012] An "exercise plan" is a set of specific guidelines provided to an individual regarding the content, intensity, and frequency of their training, optimized according to their fitness goals.

[0013] A "display device and audio output device" is a device for communicating an exercise plan to a person and providing real-time feedback during exercise.

[0014] "Data cleansing" is a process used by information processing equipment to remove abnormal values ​​and duplicate information from received data, thereby improving the accuracy of analysis. [Brief explanation of the drawing]

[0015] [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]It is a sequence diagram showing the processing flow of the data processing system in Embodiment 2 when combined with an emotion engine. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when combined with an emotion engine.

Mode for Carrying Out the Invention

[0016] Hereinafter, an example of an embodiment of the system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0017] First, the terms used in the following description will be explained.

[0018] 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.

[0019] 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.

[0020] 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.

[0021] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0022] 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."

[0023] [First Embodiment]

[0024] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0025] 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.

[0026] 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).

[0027] 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.

[0028] 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.

[0029] 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.

[0030] 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.

[0031] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] 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.

[0033] 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.

[0034] 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.

[0035] 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".

[0036] An embodiment of the AI ​​sports trainer system of the present invention will be described. This system is designed to optimize the user's exercise and consists of a detection device, a terminal, an information processing device, a display device, and an audio output device.

[0037] First, the user wears a detection device such as a wearable device to acquire physiological data such as heart rate, activity level, and location information. The terminal receives this data in real time via wireless communication and transmits it to the server.

[0038] Next, the server receives a large amount of biometric data and performs data cleansing on each piece of data. This removes unnecessary outliers and noise, generating clean data suitable for analysis. Analysis is then performed on the clean data to evaluate the user's exercise performance. Specifically, indicators such as average pace, calorie consumption, and heart rate zone duration are calculated.

[0039] The server then automatically generates a customized exercise plan based on these metrics, taking into account the user's fitness level and goals. This exercise plan includes specific details such as performing Y minutes of training at Z intensity over X sessions.

[0040] The generated exercise plan is sent to the terminal and notified to the user. The terminal uses a display device and an audio output device to provide the user with information about the plan and feedback during the exercise. For example, if the pace is faster than planned, advice such as "Please slow down a little" is output in real time.

[0041] For example, when a user is running, the device acquires data via GPS and heart rate sensors and provides real-time instructions based on pace and heart rate, such as "Your heart rate is too high, please adjust your speed." After the training is complete, a performance report is generated by the server and incorporated into the plan for the next session.

[0042] As described above, the present invention realizes an efficient and safe exercise program by providing training support tailored to the individual characteristics of the user.

[0043] The following describes the processing flow.

[0044] Step 1:

[0045] The user puts on a wearable device and begins exercising. The device continuously collects physiological data such as heart rate, steps taken, and location information.

[0046] Step 2:

[0047] The terminal receives data from the wearable device in real time. The received data is temporarily stored in storage and then prepared to be sent to the server via the communication module.

[0048] Step 3:

[0049] The server receives data sent from the terminal. It then starts a data cleansing process, removing outliers and correcting missing data to create clean data.

[0050] Step 4:

[0051] The server analyzes the user's exercise performance using clean data. During the analysis, it calculates specific metrics (e.g., average pace, calorie expenditure) and evaluates the overall exercise performance.

[0052] Step 5:

[0053] Based on the analysis results, the server generates a customized exercise plan that takes into account the user's past history data and fitness goals. The plan includes specific exercises, intensity, and frequency.

[0054] Step 6:

[0055] The device receives the generated exercise plan and notifies the user. During exercise, it provides real-time advice (e.g., "Adjust your pace") via voice and display devices as feedback.

[0056] Step 7:

[0057] As the user continues training, the device continuously collects data and provides feedback to the server. The server receives this feedback and, if further adjustments are needed, issues instructions through the device in a timely manner.

[0058] Step 8:

[0059] After the training session is complete, the server performs a final analysis and generates a user performance report. This report includes evaluation results and suggestions for the next exercise plan, and is provided to the user via their device.

[0060] (Example 1)

[0061] 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."

[0062] Systems that promote efficient and safe training by providing individually optimized activity plans in real time based on physiological indicators are limited, and there is a particular need for highly accurate analysis that removes outliers and noise, and for continuous assessment of exercise capacity based on historical data.

[0063] 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.

[0064] In this invention, the server includes means for wirelessly communicating with a detection device for collecting physiological indicators, information processing means for receiving information transmitted from the terminal and analyzing the information to evaluate biological activity, and generation means for automatically generating an activity plan individualized for the organism based on the evaluation. This makes it possible to provide a plan optimized for each organism and to provide advice in real time.

[0065] "Physiological indicators" are data that quantifies the user's physical condition and exercise status, such as heart rate, exercise volume, and location information.

[0066] A "detection device" is a device used to sense physiological indicators, such as a heart rate sensor or GPS integrated into a wearable device.

[0067] A "terminal" is a device equipped with communication functions to receive data acquired from a detection device and transmit it to a server.

[0068] An "information processing device" is a device that receives data transmitted from a terminal, analyzes that data, and evaluates user activity.

[0069] A "generation device" is a device that has the function of automatically generating individually optimized activity plans based on evaluations by an information processing device.

[0070] A "display means" is a device used to visually communicate generated activity plans and feedback to the user.

[0071] A "voice output device" is a device that provides the user with generated activity plans and real-time feedback via voice.

[0072] The system of the present invention was developed primarily to provide users with personalized exercise plans, and consists of a detector, a terminal, a server, a display means, and an audio output means.

[0073] First, the user wears a wearable device called a detector, which continuously acquires physiological indicators such as heart rate, activity level, and location information. The wearable device integrates a heart rate sensor and GPS receiver.

[0074] Next, the terminal wirelessly receives data acquired from the wearable device using Bluetooth or Wi-Fi and transmits that data to the server in real time. Typically, a smartphone or tablet is used as the terminal.

[0075] After receiving data from the terminal, the server performs a data cleansing process using an information processing device to remove outliers and noise, generating clean data suitable for analysis. Based on the analysis, the user's exercise performance is evaluated based on indicators such as average pace, calorie consumption, and time spent in different heart rate zones. This process utilizes a specialized data analysis algorithm implemented as software.

[0076] The server then uses a generative AI model to generate an exercise plan best suited to the user's fitness level and exercise goals. This exercise plan is generated automatically and includes specific guidance, such as "maintain a steady pace of 110-130 bpm for 20 minutes during your next run."

[0077] After an exercise plan is generated, the device notifies the user. A smart display and earphones are used as display and audio output means, providing the user with information visually and audibly. This allows for real-time, specific feedback during exercise, such as "Please slow down a little."

[0078] For example, when a user goes for a run, the device provides real-time voice instructions based on GPS and heart rate data, such as, "Your current heart rate is too high, please adjust your pace." This feature allows users to train safely and efficiently.

[0079] An example of a prompt message might be, "Analyze heart rate data during running training and generate an exercise plan that advises the user on the optimal pace." By entering this prompt, the system automatically generates a personalized training plan.

[0080] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0081] Step 1:

[0082] By wearing a wearable device, users can acquire physiological indicators such as heart rate, exercise volume, and location information. This information is recorded in real time and stored within the device. The input is raw data related to the user's exercise, and the output is the data stored within the device.

[0083] Step 2:

[0084] The terminal wirelessly receives data from the wearable device via Bluetooth or Wi-Fi. The input here is physiological indicator data transmitted from the wearable device, and the output is the temporary storage of this data within the terminal. Immediately after receiving the data, the terminal prepares to send it to the server.

[0085] Step 3:

[0086] The server receives data transmitted from the terminal and performs data cleansing using an information processing device. The input consists of various physiological indicator data sent from the terminal, and the output is clean data from which noise and outliers have been removed. At this stage, data processing is performed to detect and exclude rapid fluctuations in heart rate, etc.

[0087] Step 4:

[0088] The server analyzes clean data and calculates the user's exercise performance. The input is cleansed physiological indicator data, and the output is analyzed indicator data such as average pace, calorie consumption, and heart rate zone duration. This analysis generates foundational data to assess the user's current fitness level.

[0089] Step 5:

[0090] The server uses an AI model to automatically generate an optimized exercise plan for the user based on analyzed metric data. The input is the user's exercise performance analysis results, and the output is a specific exercise plan such as "maintain a constant pace of 110-130 bpm for 20 minutes during your next run." The generated exercise plan is personalized according to the user's characteristics.

[0091] Step 6:

[0092] The terminal notifies the user of the exercise plan received from the server. The plan is presented using a display or audio output means, and feedback is provided during the exercise. At this stage, the generated exercise plan is presented to the user in a practical format. The input is the exercise plan from the server, and the output is the notification to the user and real-time exercise feedback.

[0093] Step 7:

[0094] After training is complete, the server analyzes the data from the exercise session and generates a performance report. This report is then used to plan the next workout. The input is the event data from the exercise session, and the output is the performance report used for planning the next workout. This feedback loop allows the user's athletic ability to continuously improve.

[0095] (Application Example 1)

[0096] 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."

[0097] In modern times, individuals face challenges in developing efficient and effective exercise plans and obtaining real-time feedback for their independent fitness activities. Furthermore, the limited support available at home makes it difficult to receive consistent exercise guidance.

[0098] 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.

[0099] In this invention, the server includes communication means for communicating with a detection device that measures biological data, information processing means for processing the biological data received from the communication terminal and evaluating the efficiency of exercise execution, and plan generation means for constructing an exercise plan suitable for the exerciser based on the evaluation. This enables the exerciser to consistently receive appropriate exercise guidance and feedback even at home. Furthermore, by using a mobile assistive device, fitness at home can be performed more efficiently and effectively.

[0100] A "detection device that measures biometric data" is a device that has the function of collecting physiological information such as heart rate and exercise volume from an exerciser.

[0101] A "communication terminal" is a device that receives data from a detection device and transmits it to an information processing device or server.

[0102] An "information processing device" is a device that analyzes received biological data and has the function of evaluating the performance of an athlete.

[0103] A "plan generation device" is a device that automatically creates an exercise plan optimized for the individual based on the analysis results.

[0104] A "mobile assistive device" is a device that has the function of moving autonomously to support exercise within the home and plays a role in providing feedback to the user.

[0105] The embodiments for carrying out the invention are described below.

[0106] The system that realizes this application is built by combining a wearable device, a communication terminal, a mobile assistive device (a home robot), and a cloud server. By wearing the wearable device, the user's biometric data (heart rate, activity level, location information, etc.) is collected in real time. This data is transmitted to the communication terminal via Bluetooth and then delivered to the cloud server via the internet.

[0107] On the server side, data analysis software such as Python or R is used to perform data cleansing on the received biometric data. This removes outliers and noise, resulting in high-quality data suitable for analysis. Subsequently, the data is analyzed using AI models (using TENSORFLOW® or PyTorch) to evaluate the current fitness performance of the athletes.

[0108] Based on the analysis results, an AI-generated, customized exercise plan is sent to a communication terminal. This plan is communicated to the user via display or audio output, and real-time feedback is provided during exercise. The mobile assistive device supports the user's exercise within the home and provides the exercise plan visually or audibly as needed.

[0109] For example, when a user performs aerobic exercise indoors, a home robot capable of natural movements, located in another room, provides feedback such as, "Your exercise report is ready. Your exercise level was a little low this week, so we've adjusted your plan for next week." An example of a prompt for the generative AI model could be, "Please suggest an appropriate fitness plan based on the user's heart rate and exercise data."

[0110] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0111] Step 1:

[0112] The user wears a wearable device that collects biometric data in real time. This device measures data such as heart rate, activity level, and location using sensors and transmits it to a communication terminal via Bluetooth. The input is biosensor data, and the output is the reception of data by the communication terminal.

[0113] Step 2:

[0114] The device transmits the received biometric data to a cloud server via the internet. The data processing involved in this process is conversion to a data format based on the required protocol. The input is the received biometric data, and the output is the data sent to the cloud server.

[0115] Step 3:

[0116] The server performs data cleansing on the received data. A Python data analysis library is used to remove outliers and noise. The input is data sent from the terminal, and the output is cleansed, high-quality data.

[0117] Step 4:

[0118] The server inputs cleansed data into an AI model to analyze the user's exercise performance. It uses TensorFlow and PyTorch to collect statistics on average pace and heart rate. The input is cleansed data, and the output is the exercise evaluation result.

[0119] Step 5:

[0120] The server automatically generates an optimized exercise plan for the user based on the analysis results. This generating AI model considers factors such as exercise volume and heart rate to create a customized exercise plan. The input is the analyzed exercise data, and the output is the customized exercise plan.

[0121] Step 6:

[0122] The device receives the exercise plan and communicates it to the user via display and audio output. Specifically, the plan is displayed on the screen, and instructions such as "The next exercise you should do is..." are given via audio. The input is the exercise plan sent from the server, and the output is the notification to the user.

[0123] Step 7:

[0124] When a user begins exercising, the device transmits the exercise plan to a mobile assistive device, which then provides feedback at designated intervals. Specifically, the assistive device might offer voice advice such as, "Maintain your pace." The input is the exercise plan from the device, and the output is the provision of feedback.

[0125] 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.

[0126] The system of the present invention assists in optimizing the user's exercise training and, by combining it with an emotion engine, provides more personalized feedback. The system comprises a detection device, a terminal, an information processing device, a display device, and an audio output device, as well as an emotion engine.

