system

Abstract

The system according to this embodiment aims to monitor members' health status in real time and provide personalized training suggestions. [Solution] The system according to the embodiment comprises a monitoring unit, a suggestion unit, and a checking unit. The monitoring unit monitors the member's health status in real time. The suggestion unit makes personalized training suggestions based on the data monitored by the monitoring unit. The checking unit checks the training form based on the training menu suggested by the suggestion unit.

Description

Technical Field

[0006] ,

[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, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, it cannot be said that the health status of members is sufficiently monitored in real time and individualized training proposals are made, and there is room for improvement.

[0005] The system according to an embodiment aims to monitor the health status of members in real time and make individualized training proposals.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a monitoring unit, a suggestion unit, and a checking unit. The monitoring unit monitors the member's health status in real time. The suggestion unit makes personalized training suggestions based on the data monitored by the monitoring unit. The checking unit checks the training form based on the training menu suggested by the suggestion unit. [Effects of the Invention]

[0007] The system according to this embodiment can monitor the health status of members in real time and provide personalized training suggestions. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

[0019] The smart device 14 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.

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The next-generation fitness management system according to an embodiment of the present invention is a system that integrates an AI camera and a biometric authentication sensor. This system monitors the health status of members in real time and realizes personalized training suggestions and safety management. For example, the next-generation fitness management system uses an AI camera and a biometric authentication sensor to monitor the health status of members in real time. Next, based on the monitored data, the AI ​​makes personalized training suggestions. Furthermore, the AI ​​checks the member's training form in real time to prevent injuries. This system can solve problems faced by fitness club operators, 24-hour gym operators, sports club chains, and corporate fitness facilities, such as staff shortages, the burden of member safety management, limitations of individualized support, declining member retention rates, increased facility operating costs, and differentiation from competitors. For example, the next-generation fitness management system uses an AI camera and a biometric authentication sensor to monitor the health status of members in real time. At this time, entry and exit management by facial recognition and real-time monitoring of vital data are performed. For example, when a member enters the gym, facial recognition is performed and vital data such as heart rate and blood pressure are measured by the biometric authentication sensor. This makes it possible to constantly understand the health status of members. Next, based on the monitored data, the AI ​​provides personalized training suggestions. For example, it suggests the optimal training menu according to the member's health condition and training goals. For instance, for a member who needs attention to their lower back or right shoulder, it suggests a training menu that does not put strain on these areas. It also manages the member's training progress based on their training history and target deadlines, and adjusts the training menu as needed. Furthermore, the AI ​​checks the member's training form in real time to prevent injuries. For example, the AI ​​analyzes the member's training form to check if they are training with the correct form. If there is a problem with the form, the AI ​​automatically provides advice and prompts the member to correct it. This allows members to train safely.This system can solve challenges faced by fitness club operators, 24-hour gym operators, sports club chains, and corporate fitness facilities, such as staff shortages, the burden of member safety management, limitations in individualized support, declining member retention rates, increased facility operating costs, and the need for differentiation from competitors. For example, AI-powered member authentication and health monitoring can reduce the burden on staff and streamline member safety management. Real-time form checks and injury prevention can ensure member safety and enhance training effectiveness. Furthermore, personalized training suggestions can improve member retention rates and optimize facility utilization. In this way, the next-generation fitness management system can monitor members' health status in real time and realize personalized training suggestions and safety management.

[0029] The next-generation fitness management system according to this embodiment comprises a monitoring unit, a suggestion unit, and a check unit. The monitoring unit monitors the health status of members in real time. The health status of members includes, but is not limited to, heart rate, blood pressure, and body temperature. The monitoring unit monitors the health status of members using, for example, an AI camera and a biometric authentication sensor. The AI ​​camera can recognize the face of a member and manage entry and exit. The biometric authentication sensor can measure vital data such as the heart rate and blood pressure of a member in real time. For example, the monitoring unit performs facial recognition when a member enters the gym and measures vital data such as heart rate and blood pressure with the biometric authentication sensor. This allows the system to constantly monitor the health status of members. The suggestion unit makes personalized training suggestions based on the data monitored by the monitoring unit. Personalized training suggestions include, for example, suggestions for training menus that match the member's health status and training goals, but is not limited to these. The suggestion unit suggests, for example, an optimal training menu according to the member's health status and training goals. For example, for a member who needs attention to their lower back or right shoulder, the suggestion unit suggests a training menu that does not put a strain on these areas. Furthermore, the suggestion department can adjust the training menu based on the member's training history and target deadline. For example, the suggestion department manages the member's training progress based on their training history and target deadline, and adjusts the training menu as needed. The checking department checks the training form based on the training menu suggested by the suggestion department. For example, the checking department uses AI to analyze the member's training form in real time and check whether the training is being performed with the correct form. If there is a problem with the form, the checking department automatically provides advice and prompts the member to correct it to the correct form. This allows members to train safely.As a result, the next-generation fitness management system according to this embodiment can monitor members' health status in real time and provide personalized training suggestions and safety management.

[0030] The monitoring unit monitors members' health status in real time. This includes, but is not limited to, heart rate, blood pressure, and body temperature. The monitoring unit uses, for example, AI cameras and biometric sensors to monitor members' health. The AI ​​cameras recognize members' faces and manage entry and exit. The biometric sensors can measure vital data such as heart rate and blood pressure in real time. For example, the monitoring unit performs facial recognition when a member enters the gym and measures vital data such as heart rate and blood pressure using the biometric sensors. This allows for constant monitoring of members' health status. Furthermore, the monitoring unit can utilize advanced sensor technology to measure members' respiratory rate, oxygen saturation, and even stress levels. This data is crucial for a more detailed understanding of members' health status and serves as foundational data for maximizing training effectiveness. The monitoring unit transmits this data to a cloud server in real time and manages it centrally in a centralized management system. This allows trainers and medical staff to remotely check members' health status and respond quickly as needed. Furthermore, the monitoring unit can accumulate members' health data over long periods, tracking training progress and changes in health status. This allows for more effective health management of members. In addition, the monitoring unit can utilize AI technology to detect abnormal data and health risks early. For example, if heart rate or blood pressure fluctuates rapidly, the monitoring unit will immediately issue an alert and notify the member and trainer. This helps prevent health risks. To protect member privacy, the monitoring unit strictly encrypts data and controls access. This minimizes the risk of unauthorized access to members' personal information.

[0031] The Proposal Department provides personalized training suggestions based on data monitored by the Monitoring Department. These personalized suggestions include, but are not limited to, suggestions for training menus tailored to the member's health condition and training goals. For example, the Proposal Department suggests an optimal training menu based on the member's health condition and training goals. For instance, for a member who needs attention to their lower back or right shoulder, it suggests a training menu that avoids straining these areas. The Proposal Department can also adjust training menus based on the member's training history and goal deadlines. For example, it manages training progress based on the member's training history and goal deadlines, and adjusts the training menu as needed. The Proposal Department utilizes AI to analyze member data and generate optimal training plans. The AI ​​learns from the member's past training data and health condition, and suggests the most suitable training menu for each individual member. For example, the AI ​​calculates appropriate exercise intensity and duration based on the member's heart rate and calorie expenditure, and customizes the training menu accordingly. The Proposal Department also collects member feedback and evaluates the effectiveness of the training menu. Based on feedback such as fatigue and sense of accomplishment felt by members after training, the AI ​​further optimizes the training menu. This ensures that members always receive optimal training and can effectively achieve their goals. The training department also provides training suggestions tailored to members' lifestyles and schedules. For example, they propose short, effective training menus for busy members and more in-depth menus for members with more time. This allows members to continue training at their own pace without overexerting themselves. Furthermore, the training department also provides suggestions regarding members' diets and nutritional management. They propose appropriate meal plans and nutrient intake levels according to members' health conditions and training goals, maximizing the effectiveness of their training. This allows members to manage their health comprehensively, not just through training.

