System

Abstract

A system is provided.SOLUTION: A system comprising: means for measuring a heart rate in cooperation with a wearable device; means for receiving measured heart rate data in real time; means for analyzing the received heart rate data and determining whether a user is within an effective training zone; and means for notifying a determination result.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] In conventional muscle training, it has been difficult to maintain the appropriate training intensity and form to maximize the effectiveness of training. As a result, users have no way to determine in real time whether their heart rate or form is appropriate, which can result in ineffective training. It has also been difficult to obtain optimal training advice for individual users. The present invention solves these problems. [Means for solving the problem]

[0005] The present invention provides a system that includes a means for measuring heart rate in cooperation with a wearable device and receiving the measured heart rate data in real time, a means for analyzing the received heart rate data and determining whether the user is within an effective training zone, and a means for notifying the user of the result of this determination.

[0006] The system also includes a means for recording a user's exercise form, a means for analyzing the recorded data, and a means for evaluating accuracy and safety. The system also includes a means for providing feedback to the user on the evaluation results. The system also includes a means for generating personalized training advice based on the user's past training data, gender, age, and experience, and a means for presenting the generated advice to the user.

[0007] A "wearable device" is a small electronic device that can be worn on the user's body to measure and record physical condition and exercise data in real time.

[0008] "Heart rate" refers to the number of times the heart beats in a given period of time, usually expressed as the number of times per minute.

[0009] "Real-time" refers to a situation in which information and data are processed and analyzed the moment they are generated.

[0010] "Measurement" is the act of collecting and quantifying specific physical values ​​or data.

[0011] "Receiving" is the act of receiving transmitted data or signals.

[0012] "Analysis" is the process of examining collected data and information in detail to understand its meaning and status.

[0013] "Training zone" refers to the range of heart rate and load that should be achieved during exercise, and the zone is set according to the training goal.

[0014] "Judgment" is the act of reaching the most appropriate conclusion or evaluation based on the data and information obtained.

[0015] "Notification" is the act of conveying specific information or results to a user.

[0016] "Exercise form" refers to the posture and movements of the body when performing a particular exercise or training.

[0017] "Recording" is the act of recording video and audio using a camera or recording equipment.

[0018] "Evaluation" is the act of judging the content and quality of data and information.

[0019] "Feedback" is the act of returning the results of evaluation and judgment to the user and providing information that encourages improvements and adjustments.

[0020] "Personalization" is the act of reflecting individual data and characteristics in order to provide information and services optimized for each individual user.

[0021] "Training Advice" means guidance or suggestions provided to a User to help them train more effectively. [Brief explanation of the drawings]

[0022] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0023] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0024] First, the terms used in the following description will be explained.

[0025] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0026] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0027] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0028] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0029] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0030] [First embodiment]

[0031] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0032] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0033] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0034] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0035] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0036] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0037] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0038] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0039] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0040] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0041] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0042] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0043] This invention provides a system that uses a wearable device to measure heart rate and perform real-time monitoring and analysis on devices such as smartphones. It also uses a camera to record and analyze the user's exercise form, providing feedback to encourage correct form and safe training. It also generates personalized training advice for each user, enabling optimal training.

[0044] System configuration

[0045] The system mainly consists of the following components:

[0046] 1. Wearable devices (smartwatches, etc.)

[0047] Measures the user's heart rate in real time.

[0048] 2. Device (smartphone)

[0049] Connect to a wearable device via Bluetooth to receive heart rate data.

[0050] The received data is sent to the server.

[0051] Displays analysis results and feedback from the server.

[0052] 3. Server

[0053] The received heart rate data is analyzed using an AI algorithm to determine whether you are within an effective training zone.

[0054] The user's exercise form is analyzed based on the recorded data to evaluate accuracy and safety.

[0055] Generate personalized training advice.

[0056] Program processing and specific examples

[0057] Connecting wearable devices and measuring heart rate

[0058] Device: The user's smartphone launches the application and connects to the wearable device via Bluetooth. Once the connection is established, the smartwatch transmits heart rate data every second.

[0059] Example: User A puts on a wearable device and launches the smartphone app. Once pairing with the device is successful, heart rate data is sent to the smartphone every second.

[0060] Real-time data analysis and notifications

[0061] Device: Sends the received heart rate data to the server.

[0062] Server: Analyzes the data with AI algorithms to determine whether the user is in an effective training zone.

[0063] Server: Sends the analysis results to the device and generates notifications such as "You're in the right load zone" if you're in the right load zone, or "Increase your load" if your heart rate is too low.

[0064] Device: Display the notification to the user.

[0065] Example: The server determines that user A is in the fat burning zone based on the received heart rate data and sends a message saying "Appropriate" to the device. The device displays this message to the user.

[0066] Recording and analyzing exercise form

[0067] User: Uses a smartphone to record their training form.

[0068] Terminal: Sends recorded data to the server.

[0069] Server: Analyzes the recorded data, compares it with the ideal form, and generates feedback on areas for improvement and caution.

[0070] Terminal: Display feedback to the user.

[0071] Example: User A sets up a smartphone to record squat training. The recorded data is sent to a server and analyzed by an AI algorithm. The server generates feedback such as "Please straighten your back more" and sends it to the device. The device then displays this feedback to the user.

[0072] Generate personalized training advice

[0073] Server: Generates personalized advice based on the user's past training data, gender, age, and experience.

[0074] Server: Sends the generated advice to the terminal.

[0075] Device: Provides advice to users and encourages them to use it for their next workout.

[0076] Example: The server generates advice for the next training session, such as "Increase the weight by 5 kg," based on the user A's past training data. The device displays this advice to the user to help them with their next training.

[0077] The above is an embodiment of the present invention, and the system allows the user to achieve effective and safe training.

[0078] The processing flow will be explained below.

[0079] Step 1:

[0080] Device: Launch the dedicated app on the user's smartphone and open the Bluetooth settings. Once the app is launched, a screen will appear to detect wearable devices (such as smartwatches).

[0081] Step 2:

[0082] Device: Detects the smartwatch's Bluetooth and sends a pairing request. The user enters the device's pairing code and approves the pairing.

[0083] Step 3:

[0084] Device: Once paired successfully, the smartwatch will begin to maintain connection with the wearable device and begin transmitting heart rate data.

[0085] Step 4:

[0086] Terminal: Receives heart rate data from the wearable device every second and sends the received heart rate data to the server.

[0087] Step 5:

[0088] Server: Analyzes the received heart rate data using AI algorithms to determine whether the user is in an effective training zone (e.g., fat burning zone, cardiovascular zone, or maximum effort zone).

[0089] Step 6:

[0090] Server: Based on the analysis results, evaluates whether the user is in the target zone. If the user is in the fat burning zone, it generates a message saying "You're in the right zone." If the heart rate is too low, it generates a notification saying "Increase your load."

[0091] Step 7:

[0092] Server: Returns the evaluation result and a notification message to the terminal.

[0093] Step 8:

[0094] Terminal: Displays the evaluation results and notification messages from the server to the user. The user continues training based on the displayed information.

[0095] Step 9:

[0096] User: Use a smartphone to record your training form. Adjust the camera position and angle to capture your entire form.

[0097] Step 10:

[0098] Device: Start recording mode and record your training form. The recorded data is temporarily saved in the cache.

[0099] Step 11:

[0100] Device: After the training session is over, the recording data is sent to the server.

[0101] Step 12:

[0102] Server: Adds the received recording data to the analysis queue. Analyzes the form using an AI image recognition algorithm and compares it with the ideal form.

[0103] Step 13:

[0104] Server: Based on the analysis results, it identifies areas of form that need improvement or attention. For example, it generates feedback such as "Please stand up straighter" or "Your knee angle is too shallow."

[0105] Step 14:

[0106] Server: Sends the generated feedback back to the device.

[0107] Step 15:

[0108] Terminal: Feedback is displayed to the user, who can then improve the form based on the feedback.

[0109] Step 16:

[0110] Server: Retrieves user gender, age, experience, and past training data from the database.

[0111] Step 17:

[0112] Server: Based on this data, it recalculates optimal training intensity and goals and generates personalized training advice, such as "Increase the weight by 5 kg in your next training session" or "Take a rest next time."

[0113] Step 18:

[0114] Server: Sends the generated advice to the terminal.

[0115] Step 19:

[0116] On the device: Providing advice to users and encouraging them to use it for their next training session.

[0117] The above are the specific processing steps for carrying out the present invention.

[0118] Example 1

[0119] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0120] In modern training, real-time heart rate monitoring and evaluation of exercise form are important for each individual user to exercise effectively and safely. However, conventional systems have difficulty providing comprehensive support for these, making it difficult to determine whether users are in the appropriate training zone or maintain correct exercise form. For this reason, a system that allows users to train effectively while avoiding injury has been sought.

[0121] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0122] In this invention, the server includes means for measuring heart rate in cooperation with the wearable device, means for receiving measured heart rate data in real time, means for analyzing the received heart rate data and determining whether the user is within an effective training zone, means for notifying the user of the determination result, means for recording the user's exercise form, means for analyzing the recorded data and evaluating accuracy and safety, means for providing feedback to the user of the evaluation result, means for generating personalized training advice based on the user's past training data, gender, age, and experience, and means for presenting the generated advice to the user. This enables the user to check their own heart rate and exercise form in real time and perform effective and safe training.

[0123] A "wearable device" is a device that can be worn on the body and has the function of measuring biometric data, mainly heart rate, in real time and connecting to a terminal.

[0124] A "terminal" is a portable electronic device such as a smartphone or tablet that has the function of receiving data from a wearable device and transmitting it to a server.

[0125] A "server" is a high-performance computer system that exchanges data with multiple terminals via a network and analyzes the received data.

[0126] "Heart Rate Data" means information about heart rate measured through a wearable device and used to evaluate the effectiveness of training.

[0127] "Real-time" refers to data measurement and analysis occurring with almost no delay, meaning users can see the results immediately.

[0128] The "effective training zone" refers to the ideal heart rate range for the exercise the user is performing, and is the optimal range for goals such as burning fat or improving cardiopulmonary function.

[0129] The "judgment result" is information about the appropriateness of the training generated based on the heart rate data analyzed by the server.

[0130] "Notifications" are messages or alerts provided to the User via the Device to assist in adjusting their Training.

[0131] "Exercise form" refers to the body movements and positioning when a user exercises, and proper form enhances training effectiveness and prevents injury.

[0132] "Recorded data" refers to video information that records the user's exercise form and is used for exercise analysis.

[0133] The "evaluation results" are information about the accuracy and safety of the exercise form generated by the server by analyzing the recorded data.

[0134] "Feedback" refers to advice and instructions provided to users based on the evaluation results, with the aim of improving exercise form and training safety.

[0135] "Personalized training advice" refers to training suggestions and guidance that are individually tailored to the user based on their past training data, gender, age, and experience, helping them to train more effectively.

[0136] This invention is a system that uses a wearable device to measure heart rate, monitors and analyzes in real time using a terminal, and records and analyzes the user's exercise form to encourage accurate form and safe training. This system generates personalized training advice for each user, enabling optimal training.

[0137] System configuration

[0138] The system mainly consists of the following components:

[0139] 1. Wearable devices (smartwatches, etc.)

[0140] Measures the user's heart rate in real time.

[0141] 2. Device (smartphone)

[0142] Connect to a wearable device via Bluetooth to receive heart rate data.

[0143] The received data is sent to the server.

[0144] Displays analysis results and feedback from the server.

[0145] 3. Server

[0146] The received heart rate data is analyzed using an AI algorithm to determine whether you are within an effective training zone.

[0147] The user's exercise form is analyzed based on the recorded data to evaluate accuracy and safety.

[0148] Generate personalized training advice.

[0149] Connecting wearable devices and measuring heart rate

[0150] Device: The user launches the smartphone app and connects to the wearable device via Bluetooth. Once the connection is established, the wearable device sends heart rate data to the smartphone every second.

[0151] Example: User A puts on a wearable device and launches the smartphone app. Once pairing with the device is successful, heart rate data is sent to the smartphone every second. The screen displays the heart rate in real time.

[0152] Real-time data analysis and notifications

[0153] Device: Sends the received heart rate data to the server.

[0154] Server: Analyzes the data with AI algorithms to determine whether the user is in an effective training zone.

[0155] Server: Sends the analysis results to the device and generates notifications such as "You're in the right load zone" if you're in the right load zone, or "Increase your load" if your heart rate is too low.

[0156] Device: Display the notification to the user.

[0157] Example: The server determines that user A is in the fat burning zone based on the received heart rate data and sends a message saying "Appropriate" to the device. The device displays this message to the user.

[0158] Recording and analyzing exercise form

[0159] User: The user uses a smartphone to record their training form.

[0160] Terminal: Sends recorded data to the server.

[0161] Server: Analyzes the recorded data, compares it with the ideal form, and generates feedback on areas for improvement and caution.

[0162] Terminal: Display feedback to the user.

[0163] Example: User A sets up a smartphone to record squat training. The recorded data is sent to a server and analyzed by an AI algorithm. The server generates feedback such as "Please straighten your back more" and sends it to the device. The device then displays this feedback to the user.

[0164] Generate personalized training advice

[0165] Server: Generates personalized advice based on the user's past training data, gender, age, and experience.

[0166] Server: Sends the generated advice to the terminal.

[0167] Device: Provides advice to users and encourages them to use it for their next workout.

[0168] Example: The server generates advice for the next training session, such as "Increase the weight by 5 kg," based on the user A's past training data. The device displays this advice to the user to help them with their next training.

[0169] Prompt Sentence Examples

[0170] "Please tell me how the app works, measuring heart rate in conjunction with a smartwatch and analyzing the data in real time."

[0171] "Please explain how to analyze recorded data of athletic form and generate feedback."

[0172] "Please tell me more about the system that generates personalized training advice."

[0173] The above is an embodiment of the present invention, and the system allows the user to achieve effective and safe training.

[0174] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0175] Step 1:

[0176] Input: The user puts on a wearable device (smartwatch) and launches the app on their smartphone.

[0177] How it works: The phone establishes a connection with the wearable device using Bluetooth. This connection uses the Bluetooth Low Energy protocol (BLE).

[0178] Output: The connection is established and the wearable device sends heart rate data to the smartphone every second.

[0179] Step 2:

[0180] Input: Heart rate data sent from a wearable device.

[0181] Operation: The device batches the received heart rate data at regular intervals (e.g., every 5 seconds) and sends it to the server.

[0182] Output: Batched heart rate data is sent to the server.

[0183] Step 3:

[0184] Input: Heart rate data received by the server from the device.

[0185] How it works: The server analyzes the received data with AI algorithms, first filtering out noise from the raw data, and then determining which training zone the user is in.

[0186] Output: The analysis results are generated, and training zone information is derived. Messages such as "Good", "Increase load", and "Decrease load" are also generated.

[0187] Step 4:

[0188] Input: Parse results and messages generated by the server.

[0189] Operation: The server sends the analysis results and a message to the terminal.

[0190] Output: The analysis results and messages are sent to the device and ready to be displayed in the smartphone app.

[0191] Step 5:

[0192] Input: Analysis results and messages sent from the server.

[0193] Operation: The device displays the analysis results and messages it receives on the screen, allowing users to check their training status in real time.

[0194] Output: The user is notified with a message such as "Good", "Please increase load", or "Please decrease load".

[0195] Step 6:

[0196] Input: The user records their exercise form using their smartphone camera.

[0197] How it works: The device receives the recording data, applies a compression algorithm to optimize the file size, and sends it to the server.

[0198] Output: Compressed recording data is sent to the server.

[0199] Step 7:

[0200] Input: Recording data received by the server.

[0201] How it works: The server analyzes the recorded data using an AI algorithm, comparing it with the ideal form and evaluating the accuracy and safety of the user's form.

[0202] Output: As a result of the evaluation, feedback on areas for improvement and points to note is generated.

[0203] Step 8:

[0204] Input: Server-generated evaluation results and feedback.

[0205] Operation: The server sends the evaluation results and feedback to the device.

[0206] Output: Feedback is sent to the device, ready to be displayed to the user.

[0207] Step 9:

[0208] Input: Feedback sent by the server.

[0209] How it works: The device displays the feedback it receives on the screen, allowing the user to see areas of their exercise form that need improvement.

[0210] Output: The user is given specific feedback, such as "Stand up straighter."

[0211] Step 10:

[0212] Input: User's past training data, gender, age, experience.

[0213] How it works: The server uses this information to generate personalized advice for your next workout.

[0214] Output: Personalized training advice is generated.

[0215] Step 11:

[0216] Input: Server-generated personalized training advice.

[0217] Operation: The server sends advice to the terminal.

[0218] Output: The personalized advice is sent to the device and ready to be presented to the user.

[0219] Step 12:

[0220] Input: Personalized advice sent from the server.

[0221] How it works: The device displays the received advice on the screen and encourages the user to use it for their next workout.

[0222] Output: A specific piece of advice is displayed to the user, such as "Increase the weight by 5kg in your next session."

[0223] (Application example 1)

[0224] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0225] In autonomous vehicles, there are issues with insufficient management of driver health, maintaining posture while driving, and ensuring safety. Real-time monitoring of heart rate and exercise form is particularly important for drivers who drive for long periods of time, and there are concerns about health risks associated with continued improper monitoring. Furthermore, conventional systems lack personalized advice suited to individual drivers, making it difficult to ensure comfort and safety while driving.

[0226] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0227] In this invention, the server includes: means for measuring a heart rate in cooperation with a wearable device; means for receiving heart rate data in real time; means for analyzing the received heart rate data and determining whether the heart rate is within an effective exercise zone; means for notifying the driver of the determination result; means for issuing a warning if the heart rate is abnormally high to monitor the driver's health; means for recording the user's exercise form; means for analyzing the recorded data and evaluating accuracy and safety; means for providing feedback to the user on the evaluation result; means for analyzing the user's posture and exercise form in the vehicle and providing appropriate feedback; means for generating personalized training advice based on the user's past training data, gender, age, and experience; means for presenting the generated advice to the user; and means for providing personalized exercise and relaxation methods to maintain the driver's health. This improves health monitoring of drivers and passengers in autonomous vehicles, enabling a safer and more comfortable driving environment.

[0228] "Wearable device" refers to a wearable device used to measure a user's heart rate and exercise data.

[0229] "Heart Rate Data" means a user's heart rate information measured through a wearable device.

[0230] "Means for receiving in real time" refers to equipment or a system that has the functionality to instantly receive heart rate data transmitted from a wearable device.

[0231] "Effective exercise zone" refers to a specific heart rate range that a user targets to maximize the benefits of their exercise.

[0232] "Measures to monitor the driver's health and warn the driver if their heart rate is abnormally high" refers to a system that warns the driver if their heart rate exceeds the normal range.

[0233] "Means for recording the user's exercise form" refers to a device for recording the user's exercise using a video camera or the like and collecting the data.

[0234] "Means for analyzing recorded data" refers to software or algorithms used to analyze recorded exercise form data and evaluate accuracy and safety.

[0235] "Means for analyzing posture and exercise form in the vehicle and providing appropriate feedback" refers to a system that analyzes the driver's posture and exercise form captured by an in-vehicle camera and provides appropriate advice and warnings based on the results.

[0236] "Past training data" refers to historical information about the training a user has done up to now.

[0237] "Personalized Training Advice" means training advice that is tailored to you based on your individual characteristics and past training data.