[0127] First, the user wears a wearable device to collect physiological and emotional data such as heart rate, activity level, voice, and facial expressions. This data is collected in real time, and the device receives it. The device then sends the data to a server via a communication module. The server receives the data and performs data cleansing on the physiological data to create clean data suitable for analysis.

[0128] Next, the server analyzes clean data to evaluate the user's exercise performance. This evaluation includes metrics such as exercise efficiency and time spent in different heart rate zones. Simultaneously, the emotion engine analyzes the user's voice and facial expression data to identify the user's current emotional state. The server uses this information comprehensively to generate an optimized exercise plan and feedback for the user.

[0129] This exercise plan is individually tailored based on performance data and emotional state, and its content and intensity may be modified. For example, if an emotional state indicating stress is identified, the plan can be adjusted to reduce the exercise intensity. On the other hand, if high motivation is recognized, challenging training can be suggested.

[0130] The generated exercise plan is sent to the device and notified to the user. The device provides real-time feedback as the user continues exercising. Through the display and audio output devices, it provides praise such as "Great pace!" and advice such as "Let's take a short break" to boost the user's motivation.

[0131] For example, if a user is training while extremely fatigued, the emotion engine recognizes this emotional state as "fatigue." In response, the server reduces the exercise intensity and provides feedback to encourage relaxation. Conversely, if the user is motivated and energetic, the system recommends plans such as high-intensity interval training and provides feedback to encourage challenges. In this way, the system improves training effectiveness and user experience by providing dynamic feedback tailored to the user's emotional state.

[0132] The following describes the processing flow.

[0133] Step 1:

[0134] The user puts on a wearable device and begins training. The device continuously acquires physiological and emotional data such as heart rate, exercise level, voice, and facial expressions.

[0135] Step 2:

[0136] The terminal receives data from the wearable device in real time. This data includes heart rate, location information, voice data, and facial recognition data. The terminal temporarily stores this data and sends it to the server in batch processing at predetermined intervals.

[0137] Step 3:

[0138] The server receives the data sent from the terminal and starts the data cleansing process. This cleansing removes abnormal values ​​and noise, and appropriately fills in any missing data.

[0139] Step 4:

[0140] The server analyzes clean data to evaluate the user's exercise performance. Evaluation items include running pace, calorie consumption, and time spent in different heart rate zones, and the overall effect of the exercise is calculated from these factors.

[0141] Step 5:

[0142] The server uses an emotion engine to analyze voice and facial expression data to identify the user's emotional state. Emotional states are categorized into joy, stress, fatigue, etc., and each state requires a different motor plan.

[0143] Step 6:

[0144] The server generates a customized exercise plan tailored to the user based on exercise performance evaluation and emotional state. The generated exercise plan is adapted to the user's condition, for example, by adjusting the training intensity or suggesting specific exercises.

[0145] Step 7:

[0146] The device receives the generated exercise plan and notifies the user. During exercise, it also provides real-time feedback, including advice and motivational messages tailored to the user's emotional state, through a display device and audio output device.

[0147] Step 8:

[0148] As the user continues their training, the device continuously collects new data and sends it to the server. Based on this data, the server updates the exercise plan as needed and provides further advice in real time through the device.

[0149] Step 9:

[0150] After the training is complete, the server performs a final analysis and generates a feedback report for the user. This report includes the results of the exercise performed, changes in emotions, and recommendations for the next training session, and is provided to the user via their device.

[0151] (Example 2)

[0152] 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".

[0153] Many exercise instruction systems focus on collecting physiological data and evaluating exercise performance, but they fail to provide feedback that takes into account the exerciser's emotional state. As a result, it is difficult to provide training plans tailored to individual exercisers, and maintaining motivation and maximizing training effectiveness are challenging.

[0154] 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.

[0155] In this invention, the server includes means for using individual devices that communicate with a device that collects physiological and emotional data of an athlete; means for using information processing units that cleanse the data received from the individual devices and analyze exercise performance and emotional state; and means for using a generation device that generates an optimized exercise plan and emotionally appropriate feedback based on the analysis results. This makes it possible to provide personalized feedback that takes into account the athlete's emotional state in addition to their physiological data.

[0156] A "participant" is a person from whom motor and emotional data are collected using individualized devices.

[0157] "Physiological data" refers to data that quantifies the physical state of an exerciser, such as heart rate and exercise volume.

[0158] "Emotional data" refers to data that indicates the emotional state of a person, analyzed from their voice, facial expressions, and other factors.

[0159] An "individual device" is a terminal that collects physiological and emotional data from an exerciser and communicates that data to a server.

[0160] "Data cleansing" is the process of preparing data for analysis by imputing missing values ​​and removing noise.

[0161] An "information processing unit" is a system element that has the function of analyzing received data to evaluate motor performance and emotional state.

[0162] A "generation device" is a device that generates an optimized exercise plan and feedback for the exerciser based on the analysis results.

[0163] An "emotion engine" is a software or hardware component that analyzes a person's voice and facial expressions to identify their emotional state.

[0164] "Data communication means" refers to a means of communication that allows individual devices to send data about athletes to a server and receive feedback from the server.

[0165] This invention is a system that provides individually optimized feedback and exercise plans using the physiological and emotional data of the exerciser. Specifically, it begins with the exerciser collecting physiological data such as heart rate and exercise volume using a wearable device, as well as emotional data such as voice and facial expressions. This data is temporarily stored in an individual device and then transmitted to a server via communication means such as Bluetooth or Wi-Fi.

[0166] The server performs data cleansing based on the received data. This involves preprocessing such as noise reduction and missing value imputation using data processing libraries in Python or Java (registered trademark). After obtaining clean data, the server performs information processing based on this data to evaluate motor performance and emotional state. TensorFlow machine learning models are used for this evaluation, and emotional data, in particular, is analyzed by an emotion engine.

[0167] Based on the analysis results, the server generates an optimal exercise plan and feedback for the exerciser and transmits it to the individual device. The individual device then provides the exercise plan to the exerciser and offers real-time feedback via voice and display. This allows the exerciser to adjust their training on the spot.

[0168] For example, if an exerciser indicates an emotional state of "fatigue," the server will generate an exercise plan that reduces the intensity and promotes relaxation. Conversely, if the exerciser indicates "high motivation," it will suggest high-intensity training and generate feedback to further boost motivation.

[0169] Examples of prompts to input into a generative AI model are as follows:

[0170] "Generate an appropriate training plan based on the user's emotional state. Example data: Exercise efficiency = 80%, Emotional state = High motivation."

[0171] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0172] Step 1:

[0173] The user puts on a wearable device and begins exercising. The device collects physiological and emotional data in real time, such as heart rate, exercise level, voice, and facial expressions. Input is raw data from sensors, and output is stored on the device in digital data format.

[0174] Step 2:

[0175] The terminal receives data collected from wearable devices and transfers it to a server via Bluetooth or Wi-Fi. The input is digital data, and the output is the data transmission state to the server over the network. During this process, the terminal performs data format conversion and compression to ensure efficient transfer.

[0176] Step 3:

[0177] The server receives data sent from the terminal and performs data cleansing. The input is raw data containing missing values ​​and noise, and the output is clean data that can be analyzed. The server uses Python data processing libraries to remove noise and imputate data.

[0178] Step 4:

[0179] The server analyzes exercise performance and emotional state based on clean data. The input is cleansed physiological and emotional data, and the output is the evaluated metrics and emotional state. The server uses TensorFlow to run a machine learning model with an emotion engine to perform sentiment analysis.

[0180] Step 5:

[0181] The server generates an optimal exercise plan and feedback based on the analysis results. The input is analyzed exercise and emotional data, and the output is a personalized exercise plan and messages. The server considers training advice tailored to exercise efficiency and emotional state, and generates feedback using a generative AI model.

[0182] Step 6:

[0183] The terminal receives exercise plans and feedback transmitted from the server and notifies the user. Input is exercise plan data from the server, and output is message displays and audio alerts to the user. The terminal, through its display and audio output devices, provides real-time advice and encouraging messages to enhance the user's training experience.

[0184] (Application Example 2)

[0185] 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".

[0186] In exercise training, there is a need to provide optimal exercise strategies tailored to each individual's physiological and emotional state. However, existing training support systems struggle to analyze an individual's emotional state in real time and adaptively adjust the training plan based on the results. Therefore, challenges remain in improving exercise efficiency and maintaining motivation.

[0187] 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.

[0188] In this invention, the server includes means for communicating with a sensing device for collecting physiological information of a person; information processing device means for receiving information transmitted from the terminal and analyzing that information to evaluate the person's athletic ability; and emotion processing device means for analyzing the person's emotional state during exercise and dynamically adjusting the exercise strategy and feedback. This makes it possible to generate an optimal exercise plan for the user and provide personalized feedback in real time.

[0189] A "sensing device" is a device used to collect physiological information about a person in real time.

[0190] A "terminal" is a device that receives physiological information from a sensing device and transmits that information to an information processing device.

[0191] An "information processing device" is an electronic device used to analyze received physiological information and evaluate a person's motor skills.

[0192] A "generation system" is a set of devices that generates an optimized motor strategy based on data on a person's motor skills evaluated by information processing equipment.

[0193] A "display device" is a device used to visually communicate a generated motor strategy to a person.

[0194] A "voice output device" is a device that transmits generated motor strategies and feedback to a person as voice.

[0195] An "emotional processing device" is an electronic device that analyzes a person's emotional state during movement and dynamically adjusts movement strategies and feedback based on the results.

[0196] "Information cleansing means" are data correction methods that remove abnormal values ​​and duplicate information from physiological data in order to perform accurate analysis.

[0197] "Health level" is an indicator of an individual's overall health status, assessed based on their past exercise data.

[0198] One embodiment of this invention is configured as a system that optimizes exercise training and provides personalized feedback. The user collects physiological information such as heart rate and exercise volume through a sensing device. The terminal then receives the data in real time and transfers it to an information processing device.

[0199] The information processing equipment receives and analyzes physiological information from wearable devices such as smartwatches running Google® Wear OS. Furthermore, it performs facial recognition and emotion analysis using software such as Python and OpenCV. Based on the data obtained from this analysis, the generation system creates an optimized exercise strategy for the user.

[0200] This exercise strategy is dynamically adjusted according to the user's state. The server provides feedback through display devices and audio output devices. For example, if the user is highly motivated, it can recommend high-intensity exercise.

[0201] As a concrete example, when a user is jogging at home, this system provides voice feedback from a robot saying, "Your pace is good!" Furthermore, if fatigue is detected, it can advise, "Let's slow down a bit." Such feedback is provided based on generated prompts.

[0202] Example of a prompt:

[0203] "The user is experiencing fatigue during their morning exercise. Please generate advice to encourage relaxation."

[0204] This allows users to receive optimal exercise feedback tailored to their current state, which is expected to improve performance and maintain motivation.

[0205] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0206] Step 1:

[0207] The user puts on a wearable device and begins exercising. The wearable device senses physiological information such as heart rate and exercise intensity in real time and transmits it as data to the terminal. The input is the user's physiological information, and the output is the transmission of data to the terminal.

[0208] Step 2:

[0209] The terminal transfers the received physiological information to the server. Time information is added to the information, and it is formatted to be suitable for analysis on the server. The input is physiological information from the wearable device, and the output is formatted data.

[0210] Step 3:

[0211] The server cleanses the received data, removing noise and outliers. This creates clean data suitable for analysis. The input is physiological information from the terminal, and the output is clean data.

[0212] Step 4:

[0213] The server evaluates the user's athletic performance using clean data. A Python script is used to analyze the data and calculate indices. The athletic ability assessment based on this analysis is then output.

[0214] Step 5:

[0215] The server uses OpenCV to analyze the user's facial expression data and perform emotion processing. This analyzes the user's emotional state and outputs the result as emotion data. The input is the user's facial expression data, and the output is the user's emotional state.

[0216] Step 6:

[0217] The server generates an exercise strategy based on motor skill assessment and emotional data. It uses a generative AI model to generate prompts and dynamically adjust the exercise plan. The input is motor skill assessment and emotional data, and the output is the exercise strategy.

[0218] Step 7:

[0219] The generated motor strategy is communicated to the user via a terminal. Real-time feedback is provided using an audio output device and a display device. The input is the generated motor strategy, and the output is the feedback to the user.

[0220] Step 8:

[0221] The user adjusts their movements based on the feedback provided, and subsequent movement data is continuously collected, initiating the next loop. The input is the content of the feedback, and the output is the user's movement behavior.

[0222] 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.

[0223] 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.

[0224] 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.

[0225] [Second Embodiment]

[0226] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0227] 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.

[0228] 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).

[0229] 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.

[0230] 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.

[0231] 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).

[0232] 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.

[0233] 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.

[0234] 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.

[0235] 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.

[0236] 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.

[0237] 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".

[0238] An embodiment of the AI ​​sports trainer system of the present invention will be described. This system is designed to optimize the user's exercise and consists of a detection device, a terminal, an information processing device, a display device, and an audio output device.

[0239] First, the user wears a detection device such as a wearable device to acquire physiological data such as heart rate, activity level, and location information. The terminal receives this data in real time via wireless communication and transmits it to the server.