[0032] The checking unit checks the training form based on the training menu proposed by the suggestion unit. For example, the checking unit uses AI to analyze the member's training form in real time and check whether the training is being performed with the correct form. If there is a problem with the form, the checking unit automatically provides advice and prompts the member to correct it to the correct form. This allows the member to train safely. The checking unit utilizes AI technology to analyze the member's movements in detail. The AI ​​analyzes the member's posture and movements in real time based on camera footage and checks whether the training is being performed with the correct form. For example, in exercises such as squats and deadlifts, it accurately analyzes the position of the knees and hips, the angle of the back, etc., to confirm whether the exercise is being performed with the correct form. If there is a problem with the form, the AI ​​immediately issues an alert and prompts the member to correct it. The checking unit not only analyzes the member's training form but also monitors the training progress in real time. For example, it checks how much progress the member has made towards the goals they have set and adjusts the training menu as needed. This ensures that members always receive optimal training and can effectively achieve their goals. The analysis unit can also compare a member's training form with past data and data from other members. This allows it to clearly identify areas for improvement and strengthening in the member's form, and propose more effective training. Furthermore, the analysis unit can provide audio and visual feedback when analyzing a member's training form. For example, while a member is training, the AI ​​can provide real-time audio and visual feedback to encourage them to train with the correct form. This allows members to make immediate corrections during training, ensuring safe and effective training.

[0033] The monitoring unit can perform entry and exit management using facial recognition. For example, the monitoring unit can use an AI camera to recognize members' faces and manage their entry and exit. For example, the monitoring unit can perform facial recognition when a member enters the gym and manage their entry and exit. The monitoring unit can also automatically manage members' entry and exit using facial recognition technology. For example, the monitoring unit can use an AI camera to recognize members' faces and manage their entry and exit. This can streamline member entry and exit management and improve security. For facial recognition, for example, a facial recognition algorithm using deep learning is used. The facial recognition algorithm takes the member's facial image as input, extracts facial features, and performs recognition. The accuracy of facial recognition is evaluated using indicators such as recognition rate and false recognition rate. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input facial image data acquired by an AI camera into a generating AI and have the generating AI perform facial recognition.

[0034] The monitoring unit can monitor vital data such as heart rate and blood pressure in real time using biometric sensors. For example, the monitoring unit measures vital data such as a member's heart rate and blood pressure in real time using biometric sensors. For example, the monitoring unit measures vital data such as heart rate and blood pressure using biometric sensors when a member enters the gym. This allows the monitoring unit to constantly monitor the member's health status. Examples of biometric sensors include heart rate sensors and blood pressure sensors. The heart rate sensor measures the member's heart rate and provides data in real time. The blood pressure sensor measures the member's blood pressure and provides data in real time. Vital data includes, but is not limited to, heart rate, blood pressure, and body temperature. Some or all of the above processing in the monitoring unit may be performed using, for example, AI, or not using AI. For example, the monitoring unit can input the vital data acquired by the biometric sensors into a generating AI and have the generating AI perform data analysis. This allows the monitoring unit to gain a detailed understanding of the member's health status and make appropriate training suggestions.

[0035] The suggestion unit can propose the optimal training menu according to the member's health condition and training goals. For example, the suggestion unit will propose the optimal training menu according to the member's health condition and training goals. For example, for a member who needs attention to their lower back or right shoulder, the suggestion unit will propose a training menu that does not put strain on these areas. The suggestion unit can also adjust the training menu based on the member's training history and goal deadline. For example, the suggestion unit will manage the training progress based on the member's training history and goal deadline and adjust the training menu as needed. This will provide the member with the optimal training menu and maximize the training effect. Some or all of the above processes in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input the member's health condition and training goals into a generating AI and have the generating AI propose the optimal training menu.

[0036] The suggestion unit can adjust the training menu based on the member's training history and target deadline. For example, the suggestion unit can adjust the training menu based on the member's training history and target deadline. For example, the suggestion unit can manage the member's training progress based on the member's training history and target deadline and adjust the training menu as needed. The suggestion unit can also analyze the member's training history and propose the optimal training menu based on past training content, frequency, and intensity. For example, the suggestion unit can propose an effective training menu based on the member's training history. This allows for the management of the member's training progress and the provision of an appropriate menu. Some or all of the above processes in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input the member's training history and target deadline into a generating AI and have the generating AI perform the adjustment of the training menu.

[0037] The checking unit can analyze a member's training form in real time and check whether the training is being performed with the correct form. For example, the checking unit can use AI to analyze a member's training form in real time and check whether the training is being performed with the correct form. For example, the checking unit can use AI to analyze a member's training form and check whether the training is being performed with the correct form. If there is a problem with the form, the checking unit will automatically provide advice and prompt the member to correct it to the correct form. This allows members to train safely. Some or all of the above processing in the checking unit may be performed using AI, for example, or without AI. For example, the checking unit can input the member's training form into a generating AI and have the generating AI perform the form analysis and check. This allows for accurate checking of the member's training form and helps prevent injuries.

[0038] The checking unit can automatically provide advice if there are problems with the form. For example, the checking unit can use AI to analyze the member's training form and automatically provide advice if there are problems with the form. For example, the checking unit uses AI to analyze the member's training form and automatically provides advice if there are problems with the form. This allows the member to train with the correct form. Some or all of the above processing in the checking unit may be performed using AI, or not using AI. For example, the checking unit can input the member's training form into a generating AI and have the generating AI perform form analysis and provide advice. This encourages the member to train with the correct form and helps prevent injuries.

[0039] The monitoring unit can analyze a member's past health data and select the optimal monitoring method. For example, the monitoring unit can use AI to analyze a member's past health data and select the optimal monitoring method. For example, the monitoring unit can analyze a member's past heart rate data and intensify heart rate monitoring if abnormalities are found. The monitoring unit can also analyze a member's past blood pressure data and frequently monitor blood pressure if it is unstable. Furthermore, the monitoring unit can analyze a member's past exercise history and monitor their recovery state after exercise. This allows for the selection of the optimal monitoring method based on the member's past health data, thereby optimizing health management. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input a member's past health data into a generating AI and have the generating AI select the optimal monitoring method.

[0040] The monitoring unit can filter data based on the member's current activity level and stress level during monitoring. For example, the monitoring unit can use AI to filter data based on the member's current activity level and stress level. For example, if the member is at a high activity level, the monitoring unit will prioritize monitoring heart rate and respiratory rate data. The monitoring unit can also focus on monitoring stress-related vital data if the member is at a high stress level. Furthermore, if the member is at a low activity level, the monitoring unit can monitor basic health data. This allows for appropriate health management by filtering data according to the member's activity level and stress level. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input data on the member's activity level and stress level into a generating AI and have the generating AI perform data filtering.

[0041] The monitoring unit can prioritize monitoring highly relevant data based on the member's geographical location information during monitoring. For example, the monitoring unit can use AI to prioritize monitoring highly relevant data based on the member's geographical location information. For example, if the member is at high altitude, the monitoring unit will prioritize monitoring oxygen saturation data. The monitoring unit can also monitor air quality data if the member is in an urban area. Furthermore, if the member is exercising outdoors, the monitoring unit can monitor temperature and humidity data. This allows for the prioritization of monitoring of highly relevant data based on the member's geographical location information, enabling appropriate health management. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the member's geographical location information into a generating AI and have the generating AI perform the monitoring of highly relevant data.