[0238] "Means for providing personalized exercise and relaxation methods" refers to a system that suggests exercise and relaxation methods that are individually suited to the user's needs.

[0239] This invention provides a system that uses a wearable device to measure a driver's heart rate in real time and monitors it on an in-car display or smartphone. It also uses a camera to record and analyze the driver's exercise form and posture, providing safe and appropriate feedback. It also generates personalized health advice for each driver, creating a comfortable driving environment.

[0240] System configuration

[0241] The system mainly consists of the following components:

[0242] 1. Wearable devices

[0243] Measure the driver's heart rate in real time.

[0244] 2. Device (smartphone or in-car display)

[0245] Connect to a wearable device via Bluetooth to receive heart rate data.

[0246] The received data is sent to the server.

[0247] Displays analysis results and feedback from the server.

[0248] 3. Camera

[0249] The system uses an in-car camera to record the driver's posture and exercise form.

[0250] The recording data is sent to the server.

[0251] 4. Server

[0252] The received heart rate data is analyzed using an AI algorithm to determine whether the driver is within an effective heart rate zone.

[0253] The system analyzes recorded data of the driver's exercise form to evaluate accuracy and safety.

[0254] Generate personalized health advice and send it to your device.

[0255] Program processing overview

[0256] Connecting wearable devices and measuring heart rate

[0257] The smartphone or in-car display launches an application and connects to the wearable device via Bluetooth. Once the connection is established, the wearable device transmits heart rate data every second. For example, a driver puts on the wearable device and launches the app on the in-car display. If pairing with the device is successful, heart rate data will be displayed in real time.

[0258] Real-time data analysis and notifications

[0259] The device sends the received heart rate data to a server. The server analyzes the data and determines whether the driver is within an effective heart rate zone. The analysis results are sent back to the device, and if the heart rate is abnormally high, a notification such as "Take a deep breath" is sent. For example, the server may determine that the driver is under stress based on the received heart rate data and provide a relaxation suggestion.

[0260] Recording and analyzing exercise form

[0261] Users use the in-car camera to record their posture and exercise form while driving. The recorded data is sent to a server and analyzed by an AI algorithm. The server then generates feedback such as "Please adjust your seat" based on this data and sends it to the device.

[0262] Generating personalized health advice

[0263] The server generates personalized health advice based on the driver's past health data, age, and experience. The generated advice is sent to the device and used as reference for the next driving session. For example, the server generates advice based on past data, such as "Please stretch for five minutes before your next drive."

[0264] Hardware and software used

[0265] Hardware: Smartwatches (wearable devices), smartphones, in-car displays, and in-car cameras.

[0266] Software: Python program, OpenCV, Bluetooth library, HTTP library.

[0267] Prompt Sentence Examples

[0268] "Monitor the driver's heart rate in real time and display a warning message on the display if the heart rate is high."

[0269] "Use a camera to record the driver's posture and provide feedback based on the analysis results."

[0270] The system will improve health monitoring for drivers and passengers in autonomous vehicles, enabling a safer and more comfortable driving environment.

[0271] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0272] Step 1:

[0273] Connect the wearable device and the terminal via Bluetooth.

[0274] Input: Bluetooth address of the wearable device

[0275] Output: Connection success or failure status

[0276] Specific operation: The user wears the smartwatch and launches the smartphone app or the app on the car display. The app will attempt to connect to the device via Bluetooth, and if the connection is successful, it will display the connection success status.

[0277] Step 2:

[0278] Heart rate data is acquired in real time and sent to the device.

[0279] Input: Heart rate data from a wearable device

[0280] Output: Real-time heart rate data displayed on the device

[0281] What it does: The device sends heart rate data every second, and the application analyzes this data and displays it in real time.

[0282] Step 3:

[0283] The received heart rate data is sent to the server.

[0284] Input: Heart rate data received by the device

[0285] Output: Heart rate data sent to the server

[0286] Specific operation: The smartphone or in-car display sends the received heart rate data to the server at regular intervals using an HTTP POST request.

[0287] Step 4:

[0288] The server analyzes the heart rate data using an AI algorithm.

[0289] Input: Heart rate data received by the server

[0290] Output: Determining whether your heart rate is within the effective exercise zone

[0291] How it works: The server inputs the received heart rate data into an AI algorithm for real-time analysis, and determines whether the driver is in an effective exercise zone based on the analysis results.

[0292] Step 5:

[0293] The server sends the analysis results to the device and notifies the user.

[0294] Input: Analysis result notification data

[0295] Output: A notification message displayed on the terminal.

[0296] Specific operation: The server sends the analysis results to the device, and based on the results, a message such as "Please take a deep breath" is displayed.

[0297] Step 6:

[0298] The camera records the driver's exercise form.

[0299] Input: Camera image

[0300] Output: Video data of recorded exercise form

[0301] Specific operation: A camera installed inside the vehicle records the driver's posture and exercise form and acquires the video data.

[0302] Step 7:

[0303] The recorded data is sent to a server and form analysis is performed.

[0304] Input: Recorded video data

[0305] Output: Feedback of analysis results

[0306] How it works: The recorded data is sent to a server, which then analyzes it using an AI algorithm. Based on the analysis results, feedback such as "Please adjust your seat" is generated.

[0307] Step 8:

[0308] A server generates personalized health advice.

[0309] Input: Past training data, gender, age, experience

[0310] Output: Health advice data

[0311] How it works: The server generates personalized health advice based on the driver's past data, such as "Try doing five minutes of stretching before your next drive."

[0312] Step 9:

[0313] The generated advice is sent to the terminal and presented to the driver.

[0314] Input: Health advice data

[0315] Output: Health advice displayed on the device

[0316] Specific operation: The server sends the generated health advice to the device, and the driver can check the advice on the device.

[0317] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0318] This invention provides a system that uses a wearable device to measure heart rate and perform real-time monitoring and analysis on devices such as smartphones. It also uses a camera to record and analyze the user's exercise form, providing feedback to encourage correct form and safe training. It also generates personalized training advice for each user and uses an emotion engine that recognizes the user's emotions to achieve optimal training.

[0319] System configuration

[0320] The system mainly consists of the following components:

[0321] 1. Wearable devices (smartwatches, etc.)

[0322] Measures the user's heart rate in real time.

[0323] 2. Device (smartphone)

[0324] Connect to a wearable device via Bluetooth to receive heart rate data.

[0325] The received data is sent to the server.

[0326] Displays analysis results and feedback from the server.

[0327] The built-in camera records the user's exercise form.

[0328] 3. Server

[0329] The received heart rate data is analyzed using an AI algorithm to determine whether you are within an effective training zone.

[0330] The user's exercise form is analyzed based on the recorded data to evaluate accuracy and safety.

[0331] Generates personalized training advice based on the user's past training data, gender, age, and experience.

[0332] An emotion engine is used to analyze user emotion data and reflect it in training optimization.

[0333] Program processing and specific examples

[0334] Connecting wearable devices and measuring heart rate

[0335] Device: The user's smartphone runs a dedicated app and connects to the wearable device via Bluetooth. Once the connection is established, the smartwatch sends heart rate data every second.

[0336] Example: User A puts on a wearable device and launches the smartphone app. Once pairing with the device is successful, heart rate data is sent to the smartphone every second.

[0337] Real-time data analysis and notifications

[0338] Device: Sends the received heart rate data to the server.

[0339] Server: Analyzes the data with AI algorithms to determine whether the user is in an effective training zone.

[0340] Server: Based on the analysis results, evaluates whether the user is in the target zone. If the user is in the fat burning zone, it generates a message saying "You're in the right zone." If the heart rate is too low, it generates a notification saying "Increase your load."

[0341] Device: Display the notification to the user.

[0342] Example: The server determines that user A is in the fat burning zone based on the received heart rate data and sends a message saying "Appropriate" to the device. The device displays this message to the user.

[0343] Recording and analyzing exercise form

[0344] User: Use a smartphone to record your training form. Adjust the camera position and angle to capture your entire form.

[0345] Device: Start recording mode and record your training form. The recorded data is temporarily saved in the cache.

[0346] Device: After the training session is over, the recording data is sent to the server.

[0347] Server: Adds the received recording data to the analysis queue. Analyzes the form using an AI image recognition algorithm and compares it with the ideal form.

[0348] Server: Based on the analysis results, it identifies areas of form that need improvement or attention. For example, it generates feedback such as "Please stand up straighter" or "Your knee angle is too shallow."

[0349] Terminal: Feedback is displayed to the user, who can then improve the form based on the feedback.

[0350] Example: User A sets up a smartphone to record squat training. The recorded data is sent to a server and analyzed by an AI algorithm. The server generates feedback such as "Please straighten your back more" and sends it to the device. The device then displays this feedback to the user.

[0351] Generate personalized training advice

[0352] Server: Generates personalized advice based on the user's past training data, gender, age, and experience.

[0353] Server: Sends the generated advice to the terminal.

[0354] Device: Provides advice to users and encourages them to use it for their next workout.

[0355] Example: The server generates advice for the next training session, such as "Increase the weight by 5 kg," based on the user A's past training data. The device displays this advice to the user to help them with their next training.

[0356] Introducing an emotion engine and analyzing emotion data

[0357] Device: Equipped with a camera and sensors to capture emotional data during training. This data is sent to the server in real time.

[0358] Server: Analyzes the received emotional data using an emotion engine to understand the user's emotional state.

[0359] Server: Adjusts training intensity and recommended actions based on emotional data. For example, if a user feels fatigued or stressed, the server generates advice to reduce training intensity.

[0360] Terminal: Presents the generated advice to the user.

[0361] Example: The server analyzes the emotion data received in real time and determines that User A is tired. Based on this, it generates advice such as "Please do light exercise in your next training session" and sends it to the device. The device then displays this advice to the user.

[0362] The above is an embodiment of the present invention that combines an emotion engine, allowing users to train effectively and safely and receive personalized feedback that takes into account their emotional state during training.

[0363] The processing flow will be explained below.

[0364] Step 1:

[0365] Device: Launch the dedicated app on the user's smartphone and open the Bluetooth settings. Once the app is launched, a screen will appear to detect wearable devices (such as smartwatches).

[0366] Step 2:

[0367] Device: Detects the smartwatch's Bluetooth and sends a pairing request. The user enters the device's pairing code and approves the pairing.

[0368] Step 3:

[0369] Device: Once paired successfully, the smartwatch will begin to maintain connection with the wearable device and begin transmitting heart rate data.

[0370] Step 4:

[0371] Terminal: Receives heart rate data from the wearable device every second and sends the received heart rate data to the server.

[0372] Step 5:

[0373] Server: Analyzes the received heart rate data using AI algorithms to determine whether the user is in an effective training zone (e.g., fat burning zone, cardiovascular zone, or maximum effort zone).

[0374] Step 6:

[0375] Server: Based on the analysis results, evaluates whether the user is in the target zone. If the user is in the fat burning zone, it generates a message saying "You're in the right zone." If the heart rate is too low, it generates a notification saying "Increase your load."

[0376] Step 7:

[0377] Server: Returns the evaluation result and a notification message to the terminal.

[0378] Step 8:

[0379] Terminal: Displays the evaluation results and notification messages from the server to the user. The user continues training based on the displayed information.

[0380] Step 9:

[0381] User: Use a smartphone to record your training form. Adjust the camera position and angle to capture your entire form.

[0382] Step 10:

[0383] Device: Start recording mode and record your training form. The recorded data is temporarily saved in the cache.

[0384] Step 11:

[0385] Device: After the training session is over, the recording data is sent to the server.

[0386] Step 12:

[0387] Server: Adds the received recording data to the analysis queue. Analyzes the form using an AI image recognition algorithm and compares it with the ideal form.

[0388] Step 13:

[0389] Server: Based on the analysis results, it identifies areas of form that need improvement or attention. For example, it generates feedback such as "Please stand up straighter" or "Your knee angle is too shallow."

[0390] Step 14:

[0391] Server: Sends the generated feedback back to the device.

[0392] Step 15:

[0393] Terminal: Feedback is displayed to the user, who can then improve the form based on the feedback.

[0394] Step 16:

[0395] Server: Retrieves user gender, age, experience, and past training data from the database.

[0396] Step 17:

[0397] Server: Based on this data, it recalculates optimal training intensity and goals and generates personalized training advice, such as "Increase the weight by 5 kg in your next training session" or "Take a rest next time."

[0398] Step 18:

[0399] Server: Sends the generated advice to the terminal.

[0400] Step 19:

[0401] On the device: Providing advice to users and encouraging them to use it for their next training session.

[0402] Step 20:

[0403] User: Use cameras and sensors to capture emotional data during training. The cameras and sensors capture the user's facial expressions and physical reactions, monitoring their emotional state in real time.

[0404] Step 21:

[0405] Terminal: Transmits acquired emotion data to the server in real time.

[0406] Step 22:

[0407] Server: The emotion engine analyzes the received emotion data and identifies the user's emotional state. The emotion engine uses AI technology to determine emotions such as stress, joy, and fatigue from the user's facial expressions and physical reactions.

[0408] Step 23:

[0409] Server: Adjusts training intensity and recommended actions based on emotional data. For example, if a user feels fatigued or stressed, the server generates advice to reduce training intensity.

[0410] Step 24:

[0411] Server: Sends tailored feedback and advice to devices.

[0412] Step 25:

[0413] Terminal: Presents the generated advice to the user.

[0414] Examples:

[0415] Example 1: The server analyzes emotion data received in real time and determines that user A is tired. Based on this, it generates advice such as "Please do light exercise in your next training session" and sends it to the device. The device then displays this advice to the user.

[0416] Example 2: The emotion engine detects that user B is feeling joy or satisfaction during training. Based on this information, the server generates an encouraging message such as "Keep it up!" and sends it to the device. The device then displays the generated message to the user, further increasing their motivation.

[0417] The above is an embodiment of the present invention that combines an emotion engine, allowing users to train effectively and safely and receive personalized feedback that takes into account their emotional state during training.

[0418] Example 2

[0419] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0420] While conventional fitness systems using wearable devices can monitor users' biometric information such as heart rate in real time, they do not support recording and analysis of exercise form or providing personalized training advice. Furthermore, there is no training feedback that takes into account the user's emotional state. In particular, it is difficult to correct incorrect exercise form or provide advice tailored to individual needs, resulting in safety and effectiveness issues.

[0421] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes a means for measuring biometric information in cooperation with the wearable device, a means for receiving the measured biometric data in real time, a means for analyzing the received biometric data and determining whether the user is within an effective exercise zone, a means for notifying the user of the determination result, a means for recording the user's exercise movements, a means for analyzing the recorded data and evaluating accuracy and safety, a means for feeding back the evaluation result to the user, a means for generating personalized exercise advice based on the user's past exercise data and attribute data, and a means for presenting the generated advice to the user. This makes it possible to monitor biometric information in real time, as well as analyze exercise form and provide safe and effective personalized advice.

[0422] A "wearable device" is an electronic device that can be worn on the user's body and used to measure biometric information.

[0423] "Biometric information" refers to data about the user's physical condition, such as heart rate, body temperature, and blood pressure.

[0424] "Biometric data" refers to the numerical values ​​and indicators of biometric information measured by a wearable device.

[0425] "Real-time" is a time concept that refers to data being collected and processed almost immediately.

[0426] "Exercise movements" refers to movements or movements performed by a user, including training and exercise.

[0427] "Recorded data" refers to video data of exercise movements captured using a recording device such as a camera.

[0428] "Analysis" refers to the activity of processing received data, transforming it into a meaningful form, and deriving results.

[0429] "Accuracy" refers to the degree to which a user's athletic movements match the ideal form and movements they are aiming for.

[0430] "Safety" refers to the suitability of the device to prevent injury or other hazards when a user performs an exercise.

[0431] "Feedback" refers to the evaluation and advice the system provides to the user.

[0432] "Personalized exercise advice" refers to exercise advice and instructions that are optimized based on a user's individual characteristics and past data.

[0433] "Attribute data" refers to data that indicates a user's individual characteristics, such as age, gender, and experience.

[0434] This invention is a system that uses wearable devices and terminals such as smartphones to monitor and analyze a user's biometric information and exercise movements in real time, allowing the user to train appropriately and receive safer and more effective training advice.

[0435] Hardware and software used

[0436] 1. Wearable devices (e.g., smartwatches): Wearable devices measure the user's biometric information, such as heart rate, and transmit it to a smartphone via Bluetooth.

[0437] 2. Device (e.g., smartphone): The smartphone runs a dedicated app and receives biometric data sent from the wearable device. The received data is sent to a server in real time. The smartphone also uses a camera to record the user's exercise movements.

[0438] 3. Server: The server analyzes the received biometric data and recorded exercise data. It uses AI algorithms to determine whether the user is within the effective exercise zone, evaluate the accuracy and safety of the exercise form, and generate personalized training advice based on the user's past data and attributes.

[0439] Explanation of program processing

[0440] Measurement and transmission of biometric data

[0441] 1. The device launches the dedicated app and connects to the wearable device via Bluetooth. Once the connection is established, the smartwatch sends heart rate data every second.

[0442] Example: A user wears a smartwatch and launches an app on their smartphone. Once the app successfully pairs with the smartwatch, heart rate data is sent to the smartphone every second. For example, data such as "Heart rate: 75, Time: 10:00:01" is continuously sent.

[0443] Real-time data analysis

[0444] 1. The device sends the received heart rate data to the server.

[0445] 2. The server uses an AI algorithm to analyze your heart rate data and determine whether you are within an effective exercise zone.

[0446] Example: The server processes data such as "Heart rate: 135, Time: 10:05:01" and determines that the user is in the fat burning zone. Based on this, it generates a notification message saying "Appropriate" and sends it to the device. The device then displays this message to the user.

[0447] Recording and analyzing exercise form

[0448] 1. The user uses a smartphone to record their exercise movements, adjusting the camera position and angle to capture their entire form.

[0449] 2. The device will start recording mode and record your training form, temporarily saving the recorded data in the cache.

[0450] 3. After the training session ends, the device sends the recorded data to the server.

[0451] 4. The server analyzes the received recording data and uses an AI image recognition algorithm to analyze the user's form. It compares the user's form with the ideal form and identifies areas for improvement.

[0452] Example: A user records a squat and sends the recorded data to a server. The server generates a feedback message saying "Please stand up straighter" and sends it to the device. The device displays this feedback to the user.

[0453] Generate personalized training advice

[0454] 1. The server generates personalized training advice based on the user's past training data, gender, age, and experience, and sends it to the device.

[0455] 2. The device displays the advice to the user.

[0456] Example: Based on the user's training data, the server generates advice such as "Increase the weight by 5 kg in your next training session." The device notifies the user of this advice and displays it on the training guide screen.

[0457] Prompt Sentence Examples

[0458] "Today's training session was very effective as it kept my heart rate stable and allowed me to train in the fat burning zone. Next time, I'll try increasing the intensity a bit and extending the duration by 5 minutes."

[0459] The above is an embodiment of the invention. This system allows users to monitor their biological information in real time, analyze their exercise form, and perform safe and effective training.

[0460] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0461] Step 1:

[0462] Connecting wearable devices and measuring heart rate

[0463] The device launches a dedicated app and connects to the wearable device via Bluetooth. Once the connection is established, the smartwatch sends heart rate data every second.

[0464] Input: The user puts on the smartwatch and launches the app on their smartphone.

[0465] Data processing: The app pairs with the wearable device and a connection is established.