[0240] Next, the server receives a large amount of biometric data and performs data cleansing on each piece of data. This removes unnecessary outliers and noise, generating clean data suitable for analysis. Analysis is then performed on the clean data to evaluate the user's exercise performance. Specifically, indicators such as average pace, calorie consumption, and heart rate zone duration are calculated.

[0241] The server then automatically generates a customized exercise plan based on these metrics, taking into account the user's fitness level and goals. This exercise plan includes specific details such as performing Y minutes of training at Z intensity over X sessions.

[0242] The generated exercise plan is sent to the terminal and notified to the user. The terminal uses a display device and an audio output device to provide the user with information about the plan and feedback during the exercise. For example, if the pace is faster than planned, advice such as "Please slow down a little" is output in real time.

[0243] For example, when a user is running, the device acquires data via GPS and heart rate sensors and provides real-time instructions based on pace and heart rate, such as "Your heart rate is too high, please adjust your speed." After the training is complete, a performance report is generated by the server and incorporated into the plan for the next session.

[0244] As described above, the present invention realizes an efficient and safe exercise program by providing training support tailored to the individual characteristics of the user.

[0245] The following describes the processing flow.

[0246] Step 1:

[0247] The user puts on a wearable device and begins exercising. The device continuously collects physiological data such as heart rate, steps taken, and location information.

[0248] Step 2:

[0249] The terminal receives data from the wearable device in real time. The received data is temporarily stored in storage and then prepared to be sent to the server via the communication module.

[0250] Step 3:

[0251] The server receives data sent from the terminal. It starts a data cleansing process, removing outliers and correcting missing data to create clean data.

[0252] Step 4:

[0253] The server analyzes the user's exercise performance using clean data. During the analysis, it calculates specific metrics (e.g., average pace, calorie expenditure) and evaluates the overall exercise performance.

[0254] Step 5:

[0255] Based on the analysis results, the server generates a customized exercise plan that takes into account the user's past history data and fitness goals. The plan includes specific exercises, intensity, and frequency.

[0256] Step 6:

[0257] The device receives the generated exercise plan and notifies the user. During exercise, it provides real-time advice (e.g., "Adjust your pace") via voice and display devices as feedback.

[0258] Step 7:

[0259] As the user continues training, the device continuously collects data and provides feedback to the server. The server receives this feedback and, if further adjustments are needed, issues instructions through the device in a timely manner.

[0260] Step 8:

[0261] After the training session is complete, the server performs a final analysis and generates a user performance report. This report includes evaluation results and suggestions for the next exercise plan, and is provided to the user via their device.

[0262] (Example 1)

[0263] 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".

[0264] Systems that promote efficient and safe training by providing individually optimized activity plans in real time based on physiological indicators are limited, and there is a particular need for highly accurate analysis that removes outliers and noise, and for continuous assessment of exercise capacity based on historical data.

[0265] 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.

[0266] In this invention, the server includes means for wirelessly communicating with a detection device for collecting physiological indicators, information processing means for receiving information transmitted from the terminal and analyzing the information to evaluate biological activity, and generation means for automatically generating an activity plan individualized for the organism based on the evaluation. This makes it possible to provide a plan optimized for each organism and to provide advice in real time.

[0267] "Physiological indicators" are data that quantifies the user's physical condition and exercise status, such as heart rate, exercise volume, and location information.

[0268] A "detection device" is a device used to sense physiological indicators, such as a heart rate sensor or GPS integrated into a wearable device.

[0269] A "terminal" is a device equipped with communication functions for receiving data acquired from a detection device and transmitting it to a server.

[0270] An "information processing device" is a device that receives data transmitted from a terminal, analyzes that data, and evaluates user activity.

[0271] A "generation device" is a device that has the function of automatically generating individually optimized activity plans based on evaluations by an information processing device.

[0272] A "display means" is a device used to visually communicate generated activity plans and feedback to the user.

[0273] A "voice output device" is a device that provides the user with generated activity plans and real-time feedback via voice.

[0274] The system of the present invention was developed primarily to provide users with personalized exercise plans, and consists of a detector, a terminal, a server, a display means, and an audio output means.

[0275] First, the user wears a wearable device called a detector, which continuously acquires physiological indicators such as heart rate, activity level, and location information. The wearable device integrates a heart rate sensor and GPS receiver.

[0276] Next, the terminal wirelessly receives data acquired from the wearable device using Bluetooth or Wi-Fi and transmits that data to the server in real time. Typically, a smartphone or tablet is used as the terminal.

[0277] After receiving data from the terminal, the server performs a data cleansing process using an information processing device to remove outliers and noise, generating clean data suitable for analysis. Based on the analysis, the user's exercise performance is evaluated based on indicators such as average pace, calorie consumption, and time spent in different heart rate zones. This process utilizes a specialized data analysis algorithm implemented as software.

[0278] The server then uses a generative AI model to generate an exercise plan best suited to the user's fitness level and exercise goals. This exercise plan is generated automatically and includes specific guidance, such as "maintain a steady pace of 110-130 bpm for 20 minutes during your next run."

[0279] After an exercise plan is generated, the device notifies the user. A smart display and earphones are used as display and audio output means, providing the user with information visually and audibly. This allows for real-time, specific feedback during exercise, such as "Please slow down a little."

[0280] For example, when a user goes for a run, the device provides real-time voice instructions based on GPS and heart rate data, such as, "Your current heart rate is too high, please adjust your pace." This feature allows users to train safely and efficiently.

[0281] An example of a prompt message might be, "Analyze heart rate data during running training and generate an exercise plan that advises the user on the optimal pace." By entering this prompt, the system automatically generates a personalized training plan.

[0282] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0283] Step 1:

[0284] When the user wears a wearable device, physiological indicators such as heart rate, amount of exercise, and location information are acquired. This information is recorded in real time and stored in the device. The input is raw data related to the user's movement, and the output is data accumulation in the device.

[0285] Step 2:

[0286] The terminal receives data wirelessly from the wearable device via Bluetooth or Wi-Fi. The input here is the physiological indicator data transmitted from the wearable device, and the output is the temporary storage of the data in the terminal. The terminal prepares to transmit this data to the server immediately after receiving it.

[0287] Step 3:

[0288] The server receives the data transmitted from the terminal and performs data cleansing with an information processing device. The input is various physiological indicator data sent from the terminal, and the output is clean data with noise and outliers removed. At this stage, data processing is performed to detect sudden fluctuations in heart rate and the like and exclude them.

[0289] Step 4:

[0290] The server analyzes the clean data and calculates the user's exercise performance. The input is the cleansed physiological indicator data, and the output is the analyzed indicator data such as average pace, calorie consumption, and time spent in the heart rate zone. Based on this analysis, basic data for evaluating the user's current fitness level is generated.

[0291] Step 5:

[0292] The server uses an AI model to automatically generate an optimized exercise plan for the user based on analyzed metric data. The input is the user's exercise performance analysis results, and the output is a specific exercise plan such as "maintain a constant pace of 110-130 bpm for 20 minutes during your next run." The generated exercise plan is personalized according to the user's characteristics.

[0293] Step 6:

[0294] The terminal notifies the user of the exercise plan received from the server. The plan is presented using a display or audio output means, and feedback is provided during the exercise. At this stage, the generated exercise plan is presented to the user in a practical format. The input is the exercise plan from the server, and the output is the notification to the user and real-time exercise feedback.

[0295] Step 7:

[0296] After training is complete, the server analyzes the data from the exercise session and generates a performance report. This report is then used to plan the next workout. The input is the event data from the exercise session, and the output is the performance report used for planning the next workout. This feedback loop allows the user's athletic ability to continuously improve.

[0297] (Application Example 1)

[0298] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0299] In modern times, individuals face challenges in developing efficient and effective exercise plans and obtaining real-time feedback for their independent fitness activities. Furthermore, the limited support available at home makes it difficult to receive consistent exercise guidance.

[0300] 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.

[0301] In this invention, the server includes communication means for communicating with a detection apparatus that measures biological data, information processing means for processing the biological data received from the communication terminal and evaluating the execution efficiency of exercise, and plan generation means for constructing an exercise plan suitable for the exerciser based on the evaluation. Thereby, the exerciser can consistently receive appropriate exercise guidance and feedback even at home. Furthermore, by using a mobile assistive device, fitness within the home can be performed more efficiently and effectively.

[0302] The "detection apparatus that measures biological data" is a device having a function of collecting physiological information such as the heart rate and amount of exercise from an exerciser.

[0303] The "communication terminal" is a device for receiving data from the detection apparatus and transmitting it to an information processing apparatus or a server.

[0304] The "information processing apparatus" is a device having a function of analyzing the received biological data and evaluating the performance of the exerciser.

[0305] The "plan generation apparatus" is a device that automatically creates an exercise plan optimized for the exerciser based on the analysis result.

[0306] The "mobile assistive device" is a device having a function of autonomously moving to assist exercise within the home and having a role of providing feedback to the user.

[0307] A mode for carrying out the invention will be described below.

[0308] The system that realizes this application is built by combining a wearable device, a communication terminal, a mobile assistive device (a home robot), and a cloud server. By wearing the wearable device, the user's biometric data (heart rate, activity level, location information, etc.) is collected in real time. This data is transmitted to the communication terminal via Bluetooth and then delivered to the cloud server via the internet.

[0309] On the server side, data analysis software such as Python or R is used to perform data cleansing on the received biometric data. This removes outliers and noise, resulting in high-quality data suitable for analysis. Subsequently, an AI model (using TensorFlow or PyTorch) is used to analyze the data and evaluate the current fitness performance of the athletes.

[0310] Based on the analysis results, an AI-generated, customized exercise plan is sent to a communication terminal. This plan is communicated to the user via display or audio output, and real-time feedback is provided during exercise. The mobile assistive device supports the user's exercise within the home and provides the exercise plan visually or audibly as needed.

[0311] For example, when a user performs aerobic exercise indoors, a home robot capable of natural movements, located in another room, provides feedback such as, "Your exercise report is ready. Your exercise level was a little low this week, so we've adjusted your plan for next week." An example of a prompt for the generative AI model could be, "Please suggest an appropriate fitness plan based on the user's heart rate and exercise data."

[0312] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0313] Step 1:

[0314] The user wears a wearable device that collects biometric data in real time. This device measures data such as heart rate, activity level, and location using sensors and transmits it to a communication terminal via Bluetooth. The input is biosensor data, and the output is the reception of data by the communication terminal.

[0315] Step 2:

[0316] The device transmits the received biometric data to a cloud server via the internet. The data processing involved in this process is conversion to a data format based on the required protocol. The input is the received biometric data, and the output is the data sent to the cloud server.

[0317] Step 3:

[0318] The server performs data cleansing on the received data. A Python data analysis library is used to remove outliers and noise. The input is data sent from the terminal, and the output is cleansed, high-quality data.

[0319] Step 4:

[0320] The server inputs cleansed data into an AI model to analyze the user's exercise performance. It uses TensorFlow and PyTorch to collect statistics on average pace and heart rate. The input is cleansed data, and the output is the exercise evaluation result.

[0321] Step 5:

[0322] The server automatically generates an optimized exercise plan for the user based on the analysis results. This generating AI model considers factors such as exercise volume and heart rate to create a customized exercise plan. The input is the analyzed exercise data, and the output is the customized exercise plan.

[0323] Step 6:

[0324] The device receives the exercise plan and communicates it to the user via display and audio output. Specifically, the plan is displayed on the screen, and instructions such as "The next exercise to do is..." are given via audio. The input is the exercise plan sent from the server, and the output is the notification to the user.

[0325] Step 7:

[0326] When a user begins exercising, the device transmits the exercise plan to a mobile assistive device, which then provides feedback at designated intervals. Specifically, the assistive device might offer voice advice such as, "Maintain your pace." The input is the exercise plan from the device, and the output is the provision of feedback.

[0327] 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.

[0328] The system of the present invention assists in optimizing the user's exercise training and, by combining it with an emotion engine, provides more personalized feedback. The system comprises a detection device, a terminal, an information processing device, a display device, and an audio output device, as well as an emotion engine.

[0329] First, the user wears a wearable device to collect physiological and emotional data such as heart rate, activity level, voice, and facial expressions. This data is collected in real time, and the device receives it. The device then sends the data to a server via a communication module. The server receives the data and performs data cleansing on the physiological data to create clean data suitable for analysis.

[0330] Next, the server analyzes clean data to evaluate the user's exercise performance. This evaluation includes metrics such as exercise efficiency and time spent in different heart rate zones. Simultaneously, the emotion engine analyzes the user's voice and facial expression data to identify the user's current emotional state. The server uses this information comprehensively to generate an optimized exercise plan and feedback for the user.

[0331] This exercise plan is individually tailored based on performance data and emotional state, and its content and intensity may be modified. For example, if an emotional state indicating stress is identified, the plan can be adjusted to reduce the exercise intensity. On the other hand, if high motivation is recognized, challenging training can be suggested.

[0332] The generated exercise plan is sent to the device and notified to the user. The device provides real-time feedback as the user continues exercising. Through the display and audio output devices, it provides praise such as "Great pace!" and advice such as "Let's take a short break" to boost the user's motivation.

[0333] For example, if a user is training while extremely fatigued, the emotion engine recognizes this emotional state as "fatigue." In response, the server reduces the exercise intensity and provides feedback to encourage relaxation. Conversely, if the user is motivated and energetic, the system recommends plans such as high-intensity interval training and provides feedback to encourage challenges. In this way, the system improves training effectiveness and user experience by providing dynamic feedback tailored to the user's emotional state.