[0042] The monitoring unit can analyze members' social media activity and monitor related health data during monitoring. For example, the monitoring unit can use AI to analyze members' social media activity and monitor related health data. For example, if a member posts on social media indicating they are feeling stressed, the monitoring unit can monitor stress-related vital data. The monitoring unit can also monitor the recovery state after exercise if a member posts about exercise. Furthermore, if a member posts about health, the monitoring unit can monitor overall health data. This allows for monitoring of relevant health data based on members' social media activity and enables appropriate health management. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input members' social media activity data into a generating AI and have the generating AI perform the monitoring of related health data.

[0043] The suggestion unit can adjust the level of detail in the training menu based on the member's health condition when making a suggestion. For example, the suggestion unit can adjust the level of detail in the training menu based on the member's health condition. For example, if the member's health condition is good, the suggestion unit can suggest a detailed training menu. Also, if the member's health condition is unstable, the suggestion unit can suggest a simplified training menu. Furthermore, if the member's health condition is improving, the suggestion unit can suggest a progressively more detailed training menu. This allows the suggestion unit to adjust the level of detail in the training menu according to the member's health condition and make appropriate training suggestions. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the member's health condition data into a generating AI and have the generating AI perform the adjustment of the level of detail in the training menu.

[0044] The suggestion unit can apply different suggestion algorithms depending on the member's training goals when making suggestions. For example, if the member aims to improve muscle strength, the suggestion unit can apply a suggestion algorithm specialized in strength training. If the member aims to lose weight, the suggestion unit can also apply a suggestion algorithm specialized in aerobic exercise. Furthermore, if the member aims to improve flexibility, the suggestion unit can also apply a suggestion algorithm specialized in stretching. This allows the suggestion unit to apply different suggestion algorithms according to the member's training goals and provide appropriate training suggestions. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the member's training goal data into a generating AI and have the generating AI execute the application of different suggestion algorithms.

[0045] The suggestion unit can determine the priority of suggestions based on the member's training history when making suggestions. For example, the suggestion unit can determine the priority of suggestions based on the member's training history. For example, the suggestion unit can determine which menus to suggest preferentially based on the training menus the member has performed in the past. The suggestion unit can also prioritize suggesting menus that have been highly effective based on the member's training history. Furthermore, the suggestion unit can analyze the member's training history and prioritize suggesting balanced menus. This allows the suggestion unit to determine the priority of suggestions based on the member's training history and provide appropriate training suggestions. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the member's training history data into a generating AI and have the generating AI perform the determination of the suggestion priority.

[0046] The suggestion unit can adjust the order of training menus based on the member's relevance when making suggestions. For example, the suggestion unit can adjust the order of training menus based on the member's relevance. For example, the suggestion unit can suggest the most relevant menu first based on the member's training goals. The suggestion unit can also suggest the most appropriate menu first based on the member's health condition. Furthermore, the suggestion unit can suggest menus in an effective order based on the member's training history. This allows for adjusting the order of training menus based on the member's relevance and making appropriate training suggestions. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input member relevance data into a generating AI and have the generating AI perform the adjustment of the training menu order.

[0047] The checking unit can analyze the member's past training data during the checking process to select the optimal checking method. For example, the checking unit can use AI to analyze the member's past training data and select the optimal checking method. For example, the checking unit can select an effective checking method based on the member's past training data. The checking unit can also focus on checking problematic forms based on the member's past training data. Furthermore, the checking unit can analyze the member's past training data and check points that need improvement. This allows for the selection of the optimal checking method based on the member's past training data and enables appropriate form checking. Some or all of the above-described processes in the checking unit may be performed using AI, for example, or without AI. For example, the checking unit can input the member's past training data into a generating AI and have the generating AI select the optimal checking method.

[0048] The checking unit can improve the accuracy of the form check based on the member's current physical condition during the check. For example, the checking unit can use AI to improve the accuracy of the form check based on the member's current physical condition. For example, if the member's current physical condition is good, the checking unit will perform a detailed form check. Also, if the member's current physical condition is unstable, the checking unit can perform a simplified form check. Furthermore, the checking unit can perform an appropriate form check considering the member's current physical condition. This allows for improved accuracy of the form check and the performance of an appropriate form check according to the member's current physical condition. Some or all of the above-described processes in the checking unit may be performed using AI, for example, or without AI. For example, the checking unit can input the member's current physical condition data into a generating AI and have the generating AI perform the form check accuracy improvement.

[0049] The checking unit can perform form checks based on the geographical distribution of members during the check process. For example, the checking unit can use AI to perform form checks based on the geographical distribution of members. For example, if a member is at high altitude, the checking unit can perform form checks that take oxygen saturation into account. Also, if a member is in an urban area, the checking unit can perform form checks that take air quality into account. Furthermore, if a member is exercising outdoors, the checking unit can perform form checks that take temperature and humidity into account. This allows for appropriate form checks based on the geographical distribution of members. Some or all of the above processing in the checking unit may be performed using AI, for example, or without AI. For example, the checking unit can input the geographical distribution data of members into a generating AI and have the generating AI perform the form check.

[0050] The checking unit can improve the accuracy of form checking by referring to the member's relevant literature during the checking process. For example, the checking unit can use AI to improve the accuracy of form checking by referring to the member's relevant literature. For example, the checking unit can perform form checking by referring to the latest research papers related to the member's training. The checking unit can also perform form checking by referring to past literature related to the member's training. Furthermore, the checking unit can perform form checking by referring to specialized books related to the member's training. This allows the checking unit to improve the accuracy of form checking by referring to the member's relevant literature and perform appropriate form checking. Some or all of the above processing in the checking unit may be performed using AI, for example, or without using AI. For example, the checking unit can input the member's relevant literature data into a generating AI and have the generating AI perform the improvement of form checking accuracy.

[0051] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0052] Next-generation fitness management systems can also include a nutrition management department to manage members' diets. This department collects members' dietary data and proposes optimal meal plans based on their health status and training goals. For example, it can record the calories and nutrients members consume and provide balanced meal plans. It can also provide personalized meal suggestions based on members' allergy information and dietary preferences. Furthermore, it can recommend meal timings that align with members' training schedules. This comprehensively supports members' health management and maximizes training effectiveness.

[0053] Next-generation fitness management systems can also include a sleep management unit that monitors members' sleep patterns. This unit collects members' sleep data and evaluates the quality and quantity of their sleep. For example, it can record members' sleep duration and sleep cycles and provide appropriate sleep advice. It can also suggest ways to improve sleep quality based on members' sleep environment and habits. Furthermore, it can recommend optimal sleep durations in line with members' training schedules. This comprehensively supports members' health management and maximizes training effectiveness.

[0054] Next-generation fitness management systems can also include an environmental monitoring unit to further optimize members' training environments. This unit collects environmental data such as temperature, humidity, and air quality within the gym, providing an optimal training environment. For example, it can monitor temperature and humidity in the gym in real time and make appropriate environmental adjustments. It can also monitor air quality using air quality sensors and ventilate as needed. Furthermore, the environmental monitoring unit can provide an optimal training environment tailored to each member's training schedule. This optimizes the training environment for members and supports comfortable workouts.

[0055] Next-generation fitness management systems can also include a health prediction unit that analyzes members' training data to support long-term health management. This unit predicts future health risks based on members' training and health data. For example, it analyzes members' heart rate and blood pressure data to assess their risk of cardiovascular disease. It can also predict the risk of obesity and diabetes based on members' exercise habits and dietary data. Furthermore, it can analyze members' training history to predict injury risk. This allows for support of members' long-term health management and the implementation of preventative health measures.