[0466] Output: Heart rate data is sent from the smartwatch to the smartphone every second, and is simultaneously displayed in real time on the app screen.

[0467] Specific operation: The user wears the smartwatch and launches the smartphone app. Once paired with the device, heart rate data will be sent to the smartphone every second and displayed on the real-time monitoring screen within the app.

[0468] Step 2:

[0469] Real-time data analysis and notifications

[0470] The terminal transmits the received heart rate data to the server.

[0471] The server uses AI algorithms to analyze heart rate data in real time to determine whether the user is in an effective exercise zone, and generates a message based on the analysis results and sends it to the device.

[0472] Input: Heart rate data sent from the device (e.g., "Heart rate: 135, Time: 10:05:01").

[0473] Data processing: The server analyzes the heart rate data and determines whether you are in an effective exercise zone (e.g., fat burning zone).

[0474] Output: The judgment result (e.g., "Appropriate") is sent as a message to the terminal.

[0475] Specific operation: The server processes the received heart rate data and confirms that the user is in the fat burning zone. It generates a notification message saying "Suitable" and sends it to the device. The device then displays this message to the user in a pop-up.

[0476] Step 3:

[0477] Recording and analyzing exercise form

[0478] Users use their smartphones to record their exercise movements, adjusting the camera position and angle to capture their entire form.

[0479] The device will enter recording mode and record your training form. The recorded data will be temporarily saved in the cache.

[0480] After the training session ends, the terminal transmits the recorded data to the server.

[0481] The server analyzes the received recording data and uses an AI image recognition algorithm to analyze the user's form, comparing it with the ideal form and identifying areas for improvement.

[0482] Input: Video data (e.g., video of squats).

[0483] Data processing: The server analyzes the recorded data using AI image recognition algorithms and evaluates the user's form.

[0484] Output: The analysis results (e.g., "Please stand up straighter") are sent to the device as a feedback message.

[0485] How it works: The user records their squats and the recorded data is sent to the server. The server then uses an AI algorithm to analyze the data, generates a feedback message saying "Please straighten your back more," and sends it to the device. The device then displays this feedback to the user.

[0486] Step 4:

[0487] Generate personalized training advice

[0488] The server generates personalized training advice based on the user's past training data, gender, age, and experience, and sends it to the device.

[0489] The device displays the advice to the user.

[0490] Input: User's past training data and demographic data (e.g., age, gender, experience).

[0491] Data processing: The server uses this data to generate advice tailored to your individual needs.

[0492] Output: Generated advice (e.g. "Increase the weight by 5kg in your next workout") is sent to the device.

[0493] Specific operation: Based on the user's training data, the server generates advice such as "Increase the weight by 5 kg in your next training session." The device notifies the user of this advice and displays it on the training guide screen.

[0494] Step 5:

[0495] Introducing an emotion engine and analyzing emotion data

[0496] The device uses cameras and sensors to capture emotional data while the user is training, and transmits this data to a server in real time.

[0497] The server analyzes the received emotional data using an emotion engine to understand the user's emotional state and adjusts training intensity and recommended actions as needed.

[0498] Input: Emotion data obtained in real time (e.g., facial expression data from a facial recognition sensor).

[0499] Data processing: The server uses an emotion engine to analyze the emotion data and identify the user's emotional state.

[0500] Output: Advice generated based on the results of sentiment analysis (e.g., "Do light exercise during your next workout") is sent to the device.

[0501] Specific operation: The server analyzes the user's emotional data and determines that the user is tired. It generates advice such as "Try to do lighter exercise next time" and sends it to the device. The device notifies the user of this advice and displays it on the home screen.

[0502] (Application example 2)

[0503] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0504] While conventional training monitoring systems provide functions such as measuring heart rate and analyzing exercise form, they lack a mechanism for providing optimal training advice that takes into account the user's emotional state. Furthermore, there are limitations to generating personalized advice using past training data, making it difficult to provide effective feedback.

[0505] The identification processing by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for measuring heart rate in cooperation with the wearable device, means for receiving measured heart rate data in real time, means for analyzing the received heart rate data and determining whether the user is within an effective training zone, means for notifying the user of the determination result, means for acquiring user emotion data, and means for analyzing the emotion data and adjusting training intensity and recommended actions. This makes it possible to provide effective and personalized feedback that simultaneously takes into account the user's heart rate and emotional state.

[0506] A "wearable device" is a device that can be worn by a user to measure biometric information such as heart rate and share information with other devices.

[0507] "Heart rate" refers to the number of times the heart beats within a certain period of time, and is a biometric indicator generally measured in beats per minute.

[0508] "Real-time reception" is a function that means receiving measured data almost simultaneously without delay.

[0509] "Training zone" refers to a range of exercise intensity based on heart rate and other biometric information to achieve a specific exercise effect.

[0510] "Notification means" refers to the method or device for communicating analysis results or other information to the user.

[0511] "Emotional data" refers to information about a user's emotional state derived from facial expressions, tone of voice, behavior, etc.

[0512] An "emotion engine" refers to a software algorithm that analyzes emotional data and understands the user's emotional state.

[0513] "Exercise form" refers to the body posture and movement method used by a user when performing a particular exercise.

[0514] "Recorded data" refers to video files captured by a camera, which can be used to analyze the user's exercise form.

[0515] "Feedback" refers to the information and comments that the system provides to the user regarding analysis results and advice.

[0516] "Personalized Training Advice" refers to training recommendations generated based on a user's individual historical data, gender, age, and experience.

[0517] This invention is a system that uses a wearable device to measure a user's heart rate and receives and analyzes the data in real time on a smartphone or other device. It also records and analyzes the user's exercise form with a camera, provides personalized training advice, and uses an emotion engine to recognize the user's emotional state and optimize training.

[0518] The detailed configuration and usage of this system will be explained below.

[0519] Hardware and Software Configuration

[0520] 1. Wearable devices:

[0521] Function: Real-time heart rate measurement

[0522] Example: Apple Watch, Fitbit, etc.

[0523] 2. Device (smartphone or smart glasses):

[0524] Functions: Receive heart rate data, record exercise form, and display feedback

[0525] Examples: Android / iOS devices, Google Glass, Microsoft HoloLens, etc.

[0526] Software: Dedicated app (e.g., developed with Unity)

[0527] 3. Server:

[0528] Functions: Analyzing data, generating feedback, generating training advice, analyzing emotional data

[0529] Example: Cloud platforms (AWS, Google Cloud, etc.)

[0530] Software: AI algorithms (TensorFlow, PyTorch), emotion engines (Azure Emotion API, IBM Watson Tone Analyzer)

[0531] System Operation

[0532] Connecting wearable devices and measuring heart rate

[0533] Users launch a dedicated application on their smartphone or smart glasses and connect to the wearable device via Bluetooth. Once the connection is established, the wearable device transmits heart rate data every second. The device receives this data in real time and sends it to a cloud server.

[0534] Real-time data analysis and notifications

[0535] The cloud server uses an AI algorithm to analyze the received heart rate data and determine whether the user is in an effective training zone. For example, if the heart rate is within the fat-burning zone, a notification stating "Appropriate" is generated and sent to the device. The user can then view this notification on their device.

[0536] Recording and analyzing exercise form

[0537] When a user performs a specific exercise, the device's camera is used to record their exercise form. The recorded data is temporarily stored on the device and then sent to a cloud server after the workout is completed. The server analyzes the recorded data and uses an AI image recognition algorithm to compare it with ideal form and generate feedback.

[0538] Generate personalized training advice

[0539] The cloud server generates personalized training advice based on the user's past training data, gender, age, and experience, and sends the advice to the user's device, where it can be used as a reference for the next training session.

[0540] Introducing an emotion engine and analyzing emotion data

[0541] During training, the device's camera and sensors collect the user's emotional data. The collected data is sent to a cloud server where it is analyzed by the emotion engine. Based on the analysis results, if the user feels fatigued or stressed, advice to reduce the intensity of training is generated. This is also sent to the device and can be viewed by the user.

[0542] Examples of concrete examples and prompts

[0543] Specific examples

[0544] User A puts on a smartwatch at the gym and launches the smartphone app. The app automatically pairs with the smartwatch and sends heart rate data to the server. The server analyzes the heart rate data, determines that User A is in the fat burning zone, and sends a notification that the user is in the "appropriate" zone. User A can check this notification on his smartphone. When doing squat training, the smartphone camera can record his exercise form and send it to the server, allowing him to receive feedback such as "straighten your back more."

[0545] Prompt Sentence Examples

[0546] "Generate next training advice based on the user's heart rate and exercise data. Output in the following format:

[0547] User's past training data: 30 minutes of running, average heart rate 140 bpm, weight training: 50 kg

[0548] Suggestion: Next time you train, increase your running time to 35 minutes and use 55kg.

[0549] As described above, the present invention provides an optimal training environment for users by linking a wearable device with a terminal and a server.

[0550] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0551] Step 1:

[0552] The user puts on the wearable device and launches the dedicated smartphone app. The app automatically pairs with the wearable device via Bluetooth and starts transmitting heart rate data. The input is the heart rate data from the wearable device, and the output is the heart rate data received in real time.

[0553] Step 2:

[0554] The device (smartphone) sends the received heart rate data to a cloud server. The cloud server uses an AI algorithm to analyze the heart rate data and determine whether the user is within an effective training zone. The input is the heart rate data sent from the device, and the output is the analysis result (training zone status).

[0555] Step 3:

[0556] The server sends a notification to the user's device based on the analysis results. The device displays this notification in real time, providing feedback to the user. The input is the analysis results from the server, and the output is a notification message displayed on the device. A specific example of this is the notification "Inside the fat burning zone."

[0557] Step 4:

[0558] When a user exercises, the device's camera is used to record the user's exercise form. The recorded data is temporarily stored on the device. The input is the video data captured by the camera, and the output is the saved recorded data.

[0559] Step 5:

[0560] After the exercise session, the device sends the recorded data to a cloud server. The cloud server analyzes this data using an AI image recognition algorithm to evaluate the accuracy and safety of the user's exercise form. The input is the recorded data sent from the device, and the output is the analysis result (evaluation of exercise form).

[0561] Step 6:

[0562] The server generates feedback for improving the form based on the analysis results and sends it to the terminal. The terminal displays this feedback to the user. The input is the analysis results from the server, and the output is a feedback message displayed on the terminal. As a specific example, feedback such as "Please stand up straighter" is displayed.

[0563] Step 7:

[0564] The server generates personalized training advice based on the user's past training data, gender, age, and experience. The generated advice is sent to the terminal and presented to the user. The input is the user's past data and the server's analysis results, and the output is the advice presented to the user.

[0565] Step 8:

[0566] During training, the device's camera and sensors capture user emotion data. The emotion data is sent to a cloud server and analyzed by the emotion engine. The input is the emotion data captured by the camera and sensors, and the output is the emotion analysis results by the server.

[0567] Step 9:

[0568] The server generates advice to adjust training intensity and recommended actions based on the emotion analysis results and sends it to the device. The device then displays this advice to the user. The input is the emotion analysis results, and the output is the adjustment advice presented to the user. A specific example of this behavior is the display of advice such as "Please do light exercise in your next training session."

[0569] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0570] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0571] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0572] [Second embodiment]

[0573] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0574] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0575] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0576] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0577] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0578] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0579] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0580] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0581] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0582] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0583] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0584] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0585] This invention provides a system that uses a wearable device to measure heart rate and perform real-time monitoring and analysis on devices such as smartphones. It also uses a camera to record and analyze the user's exercise form, providing feedback to encourage correct form and safe training. It also generates personalized training advice for each user, enabling optimal training.

[0586] System configuration

[0587] The system mainly consists of the following components:

[0588] 1. Wearable devices (smartwatches, etc.)

[0589] Measures the user's heart rate in real time.

[0590] 2. Device (smartphone)

[0591] Connect to a wearable device via Bluetooth to receive heart rate data.

[0592] The received data is sent to the server.

[0593] Displays analysis results and feedback from the server.

[0594] 3. Server

[0595] The received heart rate data is analyzed using an AI algorithm to determine whether you are within an effective training zone.

[0596] The user's exercise form is analyzed based on the recorded data to evaluate accuracy and safety.

[0597] Generate personalized training advice.

[0598] Program processing and specific examples

[0599] Connecting wearable devices and measuring heart rate

[0600] Device: The user's smartphone launches the application and connects to the wearable device via Bluetooth. Once the connection is established, the smartwatch transmits heart rate data every second.

[0601] Example: User A puts on a wearable device and launches the smartphone app. Once pairing with the device is successful, heart rate data is sent to the smartphone every second.

[0602] Real-time data analysis and notifications

[0603] Device: Sends the received heart rate data to the server.

[0604] Server: Analyzes the data with AI algorithms to determine whether the user is in an effective training zone.

[0605] Server: Sends the analysis results to the device and generates notifications such as "You're in the right load zone" if you're in the right load zone, or "Increase your load" if your heart rate is too low.

[0606] Device: Display the notification to the user.

[0607] Example: The server determines that user A is in the fat burning zone based on the received heart rate data and sends a message saying "Appropriate" to the device. The device displays this message to the user.

[0608] Recording and analyzing exercise form

[0609] User: Uses a smartphone to record their training form.

[0610] Terminal: Sends recorded data to the server.

[0611] Server: Analyzes the recorded data, compares it with the ideal form, and generates feedback on areas for improvement and caution.

[0612] Terminal: Display feedback to the user.

[0613] Example: User A sets up a smartphone to record squat training. The recorded data is sent to a server and analyzed by an AI algorithm. The server generates feedback such as "Please straighten your back more" and sends it to the device. The device then displays this feedback to the user.

[0614] Generate personalized training advice

[0615] Server: Generates personalized advice based on the user's past training data, gender, age, and experience.

[0616] Server: Sends the generated advice to the terminal.

[0617] Device: Provides advice to users and encourages them to use it for their next workout.

[0618] Example: The server generates advice for the next training session, such as "Increase the weight by 5 kg," based on the user A's past training data. The device displays this advice to the user to help them with their next training.

[0619] The above is an embodiment of the present invention, and the system allows the user to achieve effective and safe training.

[0620] The processing flow will be explained below.

[0621] Step 1:

[0622] Device: Launch the dedicated app on the user's smartphone and open the Bluetooth settings. Once the app is launched, a screen will appear to detect wearable devices (such as smartwatches).

[0623] Step 2:

[0624] Device: Detects the smartwatch's Bluetooth and sends a pairing request. The user enters the device's pairing code and approves the pairing.

[0625] Step 3:

[0626] Device: Once paired successfully, the smartwatch will begin to maintain connection with the wearable device and begin transmitting heart rate data.

[0627] Step 4:

[0628] Terminal: Receives heart rate data from the wearable device every second and sends the received heart rate data to the server.

[0629] Step 5:

[0630] Server: Analyzes the received heart rate data using AI algorithms to determine whether the user is in an effective training zone (e.g., fat burning zone, cardiovascular zone, or maximum effort zone).

[0631] Step 6:

[0632] Server: Based on the analysis results, evaluates whether the user is in the target zone. If the user is in the fat burning zone, it generates a message saying "You're in the right zone." If the heart rate is too low, it generates a notification saying "Increase your load."

[0633] Step 7:

[0634] Server: Returns the evaluation result and a notification message to the terminal.

[0635] Step 8:

[0636] Terminal: Displays the evaluation results and notification messages from the server to the user. The user continues training based on the displayed information.

[0637] Step 9:

[0638] User: Use a smartphone to record your training form. Adjust the camera position and angle to capture your entire form.

[0639] Step 10:

[0640] Device: Start recording mode and record your training form. The recorded data is temporarily saved in the cache.

[0641] Step 11:

[0642] Device: After the training session is over, the recording data is sent to the server.

[0643] Step 12:

[0644] Server: Adds the received recording data to the analysis queue. Analyzes the form using an AI image recognition algorithm and compares it with the ideal form.

[0645] Step 13:

[0646] Server: Based on the analysis results, it identifies areas of form that need improvement or attention. For example, it generates feedback such as "Please stand up straighter" or "Your knee angle is too shallow."

[0647] Step 14:

[0648] Server: Sends the generated feedback back to the device.

[0649] Step 15:

[0650] Terminal: Feedback is displayed to the user, who can then improve the form based on the feedback.

[0651] Step 16:

[0652] Server: Retrieves user gender, age, experience, and past training data from the database.

[0653] Step 17:

[0654] Server: Based on this data, it recalculates optimal training intensity and goals and generates personalized training advice, such as "Increase the weight by 5 kg in your next training session" or "Take a rest next time."

[0655] Step 18:

[0656] Server: Sends the generated advice to the terminal.

[0657] Step 19:

[0658] On the device: Providing advice to users and encouraging them to use it for their next training session.

[0659] The above are the specific processing steps for carrying out the present invention.

[0660] Example 1

[0661] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0662] In modern training, real-time heart rate monitoring and evaluation of exercise form are important for each individual user to exercise effectively and safely. However, conventional systems have difficulty providing comprehensive support for these, making it difficult to determine whether users are in the appropriate training zone or maintain correct exercise form. For this reason, a system that allows users to train effectively while avoiding injury has been sought.

[0663] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0664] In this invention, the server includes means for measuring heart rate in cooperation with the wearable device, means for receiving measured heart rate data in real time, means for analyzing the received heart rate data and determining whether the user is within an effective training zone, means for notifying the user of the determination result, means for recording the user's exercise form, means for analyzing the recorded data and evaluating accuracy and safety, means for providing feedback to the user of the evaluation result, means for generating personalized training advice based on the user's past training data, gender, age, and experience, and means for presenting the generated advice to the user. This enables the user to check their own heart rate and exercise form in real time and perform effective and safe training.

[0665] A "wearable device" is a device that can be worn on the body and has the function of measuring biometric data, mainly heart rate, in real time and connecting to a terminal.

[0666] A "terminal" is a portable electronic device such as a smartphone or tablet that has the function of receiving data from a wearable device and transmitting it to a server.

[0667] A "server" is a high-performance computer system that exchanges data with multiple terminals via a network and analyzes the received data.

[0668] "Heart Rate Data" means information about heart rate measured through a wearable device and used to evaluate the effectiveness of training.

[0669] "Real-time" refers to data measurement and analysis occurring with almost no delay, meaning users can see the results immediately.

[0670] The "effective training zone" refers to the ideal heart rate range for the exercise the user is performing, and is the optimal range for goals such as burning fat or improving cardiopulmonary function.

[0671] The "judgment result" is information about the appropriateness of the training generated based on the heart rate data analyzed by the server.

[0672] "Notifications" are messages or alerts provided to the User via the Device to assist in adjusting their Training.

[0673] "Exercise form" refers to the body movements and positioning when a user exercises, and proper form enhances training effectiveness and prevents injury.

[0674] "Recorded data" refers to video information that records the user's exercise form and is used for exercise analysis.

[0675] The "evaluation results" are information about the accuracy and safety of the exercise form generated by the server by analyzing the recorded data.

[0676] "Feedback" refers to advice and instructions provided to users based on the evaluation results, with the aim of improving exercise form and training safety.

[0677] "Personalized training advice" refers to training suggestions and guidance that are individually tailored to the user based on their past training data, gender, age, and experience, helping them to train more effectively.

[0678] This invention is a system that uses a wearable device to measure heart rate, monitors and analyzes in real time using a terminal, and records and analyzes the user's exercise form to encourage accurate form and safe training. This system generates personalized training advice for each user, enabling optimal training.