[0334] The following describes the processing flow.

[0335] Step 1:

[0336] The user puts on a wearable device and begins training. The device continuously acquires physiological and emotional data such as heart rate, exercise level, voice, and facial expressions.

[0337] Step 2:

[0338] The terminal receives data from the wearable device in real time. This data includes heart rate, location information, voice data, and facial recognition data. The terminal temporarily stores this data and sends it to the server in batch processing at predetermined intervals.

[0339] Step 3:

[0340] The server receives the data sent from the terminal and starts the data cleansing process. This cleansing removes abnormal values ​​and noise, and appropriately fills in any missing data.

[0341] Step 4:

[0342] The server analyzes clean data to evaluate the user's exercise performance. Evaluation items include running pace, calorie consumption, and time spent in different heart rate zones, and the overall effect of the exercise is calculated from these factors.

[0343] Step 5:

[0344] The server uses an emotion engine to analyze voice and facial expression data to identify the user's emotional state. Emotional states are categorized into joy, stress, fatigue, etc., and each state requires a different motor plan.

[0345] Step 6:

[0346] The server generates a customized exercise plan tailored to the user based on exercise performance evaluation and emotional state. The generated exercise plan is adapted to the user's condition, for example, by adjusting the training intensity or suggesting specific exercises.

[0347] Step 7:

[0348] The device receives the generated exercise plan and notifies the user. During exercise, it also provides real-time feedback, including advice and motivational messages tailored to the user's emotional state, through a display device and audio output device.

[0349] Step 8:

[0350] As the user continues their training, the device continuously collects new data and sends it to the server. Based on this data, the server updates the exercise plan as needed and provides further advice in real time through the device.

[0351] Step 9:

[0352] After the training is complete, the server performs a final analysis and generates a feedback report for the user. This report includes the results of the exercise performed, changes in emotions, and recommendations for the next training session, and is provided to the user via their device.

[0353] (Example 2)

[0354] 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".

[0355] Many exercise instruction systems focus on collecting physiological data and evaluating exercise performance, but they fail to provide feedback that takes into account the exerciser's emotional state. As a result, it is difficult to provide training plans tailored to individual exercisers, and maintaining motivation and maximizing training effectiveness are challenging.

[0356] 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.

[0357] In this invention, the server includes means for using individual devices that communicate with a device that collects physiological and emotional data of an athlete; means for using information processing units that cleanse the data received from the individual devices and analyze exercise performance and emotional state; and means for using a generation device that generates an optimized exercise plan and emotionally appropriate feedback based on the analysis results. This makes it possible to provide personalized feedback that takes into account the athlete's emotional state in addition to their physiological data.

[0358] A "participant" is a person from whom motor and emotional data are collected using individualized devices.

[0359] "Physiological data" refers to data that quantifies the physical state of an exerciser, such as heart rate and exercise volume.

[0360] "Emotional data" refers to data that indicates the emotional state of a person, analyzed from their voice, facial expressions, and other factors.

[0361] An "individual device" is a terminal that collects physiological and emotional data from an exerciser and communicates that data to a server.

[0362] "Data cleansing" is the process of preparing data for analysis by imputing missing values ​​and removing noise.

[0363] An "information processing unit" is a system element that has the function of analyzing received data to evaluate motor performance and emotional state.

[0364] A "generation device" is a device that generates an optimized exercise plan and feedback for the exerciser based on the analysis results.

[0365] An "emotion engine" is a software or hardware component that analyzes a person's voice and facial expressions to identify their emotional state.

[0366] "Data communication means" refers to a means of communication that allows individual devices to send data about athletes to a server and receive feedback from the server.

[0367] This invention is a system that provides individually optimized feedback and exercise plans using the physiological and emotional data of the exerciser. Specifically, it begins with the exerciser collecting physiological data such as heart rate and exercise volume using a wearable device, as well as emotional data such as voice and facial expressions. This data is temporarily stored in an individual device and then transmitted to a server via communication means such as Bluetooth or Wi-Fi.

[0368] The server performs data cleansing based on the received data. This involves preprocessing such as noise reduction and missing value imputation using data processing libraries in Python or Java. After obtaining clean data, the server performs information processing based on this data to evaluate motor performance and emotional state. TensorFlow machine learning models are used for this evaluation, and emotional data, in particular, is analyzed by an emotion engine.

[0369] Based on the analysis results, the server generates an optimal exercise plan and feedback for the exerciser and transmits it to the individual device. The individual device then provides the exercise plan to the exerciser and offers real-time feedback via voice and display. This allows the exerciser to adjust their training on the spot.

[0370] For example, if an exerciser indicates an emotional state of "fatigue," the server will generate an exercise plan that reduces the intensity and promotes relaxation. Conversely, if the exerciser indicates "high motivation," it will suggest high-intensity training and generate feedback to further boost motivation.

[0371] Examples of prompts to input into a generative AI model are as follows:

[0372] "Generate an appropriate training plan based on the user's emotional state. Example data: Exercise efficiency = 80%, Emotional state = High motivation."

[0373] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0374] Step 1:

[0375] The user puts on a wearable device and begins exercising. The device collects physiological and emotional data in real time, such as heart rate, exercise level, voice, and facial expressions. Input is raw data from sensors, and output is stored on the device in digital data format.

[0376] Step 2:

[0377] The terminal receives data collected from wearable devices and transfers it to a server via Bluetooth or Wi-Fi. The input is digital data, and the output is the data transmission state to the server over the network. During this process, the terminal performs data format conversion and compression to ensure efficient transfer.

[0378] Step 3:

[0379] The server receives data sent from the terminal and performs data cleansing. The input is raw data containing missing values ​​and noise, and the output is clean data that can be analyzed. The server uses Python data processing libraries to remove noise and imputate data.

[0380] Step 4:

[0381] The server analyzes exercise performance and emotional state based on clean data. The input is cleansed physiological and emotional data, and the output is the evaluated metrics and emotional state. The server uses TensorFlow to run a machine learning model with an emotion engine to perform sentiment analysis.

[0382] Step 5:

[0383] The server generates an optimal exercise plan and feedback based on the analysis results. The input is analyzed exercise and emotional data, and the output is a personalized exercise plan and messages. The server considers training advice tailored to exercise efficiency and emotional state, and generates feedback using a generative AI model.

[0384] Step 6:

[0385] The terminal receives exercise plans and feedback transmitted from the server and notifies the user. Input is exercise plan data from the server, and output is message displays and audio alerts to the user. The terminal, through its display and audio output devices, provides real-time advice and encouraging messages to enhance the user's training experience.

[0386] (Application Example 2)

[0387] 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."

[0388] In exercise training, there is a need to provide optimal exercise strategies tailored to each individual's physiological and emotional state. However, existing training support systems struggle to analyze an individual's emotional state in real time and adaptively adjust the training plan based on the results. Therefore, challenges remain in improving exercise efficiency and maintaining motivation.

[0389] 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.

[0390] In this invention, the server includes means for communicating with a sensing device for collecting physiological information of a person; information processing device means for receiving information transmitted from the terminal and analyzing that information to evaluate the person's athletic ability; and emotion processing device means for analyzing the person's emotional state during exercise and dynamically adjusting the exercise strategy and feedback. This makes it possible to generate an optimal exercise plan for the user and provide personalized feedback in real time.

[0391] A "sensing device" is a device used to collect physiological information about a person in real time.

[0392] A "terminal" is a device that receives physiological information from a sensing device and transmits that information to an information processing device.

[0393] An "information processing device" is an electronic device used to analyze received physiological information and evaluate a person's motor skills.

[0394] A "generation system" is a set of devices that generates an optimized motor strategy based on data on a person's motor skills evaluated by information processing equipment.

[0395] A "display device" is a device used to visually communicate a generated motor strategy to a person.

[0396] A "voice output device" is a device that transmits generated motor strategies and feedback to a person as voice.

[0397] An "emotional processing device" is an electronic device that analyzes a person's emotional state during movement and dynamically adjusts movement strategies and feedback based on the results.

[0398] "Information cleansing means" are data correction methods that remove abnormal values ​​and duplicate information from physiological data in order to perform accurate analysis.

[0399] "Health level" is an indicator of an individual's overall health status, assessed based on their past exercise data.

[0400] One embodiment of this invention is configured as a system that optimizes exercise training and provides personalized feedback. The user collects physiological information such as heart rate and exercise volume through a sensing device. The terminal then receives the data in real time and transfers it to an information processing device.

[0401] The information processing equipment receives and analyzes physiological information from wearable devices such as smartwatches running Google Wear OS. Furthermore, it performs facial recognition and emotion analysis using software such as Python and OpenCV. Based on the data obtained from this analysis, the generation system creates an exercise strategy optimized for the user.

[0402] This exercise strategy is dynamically adjusted according to the user's state. The server provides feedback through display devices and audio output devices. For example, if the user is highly motivated, it can recommend high-intensity exercise.

[0403] As a concrete example, when a user is jogging at home, this system provides voice feedback from a robot saying, "Your pace is good!" Furthermore, if fatigue is detected, it can advise, "Let's slow down a bit." Such feedback is provided based on generated prompts.

[0404] Example of a prompt:

[0405] "The user is experiencing fatigue during their morning exercise. Please generate advice to encourage relaxation."

[0406] This allows users to receive optimal exercise feedback tailored to their current state, which is expected to improve performance and maintain motivation.

[0407] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0408] Step 1:

[0409] The user puts on a wearable device and begins exercising. The wearable device senses physiological information such as heart rate and exercise intensity in real time and transmits it as data to the terminal. The input is the user's physiological information, and the output is the transmission of data to the terminal.

[0410] Step 2:

[0411] The terminal transfers the received physiological information to the server. Time information is added to the information, and it is formatted to be suitable for analysis on the server. The input is physiological information from the wearable device, and the output is formatted data.

[0412] Step 3:

[0413] The server cleanses the received data, removing noise and outliers. This creates clean data suitable for analysis. The input is physiological information from the terminal, and the output is clean data.

[0414] Step 4:

[0415] The server evaluates the user's athletic performance using clean data. A Python script is used to analyze the data and calculate indices. The athletic ability assessment based on this analysis is then output.

[0416] Step 5:

[0417] The server uses OpenCV to analyze the user's facial expression data and perform emotion processing. This analyzes the user's emotional state and outputs the result as emotion data. The input is the user's facial expression data, and the output is the user's emotional state.

[0418] Step 6:

[0419] The server generates an exercise strategy based on motor skill assessment and emotional data. It uses a generative AI model to generate prompts and dynamically adjust the exercise plan. The input is motor skill assessment and emotional data, and the output is the exercise strategy.

[0420] Step 7:

[0421] The generated motor strategy is communicated to the user via a terminal. Real-time feedback is provided using an audio output device and a display device. The input is the generated motor strategy, and the output is the feedback to the user.

[0422] Step 8:

[0423] The user adjusts their movements based on the feedback provided, and subsequent movement data is continuously collected, initiating the next loop. The input is the content of the feedback, and the output is the user's movement behavior.

[0424] 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.

[0425] 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.

[0426] 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.

[0427] [Third Embodiment]

[0428] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0429] 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.

[0430] 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).

[0431] 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.

[0432] 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.

[0433] 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).

[0434] 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.

[0435] 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.

[0436] 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.

[0437] 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.

[0438] 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.

[0439] 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".

[0440] An embodiment of the AI ​​sports trainer system of the present invention will be described. This system is designed to optimize the user's exercise and consists of a detection device, a terminal, an information processing device, a display device, and an audio output device.

[0441] First, the user wears a detection device such as a wearable device to acquire physiological data such as heart rate, activity level, and location information. The terminal receives this data in real time via wireless communication and transmits it to the server.

[0442] Next, the server receives a large amount of biometric data and performs data cleansing on each piece of data. This removes unnecessary outliers and noise, generating clean data suitable for analysis. Analysis is then performed on the clean data to evaluate the user's exercise performance. Specifically, indicators such as average pace, calorie consumption, and heart rate zone duration are calculated.

[0443] The server then automatically generates a customized exercise plan based on these metrics, taking into account the user's fitness level and goals. This exercise plan includes specific details such as performing Y minutes of training at Z intensity over X sessions.

[0444] The generated exercise plan is sent to the terminal and notified to the user. The terminal uses a display device and an audio output device to provide the user with information about the plan and feedback during the exercise. For example, if the pace is faster than planned, advice such as "Please slow down a little" is output in real time.

[0445] For example, when a user is running, the device acquires data via GPS and heart rate sensors and provides real-time instructions based on pace and heart rate, such as "Your heart rate is too high, please adjust your speed." After the training is complete, a performance report is generated by the server and incorporated into the plan for the next session.

[0446] As described above, the present invention realizes an efficient and safe exercise program by providing training support tailored to the individual characteristics of the user.

[0447] The following describes the processing flow.

[0448] Step 1:

[0449] The user puts on a wearable device and begins exercising. The device continuously collects physiological data such as heart rate, steps taken, and location information.

[0450] Step 2:

[0451] The terminal receives data from the wearable device in real time. The received data is temporarily stored in storage and then prepared to be sent to the server via the communication module.

[0452] Step 3:

[0453] The server receives data sent from the terminal. It starts a data cleansing process, removing outliers and correcting missing data to create clean data.