[0056] Next-generation fitness management systems can also include a visualization section that visualizes the effectiveness of training based on members' training data. This visualization section displays members' training data in graphs and charts, allowing for visual confirmation of training effectiveness. For example, the visualization section can display trends in a member's heart rate and calorie expenditure in graphs. It can also display a member's training history in charts, allowing for a quick overview of progress. Furthermore, the visualization section can visually display the member's achievement level towards their training goals, providing feedback to maintain motivation. This allows for the visualization of members' training effectiveness and supports effective training.

[0057] Next-generation fitness management systems can also include an evaluation unit that assesses the effectiveness of training based on members' training data. The evaluation unit analyzes members' training data and evaluates the effectiveness of their training. For example, it can quantify training effectiveness based on data such as the member's heart rate and calories burned. Furthermore, the evaluation unit can assess training progress based on the member's training history. It can also evaluate the member's achievement of their training goals and suggest the next steps. This allows for the evaluation of members' training effectiveness and supports effective training.

[0058] The following briefly describes the processing flow for example form 1.

[0059] Step 1: The monitoring unit monitors members' health status in real time. Members' health status includes heart rate, blood pressure, and body temperature. The monitoring unit uses an AI camera and biometric sensors to monitor members' health status. The AI ​​camera can recognize members' faces and manage entry and exit, while the biometric sensors measure vital data such as heart rate and blood pressure in real time. For example, by performing facial recognition when a member enters the gym and measuring vital data such as heart rate and blood pressure with the biometric sensors, the health status of members can be constantly monitored. Step 2: The Proposal Department provides personalized training suggestions based on data monitored by the Monitoring Department. These personalized suggestions include training menus tailored to the member's health condition and training goals. The Proposal Department proposes the optimal training menu based on the member's health condition and training goals, and can also adjust the menu based on the member's training history and target deadlines. For example, for a member who needs attention to their lower back or right shoulder, the Proposal Department will propose a training menu that does not put strain on these areas. The department also manages the progress of the training and adjusts the training menu as needed. Step 3: The checking unit checks the training form based on the training menu proposed by the suggestion unit. The checking unit uses AI to analyze the member's training form in real time and check whether the training is being performed with the correct form. If there are any problems with the form, the checking unit automatically provides advice and prompts the member to correct it to the correct form. This ensures that members can train safely.

[0060] (Example of form 2) The next-generation fitness management system according to an embodiment of the present invention is a system that integrates an AI camera and a biometric authentication sensor. This system monitors the health status of members in real time and realizes personalized training suggestions and safety management. For example, the next-generation fitness management system uses an AI camera and a biometric authentication sensor to monitor the health status of members in real time. Next, based on the monitored data, the AI ​​makes personalized training suggestions. Furthermore, the AI ​​checks the member's training form in real time to prevent injuries. This system can solve problems faced by fitness club operators, 24-hour gym operators, sports club chains, and corporate fitness facilities, such as staff shortages, the burden of member safety management, limitations of individualized support, declining member retention rates, increased facility operating costs, and differentiation from competitors. For example, the next-generation fitness management system uses an AI camera and a biometric authentication sensor to monitor the health status of members in real time. At this time, entry and exit management by facial recognition and real-time monitoring of vital data are performed. For example, when a member enters the gym, facial recognition is performed and vital data such as heart rate and blood pressure are measured by the biometric authentication sensor. This makes it possible to constantly understand the health status of members. Next, based on the monitored data, the AI ​​provides personalized training suggestions. For example, it suggests the optimal training menu according to the member's health condition and training goals. For instance, for a member who needs attention to their lower back or right shoulder, it suggests a training menu that does not put strain on these areas. It also manages the member's training progress based on their training history and target deadlines, and adjusts the training menu as needed. Furthermore, the AI ​​checks the member's training form in real time to prevent injuries. For example, the AI ​​analyzes the member's training form to check if they are training with the correct form. If there is a problem with the form, the AI ​​automatically provides advice and prompts the member to correct it. This allows members to train safely.This system can solve challenges faced by fitness club operators, 24-hour gym operators, sports club chains, and corporate fitness facilities, such as staff shortages, the burden of member safety management, limitations in individualized support, declining member retention rates, increased facility operating costs, and the need for differentiation from competitors. For example, AI-powered member authentication and health monitoring can reduce the burden on staff and streamline member safety management. Real-time form checks and injury prevention can ensure member safety and enhance training effectiveness. Furthermore, personalized training suggestions can improve member retention rates and optimize facility utilization. In this way, the next-generation fitness management system can monitor members' health status in real time and realize personalized training suggestions and safety management.

[0061] The next-generation fitness management system according to this embodiment comprises a monitoring unit, a suggestion unit, and a check unit. The monitoring unit monitors the health status of members in real time. The health status of members includes, but is not limited to, heart rate, blood pressure, and body temperature. The monitoring unit monitors the health status of members using, for example, an AI camera and a biometric authentication sensor. The AI ​​camera can recognize the face of a member and manage entry and exit. The biometric authentication sensor can measure vital data such as the heart rate and blood pressure of a member in real time. For example, the monitoring unit performs facial recognition when a member enters the gym and measures vital data such as heart rate and blood pressure with the biometric authentication sensor. This allows the system to constantly monitor the health status of members. The suggestion unit makes personalized training suggestions based on the data monitored by the monitoring unit. Personalized training suggestions include, for example, suggestions for training menus that match the member's health status and training goals, but is not limited to these. The suggestion unit suggests, for example, an optimal training menu according to the member's health status and training goals. For example, for a member who needs attention to their lower back or right shoulder, the suggestion unit suggests a training menu that does not put a strain on these areas. Furthermore, the suggestion department can adjust the training menu based on the member's training history and target deadline. For example, the suggestion department manages the member's training progress based on their training history and target deadline, and adjusts the training menu as needed. The checking department checks the training form based on the training menu suggested by the suggestion department. For example, the checking department uses AI to analyze the member's training form in real time and check whether the training is being performed with the correct form. If there is a problem with the form, the checking department automatically provides advice and prompts the member to correct it to the correct form. This allows members to train safely.As a result, the next-generation fitness management system according to this embodiment can monitor members' health status in real time and provide personalized training suggestions and safety management.

[0062] The monitoring unit monitors members' health status in real time. This includes, but is not limited to, heart rate, blood pressure, and body temperature. The monitoring unit uses, for example, AI cameras and biometric sensors to monitor members' health. The AI ​​cameras recognize members' faces and manage entry and exit. The biometric sensors can measure vital data such as heart rate and blood pressure in real time. For example, the monitoring unit performs facial recognition when a member enters the gym and measures vital data such as heart rate and blood pressure using the biometric sensors. This allows for constant monitoring of members' health status. Furthermore, the monitoring unit can utilize advanced sensor technology to measure members' respiratory rate, oxygen saturation, and even stress levels. This data is crucial for a more detailed understanding of members' health status and serves as foundational data for maximizing training effectiveness. The monitoring unit transmits this data to a cloud server in real time and manages it centrally in a centralized management system. This allows trainers and medical staff to remotely check members' health status and respond quickly as needed. Furthermore, the monitoring unit can accumulate members' health data over long periods, tracking training progress and changes in health status. This allows for more effective health management of members. In addition, the monitoring unit can utilize AI technology to detect abnormal data and health risks early. For example, if heart rate or blood pressure fluctuates rapidly, the monitoring unit will immediately issue an alert and notify the member and trainer. This helps prevent health risks. To protect member privacy, the monitoring unit strictly encrypts data and controls access. This minimizes the risk of unauthorized access to members' personal information.