[0679] System configuration

[0680] The system mainly consists of the following components:

[0681] 1. Wearable devices (smartwatches, etc.)

[0682] Measures the user's heart rate in real time.

[0683] 2. Device (smartphone)

[0684] Connect to a wearable device via Bluetooth to receive heart rate data.

[0685] The received data is sent to the server.

[0686] Displays analysis results and feedback from the server.

[0687] 3. Server

[0688] The received heart rate data is analyzed using an AI algorithm to determine whether you are within an effective training zone.

[0689] The user's exercise form is analyzed based on the recorded data to evaluate accuracy and safety.

[0690] Generate personalized training advice.

[0691] Connecting wearable devices and measuring heart rate

[0692] Device: The user launches the smartphone app and connects to the wearable device via Bluetooth. Once the connection is established, the wearable device sends heart rate data to the smartphone every second.

[0693] Example: User A puts on a wearable device and launches the smartphone app. Once pairing with the device is successful, heart rate data is sent to the smartphone every second. The screen displays the heart rate in real time.

[0694] Real-time data analysis and notifications

[0695] Device: Sends the received heart rate data to the server.

[0696] Server: Analyzes the data with AI algorithms to determine whether the user is in an effective training zone.

[0697] Server: Sends the analysis results to the device and generates notifications such as "You're in the right load zone" if you're in the right load zone, or "Increase your load" if your heart rate is too low.

[0698] Device: Display the notification to the user.

[0699] Example: The server determines that user A is in the fat burning zone based on the received heart rate data and sends a message saying "Appropriate" to the device. The device displays this message to the user.

[0700] Recording and analyzing exercise form

[0701] User: The user uses a smartphone to record their training form.

[0702] Terminal: Sends recorded data to the server.

[0703] Server: Analyzes the recorded data, compares it with the ideal form, and generates feedback on areas for improvement and caution.

[0704] Terminal: Display feedback to the user.

[0705] Example: User A sets up a smartphone to record squat training. The recorded data is sent to a server and analyzed by an AI algorithm. The server generates feedback such as "Please straighten your back more" and sends it to the device. The device then displays this feedback to the user.

[0706] Generate personalized training advice

[0707] Server: Generates personalized advice based on the user's past training data, gender, age, and experience.

[0708] Server: Sends the generated advice to the terminal.

[0709] Device: Provides advice to users and encourages them to use it for their next workout.

[0710] Example: The server generates advice for the next training session, such as "Increase the weight by 5 kg," based on the user A's past training data. The device displays this advice to the user to help them with their next training.

[0711] Prompt Sentence Examples

[0712] "Please tell me how the app works, measuring heart rate in conjunction with a smartwatch and analyzing the data in real time."

[0713] "Please explain how to analyze recorded data of athletic form and generate feedback."

[0714] "Please tell me more about the system that generates personalized training advice."

[0715] The above is an embodiment of the present invention, and the system allows the user to achieve effective and safe training.

[0716] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0717] Step 1:

[0718] Input: The user puts on a wearable device (smartwatch) and launches the app on their smartphone.

[0719] How it works: The phone establishes a connection with the wearable device using Bluetooth. This connection uses the Bluetooth Low Energy protocol (BLE).

[0720] Output: The connection is established and the wearable device sends heart rate data to the smartphone every second.

[0721] Step 2:

[0722] Input: Heart rate data sent from a wearable device.

[0723] Operation: The device batches the received heart rate data at regular intervals (e.g., every 5 seconds) and sends it to the server.

[0724] Output: Batched heart rate data is sent to the server.

[0725] Step 3:

[0726] Input: Heart rate data received by the server from the device.

[0727] How it works: The server analyzes the received data with AI algorithms, first filtering out noise from the raw data, and then determining which training zone the user is in.

[0728] Output: The analysis results are generated, and training zone information is derived. Messages such as "Good", "Increase load", and "Decrease load" are also generated.

[0729] Step 4:

[0730] Input: Parse results and messages generated by the server.

[0731] Operation: The server sends the analysis results and a message to the terminal.

[0732] Output: The analysis results and messages are sent to the device and ready to be displayed in the smartphone app.

[0733] Step 5:

[0734] Input: Analysis results and messages sent from the server.

[0735] Operation: The device displays the analysis results and messages it receives on the screen, allowing users to check their training status in real time.

[0736] Output: The user is notified with a message such as "Good", "Please increase load", or "Please decrease load".

[0737] Step 6:

[0738] Input: The user records their exercise form using their smartphone camera.

[0739] How it works: The device receives the recording data, applies a compression algorithm to optimize the file size, and sends it to the server.

[0740] Output: Compressed recording data is sent to the server.

[0741] Step 7:

[0742] Input: Recording data received by the server.

[0743] How it works: The server analyzes the recorded data using an AI algorithm, comparing it with the ideal form and evaluating the accuracy and safety of the user's form.

[0744] Output: As a result of the evaluation, feedback on areas for improvement and points to note is generated.

[0745] Step 8:

[0746] Input: Server-generated evaluation results and feedback.

[0747] Operation: The server sends the evaluation results and feedback to the device.

[0748] Output: Feedback is sent to the device, ready to be displayed to the user.

[0749] Step 9:

[0750] Input: Feedback sent by the server.

[0751] How it works: The device displays the feedback it receives on the screen, allowing the user to see areas of their exercise form that need improvement.

[0752] Output: The user is given specific feedback, such as "Stand up straighter."

[0753] Step 10:

[0754] Input: User's past training data, gender, age, experience.

[0755] How it works: The server uses this information to generate personalized advice for your next workout.

[0756] Output: Personalized training advice is generated.

[0757] Step 11:

[0758] Input: Server-generated personalized training advice.

[0759] Operation: The server sends advice to the terminal.

[0760] Output: The personalized advice is sent to the device and ready to be presented to the user.

[0761] Step 12:

[0762] Input: Personalized advice sent from the server.

[0763] How it works: The device displays the received advice on the screen and encourages the user to use it for their next workout.

[0764] Output: A specific piece of advice is displayed to the user, such as "Increase the weight by 5kg in your next session."

[0765] (Application example 1)

[0766] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0767] In autonomous vehicles, there are issues with insufficient management of driver health, maintaining posture while driving, and ensuring safety. Real-time monitoring of heart rate and exercise form is particularly important for drivers who drive for long periods of time, and there are concerns about health risks associated with continued improper monitoring. Furthermore, conventional systems lack personalized advice suited to individual drivers, making it difficult to ensure comfort and safety while driving.

[0768] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0769] In this invention, the server includes: means for measuring a heart rate in cooperation with a wearable device; means for receiving heart rate data in real time; means for analyzing the received heart rate data and determining whether the heart rate is within an effective exercise zone; means for notifying the driver of the determination result; means for issuing a warning if the heart rate is abnormally high to monitor the driver's health; means for recording the user's exercise form; means for analyzing the recorded data and evaluating accuracy and safety; means for providing feedback to the user on the evaluation result; means for analyzing the user's posture and exercise form in the vehicle and providing appropriate feedback; means for generating personalized training advice based on the user's past training data, gender, age, and experience; means for presenting the generated advice to the user; and means for providing personalized exercise and relaxation methods to maintain the driver's health. This improves health monitoring of drivers and passengers in autonomous vehicles, enabling a safer and more comfortable driving environment.

[0770] "Wearable device" refers to a wearable device used to measure a user's heart rate and exercise data.

[0771] "Heart Rate Data" means a user's heart rate information measured through a wearable device.

[0772] "Means for receiving in real time" refers to equipment or a system that has the functionality to instantly receive heart rate data transmitted from a wearable device.

[0773] "Effective exercise zone" refers to a specific heart rate range that a user targets to maximize the benefits of their exercise.

[0774] "Measures to monitor the driver's health and warn the driver if their heart rate is abnormally high" refers to a system that warns the driver if their heart rate exceeds the normal range.

[0775] "Means for recording the user's exercise form" refers to a device for recording the user's exercise using a video camera or the like and collecting the data.

[0776] "Means for analyzing recorded data" refers to software or algorithms used to analyze recorded exercise form data and evaluate accuracy and safety.

[0777] "Means for analyzing posture and exercise form in the vehicle and providing appropriate feedback" refers to a system that analyzes the driver's posture and exercise form captured by an in-vehicle camera and provides appropriate advice and warnings based on the results.

[0778] "Past training data" refers to historical information about the training a user has done up to now.

[0779] "Personalized Training Advice" means training advice that is tailored to you based on your individual characteristics and past training data.

[0780] "Means for providing personalized exercise and relaxation methods" refers to a system that suggests exercise and relaxation methods that are individually suited to the user's needs.

[0781] This invention provides a system that uses a wearable device to measure a driver's heart rate in real time and monitors it on an in-car display or smartphone. It also uses a camera to record and analyze the driver's exercise form and posture, providing safe and appropriate feedback. It also generates personalized health advice for each driver, creating a comfortable driving environment.

[0782] System configuration

[0783] The system mainly consists of the following components:

[0784] 1. Wearable devices

[0785] Measure the driver's heart rate in real time.

[0786] 2. Device (smartphone or in-car display)

[0787] Connect to a wearable device via Bluetooth to receive heart rate data.

[0788] The received data is sent to the server.

[0789] Displays analysis results and feedback from the server.

[0790] 3. Camera

[0791] The system uses an in-car camera to record the driver's posture and exercise form.

[0792] The recording data is sent to the server.

[0793] 4. Server

[0794] The received heart rate data is analyzed using an AI algorithm to determine whether the driver is within an effective heart rate zone.

[0795] The system analyzes recorded data of the driver's exercise form to evaluate accuracy and safety.

[0796] Generate personalized health advice and send it to your device.

[0797] Program processing overview

[0798] Connecting wearable devices and measuring heart rate

[0799] The smartphone or in-car display launches an application and connects to the wearable device via Bluetooth. Once the connection is established, the wearable device transmits heart rate data every second. For example, a driver puts on the wearable device and launches the app on the in-car display. If pairing with the device is successful, heart rate data will be displayed in real time.

[0800] Real-time data analysis and notifications

[0801] The device sends the received heart rate data to a server. The server analyzes the data and determines whether the driver is within an effective heart rate zone. The analysis results are sent back to the device, and if the heart rate is abnormally high, a notification such as "Take a deep breath" is sent. For example, the server may determine that the driver is under stress based on the received heart rate data and provide a relaxation suggestion.

[0802] Recording and analyzing exercise form

[0803] Users use the in-car camera to record their posture and exercise form while driving. The recorded data is sent to a server and analyzed by an AI algorithm. The server then generates feedback such as "Please adjust your seat" based on this data and sends it to the device.

[0804] Generating personalized health advice

[0805] The server generates personalized health advice based on the driver's past health data, age, and experience. The generated advice is sent to the device and used as reference for the next driving session. For example, the server generates advice based on past data, such as "Please stretch for five minutes before your next drive."

[0806] Hardware and software used

[0807] Hardware: Smartwatches (wearable devices), smartphones, in-car displays, and in-car cameras.

[0808] Software: Python program, OpenCV, Bluetooth library, HTTP library.

[0809] Prompt Sentence Examples

[0810] "Monitor the driver's heart rate in real time and display a warning message on the display if the heart rate is high."

[0811] "Use a camera to record the driver's posture and provide feedback based on the analysis results."

[0812] The system will improve health monitoring for drivers and passengers in autonomous vehicles, enabling a safer and more comfortable driving environment.

[0813] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0814] Step 1:

[0815] Connect the wearable device and the terminal via Bluetooth.

[0816] Input: Bluetooth address of the wearable device

[0817] Output: Connection success or failure status

[0818] Specific operation: The user wears the smartwatch and launches the smartphone app or the app on the car display. The app will attempt to connect to the device via Bluetooth, and if the connection is successful, it will display the connection success status.

[0819] Step 2:

[0820] Heart rate data is acquired in real time and sent to the device.

[0821] Input: Heart rate data from a wearable device

[0822] Output: Real-time heart rate data displayed on the device

[0823] What it does: The device sends heart rate data every second, and the application analyzes this data and displays it in real time.

[0824] Step 3:

[0825] The received heart rate data is sent to the server.

[0826] Input: Heart rate data received by the device

[0827] Output: Heart rate data sent to the server

[0828] Specific operation: The smartphone or in-car display sends the received heart rate data to the server at regular intervals using an HTTP POST request.

[0829] Step 4:

[0830] The server analyzes the heart rate data using an AI algorithm.

[0831] Input: Heart rate data received by the server

[0832] Output: Determining whether your heart rate is within the effective exercise zone

[0833] How it works: The server inputs the received heart rate data into an AI algorithm for real-time analysis, and determines whether the driver is in an effective exercise zone based on the analysis results.

[0834] Step 5:

[0835] The server sends the analysis results to the device and notifies the user.

[0836] Input: Analysis result notification data

[0837] Output: A notification message displayed on the terminal.

[0838] Specific operation: The server sends the analysis results to the device, and based on the results, a message such as "Please take a deep breath" is displayed.

[0839] Step 6:

[0840] The camera records the driver's exercise form.

[0841] Input: Camera image

[0842] Output: Video data of recorded exercise form

[0843] Specific operation: A camera installed inside the vehicle records the driver's posture and exercise form and acquires the video data.

[0844] Step 7:

[0845] The recorded data is sent to a server and form analysis is performed.

[0846] Input: Recorded video data

[0847] Output: Feedback of analysis results

[0848] How it works: The recorded data is sent to a server, which then analyzes it using an AI algorithm. Based on the analysis results, feedback such as "Please adjust your seat" is generated.

[0849] Step 8:

[0850] A server generates personalized health advice.

[0851] Input: Past training data, gender, age, experience

[0852] Output: Health advice data

[0853] How it works: The server generates personalized health advice based on the driver's past data, such as "Try doing five minutes of stretching before your next drive."

[0854] Step 9:

[0855] The generated advice is sent to the terminal and presented to the driver.

[0856] Input: Health advice data

[0857] Output: Health advice displayed on the device

[0858] Specific operation: The server sends the generated health advice to the device, and the driver can check the advice on the device.

[0859] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0860] This invention provides a system that uses a wearable device to measure heart rate and perform real-time monitoring and analysis on devices such as smartphones. It also uses a camera to record and analyze the user's exercise form, providing feedback to encourage correct form and safe training. It also generates personalized training advice for each user and uses an emotion engine that recognizes the user's emotions to achieve optimal training.

[0861] System configuration

[0862] The system mainly consists of the following components:

[0863] 1. Wearable devices (smartwatches, etc.)

[0864] Measures the user's heart rate in real time.

[0865] 2. Device (smartphone)

[0866] Connect to a wearable device via Bluetooth to receive heart rate data.

[0867] The received data is sent to the server.

[0868] Displays analysis results and feedback from the server.

[0869] The built-in camera records the user's exercise form.

[0870] 3. Server

[0871] The received heart rate data is analyzed using an AI algorithm to determine whether you are within an effective training zone.

[0872] The user's exercise form is analyzed based on the recorded data to evaluate accuracy and safety.

[0873] Generates personalized training advice based on the user's past training data, gender, age, and experience.

[0874] An emotion engine is used to analyze user emotion data and reflect it in training optimization.

[0875] Program processing and specific examples

[0876] Connecting wearable devices and measuring heart rate

[0877] Device: The user's smartphone runs a dedicated app and connects to the wearable device via Bluetooth. Once the connection is established, the smartwatch sends heart rate data every second.

[0878] Example: User A puts on a wearable device and launches the smartphone app. Once pairing with the device is successful, heart rate data is sent to the smartphone every second.

[0879] Real-time data analysis and notifications

[0880] Device: Sends the received heart rate data to the server.

[0881] Server: Analyzes the data with AI algorithms to determine whether the user is in an effective training zone.

[0882] Server: Based on the analysis results, evaluates whether the user is in the target zone. If the user is in the fat burning zone, it generates a message saying "You're in the right zone." If the heart rate is too low, it generates a notification saying "Increase your load."

[0883] Device: Display the notification to the user.

[0884] Example: The server determines that user A is in the fat burning zone based on the received heart rate data and sends a message saying "Appropriate" to the device. The device displays this message to the user.

[0885] Recording and analyzing exercise form

[0886] User: Use a smartphone to record your training form. Adjust the camera position and angle to capture your entire form.

[0887] Device: Start recording mode and record your training form. The recorded data is temporarily saved in the cache.

[0888] Device: After the training session is over, the recording data is sent to the server.

[0889] Server: Adds the received recording data to the analysis queue. Analyzes the form using an AI image recognition algorithm and compares it with the ideal form.

[0890] Server: Based on the analysis results, it identifies areas of form that need improvement or attention. For example, it generates feedback such as "Please stand up straighter" or "Your knee angle is too shallow."

[0891] Terminal: Feedback is displayed to the user, who can then improve the form based on the feedback.

[0892] Example: User A sets up a smartphone to record squat training. The recorded data is sent to a server and analyzed by an AI algorithm. The server generates feedback such as "Please straighten your back more" and sends it to the device. The device then displays this feedback to the user.

[0893] Generate personalized training advice

[0894] Server: Generates personalized advice based on the user's past training data, gender, age, and experience.

[0895] Server: Sends the generated advice to the terminal.

[0896] Device: Provides advice to users and encourages them to use it for their next workout.

[0897] Example: The server generates advice for the next training session, such as "Increase the weight by 5 kg," based on the user A's past training data. The device displays this advice to the user to help them with their next training.

[0898] Introducing an emotion engine and analyzing emotion data

[0899] Device: Equipped with a camera and sensors to capture emotional data during training. This data is sent to the server in real time.

[0900] Server: Analyzes the received emotional data using an emotion engine to understand the user's emotional state.

[0901] Server: Adjusts training intensity and recommended actions based on emotional data. For example, if a user feels fatigued or stressed, the server generates advice to reduce training intensity.

[0902] Terminal: Presents the generated advice to the user.

[0903] Example: The server analyzes the emotion data received in real time and determines that User A is tired. Based on this, it generates advice such as "Please do light exercise in your next training session" and sends it to the device. The device then displays this advice to the user.

[0904] The above is an embodiment of the present invention that combines an emotion engine, allowing users to train effectively and safely and receive personalized feedback that takes into account their emotional state during training.

[0905] The processing flow will be explained below.

[0906] Step 1:

[0907] Device: Launch the dedicated app on the user's smartphone and open the Bluetooth settings. Once the app is launched, a screen will appear to detect wearable devices (such as smartwatches).

[0908] Step 2:

[0909] Device: Detects the smartwatch's Bluetooth and sends a pairing request. The user enters the device's pairing code and approves the pairing.

[0910] Step 3:

[0911] Device: Once paired successfully, the smartwatch will begin to maintain connection with the wearable device and begin transmitting heart rate data.

[0912] Step 4:

[0913] Terminal: Receives heart rate data from the wearable device every second and sends the received heart rate data to the server.

[0914] Step 5:

[0915] Server: Analyzes the received heart rate data using AI algorithms to determine whether the user is in an effective training zone (e.g., fat burning zone, cardiovascular zone, or maximum effort zone).

[0916] Step 6:

[0917] Server: Based on the analysis results, evaluates whether the user is in the target zone. If the user is in the fat burning zone, it generates a message saying "You're in the right zone." If the heart rate is too low, it generates a notification saying "Increase your load."