[0454] Step 4:

[0455] The server analyzes the user's exercise performance using clean data. During the analysis, it calculates specific metrics (e.g., average pace, calorie expenditure) and evaluates the overall exercise performance.

[0456] Step 5:

[0457] Based on the analysis results, the server generates a customized exercise plan that takes into account the user's past history data and fitness goals. The plan includes specific exercises, intensity, and frequency.

[0458] Step 6:

[0459] The device receives the generated exercise plan and notifies the user. During exercise, it provides real-time advice (e.g., "Adjust your pace") via voice and display devices as feedback.

[0460] Step 7:

[0461] As the user continues training, the device continuously collects data and provides feedback to the server. The server receives this feedback and, if further adjustments are needed, issues instructions through the device in a timely manner.

[0462] Step 8:

[0463] After the training session is complete, the server performs a final analysis and generates a user performance report. This report includes evaluation results and suggestions for the next exercise plan, and is provided to the user via their device.

[0464] (Example 1)

[0465] 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."

[0466] Systems that promote efficient and safe training by providing individually optimized activity plans in real time based on physiological indicators are limited, and there is a particular need for highly accurate analysis that removes outliers and noise, and for continuous assessment of exercise capacity based on historical data.

[0467] 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.

[0468] In this invention, the server includes means for wirelessly communicating with a detection device for collecting physiological indicators, information processing means for receiving information transmitted from the terminal and analyzing the information to evaluate biological activity, and generation means for automatically generating an activity plan individualized for the organism based on the evaluation. This makes it possible to provide a plan optimized for each organism and to provide advice in real time.

[0469] "Physiological indicators" are data that quantifies the user's physical condition and exercise status, such as heart rate, exercise volume, and location information.

[0470] A "detection device" is a device used to sense physiological indicators, such as a heart rate sensor or GPS integrated into a wearable device.

[0471] A "terminal" is a device equipped with communication functions for receiving data acquired from a detection device and transmitting it to a server.

[0472] An "information processing device" is a device that receives data transmitted from a terminal, analyzes that data, and evaluates user activity.

[0473] A "generation device" is a device that has the function of automatically generating individually optimized activity plans based on evaluations by an information processing device.

[0474] A "display means" is a device used to visually communicate generated activity plans and feedback to the user.

[0475] A "voice output device" is a device that provides the user with generated activity plans and real-time feedback via voice.

[0476] The system of the present invention was developed primarily to provide users with personalized exercise plans, and consists of a detector, a terminal, a server, a display means, and an audio output means.

[0477] First, the user wears a wearable device called a detector, which continuously acquires physiological indicators such as heart rate, activity level, and location information. The wearable device integrates a heart rate sensor and GPS receiver.

[0478] Next, the terminal wirelessly receives data acquired from the wearable device using Bluetooth or Wi-Fi and transmits that data to the server in real time. Typically, a smartphone or tablet is used as the terminal.

[0479] After receiving data from the terminal, the server performs a data cleansing process using an information processing device to remove outliers and noise, generating clean data suitable for analysis. Based on the analysis, the user's exercise performance is evaluated based on indicators such as average pace, calorie consumption, and time spent in different heart rate zones. This process utilizes a specialized data analysis algorithm implemented as software.

[0480] The server then uses a generative AI model to generate an exercise plan best suited to the user's fitness level and exercise goals. This exercise plan is generated automatically and includes specific guidance, such as "maintain a steady pace of 110-130 bpm for 20 minutes during your next run."

[0481] After an exercise plan is generated, the device notifies the user. A smart display and earphones are used as display and audio output means, providing the user with information visually and audibly. This allows for real-time, specific feedback during exercise, such as "Please slow down a little."

[0482] For example, when a user goes for a run, the device provides real-time voice instructions based on GPS and heart rate data, such as, "Your current heart rate is too high, please adjust your pace." This feature allows users to train safely and efficiently.

[0483] An example of a prompt message might be, "Analyze heart rate data during running training and generate an exercise plan that advises the user on the optimal pace." By entering this prompt, the system automatically generates a personalized training plan.

[0484] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0485] Step 1:

[0486] By wearing a wearable device, users can acquire physiological indicators such as heart rate, activity level, and location information. This information is recorded in real time and stored within the device. The input is raw data related to the user's exercise, and the output is the data stored within the device.

[0487] Step 2:

[0488] The terminal wirelessly receives data from the wearable device via Bluetooth or Wi-Fi. The input here is physiological indicator data transmitted from the wearable device, and the output is the temporary storage of this data within the terminal. Immediately after receiving the data, the terminal prepares to send it to the server.

[0489] Step 3:

[0490] The server receives data transmitted from the terminal and performs data cleansing using an information processing device. The input consists of various physiological indicator data sent from the terminal, and the output is clean data from which noise and outliers have been removed. At this stage, data processing is performed to detect and exclude rapid fluctuations in heart rate, etc.

[0491] Step 4:

[0492] The server analyzes clean data and calculates the user's exercise performance. The input is cleansed physiological indicator data, and the output is analyzed indicator data such as average pace, calorie consumption, and heart rate zone duration. This analysis generates foundational data to assess the user's current fitness level.

[0493] Step 5:

[0494] The server uses an AI model to automatically generate an optimized exercise plan for the user based on analyzed metric data. The input is the user's exercise performance analysis results, and the output is a specific exercise plan such as "maintain a constant pace of 110-130 bpm for 20 minutes during your next run." The generated exercise plan is personalized according to the user's characteristics.

[0495] Step 6:

[0496] The terminal notifies the user of the exercise plan received from the server. The plan is presented using a display or audio output means, and feedback is provided during the exercise. At this stage, the generated exercise plan is presented to the user in a practical format. The input is the exercise plan from the server, and the output is the notification to the user and real-time exercise feedback.

[0497] Step 7:

[0498] After training is complete, the server analyzes the data from the exercise session and generates a performance report. This report is then used to plan the next workout. The input is the event data from the exercise session, and the output is the performance report used for planning the next workout. This feedback loop allows the user's athletic ability to continuously improve.

[0499] (Application Example 1)

[0500] 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."

[0501] In modern times, individuals face challenges in developing efficient and effective exercise plans and obtaining real-time feedback for their independent fitness activities. Furthermore, the limited support available at home makes it difficult to receive consistent exercise guidance.

[0502] 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.

[0503] In this invention, the server includes communication means for communicating with a detection device that measures biological data, information processing means for processing the biological data received from the communication terminal and evaluating the efficiency of exercise execution, and plan generation means for constructing an exercise plan suitable for the exerciser based on the evaluation. This enables the exerciser to consistently receive appropriate exercise guidance and feedback even at home. Furthermore, by using a mobile assistive device, fitness at home can be performed more efficiently and effectively.

[0504] A "detection device for measuring biometric data" is a device that has the function of collecting physiological information such as heart rate and exercise volume from an exerciser.

[0505] A "communication terminal" is a device that receives data from a detection device and transmits it to an information processing device or server.

[0506] An "information processing device" is a device that analyzes received biological data and has the function of evaluating the performance of an athlete.

[0507] A "plan generation device" is a device that automatically creates an exercise plan optimized for the individual based on the analysis results.

[0508] A "mobile assistive device" is a device that has the function of moving autonomously to support exercise within the home and plays a role in providing feedback to the user.

[0509] The embodiments for carrying out the invention are described below.

[0510] The system that realizes this application is built by combining a wearable device, a communication terminal, a mobile assistive device (a home robot), and a cloud server. By wearing the wearable device, the user's biometric data (heart rate, activity level, location information, etc.) is collected in real time. This data is transmitted to the communication terminal via Bluetooth and then delivered to the cloud server via the internet.

[0511] On the server side, data analysis software such as Python or R is used to perform data cleansing on the received biometric data. This removes outliers and noise, resulting in high-quality data suitable for analysis. Subsequently, an AI model (using TensorFlow or PyTorch) is used to analyze the data and evaluate the current fitness performance of the athletes.

[0512] Based on the analysis results, an AI-generated, customized exercise plan is sent to a communication terminal. This plan is communicated to the user via display or audio output, and real-time feedback is provided during exercise. The mobile assistive device supports the user's exercise within the home and provides the exercise plan visually or audibly as needed.

[0513] For example, when a user performs aerobic exercise indoors, a home robot capable of natural movements, located in another room, provides feedback such as, "Your exercise report is ready. Your exercise level was a little low this week, so we've adjusted your plan for next week." An example of a prompt for the generative AI model could be, "Please suggest an appropriate fitness plan based on the user's heart rate and exercise data."

[0514] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0515] Step 1:

[0516] The user wears a wearable device that collects biometric data in real time. This device measures data such as heart rate, activity level, and location using sensors and transmits it to a communication terminal via Bluetooth. The input is biosensor data, and the output is the reception of data by the communication terminal.

[0517] Step 2:

[0518] The device transmits the received biometric data to a cloud server via the internet. The data processing involved in this process is conversion to a data format based on the required protocol. The input is the received biometric data, and the output is the data sent to the cloud server.

[0519] Step 3:

[0520] The server performs data cleansing on the received data. A Python data analysis library is used to remove outliers and noise. The input is data sent from the terminal, and the output is cleansed, high-quality data.

[0521] Step 4:

[0522] The server inputs cleansed data into an AI model to analyze the user's exercise performance. It uses TensorFlow and PyTorch to collect statistics on average pace and heart rate. The input is cleansed data, and the output is the exercise evaluation result.

[0523] Step 5:

[0524] The server automatically generates an optimized exercise plan for the user based on the analysis results. This generating AI model considers factors such as exercise volume and heart rate to create a customized exercise plan. The input is the analyzed exercise data, and the output is the customized exercise plan.

[0525] Step 6:

[0526] The device receives the exercise plan and communicates it to the user via display and audio output. Specifically, the plan is displayed on the screen, and instructions such as "The next exercise to do is..." are given via audio. The input is the exercise plan sent from the server, and the output is the notification to the user.

[0527] Step 7:

[0528] When a user begins exercising, the device transmits the exercise plan to a mobile assistive device, which then provides feedback at designated intervals. Specifically, the assistive device might offer voice advice such as, "Maintain your pace." The input is the exercise plan from the device, and the output is the provision of feedback.

[0529] 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.

[0530] The system of the present invention assists in optimizing the user's exercise training and, by combining it with an emotion engine, provides more personalized feedback. The system comprises a detection device, a terminal, an information processing device, a display device, and an audio output device, as well as an emotion engine.

[0531] First, the user wears a wearable device to collect physiological and emotional data such as heart rate, activity level, voice, and facial expressions. This data is collected in real time, and the device receives it. The device then sends the data to a server via a communication module. The server receives the data and performs data cleansing on the physiological data to create clean data suitable for analysis.

[0532] Next, the server analyzes clean data to evaluate the user's exercise performance. This evaluation includes metrics such as exercise efficiency and time spent in different heart rate zones. Simultaneously, the emotion engine analyzes the user's voice and facial expression data to identify the user's current emotional state. The server uses this information comprehensively to generate an optimized exercise plan and feedback for the user.

[0533] This exercise plan is individually tailored based on performance data and emotional state, and its content and intensity may be modified. For example, if an emotional state indicating stress is identified, the plan can be adjusted to reduce the exercise intensity. On the other hand, if high motivation is recognized, challenging training can be suggested.

[0534] The generated exercise plan is sent to the device and notified to the user. The device provides real-time feedback as the user continues exercising. Through the display and audio output devices, it provides praise such as "Great pace!" and advice such as "Let's take a short break" to boost the user's motivation.

[0535] For example, if a user is training while extremely fatigued, the emotion engine recognizes this emotional state as "fatigue." In response, the server reduces the exercise intensity and provides feedback to encourage relaxation. Conversely, if the user is motivated and energetic, the system recommends plans such as high-intensity interval training and provides feedback to encourage challenges. In this way, the system improves training effectiveness and user experience by providing dynamic feedback tailored to the user's emotional state.

[0536] The following describes the processing flow.

[0537] Step 1:

[0538] The user puts on a wearable device and begins training. The device continuously acquires physiological and emotional data such as heart rate, exercise level, voice, and facial expressions.

[0539] Step 2:

[0540] The terminal receives data from the wearable device in real time. This data includes heart rate, location information, voice data, and facial recognition data. The terminal temporarily stores this data and sends it to the server in batch processing at predetermined intervals.

[0541] Step 3:

[0542] The server receives the data sent from the terminal and starts the data cleansing process. This cleansing removes abnormal values ​​and noise, and appropriately fills in any missing data.

[0543] Step 4:

[0544] The server analyzes clean data to evaluate the user's exercise performance. Evaluation items include running pace, calorie consumption, and time spent in different heart rate zones, and the overall effect of the exercise is calculated from these factors.

[0545] Step 5:

[0546] The server uses an emotion engine to analyze voice and facial expression data to identify the user's emotional state. Emotional states are categorized into joy, stress, fatigue, etc., and each state requires a different motor plan.

[0547] Step 6:

[0548] The server generates a customized exercise plan tailored to the user based on exercise performance evaluation and emotional state. The generated exercise plan is adapted to the user's condition, for example, by adjusting the training intensity or suggesting specific exercises.

[0549] Step 7:

[0550] The device receives the generated exercise plan and notifies the user. During exercise, it also provides real-time feedback, including advice and motivational messages tailored to the user's emotional state, through a display device and audio output device.

[0551] Step 8:

[0552] As the user continues their training, the device continuously collects new data and sends it to the server. Based on this data, the server updates the exercise plan as needed and provides further advice in real time through the device.