[0063] The Proposal Department provides personalized training suggestions based on data monitored by the Monitoring Department. These personalized suggestions include, but are not limited to, suggestions for training menus tailored to the member's health condition and training goals. For example, the Proposal Department suggests an optimal training menu based on the member's health condition and training goals. For instance, for a member who needs attention to their lower back or right shoulder, it suggests a training menu that avoids straining these areas. The Proposal Department can also adjust training menus based on the member's training history and goal deadlines. For example, it manages training progress based on the member's training history and goal deadlines, and adjusts the training menu as needed. The Proposal Department utilizes AI to analyze member data and generate optimal training plans. The AI ​​learns from the member's past training data and health condition, and suggests the most suitable training menu for each individual member. For example, the AI ​​calculates appropriate exercise intensity and duration based on the member's heart rate and calorie expenditure, and customizes the training menu accordingly. The Proposal Department also collects member feedback and evaluates the effectiveness of the training menu. Based on feedback such as fatigue and sense of accomplishment felt by members after training, the AI ​​further optimizes the training menu. This ensures that members always receive optimal training and can effectively achieve their goals. The training department also provides training suggestions tailored to members' lifestyles and schedules. For example, they propose short, effective training menus for busy members and more in-depth menus for members with more time. This allows members to continue training at their own pace without overexerting themselves. Furthermore, the training department also provides suggestions regarding members' diets and nutritional management. They propose appropriate meal plans and nutrient intake levels according to members' health conditions and training goals, maximizing the effectiveness of their training. This allows members to manage their health comprehensively, not just through training.

[0064] The checking unit checks the training form based on the training menu proposed by the suggestion unit. For example, the checking unit uses AI to analyze the member's training form in real time and check whether the training is being performed with the correct form. If there is a problem with the form, the checking unit automatically provides advice and prompts the member to correct it to the correct form. This allows the member to train safely. The checking unit utilizes AI technology to analyze the member's movements in detail. The AI ​​analyzes the member's posture and movements in real time based on camera footage and checks whether the training is being performed with the correct form. For example, in exercises such as squats and deadlifts, it accurately analyzes the position of the knees and hips, the angle of the back, etc., to confirm whether the exercise is being performed with the correct form. If there is a problem with the form, the AI ​​immediately issues an alert and prompts the member to correct it. The checking unit not only analyzes the member's training form but also monitors the training progress in real time. For example, it checks how much progress the member has made towards the goals they have set and adjusts the training menu as needed. This ensures that members always receive optimal training and can effectively achieve their goals. The analysis unit can also compare a member's training form with past data and data from other members. This allows it to clearly identify areas for improvement and strengthening in the member's form, and propose more effective training. Furthermore, the analysis unit can provide audio and visual feedback when analyzing a member's training form. For example, while a member is training, the AI ​​can provide real-time audio and visual feedback to encourage them to train with the correct form. This allows members to make immediate corrections during training, ensuring safe and effective training.

[0065] The monitoring unit can perform entry and exit management using facial recognition. For example, the monitoring unit can use an AI camera to recognize members' faces and manage their entry and exit. For example, the monitoring unit can perform facial recognition when a member enters the gym and manage their entry and exit. The monitoring unit can also automatically manage members' entry and exit using facial recognition technology. For example, the monitoring unit can use an AI camera to recognize members' faces and manage their entry and exit. This can streamline member entry and exit management and improve security. For facial recognition, for example, a facial recognition algorithm using deep learning is used. The facial recognition algorithm takes the member's facial image as input, extracts facial features, and performs recognition. The accuracy of facial recognition is evaluated using indicators such as recognition rate and false recognition rate. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input facial image data acquired by an AI camera into a generating AI and have the generating AI perform facial recognition.

[0066] The monitoring unit can monitor vital data such as heart rate and blood pressure in real time using biometric sensors. For example, the monitoring unit measures vital data such as a member's heart rate and blood pressure in real time using biometric sensors. For example, the monitoring unit measures vital data such as heart rate and blood pressure using biometric sensors when a member enters the gym. This allows the monitoring unit to constantly monitor the member's health status. Examples of biometric sensors include heart rate sensors and blood pressure sensors. The heart rate sensor measures the member's heart rate and provides data in real time. The blood pressure sensor measures the member's blood pressure and provides data in real time. Vital data includes, but is not limited to, heart rate, blood pressure, and body temperature. Some or all of the above processing in the monitoring unit may be performed using, for example, AI, or not using AI. For example, the monitoring unit can input the vital data acquired by the biometric sensors into a generating AI and have the generating AI perform data analysis. This allows the monitoring unit to gain a detailed understanding of the member's health status and make appropriate training suggestions.

[0067] The suggestion unit can propose the optimal training menu according to the member's health condition and training goals. For example, the suggestion unit will propose the optimal training menu according to the member's health condition and training goals. For example, for a member who needs attention to their lower back or right shoulder, the suggestion unit will propose a training menu that does not put strain on these areas. The suggestion unit can also adjust the training menu based on the member's training history and goal deadline. For example, the suggestion unit will manage the training progress based on the member's training history and goal deadline and adjust the training menu as needed. This will provide the member with the optimal training menu and maximize the training effect. Some or all of the above processes in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input the member's health condition and training goals into a generating AI and have the generating AI propose the optimal training menu.

[0068] The suggestion unit can adjust the training menu based on the member's training history and target deadline. For example, the suggestion unit can adjust the training menu based on the member's training history and target deadline. For example, the suggestion unit can manage the member's training progress based on the member's training history and target deadline and adjust the training menu as needed. The suggestion unit can also analyze the member's training history and propose the optimal training menu based on past training content, frequency, and intensity. For example, the suggestion unit can propose an effective training menu based on the member's training history. This allows for the management of the member's training progress and the provision of an appropriate menu. Some or all of the above processes in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input the member's training history and target deadline into a generating AI and have the generating AI perform the adjustment of the training menu.

[0069] The checking unit can analyze a member's training form in real time and check whether the training is being performed with the correct form. For example, the checking unit can use AI to analyze a member's training form in real time and check whether the training is being performed with the correct form. For example, the checking unit can use AI to analyze a member's training form and check whether the training is being performed with the correct form. If there is a problem with the form, the checking unit will automatically provide advice and prompt the member to correct it to the correct form. This allows members to train safely. Some or all of the above processing in the checking unit may be performed using AI, for example, or without AI. For example, the checking unit can input the member's training form into a generating AI and have the generating AI perform the form analysis and check. This allows for accurate checking of the member's training form and helps prevent injuries.

[0070] The checking unit can automatically provide advice if there are problems with the form. For example, the checking unit can use AI to analyze the member's training form and automatically provide advice if there are problems with the form. For example, the checking unit uses AI to analyze the member's training form and automatically provides advice if there are problems with the form. This allows the member to train with the correct form. Some or all of the above processing in the checking unit may be performed using AI, or not using AI. For example, the checking unit can input the member's training form into a generating AI and have the generating AI perform form analysis and provide advice. This encourages the member to train with the correct form and helps prevent injuries.

[0071] The monitoring unit can estimate the emotions of members and adjust the monitoring frequency based on the estimated emotions. For example, the monitoring unit can use AI to estimate the emotions of members and adjust the monitoring frequency based on the estimated emotions. For example, if a member is feeling stressed, the monitoring unit can increase the monitoring frequency to gain a more detailed understanding of their health. The monitoring unit can also reduce the monitoring frequency to lessen the burden on members when they are relaxed. Furthermore, if a member is tired, the monitoring unit can adjust the monitoring frequency to encourage appropriate rest. This allows for appropriate health management by adjusting the monitoring frequency according to the emotions of members. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the monitoring unit may be performed using AI or not using AI. For example, the monitoring unit can input member emotion data into a generative AI and have the generative AI perform emotion estimation and adjustment of the monitoring frequency.