[0918] Step 7:

[0919] Server: Returns the evaluation result and a notification message to the terminal.

[0920] Step 8:

[0921] Terminal: Displays the evaluation results and notification messages from the server to the user. The user continues training based on the displayed information.

[0922] Step 9:

[0923] User: Use a smartphone to record your training form. Adjust the camera position and angle to capture your entire form.

[0924] Step 10:

[0925] Device: Start recording mode and record your training form. The recorded data is temporarily saved in the cache.

[0926] Step 11:

[0927] Device: After the training session is over, the recording data is sent to the server.

[0928] Step 12:

[0929] Server: Adds the received recording data to the analysis queue. Analyzes the form using an AI image recognition algorithm and compares it with the ideal form.

[0930] Step 13:

[0931] Server: Based on the analysis results, it identifies areas of form that need improvement or attention. For example, it generates feedback such as "Please stand up straighter" or "Your knee angle is too shallow."

[0932] Step 14:

[0933] Server: Sends the generated feedback back to the device.

[0934] Step 15:

[0935] Terminal: Feedback is displayed to the user, who can then improve the form based on the feedback.

[0936] Step 16:

[0937] Server: Retrieves user gender, age, experience, and past training data from the database.

[0938] Step 17:

[0939] Server: Based on this data, it recalculates optimal training intensity and goals and generates personalized training advice, such as "Increase the weight by 5 kg in your next training session" or "Take a rest next time."

[0940] Step 18:

[0941] Server: Sends the generated advice to the terminal.

[0942] Step 19:

[0943] On the device: Providing advice to users and encouraging them to use it for their next training session.

[0944] Step 20:

[0945] User: Use cameras and sensors to capture emotional data during training. The cameras and sensors capture the user's facial expressions and physical reactions, monitoring their emotional state in real time.

[0946] Step 21:

[0947] Terminal: Transmits acquired emotion data to the server in real time.

[0948] Step 22:

[0949] Server: The emotion engine analyzes the received emotion data and identifies the user's emotional state. The emotion engine uses AI technology to determine emotions such as stress, joy, and fatigue from the user's facial expressions and physical reactions.

[0950] Step 23:

[0951] Server: Adjusts training intensity and recommended actions based on emotional data. For example, if a user feels fatigued or stressed, the server generates advice to reduce training intensity.

[0952] Step 24:

[0953] Server: Sends tailored feedback and advice to devices.

[0954] Step 25:

[0955] Terminal: Presents the generated advice to the user.

[0956] Examples:

[0957] Example 1: The server analyzes emotion data received in real time and determines that user A is tired. Based on this, it generates advice such as "Please do light exercise in your next training session" and sends it to the device. The device then displays this advice to the user.

[0958] Example 2: The emotion engine detects that user B is feeling joy or satisfaction during training. Based on this information, the server generates an encouraging message such as "Keep it up!" and sends it to the device. The device then displays the generated message to the user, further increasing their motivation.

[0959] The above is an embodiment of the present invention that combines an emotion engine, allowing users to train effectively and safely and receive personalized feedback that takes into account their emotional state during training.

[0960] Example 2

[0961] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0962] While conventional fitness systems using wearable devices can monitor users' biometric information such as heart rate in real time, they do not support recording and analysis of exercise form or providing personalized training advice. Furthermore, there is no training feedback that takes into account the user's emotional state. In particular, it is difficult to correct incorrect exercise form or provide advice tailored to individual needs, resulting in safety and effectiveness issues.

[0963] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes a means for measuring biometric information in cooperation with the wearable device, a means for receiving the measured biometric data in real time, a means for analyzing the received biometric data and determining whether the user is within an effective exercise zone, a means for notifying the user of the determination result, a means for recording the user's exercise movements, a means for analyzing the recorded data and evaluating accuracy and safety, a means for feeding back the evaluation result to the user, a means for generating personalized exercise advice based on the user's past exercise data and attribute data, and a means for presenting the generated advice to the user. This makes it possible to monitor biometric information in real time, as well as analyze exercise form and provide safe and effective personalized advice.

[0964] A "wearable device" is an electronic device that can be worn on the user's body and used to measure biometric information.

[0965] "Biometric information" refers to data about the user's physical condition, such as heart rate, body temperature, and blood pressure.

[0966] "Biometric data" refers to the numerical values ​​and indicators of biometric information measured by a wearable device.

[0967] "Real-time" is a time concept that refers to data being collected and processed almost immediately.

[0968] "Exercise movements" refers to movements or movements performed by a user, including training and exercise.

[0969] "Recorded data" refers to video data of exercise movements captured using a recording device such as a camera.

[0970] "Analysis" refers to the activity of processing received data, transforming it into a meaningful form, and deriving results.

[0971] "Accuracy" refers to the degree to which a user's athletic movements match the ideal form and movements they are aiming for.

[0972] "Safety" refers to the suitability of the device to prevent injury or other hazards when a user performs an exercise.

[0973] "Feedback" refers to the evaluation and advice the system provides to the user.

[0974] "Personalized exercise advice" refers to exercise advice and instructions that are optimized based on a user's individual characteristics and past data.

[0975] "Attribute data" refers to data that indicates a user's individual characteristics, such as age, gender, and experience.

[0976] This invention is a system that uses wearable devices and terminals such as smartphones to monitor and analyze a user's biometric information and exercise movements in real time, allowing the user to train appropriately and receive safer and more effective training advice.

[0977] Hardware and software used

[0978] 1. Wearable devices (e.g., smartwatches): Wearable devices measure the user's biometric information, such as heart rate, and transmit it to a smartphone via Bluetooth.

[0979] 2. Device (e.g., smartphone): The smartphone runs a dedicated app and receives biometric data sent from the wearable device. The received data is sent to a server in real time. The smartphone also uses a camera to record the user's exercise movements.

[0980] 3. Server: The server analyzes the received biometric data and recorded exercise data. It uses AI algorithms to determine whether the user is within the effective exercise zone, evaluate the accuracy and safety of the exercise form, and generate personalized training advice based on the user's past data and attributes.

[0981] Explanation of program processing

[0982] Measurement and transmission of biometric data

[0983] 1. The device launches the dedicated app and connects to the wearable device via Bluetooth. Once the connection is established, the smartwatch sends heart rate data every second.

[0984] Example: A user wears a smartwatch and launches an app on their smartphone. Once the app successfully pairs with the smartwatch, heart rate data is sent to the smartphone every second. For example, data such as "Heart rate: 75, Time: 10:00:01" is continuously sent.

[0985] Real-time data analysis

[0986] 1. The device sends the received heart rate data to the server.

[0987] 2. The server uses an AI algorithm to analyze your heart rate data and determine whether you are within an effective exercise zone.

[0988] Example: The server processes data such as "Heart rate: 135, Time: 10:05:01" and determines that the user is in the fat burning zone. Based on this, it generates a notification message saying "Appropriate" and sends it to the device. The device then displays this message to the user.

[0989] Recording and analyzing exercise form

[0990] 1. The user uses a smartphone to record their exercise movements, adjusting the camera position and angle to capture their entire form.

[0991] 2. The device will start recording mode and record your training form, temporarily saving the recorded data in the cache.

[0992] 3. After the training session ends, the device sends the recorded data to the server.

[0993] 4. The server analyzes the received recording data and uses an AI image recognition algorithm to analyze the user's form. It compares the user's form with the ideal form and identifies areas for improvement.

[0994] Example: A user records a squat and sends the recorded data to a server. The server generates a feedback message saying "Please stand up straighter" and sends it to the device. The device displays this feedback to the user.

[0995] Generate personalized training advice

[0996] 1. The server generates personalized training advice based on the user's past training data, gender, age, and experience, and sends it to the device.

[0997] 2. The device displays the advice to the user.

[0998] Example: Based on the user's training data, the server generates advice such as "Increase the weight by 5 kg in your next training session." The device notifies the user of this advice and displays it on the training guide screen.

[0999] Prompt Sentence Examples

[1000] "Today's training session was very effective as it kept my heart rate stable and allowed me to train in the fat burning zone. Next time, I'll try increasing the intensity a bit and extending the duration by 5 minutes."

[1001] The above is an embodiment of the invention. This system allows users to monitor their biological information in real time, analyze their exercise form, and perform safe and effective training.

[1002] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1003] Step 1:

[1004] Connecting wearable devices and measuring heart rate

[1005] The device launches a dedicated app and connects to the wearable device via Bluetooth. Once the connection is established, the smartwatch sends heart rate data every second.

[1006] Input: The user puts on the smartwatch and launches the app on their smartphone.

[1007] Data processing: The app pairs with the wearable device and a connection is established.

[1008] Output: Heart rate data is sent from the smartwatch to the smartphone every second, and is simultaneously displayed in real time on the app screen.

[1009] Specific operation: The user wears the smartwatch and launches the smartphone app. Once paired with the device, heart rate data will be sent to the smartphone every second and displayed on the real-time monitoring screen within the app.

[1010] Step 2:

[1011] Real-time data analysis and notifications

[1012] The terminal transmits the received heart rate data to the server.

[1013] The server uses AI algorithms to analyze heart rate data in real time to determine whether the user is in an effective exercise zone, and generates a message based on the analysis results and sends it to the device.

[1014] Input: Heart rate data sent from the device (e.g., "Heart rate: 135, Time: 10:05:01").

[1015] Data processing: The server analyzes the heart rate data and determines whether you are in an effective exercise zone (e.g., fat burning zone).

[1016] Output: The judgment result (e.g., "Appropriate") is sent as a message to the terminal.

[1017] Specific operation: The server processes the received heart rate data and confirms that the user is in the fat burning zone. It generates a notification message saying "Suitable" and sends it to the device. The device then displays this message to the user in a pop-up.

[1018] Step 3:

[1019] Recording and analyzing exercise form

[1020] Users use their smartphones to record their exercise movements, adjusting the camera position and angle to capture their entire form.

[1021] The device will enter recording mode and record your training form. The recorded data will be temporarily saved in the cache.

[1022] After the training session ends, the terminal transmits the recorded data to the server.

[1023] The server analyzes the received recording data and uses an AI image recognition algorithm to analyze the user's form, comparing it with the ideal form and identifying areas for improvement.

[1024] Input: Video data (e.g., video of squats).

[1025] Data processing: The server analyzes the recorded data using AI image recognition algorithms and evaluates the user's form.

[1026] Output: The analysis results (e.g., "Please stand up straighter") are sent to the device as a feedback message.

[1027] How it works: The user records their squats and the recorded data is sent to the server. The server then uses an AI algorithm to analyze the data, generates a feedback message saying "Please straighten your back more," and sends it to the device. The device then displays this feedback to the user.

[1028] Step 4:

[1029] Generate personalized training advice

[1030] The server generates personalized training advice based on the user's past training data, gender, age, and experience, and sends it to the device.

[1031] The device displays the advice to the user.

[1032] Input: User's past training data and demographic data (e.g., age, gender, experience).

[1033] Data processing: The server uses this data to generate advice tailored to your individual needs.

[1034] Output: Generated advice (e.g. "Increase the weight by 5kg in your next workout") is sent to the device.

[1035] Specific operation: Based on the user's training data, the server generates advice such as "Increase the weight by 5 kg in your next training session." The device notifies the user of this advice and displays it on the training guide screen.

[1036] Step 5:

[1037] Introducing an emotion engine and analyzing emotion data

[1038] The device uses cameras and sensors to capture emotional data while the user is training, and transmits this data to a server in real time.

[1039] The server analyzes the received emotional data using an emotion engine to understand the user's emotional state and adjusts training intensity and recommended actions as needed.

[1040] Input: Emotion data obtained in real time (e.g., facial expression data from a facial recognition sensor).

[1041] Data processing: The server uses an emotion engine to analyze the emotion data and identify the user's emotional state.

[1042] Output: Advice generated based on the results of sentiment analysis (e.g., "Do light exercise during your next workout") is sent to the device.

[1043] Specific operation: The server analyzes the user's emotional data and determines that the user is tired. It generates advice such as "Try to do lighter exercise next time" and sends it to the device. The device notifies the user of this advice and displays it on the home screen.

[1044] (Application example 2)

[1045] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1046] While conventional training monitoring systems provide functions such as measuring heart rate and analyzing exercise form, they lack a mechanism for providing optimal training advice that takes into account the user's emotional state. Furthermore, there are limitations to generating personalized advice using past training data, making it difficult to provide effective feedback.

[1047] The identification processing by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for measuring heart rate in cooperation with the wearable device, means for receiving measured heart rate data in real time, means for analyzing the received heart rate data and determining whether the user is within an effective training zone, means for notifying the user of the determination result, means for acquiring user emotion data, and means for analyzing the emotion data and adjusting training intensity and recommended actions. This makes it possible to provide effective and personalized feedback that simultaneously takes into account the user's heart rate and emotional state.

[1048] A "wearable device" is a device that can be worn by a user to measure biometric information such as heart rate and share information with other devices.

[1049] "Heart rate" refers to the number of times the heart beats within a certain period of time, and is a biometric indicator generally measured in beats per minute.

[1050] "Real-time reception" is a function that means receiving measured data almost simultaneously without delay.

[1051] "Training zone" refers to a range of exercise intensity based on heart rate and other biometric information to achieve a specific exercise effect.

[1052] "Notification means" refers to the method or device for communicating analysis results or other information to the user.

[1053] "Emotional data" refers to information about a user's emotional state derived from facial expressions, tone of voice, behavior, etc.

[1054] An "emotion engine" refers to a software algorithm that analyzes emotional data and understands the user's emotional state.

[1055] "Exercise form" refers to the body posture and movement method used by a user when performing a particular exercise.

[1056] "Recorded data" refers to video files captured by a camera, which can be used to analyze the user's exercise form.

[1057] "Feedback" refers to the information and comments that the system provides to the user regarding analysis results and advice.

[1058] "Personalized Training Advice" refers to training recommendations generated based on a user's individual historical data, gender, age, and experience.

[1059] This invention is a system that uses a wearable device to measure a user's heart rate and receives and analyzes the data in real time on a smartphone or other device. It also records and analyzes the user's exercise form with a camera, provides personalized training advice, and uses an emotion engine to recognize the user's emotional state and optimize training.

[1060] The detailed configuration and usage of this system will be explained below.

[1061] Hardware and Software Configuration

[1062] 1. Wearable devices:

[1063] Function: Real-time heart rate measurement

[1064] Example: Apple Watch, Fitbit, etc.

[1065] 2. Device (smartphone or smart glasses):

[1066] Functions: Receive heart rate data, record exercise form, and display feedback

[1067] Examples: Android / iOS devices, Google Glass, Microsoft HoloLens, etc.

[1068] Software: Dedicated app (e.g., developed with Unity)

[1069] 3. Server:

[1070] Functions: Analyzing data, generating feedback, generating training advice, analyzing emotional data

[1071] Example: Cloud platforms (AWS, Google Cloud, etc.)

[1072] Software: AI algorithms (TensorFlow, PyTorch), emotion engines (Azure Emotion API, IBM Watson Tone Analyzer)

[1073] System Operation

[1074] Connecting wearable devices and measuring heart rate

[1075] Users launch a dedicated application on their smartphone or smart glasses and connect to the wearable device via Bluetooth. Once the connection is established, the wearable device transmits heart rate data every second. The device receives this data in real time and sends it to a cloud server.

[1076] Real-time data analysis and notifications

[1077] The cloud server uses an AI algorithm to analyze the received heart rate data and determine whether the user is in an effective training zone. For example, if the heart rate is within the fat-burning zone, a notification stating "Appropriate" is generated and sent to the device. The user can then view this notification on their device.

[1078] Recording and analyzing exercise form

[1079] When a user performs a specific exercise, the device's camera is used to record their exercise form. The recorded data is temporarily stored on the device and then sent to a cloud server after the workout is completed. The server analyzes the recorded data and uses an AI image recognition algorithm to compare it with ideal form and generate feedback.

[1080] Generate personalized training advice

[1081] The cloud server generates personalized training advice based on the user's past training data, gender, age, and experience, and sends the advice to the user's device, where it can be used as a reference for the next training session.

[1082] Introducing an emotion engine and analyzing emotion data

[1083] During training, the device's camera and sensors collect the user's emotional data. The collected data is sent to a cloud server where it is analyzed by the emotion engine. Based on the analysis results, if the user feels fatigued or stressed, advice to reduce the intensity of training is generated. This is also sent to the device and can be viewed by the user.

[1084] Examples of concrete examples and prompts

[1085] Specific examples

[1086] User A puts on a smartwatch at the gym and launches the smartphone app. The app automatically pairs with the smartwatch and sends heart rate data to the server. The server analyzes the heart rate data, determines that User A is in the fat burning zone, and sends a notification that the user is in the "appropriate" zone. User A can check this notification on his smartphone. When doing squat training, the smartphone camera can record his exercise form and send it to the server, allowing him to receive feedback such as "straighten your back more."

[1087] Prompt Sentence Examples

[1088] "Generate next training advice based on the user's heart rate and exercise data. Output in the following format:

[1089] User's past training data: 30 minutes of running, average heart rate 140 bpm, weight training: 50 kg

[1090] Suggestion: Next time you train, increase your running time to 35 minutes and use 55kg.

[1091] As described above, the present invention provides an optimal training environment for users by linking a wearable device with a terminal and a server.

[1092] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1093] Step 1:

[1094] The user puts on the wearable device and launches the dedicated smartphone app. The app automatically pairs with the wearable device via Bluetooth and starts transmitting heart rate data. The input is the heart rate data from the wearable device, and the output is the heart rate data received in real time.

[1095] Step 2:

[1096] The device (smartphone) sends the received heart rate data to a cloud server. The cloud server uses an AI algorithm to analyze the heart rate data and determine whether the user is within an effective training zone. The input is the heart rate data sent from the device, and the output is the analysis result (training zone status).

[1097] Step 3:

[1098] The server sends a notification to the user's device based on the analysis results. The device displays this notification in real time, providing feedback to the user. The input is the analysis results from the server, and the output is a notification message displayed on the device. A specific example of this is the notification "Inside the fat burning zone."

[1099] Step 4:

[1100] When a user exercises, the device's camera is used to record the user's exercise form. The recorded data is temporarily stored on the device. The input is the video data captured by the camera, and the output is the saved recorded data.

[1101] Step 5:

[1102] After the exercise session, the device sends the recorded data to a cloud server. The cloud server analyzes this data using an AI image recognition algorithm to evaluate the accuracy and safety of the user's exercise form. The input is the recorded data sent from the device, and the output is the analysis result (evaluation of exercise form).

[1103] Step 6:

[1104] The server generates feedback for improving the form based on the analysis results and sends it to the terminal. The terminal displays this feedback to the user. The input is the analysis results from the server, and the output is a feedback message displayed on the terminal. As a specific example, feedback such as "Please stand up straighter" is displayed.

[1105] Step 7:

[1106] The server generates personalized training advice based on the user's past training data, gender, age, and experience. The generated advice is sent to the terminal and presented to the user. The input is the user's past data and the server's analysis results, and the output is the advice presented to the user.

[1107] Step 8:

[1108] During training, the device's camera and sensors capture user emotion data. The emotion data is sent to a cloud server and analyzed by the emotion engine. The input is the emotion data captured by the camera and sensors, and the output is the emotion analysis results by the server.