[0553] Step 9:

[0554] After the training is complete, the server performs a final analysis and generates a feedback report for the user. This report includes the results of the exercise performed, changes in emotions, and recommendations for the next training session, and is provided to the user via their device.

[0555] (Example 2)

[0556] 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."

[0557] Many exercise instruction systems focus on collecting physiological data and evaluating exercise performance, but they fail to provide feedback that takes into account the exerciser's emotional state. As a result, it is difficult to provide training plans tailored to individual exercisers, and maintaining motivation and maximizing training effectiveness are challenging.

[0558] 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.

[0559] In this invention, the server includes means for using individual devices that communicate with a device that collects physiological and emotional data of an athlete; means for using information processing units that cleanse the data received from the individual devices and analyze exercise performance and emotional state; and means for using a generation device that generates an optimized exercise plan and emotionally appropriate feedback based on the analysis results. This makes it possible to provide personalized feedback that takes into account the athlete's emotional state in addition to their physiological data.

[0560] A "participant" is a person from whom motor and emotional data are collected using individualized devices.

[0561] "Physiological data" refers to data that quantifies the physical state of an exerciser, such as heart rate and exercise volume.

[0562] "Emotional data" refers to data that indicates the emotional state of a person, analyzed from their voice, facial expressions, and other factors.

[0563] An "individual device" is a terminal that collects physiological and emotional data from an exerciser and communicates that data to a server.

[0564] "Data cleansing" is the process of preparing data for analysis by imputing missing values ​​and removing noise.

[0565] An "information processing unit" is a system element that has the function of analyzing received data to evaluate motor performance and emotional state.

[0566] A "generation device" is a device that generates an optimized exercise plan and feedback for the exerciser based on the analysis results.

[0567] An "emotion engine" is a software or hardware component that analyzes a person's voice and facial expressions to identify their emotional state.

[0568] "Data communication means" refers to a means of communication that allows individual devices to send data about athletes to a server and receive feedback from the server.

[0569] This invention is a system that provides individually optimized feedback and exercise plans using the physiological and emotional data of the exerciser. Specifically, it begins with the exerciser collecting physiological data such as heart rate and exercise volume using a wearable device, as well as emotional data such as voice and facial expressions. This data is temporarily stored in an individual device and then transmitted to a server via communication means such as Bluetooth or Wi-Fi.

[0570] The server performs data cleansing based on the received data. This involves preprocessing such as noise reduction and missing value imputation using data processing libraries in Python or Java. After obtaining clean data, the server performs information processing based on this data to evaluate motor performance and emotional state. TensorFlow machine learning models are used for this evaluation, and emotional data, in particular, is analyzed by an emotion engine.

[0571] Based on the analysis results, the server generates an optimal exercise plan and feedback for the exerciser and transmits it to the individual device. The individual device then provides the exercise plan to the exerciser and offers real-time feedback via voice and display. This allows the exerciser to adjust their training on the spot.

[0572] For example, if an exerciser indicates an emotional state of "fatigue," the server will generate an exercise plan that reduces the intensity and promotes relaxation. Conversely, if the exerciser indicates "high motivation," it will suggest high-intensity training and generate feedback to further boost motivation.

[0573] Examples of prompts to input into a generative AI model are as follows:

[0574] "Generate an appropriate training plan based on the user's emotional state. Example data: Exercise efficiency = 80%, Emotional state = High motivation."

[0575] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0576] Step 1:

[0577] The user puts on a wearable device and begins exercising. The device collects physiological and emotional data in real time, such as heart rate, exercise level, voice, and facial expressions. Input is raw data from sensors, and output is stored on the device in digital data format.

[0578] Step 2:

[0579] The terminal receives data collected from wearable devices and transfers it to a server via Bluetooth or Wi-Fi. The input is digital data, and the output is the data transmission state to the server over the network. During this process, the terminal performs data format conversion and compression to ensure efficient transfer.

[0580] Step 3:

[0581] The server receives data sent from the terminal and performs data cleansing. The input is raw data containing missing values ​​and noise, and the output is clean data that can be analyzed. The server uses Python data processing libraries to remove noise and imputate data.

[0582] Step 4:

[0583] The server analyzes exercise performance and emotional state based on clean data. The input is cleansed physiological and emotional data, and the output is the evaluated metrics and emotional state. The server uses TensorFlow to run a machine learning model with an emotion engine to perform sentiment analysis.

[0584] Step 5:

[0585] The server generates an optimal exercise plan and feedback based on the analysis results. The input is analyzed exercise and emotional data, and the output is a personalized exercise plan and messages. The server considers training advice tailored to exercise efficiency and emotional state, and generates feedback using a generative AI model.

[0586] Step 6:

[0587] The terminal receives exercise plans and feedback transmitted from the server and notifies the user. Input is exercise plan data from the server, and output is message displays and audio alerts to the user. The terminal, through its display and audio output devices, provides real-time advice and encouraging messages to enhance the user's training experience.

[0588] (Application Example 2)

[0589] 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."

[0590] In exercise training, there is a need to provide optimal exercise strategies tailored to each individual's physiological and emotional state. However, existing training support systems struggle to analyze an individual's emotional state in real time and adaptively adjust the training plan based on the results. Therefore, challenges remain in improving exercise efficiency and maintaining motivation.

[0591] 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.

[0592] In this invention, the server includes means for communicating with a sensing device for collecting physiological information of a person; information processing device means for receiving information transmitted from the terminal and analyzing that information to evaluate the person's athletic ability; and emotion processing device means for analyzing the person's emotional state during exercise and dynamically adjusting the exercise strategy and feedback. This makes it possible to generate an optimal exercise plan for the user and provide personalized feedback in real time.

[0593] A "sensing device" is a device used to collect physiological information about a person in real time.

[0594] A "terminal" is a device that receives physiological information from a sensing device and transmits that information to an information processing device.

[0595] An "information processing device" is an electronic device used to analyze received physiological information and evaluate a person's motor skills.

[0596] A "generation system" is a set of devices that generates an optimized motor strategy based on data on a person's motor skills evaluated by information processing equipment.

[0597] A "display device" is a device used to visually communicate a generated motor strategy to a person.

[0598] A "voice output device" is a device that transmits generated motor strategies and feedback to a person as voice.

[0599] An "emotional processing device" is an electronic device that analyzes a person's emotional state during movement and dynamically adjusts movement strategies and feedback based on the results.

[0600] "Information cleansing means" are data correction methods that remove abnormal values ​​and duplicate information from physiological data in order to perform accurate analysis.

[0601] "Health level" is an indicator of an individual's overall health status, assessed based on their past exercise data.

[0602] One embodiment of this invention is configured as a system that optimizes exercise training and provides personalized feedback. The user collects physiological information such as heart rate and exercise volume through a sensing device. The terminal then receives the data in real time and transfers it to an information processing device.

[0603] The information processing equipment receives and analyzes physiological information from wearable devices such as smartwatches running Google Wear OS. Furthermore, it performs facial recognition and emotion analysis using software such as Python and OpenCV. Based on the data obtained from this analysis, the generation system creates an exercise strategy optimized for the user.

[0604] This exercise strategy is dynamically adjusted according to the user's state. The server provides feedback through display devices and audio output devices. For example, if the user is highly motivated, it can recommend high-intensity exercise.

[0605] As a concrete example, when a user is jogging at home, this system provides voice feedback from a robot saying, "Your pace is good!" Furthermore, if fatigue is detected, it can advise, "Let's slow down a bit." Such feedback is provided based on generated prompts.

[0606] Example of a prompt:

[0607] "The user is experiencing fatigue during their morning exercise. Please generate advice to encourage relaxation."

[0608] This allows users to receive optimal exercise feedback tailored to their current state, which is expected to improve performance and maintain motivation.

[0609] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0610] Step 1:

[0611] The user puts on a wearable device and begins exercising. The wearable device senses physiological information such as heart rate and exercise intensity in real time and transmits it as data to the terminal. The input is the user's physiological information, and the output is the transmission of data to the terminal.

[0612] Step 2:

[0613] The terminal transfers the received physiological information to the server. Time information is added to the information, and it is formatted to be suitable for analysis on the server. The input is physiological information from the wearable device, and the output is formatted data.

[0614] Step 3:

[0615] The server cleanses the received data, removing noise and outliers. This creates clean data suitable for analysis. The input is physiological information from the terminal, and the output is clean data.

[0616] Step 4:

[0617] The server evaluates the user's athletic performance using clean data. A Python script is used to analyze the data and calculate indices. The athletic ability assessment based on this analysis is then output.

[0618] Step 5:

[0619] The server uses OpenCV to analyze the user's facial expression data and perform emotion processing. This analyzes the user's emotional state and outputs the result as emotion data. The input is the user's facial expression data, and the output is the user's emotional state.

[0620] Step 6:

[0621] The server generates an exercise strategy based on motor skill assessment and emotional data. It uses a generative AI model to generate prompts and dynamically adjust the exercise plan. The input is motor skill assessment and emotional data, and the output is the exercise strategy.

[0622] Step 7:

[0623] The generated motor strategy is communicated to the user via a terminal. Real-time feedback is provided using an audio output device and a display device. The input is the generated motor strategy, and the output is the feedback to the user.

[0624] Step 8:

[0625] The user adjusts their movements based on the feedback provided, and subsequent movement data is continuously collected, initiating the next loop. The input is the content of the feedback, and the output is the user's movement behavior.

[0626] 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.

[0627] 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.

[0628] 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.

[0629] [Fourth Embodiment]

[0630] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0631] 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.

[0632] 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).

[0633] 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.

[0634] 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.

[0635] 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).

[0636] 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.

[0637] 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.

[0638] 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.

[0639] 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.

[0640] 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.

[0641] 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.

[0642] 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".

[0643] An embodiment of the AI ​​sports trainer system of the present invention will be described. This system is designed to optimize the user's exercise and consists of a detection device, a terminal, an information processing device, a display device, and an audio output device.

[0644] First, the user wears a detection device such as a wearable device to acquire physiological data such as heart rate, activity level, and location information. The terminal receives this data in real time via wireless communication and transmits it to the server.

[0645] Next, the server receives a large amount of biometric data and performs data cleansing on each piece of data. This removes unnecessary outliers and noise, generating clean data suitable for analysis. Analysis is then performed on the clean data to evaluate the user's exercise performance. Specifically, indicators such as average pace, calorie consumption, and heart rate zone duration are calculated.

[0646] The server then automatically generates a customized exercise plan based on these metrics, taking into account the user's fitness level and goals. This exercise plan includes specific details such as performing Y minutes of training at Z intensity over X sessions.

[0647] The generated exercise plan is sent to the terminal and notified to the user. The terminal uses a display device and an audio output device to provide the user with information about the plan and feedback during the exercise. For example, if the pace is faster than planned, advice such as "Please slow down a little" is output in real time.

[0648] For example, when a user is running, the device acquires data via GPS and heart rate sensors and provides real-time instructions based on pace and heart rate, such as "Your heart rate is too high, please adjust your speed." After the training is complete, a performance report is generated by the server and incorporated into the plan for the next session.

[0649] As described above, the present invention realizes an efficient and safe exercise program by providing training support tailored to the individual characteristics of the user.

[0650] The following describes the processing flow.

[0651] Step 1:

[0652] The user puts on a wearable device and begins exercising. The device continuously collects physiological data such as heart rate, steps taken, and location information.

[0653] Step 2:

[0654] The terminal receives data from the wearable device in real time. The received data is temporarily stored in storage and then prepared to be sent to the server via the communication module.

[0655] Step 3:

[0656] The server receives data sent from the terminal. It starts a data cleansing process, removing outliers and correcting missing data to create clean data.

[0657] Step 4:

[0658] The server analyzes the user's exercise performance using clean data. During the analysis, it calculates specific metrics (e.g., average pace, calorie expenditure) and evaluates the overall exercise performance.

[0659] Step 5:

[0660] Based on the analysis results, the server generates a customized exercise plan that takes into account the user's past history data and fitness goals. The plan includes specific exercises, intensity, and frequency.

[0661] Step 6:

[0662] The device receives the generated exercise plan and notifies the user. During exercise, it provides real-time advice (e.g., "Adjust your pace") via voice and display devices as feedback.

[0663] Step 7:

[0664] As the user continues training, the device continuously collects data and provides feedback to the server. The server receives this feedback and, if further adjustments are needed, issues instructions through the device in a timely manner.

[0665] Step 8:

[0666] After the training session is complete, the server performs a final analysis and generates a user performance report. This report includes evaluation results and suggestions for the next exercise plan, and is provided to the user via their device.

[0667] (Example 1)

[0668] 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".

[0669] Systems that promote efficient and safe training by providing individually optimized activity plans in real time based on physiological indicators are limited, and there is a particular need for highly accurate analysis that removes outliers and noise, and for continuous assessment of exercise capacity based on historical data.

[0670] 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.

[0671] In this invention, the server includes means for wirelessly communicating with a detection device for collecting physiological indicators, information processing means for receiving information transmitted from the terminal and analyzing the information to evaluate biological activity, and generation means for automatically generating an activity plan individualized for the organism based on the evaluation. This makes it possible to provide a plan optimized for each organism and to provide advice in real time.

[0672] "Physiological indicators" are data that quantifies the user's physical condition and exercise status, such as heart rate, exercise volume, and location information.