[0072] The monitoring unit can analyze a member's past health data and select the optimal monitoring method. For example, the monitoring unit can use AI to analyze a member's past health data and select the optimal monitoring method. For example, the monitoring unit can analyze a member's past heart rate data and intensify heart rate monitoring if abnormalities are found. The monitoring unit can also analyze a member's past blood pressure data and frequently monitor blood pressure if it is unstable. Furthermore, the monitoring unit can analyze a member's past exercise history and monitor their recovery state after exercise. This allows for the selection of the optimal monitoring method based on the member's past health data, thereby optimizing health management. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input a member's past health data into a generating AI and have the generating AI select the optimal monitoring method.

[0073] The monitoring unit can filter data based on the member's current activity level and stress level during monitoring. For example, the monitoring unit can use AI to filter data based on the member's current activity level and stress level. For example, if the member is at a high activity level, the monitoring unit will prioritize monitoring heart rate and respiratory rate data. The monitoring unit can also focus on monitoring stress-related vital data if the member is at a high stress level. Furthermore, if the member is at a low activity level, the monitoring unit can monitor basic health data. This allows for appropriate health management by filtering data according to the member's activity level and stress level. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input data on the member's activity level and stress level into a generating AI and have the generating AI perform data filtering.

[0074] The monitoring unit can estimate a member's emotions and determine the priority of data to monitor based on the estimated emotions. For example, the monitoring unit can use AI to estimate a member's emotions and determine the priority of data to monitor based on the estimated emotions. For example, if a member is stressed, the monitoring unit will prioritize monitoring stress-related vital data. If a member is relaxed, the monitoring unit can also monitor overall health data in a balanced manner. Furthermore, if a member is tired, the monitoring unit can prioritize monitoring data related to fatigue recovery. This allows for the prioritization of data to monitor according to the member's emotions, enabling appropriate health management. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the monitoring unit may be performed using AI or not. For example, the monitoring unit can input member emotion data into a generative AI and have the generative AI perform emotion estimation and data priority determination.

[0075] The monitoring unit can prioritize monitoring highly relevant data based on the member's geographical location information during monitoring. For example, the monitoring unit can use AI to prioritize monitoring highly relevant data based on the member's geographical location information. For example, if the member is at high altitude, the monitoring unit will prioritize monitoring oxygen saturation data. The monitoring unit can also monitor air quality data if the member is in an urban area. Furthermore, if the member is exercising outdoors, the monitoring unit can monitor temperature and humidity data. This allows for the prioritization of monitoring of highly relevant data based on the member's geographical location information, enabling appropriate health management. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the member's geographical location information into a generating AI and have the generating AI perform the monitoring of highly relevant data.

[0076] The monitoring unit can analyze members' social media activity and monitor related health data during monitoring. For example, the monitoring unit can use AI to analyze members' social media activity and monitor related health data. For example, if a member posts on social media indicating they are feeling stressed, the monitoring unit can monitor stress-related vital data. The monitoring unit can also monitor the recovery state after exercise if a member posts about exercise. Furthermore, if a member posts about health, the monitoring unit can monitor overall health data. This allows for monitoring of relevant health data based on members' social media activity and enables appropriate health management. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input members' social media activity data into a generating AI and have the generating AI perform the monitoring of related health data.

[0077] The suggestion unit can estimate a member's emotions and adjust the way training suggestions are presented based on the estimated emotions. For example, the suggestion unit can use AI to estimate a member's emotions and adjust the way training suggestions are presented based on the estimated emotions. For example, if a member is feeling stressed, the suggestion unit can suggest a relaxing training menu. If a member is relaxed, the suggestion unit can also suggest a challenging training menu. Furthermore, if a member is tired, the suggestion unit can suggest a lighter training menu. This allows the suggestion unit to adjust the way training suggestions are presented according to the member's emotions and provide appropriate training suggestions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input member emotion data into a generative AI and have the generative AI perform emotion estimation and adjust the way training suggestions are presented.

[0078] The suggestion unit can adjust the level of detail in the training menu based on the member's health condition when making a suggestion. For example, the suggestion unit can adjust the level of detail in the training menu based on the member's health condition. For example, if the member's health condition is good, the suggestion unit can suggest a detailed training menu. Also, if the member's health condition is unstable, the suggestion unit can suggest a simplified training menu. Furthermore, if the member's health condition is improving, the suggestion unit can suggest a progressively more detailed training menu. This allows the suggestion unit to adjust the level of detail in the training menu according to the member's health condition and make appropriate training suggestions. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the member's health condition data into a generating AI and have the generating AI perform the adjustment of the level of detail in the training menu.

[0079] The suggestion unit can apply different suggestion algorithms depending on the member's training goals when making suggestions. For example, if the member aims to improve muscle strength, the suggestion unit can apply a suggestion algorithm specialized in strength training. If the member aims to lose weight, the suggestion unit can also apply a suggestion algorithm specialized in aerobic exercise. Furthermore, if the member aims to improve flexibility, the suggestion unit can also apply a suggestion algorithm specialized in stretching. This allows the suggestion unit to apply different suggestion algorithms according to the member's training goals and provide appropriate training suggestions. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the member's training goal data into a generating AI and have the generating AI execute the application of different suggestion algorithms.

[0080] The suggestion unit can estimate a member's emotions and adjust the length of training suggestions based on the estimated emotions. For example, the suggestion unit can use AI to estimate a member's emotions and adjust the length of training suggestions based on the estimated emotions. For example, if a member is feeling stressed, the suggestion unit can suggest a short, effective training menu. If a member is relaxed, the suggestion unit can also suggest a longer training menu. Furthermore, if a member is tired, the suggestion unit can suggest a short, refreshing training menu. This allows the length of training suggestions to be adjusted according to the member's emotions, enabling appropriate training suggestions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input member emotion data into a generative AI and have the generative AI perform emotion estimation and adjustment of training suggestion length.

[0081] The suggestion unit can determine the priority of suggestions based on the member's training history when making suggestions. For example, the suggestion unit can determine the priority of suggestions based on the member's training history. For example, the suggestion unit can determine which menus to suggest preferentially based on the training menus the member has performed in the past. The suggestion unit can also prioritize suggesting menus that have been highly effective based on the member's training history. Furthermore, the suggestion unit can analyze the member's training history and prioritize suggesting balanced menus. This allows the suggestion unit to determine the priority of suggestions based on the member's training history and provide appropriate training suggestions. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the member's training history data into a generating AI and have the generating AI perform the determination of the suggestion priority.

[0082] The suggestion unit can adjust the order of training menus based on the member's relevance when making suggestions. For example, the suggestion unit can adjust the order of training menus based on the member's relevance. For example, the suggestion unit can suggest the most relevant menu first based on the member's training goals. The suggestion unit can also suggest the most appropriate menu first based on the member's health condition. Furthermore, the suggestion unit can suggest menus in an effective order based on the member's training history. This allows for adjusting the order of training menus based on the member's relevance and making appropriate training suggestions. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input member relevance data into a generating AI and have the generating AI perform the adjustment of the training menu order.

[0083] The checking unit can estimate the member's emotions and adjust the training form checking method based on the estimated emotions. For example, the checking unit can use AI to estimate the member's emotions and adjust the training form checking method based on the estimated emotions. For example, if the member is feeling stressed, the checking unit can provide a simple and easy-to-understand checking method. The checking unit can also provide a detailed checking method if the member is relaxed. Furthermore, if the member is tired, the checking unit can provide a simplified checking method. This allows the training form checking method to be adjusted according to the member's emotions, enabling appropriate form checking. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the checking unit may be performed using AI or not using AI. For example, the checking unit can input the member's emotion data into the generative AI and have the generative AI perform emotion estimation and adjustment of the checking method.