[1109] Step 9:

[1110] The server generates advice to adjust training intensity and recommended actions based on the emotion analysis results and sends it to the device. The device then displays this advice to the user. The input is the emotion analysis results, and the output is the adjustment advice presented to the user. A specific example of this behavior is the display of advice such as "Please do light exercise in your next training session."

[1111] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1112] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1113] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[1114] [Third embodiment]

[1115] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1116] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[1117] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1118] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[1119] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1120] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1121] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1122] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1123] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1124] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1125] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1126] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[1127] This invention provides a system that uses a wearable device to measure heart rate and perform real-time monitoring and analysis on devices such as smartphones. It also uses a camera to record and analyze the user's exercise form, providing feedback to encourage correct form and safe training. It also generates personalized training advice for each user, enabling optimal training.

[1128] System configuration

[1129] The system mainly consists of the following components:

[1130] 1. Wearable devices (smartwatches, etc.)

[1131] Measures the user's heart rate in real time.

[1132] 2. Device (smartphone)

[1133] Connect to a wearable device via Bluetooth to receive heart rate data.

[1134] The received data is sent to the server.

[1135] Displays analysis results and feedback from the server.

[1136] 3. Server

[1137] The received heart rate data is analyzed using an AI algorithm to determine whether you are within an effective training zone.

[1138] The user's exercise form is analyzed based on the recorded data to evaluate accuracy and safety.

[1139] Generate personalized training advice.

[1140] Program processing and specific examples

[1141] Connecting wearable devices and measuring heart rate

[1142] Device: The user's smartphone launches the application and connects to the wearable device via Bluetooth. Once the connection is established, the smartwatch transmits heart rate data every second.

[1143] Example: User A puts on a wearable device and launches the smartphone app. Once pairing with the device is successful, heart rate data is sent to the smartphone every second.

[1144] Real-time data analysis and notifications

[1145] Device: Sends the received heart rate data to the server.

[1146] Server: Analyzes the data with AI algorithms to determine whether the user is in an effective training zone.

[1147] Server: Sends the analysis results to the device and generates notifications such as "You're in the right load zone" if you're in the right load zone, or "Increase your load" if your heart rate is too low.

[1148] Device: Display the notification to the user.

[1149] Example: The server determines that user A is in the fat burning zone based on the received heart rate data and sends a message saying "Appropriate" to the device. The device displays this message to the user.

[1150] Recording and analyzing exercise form

[1151] User: Uses a smartphone to record their training form.

[1152] Terminal: Sends recorded data to the server.

[1153] Server: Analyzes the recorded data, compares it with the ideal form, and generates feedback on areas for improvement and caution.

[1154] Terminal: Display feedback to the user.

[1155] Example: User A sets up a smartphone to record squat training. The recorded data is sent to a server and analyzed by an AI algorithm. The server generates feedback such as "Please straighten your back more" and sends it to the device. The device then displays this feedback to the user.

[1156] Generate personalized training advice

[1157] Server: Generates personalized advice based on the user's past training data, gender, age, and experience.

[1158] Server: Sends the generated advice to the terminal.

[1159] Device: Provides advice to users and encourages them to use it for their next workout.

[1160] Example: The server generates advice for the next training session, such as "Increase the weight by 5 kg," based on the user A's past training data. The device displays this advice to the user to help them with their next training.

[1161] The above is an embodiment of the present invention, and the system allows the user to achieve effective and safe training.

[1162] The processing flow will be explained below.

[1163] Step 1:

[1164] Device: Launch the dedicated app on the user's smartphone and open the Bluetooth settings. Once the app is launched, a screen will appear to detect wearable devices (such as smartwatches).

[1165] Step 2:

[1166] Device: Detects the smartwatch's Bluetooth and sends a pairing request. The user enters the device's pairing code and approves the pairing.

[1167] Step 3:

[1168] Device: Once paired successfully, the smartwatch will begin to maintain connection with the wearable device and begin transmitting heart rate data.

[1169] Step 4:

[1170] Terminal: Receives heart rate data from the wearable device every second and sends the received heart rate data to the server.

[1171] Step 5:

[1172] Server: Analyzes the received heart rate data using AI algorithms to determine whether the user is in an effective training zone (e.g., fat burning zone, cardiovascular zone, or maximum effort zone).

[1173] Step 6:

[1174] Server: Based on the analysis results, evaluates whether the user is in the target zone. If the user is in the fat burning zone, it generates a message saying "You're in the right zone." If the heart rate is too low, it generates a notification saying "Increase your load."

[1175] Step 7:

[1176] Server: Returns the evaluation result and a notification message to the terminal.

[1177] Step 8:

[1178] Terminal: Displays the evaluation results and notification messages from the server to the user. The user continues training based on the displayed information.

[1179] Step 9:

[1180] User: Use a smartphone to record your training form. Adjust the camera position and angle to capture your entire form.

[1181] Step 10:

[1182] Device: Start recording mode and record your training form. The recorded data is temporarily saved in the cache.

[1183] Step 11:

[1184] Device: After the training session is over, the recording data is sent to the server.

[1185] Step 12:

[1186] Server: Adds the received recording data to the analysis queue. Analyzes the form using an AI image recognition algorithm and compares it with the ideal form.

[1187] Step 13:

[1188] Server: Based on the analysis results, it identifies areas of form that need improvement or attention. For example, it generates feedback such as "Please stand up straighter" or "Your knee angle is too shallow."

[1189] Step 14:

[1190] Server: Sends the generated feedback back to the device.

[1191] Step 15:

[1192] Terminal: Feedback is displayed to the user, who can then improve the form based on the feedback.

[1193] Step 16:

[1194] Server: Retrieves user gender, age, experience, and past training data from the database.

[1195] Step 17:

[1196] Server: Based on this data, it recalculates optimal training intensity and goals and generates personalized training advice, such as "Increase the weight by 5 kg in your next training session" or "Take a rest next time."

[1197] Step 18:

[1198] Server: Sends the generated advice to the terminal.

[1199] Step 19:

[1200] On the device: Providing advice to users and encouraging them to use it for their next training session.

[1201] The above are the specific processing steps for carrying out the present invention.

[1202] Example 1

[1203] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1204] In modern training, real-time heart rate monitoring and evaluation of exercise form are important for each individual user to exercise effectively and safely. However, conventional systems have difficulty providing comprehensive support for these, making it difficult to determine whether users are in the appropriate training zone or maintain correct exercise form. For this reason, a system that allows users to train effectively while avoiding injury has been sought.

[1205] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1206] In this invention, the server includes means for measuring heart rate in cooperation with the wearable device, means for receiving measured heart rate data in real time, means for analyzing the received heart rate data and determining whether the user is within an effective training zone, means for notifying the user of the determination result, means for recording the user's exercise form, means for analyzing the recorded data and evaluating accuracy and safety, means for providing feedback to the user of the evaluation result, means for generating personalized training advice based on the user's past training data, gender, age, and experience, and means for presenting the generated advice to the user. This enables the user to check their own heart rate and exercise form in real time and perform effective and safe training.

[1207] A "wearable device" is a device that can be worn on the body and has the function of measuring biometric data, mainly heart rate, in real time and connecting to a terminal.

[1208] A "terminal" is a portable electronic device such as a smartphone or tablet that has the function of receiving data from a wearable device and transmitting it to a server.

[1209] A "server" is a high-performance computer system that exchanges data with multiple terminals via a network and analyzes the received data.

[1210] "Heart Rate Data" means information about heart rate measured through a wearable device and used to evaluate the effectiveness of training.

[1211] "Real-time" refers to data measurement and analysis occurring with almost no delay, meaning users can see the results immediately.

[1212] The "effective training zone" refers to the ideal heart rate range for the exercise the user is performing, and is the optimal range for goals such as burning fat or improving cardiopulmonary function.

[1213] The "judgment result" is information about the appropriateness of the training generated based on the heart rate data analyzed by the server.

[1214] "Notifications" are messages or alerts provided to the User via the Device to assist in adjusting their Training.

[1215] "Exercise form" refers to the body movements and positioning when a user exercises, and proper form enhances training effectiveness and prevents injury.

[1216] "Recorded data" refers to video information that records the user's exercise form and is used for exercise analysis.

[1217] The "evaluation results" are information about the accuracy and safety of the exercise form generated by the server by analyzing the recorded data.

[1218] "Feedback" refers to advice and instructions provided to users based on the evaluation results, with the aim of improving exercise form and training safety.

[1219] "Personalized training advice" refers to training suggestions and guidance that are individually tailored to the user based on their past training data, gender, age, and experience, helping them to train more effectively.

[1220] This invention is a system that uses a wearable device to measure heart rate, monitors and analyzes in real time using a terminal, and records and analyzes the user's exercise form to encourage accurate form and safe training. This system generates personalized training advice for each user, enabling optimal training.

[1221] System configuration

[1222] The system mainly consists of the following components:

[1223] 1. Wearable devices (smartwatches, etc.)

[1224] Measures the user's heart rate in real time.

[1225] 2. Device (smartphone)

[1226] Connect to a wearable device via Bluetooth to receive heart rate data.

[1227] The received data is sent to the server.

[1228] Displays analysis results and feedback from the server.

[1229] 3. Server

[1230] The received heart rate data is analyzed using an AI algorithm to determine whether you are within an effective training zone.

[1231] The user's exercise form is analyzed based on the recorded data to evaluate accuracy and safety.

[1232] Generate personalized training advice.

[1233] Connecting wearable devices and measuring heart rate

[1234] Device: The user launches the smartphone app and connects to the wearable device via Bluetooth. Once the connection is established, the wearable device sends heart rate data to the smartphone every second.

[1235] Example: User A puts on a wearable device and launches the smartphone app. Once pairing with the device is successful, heart rate data is sent to the smartphone every second. The screen displays the heart rate in real time.

[1236] Real-time data analysis and notifications

[1237] Device: Sends the received heart rate data to the server.

[1238] Server: Analyzes the data with AI algorithms to determine whether the user is in an effective training zone.

[1239] Server: Sends the analysis results to the device and generates notifications such as "You're in the right load zone" if you're in the right load zone, or "Increase your load" if your heart rate is too low.

[1240] Device: Display the notification to the user.

[1241] Example: The server determines that user A is in the fat burning zone based on the received heart rate data and sends a message saying "Appropriate" to the device. The device displays this message to the user.

[1242] Recording and analyzing exercise form

[1243] User: The user uses a smartphone to record their training form.

[1244] Terminal: Sends recorded data to the server.

[1245] Server: Analyzes the recorded data, compares it with the ideal form, and generates feedback on areas for improvement and caution.

[1246] Terminal: Display feedback to the user.

[1247] Example: User A sets up a smartphone to record squat training. The recorded data is sent to a server and analyzed by an AI algorithm. The server generates feedback such as "Please straighten your back more" and sends it to the device. The device then displays this feedback to the user.

[1248] Generate personalized training advice

[1249] Server: Generates personalized advice based on the user's past training data, gender, age, and experience.

[1250] Server: Sends the generated advice to the terminal.

[1251] Device: Provides advice to users and encourages them to use it for their next workout.

[1252] Example: The server generates advice for the next training session, such as "Increase the weight by 5 kg," based on the user A's past training data. The device displays this advice to the user to help them with their next training.

[1253] Prompt Sentence Examples

[1254] "Please tell me how the app works, measuring heart rate in conjunction with a smartwatch and analyzing the data in real time."

[1255] "Please explain how to analyze recorded data of athletic form and generate feedback."

[1256] "Please tell me more about the system that generates personalized training advice."

[1257] The above is an embodiment of the present invention, and the system allows the user to achieve effective and safe training.

[1258] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1259] Step 1:

[1260] Input: The user puts on a wearable device (smartwatch) and launches the app on their smartphone.

[1261] How it works: The phone establishes a connection with the wearable device using Bluetooth. This connection uses the Bluetooth Low Energy protocol (BLE).

[1262] Output: The connection is established and the wearable device sends heart rate data to the smartphone every second.

[1263] Step 2:

[1264] Input: Heart rate data sent from a wearable device.

[1265] Operation: The device batches the received heart rate data at regular intervals (e.g., every 5 seconds) and sends it to the server.

[1266] Output: Batched heart rate data is sent to the server.

[1267] Step 3:

[1268] Input: Heart rate data received by the server from the device.

[1269] How it works: The server analyzes the received data with AI algorithms, first filtering out noise from the raw data, and then determining which training zone the user is in.

[1270] Output: The analysis results are generated, and training zone information is derived. Messages such as "Good", "Increase load", and "Decrease load" are also generated.

[1271] Step 4:

[1272] Input: Parse results and messages generated by the server.

[1273] Operation: The server sends the analysis results and a message to the terminal.

[1274] Output: The analysis results and messages are sent to the device and ready to be displayed in the smartphone app.

[1275] Step 5:

[1276] Input: Analysis results and messages sent from the server.

[1277] Operation: The device displays the analysis results and messages it receives on the screen, allowing users to check their training status in real time.

[1278] Output: The user is notified with a message such as "Good", "Please increase load", or "Please decrease load".

[1279] Step 6:

[1280] Input: The user records their exercise form using their smartphone camera.

[1281] How it works: The device receives the recording data, applies a compression algorithm to optimize the file size, and sends it to the server.

[1282] Output: Compressed recording data is sent to the server.

[1283] Step 7:

[1284] Input: Recording data received by the server.

[1285] How it works: The server analyzes the recorded data using an AI algorithm, comparing it with the ideal form and evaluating the accuracy and safety of the user's form.

[1286] Output: As a result of the evaluation, feedback on areas for improvement and points to note is generated.

[1287] Step 8:

[1288] Input: Server-generated evaluation results and feedback.

[1289] Operation: The server sends the evaluation results and feedback to the device.

[1290] Output: Feedback is sent to the device, ready to be displayed to the user.

[1291] Step 9:

[1292] Input: Feedback sent by the server.

[1293] How it works: The device displays the feedback it receives on the screen, allowing the user to see areas of their exercise form that need improvement.

[1294] Output: The user is given specific feedback, such as "Stand up straighter."

[1295] Step 10:

[1296] Input: User's past training data, gender, age, experience.

[1297] How it works: The server uses this information to generate personalized advice for your next workout.

[1298] Output: Personalized training advice is generated.

[1299] Step 11:

[1300] Input: Server-generated personalized training advice.

[1301] Operation: The server sends advice to the terminal.

[1302] Output: The personalized advice is sent to the device and ready to be presented to the user.

[1303] Step 12:

[1304] Input: Personalized advice sent from the server.

[1305] How it works: The device displays the received advice on the screen and encourages the user to use it for their next workout.

[1306] Output: A specific piece of advice is displayed to the user, such as "Increase the weight by 5kg in your next session."

[1307] (Application example 1)

[1308] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1309] In autonomous vehicles, there are issues with insufficient management of driver health, maintaining posture while driving, and ensuring safety. Real-time monitoring of heart rate and exercise form is particularly important for drivers who drive for long periods of time, and there are concerns about health risks associated with continued improper monitoring. Furthermore, conventional systems lack personalized advice suited to individual drivers, making it difficult to ensure comfort and safety while driving.

[1310] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1311] In this invention, the server includes: means for measuring a heart rate in cooperation with a wearable device; means for receiving heart rate data in real time; means for analyzing the received heart rate data and determining whether the heart rate is within an effective exercise zone; means for notifying the driver of the determination result; means for issuing a warning if the heart rate is abnormally high to monitor the driver's health; means for recording the user's exercise form; means for analyzing the recorded data and evaluating accuracy and safety; means for providing feedback to the user on the evaluation result; means for analyzing the user's posture and exercise form in the vehicle and providing appropriate feedback; means for generating personalized training advice based on the user's past training data, gender, age, and experience; means for presenting the generated advice to the user; and means for providing personalized exercise and relaxation methods to maintain the driver's health. This improves health monitoring of drivers and passengers in autonomous vehicles, enabling a safer and more comfortable driving environment.

[1312] "Wearable device" refers to a wearable device used to measure a user's heart rate and exercise data.

[1313] "Heart Rate Data" means a user's heart rate information measured through a wearable device.

[1314] "Means for receiving in real time" refers to equipment or a system that has the functionality to instantly receive heart rate data transmitted from a wearable device.

[1315] "Effective exercise zone" refers to a specific heart rate range that a user targets to maximize the benefits of their exercise.

[1316] "Measures to monitor the driver's health and warn the driver if their heart rate is abnormally high" refers to a system that warns the driver if their heart rate exceeds the normal range.

[1317] "Means for recording the user's exercise form" refers to a device for recording the user's exercise using a video camera or the like and collecting the data.

[1318] "Means for analyzing recorded data" refers to software or algorithms used to analyze recorded exercise form data and evaluate accuracy and safety.

[1319] "Means for analyzing posture and exercise form in the vehicle and providing appropriate feedback" refers to a system that analyzes the driver's posture and exercise form captured by an in-vehicle camera and provides appropriate advice and warnings based on the results.

[1320] "Past training data" refers to historical information about the training a user has done up to now.

[1321] "Personalized Training Advice" means training advice that is tailored to you based on your individual characteristics and past training data.

[1322] "Means for providing personalized exercise and relaxation methods" refers to a system that suggests exercise and relaxation methods that are individually suited to the user's needs.

[1323] This invention provides a system that uses a wearable device to measure a driver's heart rate in real time and monitors it on an in-car display or smartphone. It also uses a camera to record and analyze the driver's exercise form and posture, providing safe and appropriate feedback. It also generates personalized health advice for each driver, creating a comfortable driving environment.

[1324] System configuration

[1325] The system mainly consists of the following components:

[1326] 1. Wearable devices

[1327] Measure the driver's heart rate in real time.

[1328] 2. Device (smartphone or in-car display)

[1329] Connect to a wearable device via Bluetooth to receive heart rate data.

[1330] The received data is sent to the server.

[1331] Displays analysis results and feedback from the server.

[1332] 3. Camera

[1333] The system uses an in-car camera to record the driver's posture and exercise form.

[1334] The recording data is sent to the server.

[1335] 4. Server

[1336] The received heart rate data is analyzed using an AI algorithm to determine whether the driver is within an effective heart rate zone.

[1337] The system analyzes recorded data of the driver's exercise form to evaluate accuracy and safety.

[1338] Generate personalized health advice and send it to your device.

[1339] Program processing overview

[1340] Connecting wearable devices and measuring heart rate

[1341] The smartphone or in-car display launches an application and connects to the wearable device via Bluetooth. Once the connection is established, the wearable device transmits heart rate data every second. For example, a driver puts on the wearable device and launches the app on the in-car display. If pairing with the device is successful, heart rate data will be displayed in real time.

[1342] Real-time data analysis and notifications

[1343] The device sends the received heart rate data to a server. The server analyzes the data and determines whether the driver is within an effective heart rate zone. The analysis results are sent back to the device, and if the heart rate is abnormally high, a notification such as "Take a deep breath" is sent. For example, the server may determine that the driver is under stress based on the received heart rate data and provide a relaxation suggestion.

[1344] Recording and analyzing exercise form

[1345] Users use the in-car camera to record their posture and exercise form while driving. The recorded data is sent to a server and analyzed by an AI algorithm. The server then generates feedback such as "Please adjust your seat" based on this data and sends it to the device.

[1346] Generating personalized health advice

[1347] The server generates personalized health advice based on the driver's past health data, age, and experience. The generated advice is sent to the device and used as reference for the next driving session. For example, the server generates advice based on past data, such as "Please stretch for five minutes before your next drive."