[0673] A "detection device" is a device used to sense physiological indicators, such as a heart rate sensor or GPS integrated into a wearable device.

[0674] A "terminal" is a device equipped with communication functions for receiving data acquired from a detection device and transmitting it to a server.

[0675] An "information processing device" is a device that receives data transmitted from a terminal, analyzes that data, and evaluates user activity.

[0676] A "generation device" is a device that has the function of automatically generating individually optimized activity plans based on evaluations by an information processing device.

[0677] A "display means" is a device used to visually communicate generated activity plans and feedback to the user.

[0678] A "voice output device" is a device that provides the user with generated activity plans and real-time feedback via voice.

[0679] The system of the present invention was developed primarily to provide users with personalized exercise plans, and consists of a detector, a terminal, a server, a display means, and an audio output means.

[0680] First, the user wears a wearable device called a detector, which continuously acquires physiological indicators such as heart rate, activity level, and location information. The wearable device integrates a heart rate sensor and GPS receiver.

[0681] Next, the terminal wirelessly receives data acquired from the wearable device using Bluetooth or Wi-Fi and transmits that data to the server in real time. Typically, a smartphone or tablet is used as the terminal.

[0682] After receiving data from the terminal, the server performs a data cleansing process using an information processing device to remove outliers and noise, generating clean data suitable for analysis. Based on the analysis, the user's exercise performance is evaluated based on indicators such as average pace, calorie consumption, and time spent in different heart rate zones. This process utilizes a specialized data analysis algorithm implemented as software.

[0683] The server then uses a generative AI model to generate an exercise plan best suited to the user's fitness level and exercise goals. This exercise plan is generated automatically and includes specific guidance, such as "maintain a steady pace of 110-130 bpm for 20 minutes during your next run."

[0684] After an exercise plan is generated, the device notifies the user. A smart display and earphones are used as display and audio output means, providing the user with information visually and audibly. This allows for real-time, specific feedback during exercise, such as "Please slow down a little."

[0685] For example, when a user goes for a run, the device provides real-time voice instructions based on GPS and heart rate data, such as, "Your current heart rate is too high, please adjust your pace." This feature allows users to train safely and efficiently.

[0686] An example of a prompt message might be, "Analyze heart rate data during running training and generate an exercise plan that advises the user on the optimal pace." By entering this prompt, the system automatically generates a personalized training plan.

[0687] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0688] Step 1:

[0689] By wearing a wearable device, users can acquire physiological indicators such as heart rate, activity level, and location information. This information is recorded in real time and stored within the device. The input is raw data related to the user's exercise, and the output is the data stored within the device.

[0690] Step 2:

[0691] The terminal wirelessly receives data from the wearable device via Bluetooth or Wi-Fi. The input here is physiological indicator data transmitted from the wearable device, and the output is the temporary storage of this data within the terminal. Immediately after receiving the data, the terminal prepares to send it to the server.

[0692] Step 3:

[0693] The server receives data transmitted from the terminal and performs data cleansing using an information processing device. The input consists of various physiological indicator data sent from the terminal, and the output is clean data from which noise and outliers have been removed. At this stage, data processing is performed to detect and exclude rapid fluctuations in heart rate, etc.

[0694] Step 4:

[0695] The server analyzes clean data and calculates the user's exercise performance. The input is cleansed physiological indicator data, and the output is analyzed indicator data such as average pace, calorie consumption, and heart rate zone duration. This analysis generates foundational data to assess the user's current fitness level.

[0696] Step 5:

[0697] The server uses an AI model to automatically generate an optimized exercise plan for the user based on analyzed metric data. The input is the user's exercise performance analysis results, and the output is a specific exercise plan such as "maintain a constant pace of 110-130 bpm for 20 minutes during your next run." The generated exercise plan is personalized according to the user's characteristics.

[0698] Step 6:

[0699] The terminal notifies the user of the exercise plan received from the server. The plan is presented using a display or audio output means, and feedback is provided during the exercise. At this stage, the generated exercise plan is presented to the user in a practical format. The input is the exercise plan from the server, and the output is the notification to the user and real-time exercise feedback.

[0700] Step 7:

[0701] After training is complete, the server analyzes the data from the exercise session and generates a performance report. This report is then used to plan the next workout. The input is the event data from the exercise session, and the output is the performance report used for planning the next workout. This feedback loop allows the user's athletic ability to continuously improve.

[0702] (Application Example 1)

[0703] 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".

[0704] In modern times, individuals face challenges in developing efficient and effective exercise plans and obtaining real-time feedback for their independent fitness activities. Furthermore, the limited support available at home makes it difficult to receive consistent exercise guidance.

[0705] 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.

[0706] In this invention, the server includes communication means for communicating with a detection device that measures biological data, information processing means for processing the biological data received from the communication terminal and evaluating the efficiency of exercise execution, and plan generation means for constructing an exercise plan suitable for the exerciser based on the evaluation. This enables the exerciser to consistently receive appropriate exercise guidance and feedback even at home. Furthermore, by using a mobile assistive device, fitness at home can be performed more efficiently and effectively.

[0707] A "detection device for measuring biometric data" is a device that has the function of collecting physiological information such as heart rate and exercise volume from an exerciser.

[0708] A "communication terminal" is a device that receives data from a detection device and transmits it to an information processing device or server.

[0709] An "information processing device" is a device that analyzes received biological data and has the function of evaluating the performance of an athlete.

[0710] A "plan generation device" is a device that automatically creates an exercise plan optimized for the individual based on the analysis results.

[0711] A "mobile assistive device" is a device that has the function of moving autonomously to support exercise within the home and plays a role in providing feedback to the user.

[0712] The embodiments for carrying out the invention are described below.

[0713] The system that realizes this application is built by combining a wearable device, a communication terminal, a mobile assistive device (a home robot), and a cloud server. By wearing the wearable device, the user's biometric data (heart rate, activity level, location information, etc.) is collected in real time. This data is transmitted to the communication terminal via Bluetooth and then delivered to the cloud server via the internet.

[0714] On the server side, data analysis software such as Python or R is used to perform data cleansing on the received biometric data. This removes outliers and noise, resulting in high-quality data suitable for analysis. Subsequently, an AI model (using TensorFlow or PyTorch) is used to analyze the data and evaluate the current fitness performance of the athletes.

[0715] Based on the analysis results, an AI-generated, customized exercise plan is sent to a communication terminal. This plan is communicated to the user via display or audio output, and real-time feedback is provided during exercise. The mobile assistive device supports the user's exercise within the home and provides the exercise plan visually or audibly as needed.

[0716] For example, when a user performs aerobic exercise indoors, a home robot capable of natural movements, located in another room, provides feedback such as, "Your exercise report is ready. Your exercise level was a little low this week, so we've adjusted your plan for next week." An example of a prompt for the generative AI model could be, "Please suggest an appropriate fitness plan based on the user's heart rate and exercise data."

[0717] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0718] Step 1:

[0719] The user wears a wearable device that collects biometric data in real time. This device measures data such as heart rate, activity level, and location using sensors and transmits it to a communication terminal via Bluetooth. The input is biosensor data, and the output is the reception of data by the communication terminal.

[0720] Step 2:

[0721] The device transmits the received biometric data to a cloud server via the internet. The data processing involved in this process is conversion to a data format based on the required protocol. The input is the received biometric data, and the output is the data sent to the cloud server.

[0722] Step 3:

[0723] The server performs data cleansing on the received data. A Python data analysis library is used to remove outliers and noise. The input is data sent from the terminal, and the output is cleansed, high-quality data.

[0724] Step 4:

[0725] The server inputs cleansed data into an AI model to analyze the user's exercise performance. It uses TensorFlow and PyTorch to collect statistics on average pace and heart rate. The input is cleansed data, and the output is the exercise evaluation result.

[0726] Step 5:

[0727] The server automatically generates an optimized exercise plan for the user based on the analysis results. This generating AI model considers factors such as exercise volume and heart rate to create a customized exercise plan. The input is the analyzed exercise data, and the output is the customized exercise plan.

[0728] Step 6:

[0729] The device receives the exercise plan and communicates it to the user via display and audio output. Specifically, the plan is displayed on the screen, and instructions such as "The next exercise to do is..." are given via audio. The input is the exercise plan sent from the server, and the output is the notification to the user.

[0730] Step 7:

[0731] When a user begins exercising, the device transmits the exercise plan to a mobile assistive device, which then provides feedback at designated intervals. Specifically, the assistive device might offer voice advice such as, "Maintain your pace." The input is the exercise plan from the device, and the output is the provision of feedback.

[0732] 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.

[0733] The system of the present invention assists in optimizing the user's exercise training and, by combining it with an emotion engine, provides more personalized feedback. The system comprises a detection device, a terminal, an information processing device, a display device, and an audio output device, as well as an emotion engine.

[0734] First, the user wears a wearable device to collect physiological and emotional data such as heart rate, activity level, voice, and facial expressions. This data is collected in real time, and the device receives it. The device then sends the data to a server via a communication module. The server receives the data and performs data cleansing on the physiological data to create clean data suitable for analysis.

[0735] Next, the server analyzes clean data to evaluate the user's exercise performance. This evaluation includes metrics such as exercise efficiency and time spent in different heart rate zones. Simultaneously, the emotion engine analyzes the user's voice and facial expression data to identify the user's current emotional state. The server uses this information comprehensively to generate an optimized exercise plan and feedback for the user.

[0736] This exercise plan is individually tailored based on performance data and emotional state, and its content and intensity may be modified. For example, if an emotional state indicating stress is identified, the plan can be adjusted to reduce the exercise intensity. On the other hand, if high motivation is recognized, challenging training can be suggested.

[0737] The generated exercise plan is sent to the device and notified to the user. The device provides real-time feedback as the user continues exercising. Through the display and audio output devices, it provides praise such as "Great pace!" and advice such as "Let's take a short break" to boost the user's motivation.

[0738] For example, if a user is training while extremely fatigued, the emotion engine recognizes this emotional state as "fatigue." In response, the server reduces the exercise intensity and provides feedback to encourage relaxation. Conversely, if the user is motivated and energetic, the system recommends plans such as high-intensity interval training and provides feedback to encourage challenges. In this way, the system improves training effectiveness and user experience by providing dynamic feedback tailored to the user's emotional state.

[0739] The following describes the processing flow.

[0740] Step 1:

[0741] The user puts on a wearable device and begins training. The device continuously acquires physiological and emotional data such as heart rate, exercise level, voice, and facial expressions.

[0742] Step 2:

[0743] The terminal receives data from the wearable device in real time. This data includes heart rate, location information, voice data, and facial recognition data. The terminal temporarily stores this data and sends it to the server in batch processing at predetermined intervals.

[0744] Step 3:

[0745] The server receives the data sent from the terminal and starts the data cleansing process. This cleansing removes abnormal values ​​and noise, and appropriately fills in any missing data.

[0746] Step 4:

[0747] The server analyzes clean data to evaluate the user's exercise performance. Evaluation items include running pace, calorie consumption, and time spent in different heart rate zones, and the overall effect of the exercise is calculated from these factors.

[0748] Step 5:

[0749] The server uses an emotion engine to analyze voice and facial expression data to identify the user's emotional state. Emotional states are categorized into joy, stress, fatigue, etc., and each state requires a different motor plan.

[0750] Step 6:

[0751] The server generates a customized exercise plan tailored to the user based on exercise performance evaluation and emotional state. The generated exercise plan is adapted to the user's condition, for example, by adjusting the training intensity or suggesting specific exercises.

[0752] Step 7:

[0753] The device receives the generated exercise plan and notifies the user. During exercise, it also provides real-time feedback, including advice and motivational messages tailored to the user's emotional state, through a display device and audio output device.

[0754] Step 8:

[0755] As the user continues their training, the device continuously collects new data and sends it to the server. Based on this data, the server updates the exercise plan as needed and provides further advice in real time through the device.

[0756] Step 9:

[0757] After the training is complete, the server performs a final analysis and generates a feedback report for the user. This report includes the results of the exercise performed, changes in emotions, and recommendations for the next training session, and is provided to the user via their device.

[0758] (Example 2)

[0759] 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".

[0760] Many exercise instruction systems focus on collecting physiological data and evaluating exercise performance, but they fail to provide feedback that takes into account the exerciser's emotional state. As a result, it is difficult to provide training plans tailored to individual exercisers, and maintaining motivation and maximizing training effectiveness are challenging.

[0761] 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.

[0762] In this invention, the server includes means for using individual devices that communicate with a device that collects physiological and emotional data of an athlete; means for using information processing units that cleanse the data received from the individual devices and analyze exercise performance and emotional state; and means for using a generation device that generates an optimized exercise plan and emotionally appropriate feedback based on the analysis results. This makes it possible to provide personalized feedback that takes into account the athlete's emotional state in addition to their physiological data.

[0763] A "participant" is a person from whom motor and emotional data are collected using individualized devices.

[0764] "Physiological data" refers to data that quantifies the physical state of an exerciser, such as heart rate and exercise volume.

[0765] "Emotional data" refers to data that indicates the emotional state of a person, analyzed from their voice, facial expressions, and other factors.

[0766] An "individual device" is a terminal that collects physiological and emotional data from an exerciser and communicates that data to a server.

[0767] "Data cleansing" is the process of preparing data for analysis by imputing missing values ​​and removing noise.