[0084] The checking unit can analyze the member's past training data during the checking process to select the optimal checking method. For example, the checking unit can use AI to analyze the member's past training data and select the optimal checking method. For example, the checking unit can select an effective checking method based on the member's past training data. The checking unit can also focus on checking problematic forms based on the member's past training data. Furthermore, the checking unit can analyze the member's past training data and check points that need improvement. This allows for the selection of the optimal checking method based on the member's past training data and enables appropriate form checking. Some or all of the above-described processes in the checking unit may be performed using AI, for example, or without AI. For example, the checking unit can input the member's past training data into a generating AI and have the generating AI select the optimal checking method.

[0085] The checking unit can improve the accuracy of the form check based on the member's current physical condition during the check. For example, the checking unit can use AI to improve the accuracy of the form check based on the member's current physical condition. For example, if the member's current physical condition is good, the checking unit will perform a detailed form check. Also, if the member's current physical condition is unstable, the checking unit can perform a simplified form check. Furthermore, the checking unit can perform an appropriate form check considering the member's current physical condition. This allows for improved accuracy of the form check and the performance of an appropriate form check according to the member's current physical condition. Some or all of the above-described processes in the checking unit may be performed using AI, for example, or without AI. For example, the checking unit can input the member's current physical condition data into a generating AI and have the generating AI perform the form check accuracy improvement.

[0086] The checking unit can estimate the member's emotions and adjust the order in which the training form check results are displayed based on the estimated emotions. For example, the checking unit can use AI to estimate the member's emotions and adjust the order in which the training form check results are displayed based on the estimated emotions. For example, if the member is feeling stressed, the checking unit can display important check results first. Also, if the member is relaxed, the checking unit can display detailed check results in a sequential manner. Furthermore, if the member is tired, the checking unit can display concise check results first. This allows for adjusting the order in which the training form check results are displayed according to the member's emotions, enabling appropriate form checks. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the checking unit may be performed using AI, for example, or without AI. For example, the checking unit can input the member's emotional data into a generating AI, which can then perform emotion estimation and adjust the display order of the check results.

[0087] The checking unit can perform form checks based on the geographical distribution of members during the check process. For example, the checking unit can use AI to perform form checks based on the geographical distribution of members. For example, if a member is at high altitude, the checking unit can perform form checks that take oxygen saturation into account. Also, if a member is in an urban area, the checking unit can perform form checks that take air quality into account. Furthermore, if a member is exercising outdoors, the checking unit can perform form checks that take temperature and humidity into account. This allows for appropriate form checks based on the geographical distribution of members. Some or all of the above processing in the checking unit may be performed using AI, for example, or without AI. For example, the checking unit can input the geographical distribution data of members into a generating AI and have the generating AI perform the form check.

[0088] The checking unit can improve the accuracy of form checking by referring to the member's relevant literature during the checking process. For example, the checking unit can use AI to improve the accuracy of form checking by referring to the member's relevant literature. For example, the checking unit can perform form checking by referring to the latest research papers related to the member's training. The checking unit can also perform form checking by referring to past literature related to the member's training. Furthermore, the checking unit can perform form checking by referring to specialized books related to the member's training. This allows the checking unit to improve the accuracy of form checking by referring to the member's relevant literature and perform appropriate form checking. Some or all of the above processing in the checking unit may be performed using AI, for example, or without using AI. For example, the checking unit can input the member's relevant literature data into a generating AI and have the generating AI perform the improvement of form checking accuracy.

[0089] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0090] Next-generation fitness management systems can also include a nutrition management department to manage members' diets. This department collects members' dietary data and proposes optimal meal plans based on their health status and training goals. For example, it can record the calories and nutrients members consume and provide balanced meal plans. It can also provide personalized meal suggestions based on members' allergy information and dietary preferences. Furthermore, it can recommend meal timings that align with members' training schedules. This comprehensively supports members' health management and maximizes training effectiveness.

[0091] Next-generation fitness management systems can also include a sleep management unit that monitors members' sleep patterns. This unit collects members' sleep data and evaluates the quality and quantity of their sleep. For example, it can record members' sleep duration and sleep cycles and provide appropriate sleep advice. It can also suggest ways to improve sleep quality based on members' sleep environment and habits. Furthermore, it can recommend optimal sleep durations in line with members' training schedules. This comprehensively supports members' health management and maximizes training effectiveness.

[0092] Next-generation fitness management systems can also include a stress management unit that monitors members' stress levels. This unit collects members' stress data and assesses their stress levels. For example, it can measure members' heart rate variability and skin electrical activity to monitor stress levels in real time. It can also identify members' stressors and provide advice for stress reduction. Furthermore, it can provide relaxation and mental health support tailored to members' training schedules. This allows for comprehensive support of members' health management and maximizes training effectiveness.

[0093] Next-generation fitness management systems can incorporate gamification features to further enhance member motivation. Gamification features set achievement goals and rewards based on members' training data. For example, a gamification feature could award points each time a member achieves their set training goal, offering rewards based on accumulated points. Gamification features can also implement ranking systems to encourage competition and cooperation among members. Furthermore, gamification features can visualize members' training progress and provide feedback to maintain motivation. This increases members' motivation to train and supports consistent training.

[0094] Next-generation fitness management systems can also incorporate virtual coaching features to further enhance members' training performance. These virtual coaches provide real-time coaching based on members' training data. For example, they can analyze members' training form and encourage them to train with correct form. They can also suggest appropriate training menus based on members' training progress. Furthermore, they can provide encouragement and advice to maintain members' motivation. This helps improve members' training performance and supports effective training.

[0095] Next-generation fitness management systems can also include an environmental monitoring unit to further optimize members' training environments. This unit collects environmental data such as temperature, humidity, and air quality within the gym, providing an optimal training environment. For example, it can monitor temperature and humidity in the gym in real time and make appropriate environmental adjustments. It can also monitor air quality using air quality sensors and ventilate as needed. Furthermore, the environmental monitoring unit can provide an optimal training environment tailored to each member's training schedule. This optimizes the training environment for members and supports comfortable workouts.

[0096] Next-generation fitness management systems can also include a health prediction unit that analyzes members' training data to support long-term health management. This unit predicts future health risks based on members' training and health data. For example, it analyzes members' heart rate and blood pressure data to assess their risk of cardiovascular disease. It can also predict the risk of obesity and diabetes based on members' exercise habits and dietary data. Furthermore, it can analyze members' training history to predict injury risk. This allows for support of members' long-term health management and the implementation of preventative health measures.

[0097] Next-generation fitness management systems can also include a feedback unit that provides personalized feedback based on members' training data. The feedback unit analyzes members' training data and provides individualized feedback. For example, it can analyze a member's training form and point out areas for improvement. It can also provide advice to maintain motivation based on the member's training progress. Furthermore, it can evaluate the member's achievement level in line with their training goals and suggest the next steps. This maximizes the effectiveness of members' training and supports consistent training.

[0098] Next-generation fitness management systems can also include a visualization section that visualizes the effectiveness of training based on members' training data. This visualization section displays members' training data in graphs and charts, allowing for visual confirmation of training effectiveness. For example, the visualization section can display trends in a member's heart rate and calorie expenditure in graphs. It can also display a member's training history in charts, allowing for a quick overview of progress. Furthermore, the visualization section can visually display the member's achievement level towards their training goals, providing feedback to maintain motivation. This allows for the visualization of members' training effectiveness and supports effective training.

[0099] Next-generation fitness management systems can also include an evaluation unit that assesses the effectiveness of training based on members' training data. The evaluation unit analyzes members' training data and evaluates the effectiveness of their training. For example, it can quantify training effectiveness based on data such as the member's heart rate and calories burned. Furthermore, the evaluation unit can assess training progress based on the member's training history. It can also evaluate the member's achievement of their training goals and suggest the next steps. This allows for the evaluation of members' training effectiveness and supports effective training.