[1348] Hardware and software used

[1349] Hardware: Smartwatches (wearable devices), smartphones, in-car displays, and in-car cameras.

[1350] Software: Python program, OpenCV, Bluetooth library, HTTP library.

[1351] Prompt Sentence Examples

[1352] "Monitor the driver's heart rate in real time and display a warning message on the display if the heart rate is high."

[1353] "Use a camera to record the driver's posture and provide feedback based on the analysis results."

[1354] The system will improve health monitoring for drivers and passengers in autonomous vehicles, enabling a safer and more comfortable driving environment.

[1355] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1356] Step 1:

[1357] Connect the wearable device and the terminal via Bluetooth.

[1358] Input: Bluetooth address of the wearable device

[1359] Output: Connection success or failure status

[1360] Specific operation: The user wears the smartwatch and launches the smartphone app or the app on the car display. The app will attempt to connect to the device via Bluetooth, and if the connection is successful, it will display the connection success status.

[1361] Step 2:

[1362] Heart rate data is acquired in real time and sent to the device.

[1363] Input: Heart rate data from a wearable device

[1364] Output: Real-time heart rate data displayed on the device

[1365] What it does: The device sends heart rate data every second, and the application analyzes this data and displays it in real time.

[1366] Step 3:

[1367] The received heart rate data is sent to the server.

[1368] Input: Heart rate data received by the device

[1369] Output: Heart rate data sent to the server

[1370] Specific operation: The smartphone or in-car display sends the received heart rate data to the server at regular intervals using an HTTP POST request.

[1371] Step 4:

[1372] The server analyzes the heart rate data using an AI algorithm.

[1373] Input: Heart rate data received by the server

[1374] Output: Determining whether your heart rate is within the effective exercise zone

[1375] How it works: The server inputs the received heart rate data into an AI algorithm for real-time analysis, and determines whether the driver is in an effective exercise zone based on the analysis results.

[1376] Step 5:

[1377] The server sends the analysis results to the device and notifies the user.

[1378] Input: Analysis result notification data

[1379] Output: A notification message displayed on the terminal.

[1380] Specific operation: The server sends the analysis results to the device, and based on the results, a message such as "Please take a deep breath" is displayed.

[1381] Step 6:

[1382] The camera records the driver's exercise form.

[1383] Input: Camera image

[1384] Output: Video data of recorded exercise form

[1385] Specific operation: A camera installed inside the vehicle records the driver's posture and exercise form and acquires the video data.

[1386] Step 7:

[1387] The recorded data is sent to a server and form analysis is performed.

[1388] Input: Recorded video data

[1389] Output: Feedback of analysis results

[1390] How it works: The recorded data is sent to a server, which then analyzes it using an AI algorithm. Based on the analysis results, feedback such as "Please adjust your seat" is generated.

[1391] Step 8:

[1392] A server generates personalized health advice.

[1393] Input: Past training data, gender, age, experience

[1394] Output: Health advice data

[1395] How it works: The server generates personalized health advice based on the driver's past data, such as "Try doing five minutes of stretching before your next drive."

[1396] Step 9:

[1397] The generated advice is sent to the terminal and presented to the driver.

[1398] Input: Health advice data

[1399] Output: Health advice displayed on the device

[1400] Specific operation: The server sends the generated health advice to the device, and the driver can check the advice on the device.

[1401] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1402] This invention provides a system that uses a wearable device to measure heart rate and perform real-time monitoring and analysis on devices such as smartphones. It also uses a camera to record and analyze the user's exercise form, providing feedback to encourage correct form and safe training. It also generates personalized training advice for each user and uses an emotion engine that recognizes the user's emotions to achieve optimal training.

[1403] System configuration

[1404] The system mainly consists of the following components:

[1405] 1. Wearable devices (smartwatches, etc.)

[1406] Measures the user's heart rate in real time.

[1407] 2. Device (smartphone)

[1408] Connect to a wearable device via Bluetooth to receive heart rate data.

[1409] The received data is sent to the server.

[1410] Displays analysis results and feedback from the server.

[1411] The built-in camera records the user's exercise form.

[1412] 3. Server

[1413] The received heart rate data is analyzed using an AI algorithm to determine whether you are within an effective training zone.

[1414] The user's exercise form is analyzed based on the recorded data to evaluate accuracy and safety.

[1415] Generates personalized training advice based on the user's past training data, gender, age, and experience.

[1416] An emotion engine is used to analyze user emotion data and reflect it in training optimization.

[1417] Program processing and specific examples

[1418] Connecting wearable devices and measuring heart rate

[1419] Device: The user's smartphone runs a dedicated app and connects to the wearable device via Bluetooth. Once the connection is established, the smartwatch sends heart rate data every second.

[1420] Example: User A puts on a wearable device and launches the smartphone app. Once pairing with the device is successful, heart rate data is sent to the smartphone every second.

[1421] Real-time data analysis and notifications

[1422] Device: Sends the received heart rate data to the server.

[1423] Server: Analyzes the data with AI algorithms to determine whether the user is in an effective training zone.

[1424] Server: Based on the analysis results, evaluates whether the user is in the target zone. If the user is in the fat burning zone, it generates a message saying "You're in the right zone." If the heart rate is too low, it generates a notification saying "Increase your load."

[1425] Device: Display the notification to the user.

[1426] Example: The server determines that user A is in the fat burning zone based on the received heart rate data and sends a message saying "Appropriate" to the device. The device displays this message to the user.

[1427] Recording and analyzing exercise form

[1428] User: Use a smartphone to record your training form. Adjust the camera position and angle to capture your entire form.

[1429] Device: Start recording mode and record your training form. The recorded data is temporarily saved in the cache.

[1430] Device: After the training session is over, the recording data is sent to the server.

[1431] Server: Adds the received recording data to the analysis queue. Analyzes the form using an AI image recognition algorithm and compares it with the ideal form.

[1432] Server: Based on the analysis results, it identifies areas of form that need improvement or attention. For example, it generates feedback such as "Please stand up straighter" or "Your knee angle is too shallow."

[1433] Terminal: Feedback is displayed to the user, who can then improve the form based on the feedback.

[1434] Example: User A sets up a smartphone to record squat training. The recorded data is sent to a server and analyzed by an AI algorithm. The server generates feedback such as "Please straighten your back more" and sends it to the device. The device then displays this feedback to the user.

[1435] Generate personalized training advice

[1436] Server: Generates personalized advice based on the user's past training data, gender, age, and experience.

[1437] Server: Sends the generated advice to the terminal.

[1438] Device: Provides advice to users and encourages them to use it for their next workout.

[1439] Example: The server generates advice for the next training session, such as "Increase the weight by 5 kg," based on the user A's past training data. The device displays this advice to the user to help them with their next training.

[1440] Introducing an emotion engine and analyzing emotion data

[1441] Device: Equipped with a camera and sensors to capture emotional data during training. This data is sent to the server in real time.

[1442] Server: Analyzes the received emotional data using an emotion engine to understand the user's emotional state.

[1443] Server: Adjusts training intensity and recommended actions based on emotional data. For example, if a user feels fatigued or stressed, the server generates advice to reduce training intensity.

[1444] Terminal: Presents the generated advice to the user.

[1445] Example: The server analyzes the emotion data received in real time and determines that User A is tired. Based on this, it generates advice such as "Please do light exercise in your next training session" and sends it to the device. The device then displays this advice to the user.

[1446] The above is an embodiment of the present invention that combines an emotion engine, allowing users to train effectively and safely and receive personalized feedback that takes into account their emotional state during training.

[1447] The processing flow will be explained below.

[1448] Step 1:

[1449] Device: Launch the dedicated app on the user's smartphone and open the Bluetooth settings. Once the app is launched, a screen will appear to detect wearable devices (such as smartwatches).

[1450] Step 2:

[1451] Device: Detects the smartwatch's Bluetooth and sends a pairing request. The user enters the device's pairing code and approves the pairing.

[1452] Step 3:

[1453] Device: Once paired successfully, the smartwatch will begin to maintain connection with the wearable device and begin transmitting heart rate data.

[1454] Step 4:

[1455] Terminal: Receives heart rate data from the wearable device every second and sends the received heart rate data to the server.

[1456] Step 5:

[1457] Server: Analyzes the received heart rate data using AI algorithms to determine whether the user is in an effective training zone (e.g., fat burning zone, cardiovascular zone, or maximum effort zone).

[1458] Step 6:

[1459] Server: Based on the analysis results, evaluates whether the user is in the target zone. If the user is in the fat burning zone, it generates a message saying "You're in the right zone." If the heart rate is too low, it generates a notification saying "Increase your load."

[1460] Step 7:

[1461] Server: Returns the evaluation result and a notification message to the terminal.

[1462] Step 8:

[1463] Terminal: Displays the evaluation results and notification messages from the server to the user. The user continues training based on the displayed information.

[1464] Step 9:

[1465] User: Use a smartphone to record your training form. Adjust the camera position and angle to capture your entire form.

[1466] Step 10:

[1467] Device: Start recording mode and record your training form. The recorded data is temporarily saved in the cache.

[1468] Step 11:

[1469] Device: After the training session is over, the recording data is sent to the server.

[1470] Step 12:

[1471] Server: Adds the received recording data to the analysis queue. Analyzes the form using an AI image recognition algorithm and compares it with the ideal form.

[1472] Step 13:

[1473] Server: Based on the analysis results, it identifies areas of form that need improvement or attention. For example, it generates feedback such as "Please stand up straighter" or "Your knee angle is too shallow."

[1474] Step 14:

[1475] Server: Sends the generated feedback back to the device.

[1476] Step 15:

[1477] Terminal: Feedback is displayed to the user, who can then improve the form based on the feedback.

[1478] Step 16:

[1479] Server: Retrieves user gender, age, experience, and past training data from the database.

[1480] Step 17:

[1481] Server: Based on this data, it recalculates optimal training intensity and goals and generates personalized training advice, such as "Increase the weight by 5 kg in your next training session" or "Take a rest next time."

[1482] Step 18:

[1483] Server: Sends the generated advice to the terminal.

[1484] Step 19:

[1485] On the device: Providing advice to users and encouraging them to use it for their next training session.

[1486] Step 20:

[1487] User: Use cameras and sensors to capture emotional data during training. The cameras and sensors capture the user's facial expressions and physical reactions, monitoring their emotional state in real time.

[1488] Step 21:

[1489] Terminal: Transmits acquired emotion data to the server in real time.

[1490] Step 22:

[1491] Server: The emotion engine analyzes the received emotion data and identifies the user's emotional state. The emotion engine uses AI technology to determine emotions such as stress, joy, and fatigue from the user's facial expressions and physical reactions.

[1492] Step 23:

[1493] Server: Adjusts training intensity and recommended actions based on emotional data. For example, if a user feels fatigued or stressed, the server generates advice to reduce training intensity.

[1494] Step 24:

[1495] Server: Sends tailored feedback and advice to devices.

[1496] Step 25:

[1497] Terminal: Presents the generated advice to the user.

[1498] Examples:

[1499] Example 1: The server analyzes emotion data received in real time and determines that user A is tired. Based on this, it generates advice such as "Please do light exercise in your next training session" and sends it to the device. The device then displays this advice to the user.

[1500] Example 2: The emotion engine detects that user B is feeling joy or satisfaction during training. Based on this information, the server generates an encouraging message such as "Keep it up!" and sends it to the device. The device then displays the generated message to the user, further increasing their motivation.

[1501] The above is an embodiment of the present invention that combines an emotion engine, allowing users to train effectively and safely and receive personalized feedback that takes into account their emotional state during training.

[1502] Example 2

[1503] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1504] While conventional fitness systems using wearable devices can monitor users' biometric information such as heart rate in real time, they do not support recording and analysis of exercise form or providing personalized training advice. Furthermore, there is no training feedback that takes into account the user's emotional state. In particular, it is difficult to correct incorrect exercise form or provide advice tailored to individual needs, resulting in safety and effectiveness issues.

[1505] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes a means for measuring biometric information in cooperation with the wearable device, a means for receiving the measured biometric data in real time, a means for analyzing the received biometric data and determining whether the user is within an effective exercise zone, a means for notifying the user of the determination result, a means for recording the user's exercise movements, a means for analyzing the recorded data and evaluating accuracy and safety, a means for feeding back the evaluation result to the user, a means for generating personalized exercise advice based on the user's past exercise data and attribute data, and a means for presenting the generated advice to the user. This makes it possible to monitor biometric information in real time, as well as analyze exercise form and provide safe and effective personalized advice.

[1506] A "wearable device" is an electronic device that can be worn on the user's body and used to measure biometric information.

[1507] "Biometric information" refers to data about the user's physical condition, such as heart rate, body temperature, and blood pressure.

[1508] "Biometric data" refers to the numerical values ​​and indicators of biometric information measured by a wearable device.

[1509] "Real-time" is a time concept that refers to data being collected and processed almost immediately.

[1510] "Exercise movements" refers to movements or movements performed by a user, including training and exercise.

[1511] "Recorded data" refers to video data of exercise movements captured using a recording device such as a camera.

[1512] "Analysis" refers to the activity of processing received data, transforming it into a meaningful form, and deriving results.

[1513] "Accuracy" refers to the degree to which a user's athletic movements match the ideal form and movements they are aiming for.

[1514] "Safety" refers to the suitability of the device to prevent injury or other hazards when a user performs an exercise.

[1515] "Feedback" refers to the evaluation and advice the system provides to the user.

[1516] "Personalized exercise advice" refers to exercise advice and instructions that are optimized based on a user's individual characteristics and past data.

[1517] "Attribute data" refers to data that indicates a user's individual characteristics, such as age, gender, and experience.

[1518] This invention is a system that uses wearable devices and terminals such as smartphones to monitor and analyze a user's biometric information and exercise movements in real time, allowing the user to train appropriately and receive safer and more effective training advice.

[1519] Hardware and software used

[1520] 1. Wearable devices (e.g., smartwatches): Wearable devices measure the user's biometric information, such as heart rate, and transmit it to a smartphone via Bluetooth.

[1521] 2. Device (e.g., smartphone): The smartphone runs a dedicated app and receives biometric data sent from the wearable device. The received data is sent to a server in real time. The smartphone also uses a camera to record the user's exercise movements.

[1522] 3. Server: The server analyzes the received biometric data and recorded exercise data. It uses AI algorithms to determine whether the user is within the effective exercise zone, evaluate the accuracy and safety of the exercise form, and generate personalized training advice based on the user's past data and attributes.

[1523] Explanation of program processing

[1524] Measurement and transmission of biometric data

[1525] 1. The device launches the dedicated app and connects to the wearable device via Bluetooth. Once the connection is established, the smartwatch sends heart rate data every second.

[1526] Example: A user wears a smartwatch and launches an app on their smartphone. Once the app successfully pairs with the smartwatch, heart rate data is sent to the smartphone every second. For example, data such as "Heart rate: 75, Time: 10:00:01" is continuously sent.

[1527] Real-time data analysis

[1528] 1. The device sends the received heart rate data to the server.

[1529] 2. The server uses an AI algorithm to analyze your heart rate data and determine whether you are within an effective exercise zone.

[1530] Example: The server processes data such as "Heart rate: 135, Time: 10:05:01" and determines that the user is in the fat burning zone. Based on this, it generates a notification message saying "Appropriate" and sends it to the device. The device then displays this message to the user.

[1531] Recording and analyzing exercise form

[1532] 1. The user uses a smartphone to record their exercise movements, adjusting the camera position and angle to capture their entire form.

[1533] 2. The device will start recording mode and record your training form, temporarily saving the recorded data in the cache.

[1534] 3. After the training session ends, the device sends the recorded data to the server.

[1535] 4. The server analyzes the received recording data and uses an AI image recognition algorithm to analyze the user's form. It compares the user's form with the ideal form and identifies areas for improvement.

[1536] Example: A user records a squat and sends the recorded data to a server. The server generates a feedback message saying "Please stand up straighter" and sends it to the device. The device displays this feedback to the user.

[1537] Generate personalized training advice

[1538] 1. The server generates personalized training advice based on the user's past training data, gender, age, and experience, and sends it to the device.

[1539] 2. The device displays the advice to the user.

[1540] Example: Based on the user's training data, the server generates advice such as "Increase the weight by 5 kg in your next training session." The device notifies the user of this advice and displays it on the training guide screen.

[1541] Prompt Sentence Examples

[1542] "Today's training session was very effective as it kept my heart rate stable and allowed me to train in the fat burning zone. Next time, I'll try increasing the intensity a bit and extending the duration by 5 minutes."

[1543] The above is an embodiment of the invention. This system allows users to monitor their biological information in real time, analyze their exercise form, and perform safe and effective training.

[1544] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1545] Step 1:

[1546] Connecting wearable devices and measuring heart rate

[1547] The device launches a dedicated app and connects to the wearable device via Bluetooth. Once the connection is established, the smartwatch sends heart rate data every second.

[1548] Input: The user puts on the smartwatch and launches the app on their smartphone.

[1549] Data processing: The app pairs with the wearable device and a connection is established.

[1550] Output: Heart rate data is sent from the smartwatch to the smartphone every second, and is simultaneously displayed in real time on the app screen.

[1551] Specific operation: The user wears the smartwatch and launches the smartphone app. Once paired with the device, heart rate data will be sent to the smartphone every second and displayed on the real-time monitoring screen within the app.

[1552] Step 2:

[1553] Real-time data analysis and notifications

[1554] The terminal transmits the received heart rate data to the server.

[1555] The server uses AI algorithms to analyze heart rate data in real time to determine whether the user is in an effective exercise zone, and generates a message based on the analysis results and sends it to the device.

[1556] Input: Heart rate data sent from the device (e.g., "Heart rate: 135, Time: 10:05:01").

[1557] Data processing: The server analyzes the heart rate data and determines whether you are in an effective exercise zone (e.g., fat burning zone).

[1558] Output: The judgment result (e.g., "Appropriate") is sent as a message to the terminal.

[1559] Specific operation: The server processes the received heart rate data and confirms that the user is in the fat burning zone. It generates a notification message saying "Suitable" and sends it to the device. The device then displays this message to the user in a pop-up.

[1560] Step 3:

[1561] Recording and analyzing exercise form

[1562] Users use their smartphones to record their exercise movements, adjusting the camera position and angle to capture their entire form.

[1563] The device will enter recording mode and record your training form. The recorded data will be temporarily saved in the cache.

[1564] After the training session ends, the terminal transmits the recorded data to the server.

[1565] The server analyzes the received recording data and uses an AI image recognition algorithm to analyze the user's form, comparing it with the ideal form and identifying areas for improvement.

[1566] Input: Video data (e.g., video of squats).

[1567] Data processing: The server analyzes the recorded data using AI image recognition algorithms and evaluates the user's form.

[1568] Output: The analysis results (e.g., "Please stand up straighter") are sent to the device as a feedback message.

[1569] How it works: The user records their squats and the recorded data is sent to the server. The server then uses an AI algorithm to analyze the data, generates a feedback message saying "Please straighten your back more," and sends it to the device. The device then displays this feedback to the user.

[1570] Step 4:

[1571] Generate personalized training advice

[1572] The server generates personalized training advice based on the user's past training data, gender, age, and experience, and sends it to the device.

[1573] The device displays the advice to the user.

[1574] Input: User's past training data and demographic data (e.g., age, gender, experience).

[1575] Data processing: The server uses this data to generate advice tailored to your individual needs.