[0768] An "information processing unit" is a system element that has the function of analyzing received data to evaluate motor performance and emotional state.

[0769] A "generation device" is a device that generates an optimized exercise plan and feedback for the exerciser based on the analysis results.

[0770] An "emotion engine" is a software or hardware component that analyzes a person's voice and facial expressions to identify their emotional state.

[0771] "Data communication means" refers to a means of communication that allows individual devices to send data about athletes to a server and receive feedback from the server.

[0772] This invention is a system that provides individually optimized feedback and exercise plans using the physiological and emotional data of the exerciser. Specifically, it begins with the exerciser collecting physiological data such as heart rate and exercise volume using a wearable device, as well as emotional data such as voice and facial expressions. This data is temporarily stored in an individual device and then transmitted to a server via communication means such as Bluetooth or Wi-Fi.

[0773] The server performs data cleansing based on the received data. This involves preprocessing such as noise reduction and missing value imputation using data processing libraries in Python or Java. After obtaining clean data, the server performs information processing based on this data to evaluate motor performance and emotional state. TensorFlow machine learning models are used for this evaluation, and emotional data, in particular, is analyzed by an emotion engine.

[0774] Based on the analysis results, the server generates an optimal exercise plan and feedback for the exerciser and transmits it to the individual device. The individual device then provides the exercise plan to the exerciser and offers real-time feedback via voice and display. This allows the exerciser to adjust their training on the spot.

[0775] For example, if an exerciser indicates an emotional state of "fatigue," the server will generate an exercise plan that reduces the intensity and promotes relaxation. Conversely, if the exerciser indicates "high motivation," it will suggest high-intensity training and generate feedback to further boost motivation.

[0776] Examples of prompts to input into a generative AI model are as follows:

[0777] "Generate an appropriate training plan based on the user's emotional state. Example data: Exercise efficiency = 80%, Emotional state = High motivation."

[0778] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0779] Step 1:

[0780] The user puts on a wearable device and begins exercising. The device collects physiological and emotional data in real time, such as heart rate, exercise level, voice, and facial expressions. Input is raw data from sensors, and output is stored on the device in digital data format.

[0781] Step 2:

[0782] The terminal receives data collected from wearable devices and transfers it to a server via Bluetooth or Wi-Fi. The input is digital data, and the output is the data transmission state to the server over the network. During this process, the terminal performs data format conversion and compression to ensure efficient transfer.

[0783] Step 3:

[0784] The server receives data sent from the terminal and performs data cleansing. The input is raw data containing missing values ​​and noise, and the output is clean data that can be analyzed. The server uses Python data processing libraries to remove noise and imputate data.

[0785] Step 4:

[0786] The server analyzes exercise performance and emotional state based on clean data. The input is cleansed physiological and emotional data, and the output is the evaluated metrics and emotional state. The server uses TensorFlow to run a machine learning model with an emotion engine to perform sentiment analysis.

[0787] Step 5:

[0788] The server generates an optimal exercise plan and feedback based on the analysis results. The input is analyzed exercise and emotional data, and the output is a personalized exercise plan and messages. The server considers training advice tailored to exercise efficiency and emotional state, and generates feedback using a generative AI model.

[0789] Step 6:

[0790] The terminal receives exercise plans and feedback transmitted from the server and notifies the user. Input is exercise plan data from the server, and output is message displays and audio alerts to the user. The terminal, through its display and audio output devices, provides real-time advice and encouraging messages to enhance the user's training experience.

[0791] (Application Example 2)

[0792] 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".

[0793] In exercise training, there is a need to provide optimal exercise strategies tailored to each individual's physiological and emotional state. However, existing training support systems struggle to analyze an individual's emotional state in real time and adaptively adjust the training plan based on the results. Therefore, challenges remain in improving exercise efficiency and maintaining motivation.

[0794] 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.

[0795] In this invention, the server includes means for communicating with a sensing device for collecting physiological information of a person; information processing device means for receiving information transmitted from the terminal and analyzing that information to evaluate the person's athletic ability; and emotion processing device means for analyzing the person's emotional state during exercise and dynamically adjusting the exercise strategy and feedback. This makes it possible to generate an optimal exercise plan for the user and provide personalized feedback in real time.

[0796] A "sensing device" is a device used to collect physiological information about a person in real time.

[0797] A "terminal" is a device that receives physiological information from a sensing device and transmits that information to an information processing device.

[0798] An "information processing device" is an electronic device used to analyze received physiological information and evaluate a person's motor skills.

[0799] A "generation system" is a set of devices that generates an optimized motor strategy based on data on a person's motor skills evaluated by information processing equipment.

[0800] A "display device" is a device used to visually communicate a generated motor strategy to a person.

[0801] A "voice output device" is a device that transmits generated motor strategies and feedback to a person as voice.

[0802] An "emotional processing device" is an electronic device that analyzes a person's emotional state during movement and dynamically adjusts movement strategies and feedback based on the results.

[0803] "Information cleansing means" are data correction methods that remove abnormal values ​​and duplicate information from physiological data in order to perform accurate analysis.

[0804] "Health level" is an indicator of an individual's overall health status, assessed based on their past exercise data.

[0805] One embodiment of this invention is configured as a system that optimizes exercise training and provides personalized feedback. The user collects physiological information such as heart rate and exercise volume through a sensing device. The terminal then receives the data in real time and transfers it to an information processing device.

[0806] The information processing equipment receives and analyzes physiological information from wearable devices such as smartwatches running Google Wear OS. Furthermore, it performs facial recognition and emotion analysis using software such as Python and OpenCV. Based on the data obtained from this analysis, the generation system creates an exercise strategy optimized for the user.

[0807] This exercise strategy is dynamically adjusted according to the user's state. The server provides feedback through display devices and audio output devices. For example, if the user is highly motivated, it can recommend high-intensity exercise.

[0808] As a concrete example, when a user is jogging at home, this system provides voice feedback from a robot saying, "Your pace is good!" Furthermore, if fatigue is detected, it can advise, "Let's slow down a bit." Such feedback is provided based on generated prompts.

[0809] Example of a prompt:

[0810] "The user is experiencing fatigue during their morning exercise. Please generate advice to encourage relaxation."

[0811] This allows users to receive optimal exercise feedback tailored to their current state, which is expected to improve performance and maintain motivation.

[0812] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0813] Step 1:

[0814] The user puts on a wearable device and begins exercising. The wearable device senses physiological information such as heart rate and exercise intensity in real time and transmits it as data to the terminal. The input is the user's physiological information, and the output is the transmission of data to the terminal.

[0815] Step 2:

[0816] The terminal transfers the received physiological information to the server. Time information is added to the information, and it is formatted to be suitable for analysis on the server. The input is physiological information from the wearable device, and the output is formatted data.

[0817] Step 3:

[0818] The server cleanses the received data, removing noise and outliers. This creates clean data suitable for analysis. The input is physiological information from the terminal, and the output is clean data.

[0819] Step 4:

[0820] The server evaluates the user's athletic performance using clean data. A Python script is used to analyze the data and calculate indices. The athletic ability assessment based on this analysis is then output.

[0821] Step 5:

[0822] The server uses OpenCV to analyze the user's facial expression data and perform emotion processing. This analyzes the user's emotional state and outputs the result as emotion data. The input is the user's facial expression data, and the output is the user's emotional state.

[0823] Step 6:

[0824] The server generates an exercise strategy based on motor skill assessment and emotional data. It uses a generative AI model to generate prompts and dynamically adjust the exercise plan. The input is motor skill assessment and emotional data, and the output is the exercise strategy.

[0825] Step 7:

[0826] The generated motor strategy is communicated to the user via a terminal. Real-time feedback is provided using an audio output device and a display device. The input is the generated motor strategy, and the output is the feedback to the user.

[0827] Step 8:

[0828] The user adjusts their movements based on the feedback provided, and subsequent movement data is continuously collected, initiating the next loop. The input is the content of the feedback, and the output is the user's movement behavior.

[0829] 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.

[0830] 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.

[0831] 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.

[0832] 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.

[0833] 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.

[0834] 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.

[0835] 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.

[0836] 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.

[0837] 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."

[0838] 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.

[0839] 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.

[0840] 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.

[0841] 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.

[0842] 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.

[0843] 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.

[0844] 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.

[0845] 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.

[0846] 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.

[0847] 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.

[0848] 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.

[0849] 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.

[0850] The following is further disclosed regarding the embodiments described above.

[0851] (Claim 1)

[0852] A means of communication with a detection device for collecting physiological data of a person,

[0853] Information processing means that receives data transmitted from the aforementioned terminal, analyzes the data, and evaluates the person's motor performance.

[0854] Based on the above evaluation, a generation means for generating an optimized movement plan for a person,

[0855] Display means and audio output means for communicating the aforementioned exercise plan to a person and providing real-time feedback during exercise,

[0856] A system that includes this.

[0857] (Claim 2)

[0858] The system according to claim 1, further comprising data cleansing means for removing abnormal values ​​and duplicate data and performing accurate analysis, wherein the information processing device is further a data cleansing means.

[0859] (Claim 3)

[0860] The system according to claim 1, further comprising means for evaluating and updating a person's fitness level by comparing it with the person's past exercise data.

[0861] "Example 1"

[0862] (Claim 1)

[0863] A detection device for collecting physiological indicators and means for wireless communication,

[0864] Information processing means for receiving information transmitted from the aforementioned terminal and analyzing that information to evaluate biological activity,

[0865] Based on the aforementioned evaluation, a generation means for automatically generating an activity plan individualized for the living organism,

[0866] A system including display means and voice output means for notifying a living organism of the activity plan and providing real-time advice during the activity.

[0867] (Claim 2)

[0868] The system according to claim 1, wherein the information processing device further comprises data processing means for removing abnormal values ​​and noise and performing highly accurate analysis.

[0869] (Claim 3)

[0870] The system according to claim 1, further comprising means for evaluating and updating motor skills by comparing them with past activity information of the living organism.

[0871] "Application Example 1"

[0872] (Claim 1)

[0873] A detection device for measuring biological data and a communication means for communicating with it,

[0874] Information processing means for processing biometric data received from the aforementioned communication terminal and evaluating the efficiency of exercise execution,

[0875] Based on the aforementioned evaluation, a plan generation means for constructing an exercise plan suitable for the exerciser,

[0876] A display means and an audio output means for communicating the exercise plan to the exerciser and providing dynamic feedback during the execution of the exercise,

[0877] A mobile assistive device to support exercise for people within the home,

[0878] A system that includes this.

[0879] (Claim 2)

[0880] The system according to claim 1, wherein the information processing device further comprises a data cleansing function that removes abnormal or redundant data and performs accurate analysis.

[0881] (Claim 3)

[0882] The system according to claim 1, further comprising a communication terminal that evaluates and updates the fitness status by comparing it with the exerciser's past activity data.

[0883] "Example 2 of combining an emotion engine"

[0884] (Claim 1)

[0885] A device for collecting physiological and emotional data of an athlete and individual means for communicating with it,

[0886] An information processing unit means that receives data transmitted from the individual devices, performs data cleansing, and analyzes the exercise performance and emotional state of the athlete,

[0887] Based on the analysis results, a generation means generates an exercise plan optimized for the exerciser and feedback tailored to their emotions.

[0888] A presentation means including display and audio output for providing the exercise plan and feedback to the exerciser and for providing real-time motivation during the exercise,

[0889] A system that includes this.

[0890] (Claim 2)

[0891] The system according to claim 1, wherein the information processing unit further comprises an emotion engine that analyzes voice and facial expressions to identify the emotional state of the person performing the action.

[0892] (Claim 3)

[0893] The system according to claim 1, further comprising data communication means for the individual device to transmit data based on the emotional state of the person performing the exercise to a server and to receive feedback.

[0894] "Application example 2 when combining with an emotional engine"

[0895] (Claim 1)

[0896] A sensing device and means for communicating with a person to collect physiological information about that person,

[0897] Information processing equipment means that receives information transmitted from the aforementioned terminal, analyzes that information, and evaluates the person's athletic ability,

[0898] Based on the above evaluation, a generation system means for generating an optimized movement strategy for a person,

[0899] A display device and audio output device means for communicating the aforementioned exercise strategy to a person and providing real-time feedback during exercise,

[0900] An emotional processing device that analyzes a person's emotional state during exercise and dynamically adjusts exercise strategies and feedback,

[0901] A system that includes this.

[0902] (Claim 2)

[0903] The system according to claim 1, wherein the information processing device further comprises information cleansing means for removing abnormal values ​​and duplicate information and performing accurate analysis.

[0904] (Claim 3)

[0905] The system according to claim 1, further comprising means for evaluating and updating a person's health level by comparing it with the person's past exercise data. [Explanation of symbols]

[0906] 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 detection device for measuring biological data and a communication means for communicating with it, Information processing means for processing biometric data received from the aforementioned communication terminal and evaluating the efficiency of exercise execution, Based on the aforementioned evaluation, a plan generation means for constructing an exercise plan suitable for the exerciser, A display means and an audio output means for communicating the exercise plan to the exerciser and providing dynamic feedback during the execution of the exercise, A mobile assistive device to support exercise for people within the home, A system that includes this.

2. The system according to claim 1, wherein the information processing device further comprises a data cleansing function that removes abnormal or redundant data and performs accurate analysis.

3. The system according to claim 1, further comprising a function of evaluating and updating the fitness status by comparing it with the exerciser's past activity data.

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