[0100] The following briefly describes the processing flow for example form 2.

[0101] Step 1: The monitoring unit monitors members' health status in real time. Members' health status includes heart rate, blood pressure, and body temperature. The monitoring unit uses an AI camera and biometric sensors to monitor members' health status. The AI ​​camera can recognize members' faces and manage entry and exit, while the biometric sensors measure vital data such as heart rate and blood pressure in real time. For example, by performing facial recognition when a member enters the gym and measuring vital data such as heart rate and blood pressure with the biometric sensors, the health status of members can be constantly monitored. Step 2: The Proposal Department provides personalized training suggestions based on data monitored by the Monitoring Department. These personalized suggestions include training menus tailored to the member's health condition and training goals. The Proposal Department proposes the optimal training menu based on the member's health condition and training goals, and can also adjust the menu based on the member's training history and target deadlines. For example, for a member who needs attention to their lower back or right shoulder, the Proposal Department will propose a training menu that does not put strain on these areas. The department also manages the progress of the training and adjusts the training menu as needed. Step 3: The checking unit checks the training form based on the training menu proposed by the suggestion unit. The checking unit uses AI to analyze the member's training form in real time and check whether the training is being performed with the correct form. If there are any problems with the form, the checking unit automatically provides advice and prompts the member to correct it to the correct form. This ensures that members can train safely.

[0102] 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.

[0103] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0104] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0105] Each of the multiple elements described above, including the monitoring unit, proposal unit, and check unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the monitoring unit monitors the member's health status in real time using the AI ​​camera and biometric authentication sensor of the smart device 14. The proposal unit is implemented in real time by the identification processing unit 290 of the data processing unit 12 and makes personalized training suggestions based on the monitored data. The check unit is implemented in real time by the control unit 46A of the smart device 14 and analyzes the member's training form in real time to check whether the training is being performed with the correct form. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0106] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0107] 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.

[0108] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0109] 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.

[0110] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0111] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0112] 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.

[0113] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0114] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0115] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0116] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0117] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0118] 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.

[0119] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0120] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0121] Each of the multiple elements described above, including the monitoring unit, suggestion unit, and check unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the monitoring unit monitors the member's health status in real time using the AI ​​camera and biometric authentication sensor of the smart glasses 214. The suggestion unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and makes personalized training suggestions based on the monitored data. The check unit is implemented, for example, by the control unit 46A of the smart glasses 214, and analyzes the member's training form in real time to check whether the training is being performed with the correct form. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0122] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0123] 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.

[0124] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0125] 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.

[0126] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0127] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0128] 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.

[0129] 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.

[0130] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0131] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0132] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0133] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0134] 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.

[0135] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0136] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0137] Each of the multiple elements described above, including the monitoring unit, suggestion unit, and check unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the monitoring unit monitors the member's health status in real time using the AI ​​camera and biometric authentication sensor of the headset terminal 314. The suggestion unit is implemented in real time by the identification processing unit 290 of the data processing unit 12 and makes personalized training suggestions based on the monitored data. The check unit is implemented in real time by the control unit 46A of the headset terminal 314 and checks whether the member's training form is being performed correctly. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0138] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0139] 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.

[0140] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0141] 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.

[0142] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0143] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0144] 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.

[0145] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0146] 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.

[0147] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0148] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0149] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0150] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0151] 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.

[0152] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0153] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0154] Each of the multiple elements described above, including the monitoring unit, proposal unit, and checking unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the monitoring unit monitors the member's health status in real time using the robot 414's AI camera and biometric authentication sensor. The proposal unit is implemented in real time by the identification processing unit 290 of the data processing unit 12 and makes personalized training suggestions based on the monitored data. The checking unit is implemented in real time by the control unit 46A of the robot 414 and checks whether the member's training form is being performed correctly. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0155] 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.

[0156] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0157] 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.

[0158] 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.

[0159] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0160] 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."

[0161] 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.

[0162] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0171] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0172] 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.

[0173] (Note 1) The Monitoring Department monitors the health status of members in real time, A proposal unit provides personalized training suggestions based on data monitored by the aforementioned monitoring unit, The system includes a checking unit that checks the training form based on the training menu proposed by the aforementioned proposal unit. A system characterized by the following features. (Note 2) The monitoring unit, Entry and exit management will be conducted using facial recognition. The system described in Appendix 1, characterized by the features described herein. (Note 3) The monitoring unit, Biometric sensors monitor vital data such as heart rate and blood pressure in real time. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, We propose the optimal training menu based on each member's health condition and training goals. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned proposal section is, We adjust training menus based on members' training history and goal deadlines. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned checking unit is The system analyzes members' training form in real time to check whether they are training with the correct form. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned checking unit is Automatically provides advice if there are problems with the form. The system described in Appendix 1, characterized by the features described herein. (Note 8) The monitoring unit, The system estimates the emotions of its members and adjusts the monitoring frequency based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The monitoring unit, We analyze members' past health data and select the optimal monitoring method. The system described in Appendix 1, characterized by the features described herein. (Note 10) The monitoring unit, During monitoring, data is filtered based on the member's current activity level and stress level. The system described in Appendix 1, characterized by the features described herein. (Note 11) The monitoring unit, The system estimates members' emotions and prioritizes the data to monitor based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The monitoring unit, During monitoring, the system prioritizes monitoring of highly relevant data based on members' geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The monitoring unit, During monitoring, the social media activity of members is analyzed, and relevant health data is monitored. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned proposal section is, The system estimates the emotions of its members and adjusts the way training suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned proposal section is, When making a proposal, adjust the level of detail in the training menu based on the member's health condition. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned proposal section is, When making a proposal, different proposal algorithms are applied depending on the member's training goals. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned proposal section is, The system estimates the emotions of its members and adjusts the length of training suggestions based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned proposal section is, When making a proposal, we determine the priority of the proposal based on the member's training history. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, When making a proposal, the order of the training menu will be adjusted based on the member's relevance. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned checking unit is The system estimates the emotions of its members and adjusts the training form checking method based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned checking unit is During the check-up, the system analyzes the member's past training data to select the most suitable check-up method. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned checking unit is During the check, we improve the accuracy of the form check based on the member's current physical condition. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned checking unit is The system estimates the emotions of its members and adjusts the order in which training form check results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned checking unit is During the check, form verification will be performed based on the geographical distribution of members. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned checking unit is During the check process, we refer to the member's relevant literature to improve the accuracy of the form check. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0174] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The Monitoring Department monitors the health status of members in real time, A proposal unit provides personalized training suggestions based on data monitored by the aforementioned monitoring unit, The system includes a checking unit that checks the training form based on the training menu proposed by the aforementioned proposal unit. A system characterized by the following features.

2. The monitoring unit, Entry and exit management will be conducted using facial recognition. The system according to feature 1.

3. The monitoring unit, Biometric sensors monitor vital data such as heart rate and blood pressure in real time. The system according to feature 1.

4. The aforementioned proposal section is, We propose the optimal training menu based on each member's health condition and training goals. The system according to feature 1.

5. The aforementioned proposal section is, We adjust training menus based on members' training history and goal deadlines. The system according to feature 1.

6. The aforementioned checking unit is The system analyzes members' training form in real time to check whether they are training with the correct form. The system according to feature 1.

7. The aforementioned checking unit is Automatically provides advice if there are problems with the form. The system according to feature 1.

8. The monitoring unit, The system estimates the emotions of its members and adjusts the monitoring frequency based on those estimated emotions. The system according to feature 1.

9. The monitoring unit, We analyze members' past health data and select the optimal monitoring method. The system according to feature 1.

10. The monitoring unit, During monitoring, data is filtered based on the member's current activity level and stress level. The system according to feature 1.

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