[1576] Output: Generated advice (e.g. "Increase the weight by 5kg in your next workout") is sent to the device.

[1577] Specific operation: Based on the user's training data, the server generates advice such as "Increase the weight by 5 kg in your next training session." The device notifies the user of this advice and displays it on the training guide screen.

[1578] Step 5:

[1579] Introducing an emotion engine and analyzing emotion data

[1580] The device uses cameras and sensors to capture emotional data while the user is training, and transmits this data to a server in real time.

[1581] The server analyzes the received emotional data using an emotion engine to understand the user's emotional state and adjusts training intensity and recommended actions as needed.

[1582] Input: Emotion data obtained in real time (e.g., facial expression data from a facial recognition sensor).

[1583] Data processing: The server uses an emotion engine to analyze the emotion data and identify the user's emotional state.

[1584] Output: Advice generated based on the results of sentiment analysis (e.g., "Do light exercise during your next workout") is sent to the device.

[1585] Specific operation: The server analyzes the user's emotional data and determines that the user is tired. It generates advice such as "Try to do lighter exercise next time" and sends it to the device. The device notifies the user of this advice and displays it on the home screen.

[1586] (Application example 2)

[1587] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1588] While conventional training monitoring systems provide functions such as measuring heart rate and analyzing exercise form, they lack a mechanism for providing optimal training advice that takes into account the user's emotional state. Furthermore, there are limitations to generating personalized advice using past training data, making it difficult to provide effective feedback.

[1589] The identification processing by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for measuring heart rate in cooperation with the wearable device, means for receiving measured heart rate data in real time, means for analyzing the received heart rate data and determining whether the user is within an effective training zone, means for notifying the user of the determination result, means for acquiring user emotion data, and means for analyzing the emotion data and adjusting training intensity and recommended actions. This makes it possible to provide effective and personalized feedback that simultaneously takes into account the user's heart rate and emotional state.

[1590] A "wearable device" is a device that can be worn by a user to measure biometric information such as heart rate and share information with other devices.

[1591] "Heart rate" refers to the number of times the heart beats within a certain period of time, and is a biometric indicator generally measured in beats per minute.

[1592] "Real-time reception" is a function that means receiving measured data almost simultaneously without delay.

[1593] "Training zone" refers to a range of exercise intensity based on heart rate and other biometric information to achieve a specific exercise effect.

[1594] "Notification means" refers to the method or device for communicating analysis results or other information to the user.

[1595] "Emotional data" refers to information about a user's emotional state derived from facial expressions, tone of voice, behavior, etc.

[1596] An "emotion engine" refers to a software algorithm that analyzes emotional data and understands the user's emotional state.

[1597] "Exercise form" refers to the body posture and movement method used by a user when performing a particular exercise.

[1598] "Recorded data" refers to video files captured by a camera, which can be used to analyze the user's exercise form.

[1599] "Feedback" refers to the information and comments that the system provides to the user regarding analysis results and advice.

[1600] "Personalized Training Advice" refers to training recommendations generated based on a user's individual historical data, gender, age, and experience.

[1601] This invention is a system that uses a wearable device to measure a user's heart rate and receives and analyzes the data in real time on a smartphone or other device. It also records and analyzes the user's exercise form with a camera, provides personalized training advice, and uses an emotion engine to recognize the user's emotional state and optimize training.

[1602] The detailed configuration and usage of this system will be explained below.

[1603] Hardware and Software Configuration

[1604] 1. Wearable devices:

[1605] Function: Real-time heart rate measurement

[1606] Example: Apple Watch, Fitbit, etc.

[1607] 2. Device (smartphone or smart glasses):

[1608] Functions: Receive heart rate data, record exercise form, and display feedback

[1609] Examples: Android / iOS devices, Google Glass, Microsoft HoloLens, etc.

[1610] Software: Dedicated app (e.g., developed with Unity)

[1611] 3. Server:

[1612] Functions: Analyzing data, generating feedback, generating training advice, analyzing emotional data

[1613] Example: Cloud platforms (AWS, Google Cloud, etc.)

[1614] Software: AI algorithms (TensorFlow, PyTorch), emotion engines (Azure Emotion API, IBM Watson Tone Analyzer)

[1615] System Operation

[1616] Connecting wearable devices and measuring heart rate

[1617] Users launch a dedicated application on their smartphone or smart glasses and connect to the wearable device via Bluetooth. Once the connection is established, the wearable device transmits heart rate data every second. The device receives this data in real time and sends it to a cloud server.

[1618] Real-time data analysis and notifications

[1619] The cloud server uses an AI algorithm to analyze the received heart rate data and determine whether the user is in an effective training zone. For example, if the heart rate is within the fat-burning zone, a notification stating "Appropriate" is generated and sent to the device. The user can then view this notification on their device.

[1620] Recording and analyzing exercise form

[1621] When a user performs a specific exercise, the device's camera is used to record their exercise form. The recorded data is temporarily stored on the device and then sent to a cloud server after the workout is completed. The server analyzes the recorded data and uses an AI image recognition algorithm to compare it with ideal form and generate feedback.

[1622] Generate personalized training advice

[1623] The cloud server generates personalized training advice based on the user's past training data, gender, age, and experience, and sends the advice to the user's device, where it can be used as a reference for the next training session.

[1624] Introducing an emotion engine and analyzing emotion data

[1625] During training, the device's camera and sensors collect the user's emotional data. The collected data is sent to a cloud server where it is analyzed by the emotion engine. Based on the analysis results, if the user feels fatigued or stressed, advice to reduce the intensity of training is generated. This is also sent to the device and can be viewed by the user.

[1626] Examples of concrete examples and prompts

[1627] Specific examples

[1628] User A puts on a smartwatch at the gym and launches the smartphone app. The app automatically pairs with the smartwatch and sends heart rate data to the server. The server analyzes the heart rate data, determines that User A is in the fat burning zone, and sends a notification that the user is in the "appropriate" zone. User A can check this notification on his smartphone. When doing squat training, the smartphone camera can record his exercise form and send it to the server, allowing him to receive feedback such as "straighten your back more."

[1629] Prompt Sentence Examples

[1630] "Generate next training advice based on the user's heart rate and exercise data. Output in the following format:

[1631] User's past training data: 30 minutes of running, average heart rate 140 bpm, weight training: 50 kg

[1632] Suggestion: Next time you train, increase your running time to 35 minutes and use 55kg.

[1633] As described above, the present invention provides an optimal training environment for users by linking a wearable device with a terminal and a server.

[1634] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1635] Step 1:

[1636] The user puts on the wearable device and launches the dedicated smartphone app. The app automatically pairs with the wearable device via Bluetooth and starts transmitting heart rate data. The input is the heart rate data from the wearable device, and the output is the heart rate data received in real time.

[1637] Step 2:

[1638] The device (smartphone) sends the received heart rate data to a cloud server. The cloud server uses an AI algorithm to analyze the heart rate data and determine whether the user is within an effective training zone. The input is the heart rate data sent from the device, and the output is the analysis result (training zone status).

[1639] Step 3:

[1640] The server sends a notification to the user's device based on the analysis results. The device displays this notification in real time, providing feedback to the user. The input is the analysis results from the server, and the output is a notification message displayed on the device. A specific example of this is the notification "Inside the fat burning zone."

[1641] Step 4:

[1642] When a user exercises, the device's camera is used to record the user's exercise form. The recorded data is temporarily stored on the device. The input is the video data captured by the camera, and the output is the saved recorded data.

[1643] Step 5:

[1644] After the exercise session, the device sends the recorded data to a cloud server. The cloud server analyzes this data using an AI image recognition algorithm to evaluate the accuracy and safety of the user's exercise form. The input is the recorded data sent from the device, and the output is the analysis result (evaluation of exercise form).

[1645] Step 6:

[1646] The server generates feedback for improving the form based on the analysis results and sends it to the terminal. The terminal displays this feedback to the user. The input is the analysis results from the server, and the output is a feedback message displayed on the terminal. As a specific example, feedback such as "Please stand up straighter" is displayed.

[1647] Step 7:

[1648] The server generates personalized training advice based on the user's past training data, gender, age, and experience. The generated advice is sent to the terminal and presented to the user. The input is the user's past data and the server's analysis results, and the output is the advice presented to the user.

[1649] Step 8:

[1650] During training, the device's camera and sensors capture user emotion data. The emotion data is sent to a cloud server and analyzed by the emotion engine. The input is the emotion data captured by the camera and sensors, and the output is the emotion analysis results by the server.

[1651] Step 9:

[1652] The server generates advice to adjust training intensity and recommended actions based on the emotion analysis results and sends it to the device. The device then displays this advice to the user. The input is the emotion analysis results, and the output is the adjustment advice presented to the user. A specific example of this behavior is the display of advice such as "Please do light exercise in your next training session."

[1653] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1654] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1655] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1656] [Fourth embodiment]

[1657] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1658] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1659] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1660] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1661] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1662] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1663] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1664] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1665] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1666] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1667] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1668] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1669] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1670] This invention provides a system that uses a wearable device to measure heart rate and perform real-time monitoring and analysis on devices such as smartphones. It also uses a camera to record and analyze the user's exercise form, providing feedback to encourage correct form and safe training. It also generates personalized training advice for each user, enabling optimal training.

[1671] System configuration

[1672] The system mainly consists of the following components:

[1673] 1. Wearable devices (smartwatches, etc.)

[1674] Measures the user's heart rate in real time.

[1675] 2. Device (smartphone)

[1676] Connect to a wearable device via Bluetooth to receive heart rate data.

[1677] The received data is sent to the server.

[1678] Displays analysis results and feedback from the server.

[1679] 3. Server

[1680] The received heart rate data is analyzed using an AI algorithm to determine whether you are within an effective training zone.

[1681] The user's exercise form is analyzed based on the recorded data to evaluate accuracy and safety.

[1682] Generate personalized training advice.

[1683] Program processing and specific examples

[1684] Connecting wearable devices and measuring heart rate

[1685] Device: The user's smartphone launches the application and connects to the wearable device via Bluetooth. Once the connection is established, the smartwatch transmits heart rate data every second.

[1686] Example: User A puts on a wearable device and launches the smartphone app. Once pairing with the device is successful, heart rate data is sent to the smartphone every second.

[1687] Real-time data analysis and notifications

[1688] Device: Sends the received heart rate data to the server.

[1689] Server: Analyzes the data with AI algorithms to determine whether the user is in an effective training zone.

[1690] Server: Sends the analysis results to the device and generates notifications such as "You're in the right load zone" if you're in the right load zone, or "Increase your load" if your heart rate is too low.

[1691] Device: Display the notification to the user.

[1692] Example: The server determines that user A is in the fat burning zone based on the received heart rate data and sends a message saying "Appropriate" to the device. The device displays this message to the user.

[1693] Recording and analyzing exercise form

[1694] User: Uses a smartphone to record their training form.

[1695] Terminal: Sends recorded data to the server.

[1696] Server: Analyzes the recorded data, compares it with the ideal form, and generates feedback on areas for improvement and caution.

[1697] Terminal: Display feedback to the user.

[1698] Example: User A sets up a smartphone to record squat training. The recorded data is sent to a server and analyzed by an AI algorithm. The server generates feedback such as "Please straighten your back more" and sends it to the device. The device then displays this feedback to the user.

[1699] Generate personalized training advice

[1700] Server: Generates personalized advice based on the user's past training data, gender, age, and experience.

[1701] Server: Sends the generated advice to the terminal.

[1702] Device: Provides advice to users and encourages them to use it for their next workout.

[1703] Example: The server generates advice for the next training session, such as "Increase the weight by 5 kg," based on the user A's past training data. The device displays this advice to the user to help them with their next training.

[1704] The above is an embodiment of the present invention, and the system allows the user to achieve effective and safe training.

[1705] The processing flow will be explained below.

[1706] Step 1:

[1707] Device: Launch the dedicated app on the user's smartphone and open the Bluetooth settings. Once the app is launched, a screen will appear to detect wearable devices (such as smartwatches).

[1708] Step 2:

[1709] Device: Detects the smartwatch's Bluetooth and sends a pairing request. The user enters the device's pairing code and approves the pairing.

[1710] Step 3:

[1711] Device: Once paired successfully, the smartwatch will begin to maintain connection with the wearable device and begin transmitting heart rate data.

[1712] Step 4:

[1713] Terminal: Receives heart rate data from the wearable device every second and sends the received heart rate data to the server.

[1714] Step 5:

[1715] Server: Analyzes the received heart rate data using AI algorithms to determine whether the user is in an effective training zone (e.g., fat burning zone, cardiovascular zone, or maximum effort zone).

[1716] Step 6:

[1717] Server: Based on the analysis results, evaluates whether the user is in the target zone. If the user is in the fat burning zone, it generates a message saying "You're in the right zone." If the heart rate is too low, it generates a notification saying "Increase your load."

[1718] Step 7:

[1719] Server: Returns the evaluation result and a notification message to the terminal.

[1720] Step 8:

[1721] Terminal: Displays the evaluation results and notification messages from the server to the user. The user continues training based on the displayed information.

[1722] Step 9:

[1723] User: Use a smartphone to record your training form. Adjust the camera position and angle to capture your entire form.

[1724] Step 10:

[1725] Device: Start recording mode and record your training form. The recorded data is temporarily saved in the cache.

[1726] Step 11:

[1727] Device: After the training session is over, the recording data is sent to the server.

[1728] Step 12:

[1729] Server: Adds the received recording data to the analysis queue. Analyzes the form using an AI image recognition algorithm and compares it with the ideal form.

[1730] Step 13:

[1731] Server: Based on the analysis results, it identifies areas of form that need improvement or attention. For example, it generates feedback such as "Please stand up straighter" or "Your knee angle is too shallow."

[1732] Step 14:

[1733] Server: Sends the generated feedback back to the device.

[1734] Step 15:

[1735] Terminal: Feedback is displayed to the user, who can then improve the form based on the feedback.

[1736] Step 16:

[1737] Server: Retrieves user gender, age, experience, and past training data from the database.

[1738] Step 17:

[1739] Server: Based on this data, it recalculates optimal training intensity and goals and generates personalized training advice, such as "Increase the weight by 5 kg in your next training session" or "Take a rest next time."

[1740] Step 18:

[1741] Server: Sends the generated advice to the terminal.

[1742] Step 19:

[1743] On the device: Providing advice to users and encouraging them to use it for their next training session.

[1744] The above are the specific processing steps for carrying out the present invention.

[1745] Example 1

[1746] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1747] In modern training, real-time heart rate monitoring and evaluation of exercise form are important for each individual user to exercise effectively and safely. However, conventional systems have difficulty providing comprehensive support for these, making it difficult to determine whether users are in the appropriate training zone or maintain correct exercise form. For this reason, a system that allows users to train effectively while avoiding injury has been sought.

[1748] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1749] In this invention, the server includes means for measuring heart rate in cooperation with the wearable device, means for receiving measured heart rate data in real time, means for analyzing the received heart rate data and determining whether the user is within an effective training zone, means for notifying the user of the determination result, means for recording the user's exercise form, means for analyzing the recorded data and evaluating accuracy and safety, means for providing feedback to the user of the evaluation result, means for generating personalized training advice based on the user's past training data, gender, age, and experience, and means for presenting the generated advice to the user. This enables the user to check their own heart rate and exercise form in real time and perform effective and safe training.

[1750] A "wearable device" is a device that can be worn on the body and has the function of measuring biometric data, mainly heart rate, in real time and connecting to a terminal.

[1751] A "terminal" is a portable electronic device such as a smartphone or tablet that has the function of receiving data from a wearable device and transmitting it to a server.

[1752] A "server" is a high-performance computer system that exchanges data with multiple terminals via a network and analyzes the received data.

[1753] "Heart Rate Data" means information about heart rate measured through a wearable device and used to evaluate the effectiveness of training.

[1754] "Real-time" refers to data measurement and analysis occurring with almost no delay, meaning users can see the results immediately.

[1755] The "effective training zone" refers to the ideal heart rate range for the exercise the user is performing, and is the optimal range for goals such as burning fat or improving cardiopulmonary function.

[1756] The "judgment result" is information about the appropriateness of the training generated based on the heart rate data analyzed by the server.

[1757] "Notifications" are messages or alerts provided to the User via the Device to assist in adjusting their Training.

[1758] "Exercise form" refers to the body movements and positioning when a user exercises, and proper form enhances training effectiveness and prevents injury.

[1759] "Recorded data" refers to video information that records the user's exercise form and is used for exercise analysis.

[1760] The "evaluation results" are information about the accuracy and safety of the exercise form generated by the server by analyzing the recorded data.

[1761] "Feedback" refers to advice and instructions provided to users based on the evaluation results, with the aim of improving exercise form and training safety.

[1762] "Personalized training advice" refers to training suggestions and guidance that are individually tailored to the user based on their past training data, gender, age, and experience, helping them to train more effectively.

[1763] This invention is a system that uses a wearable device to measure heart rate, monitors and analyzes in real time using a terminal, and records and analyzes the user's exercise form to encourage accurate form and safe training. This system generates personalized training advice for each user, enabling optimal training.

[1764] System configuration

[1765] The system mainly consists of the following components:

[1766] 1. Wearable devices (smartwatches, etc.)

[1767] Measures the user's heart rate in real time.

[1768] 2. Device (smartphone)

[1769] Connect to a wearable device via Bluetooth to receive heart rate data.

[1770] The received data is sent to the server.

[1771] Displays analysis results and feedback from the server.

[1772] 3. Server

[1773] The received heart rate data is analyzed using an AI algorithm to determine whether you are within an effective training zone.

[1774] The user's exercise form is analyzed based on the recorded data to evaluate accuracy and safety.

[1775] Generate personalized training advice.

[1776] Connecting wearable devices and measuring heart rate

[1777] Device: The user launches the smartphone app and connects to the wearable device via Bluetooth. Once the connection is established, the wearable device sends heart rate data to the smartphone every second.

[1778] Example: User A puts on a wearable device and launches the smartphone app. Once pairing with the device is successful, heart rate data is sent to the smartphone every second. The screen displays the heart rate in real time.

[1779] Real-time data analysis and notifications

[1780] Device: Sends the received heart rate data to the server.

[1781] Server: Analyzes the data with AI algorithms to determine whether the user is in an effective training zone.

[1782] Server: Sends the analysis results to the device and generates notifications such as "You're in the right load zone" if you're in the right load zone, or "Increase your load" if your heart rate is too low.

[1783] Device: Display the notification to the user.

[1784] Example: The server determines that user A is in the fat burning zone based on the received heart rate data and sends a message saying "Appropriate" to the device. The device displays this message to the user.

[1785] Recording and analyzing exercise form

[1786] User: The user uses a smartphone to record their training form.

[1787] Terminal: Sends recorded data to the server.

[1788] Server: Analyzes the recorded data, compares it with the ideal form, and generates feedback on areas for improvement and caution.

[1789] Terminal: Display feedback to the user.

[1790] Example: User A sets up a smartphone to record squat training. The recorded data is sent to a server and analyzed by an AI algorithm. The server generates feedback such as "Please straighten your back more" and sends it to the device. The device then displays this feedback to the user.

[1791] Generate personalized training advice

[1792] Server: Generates personalized advice based on the user's past training data, gender, age, and experience.

[1793] Server: Sends the generated advice to the terminal.

[1794] Device: Provides advice to users and encourages them to use it for their next workout.

[1795] Example: The server generates advice for the next training session, such as "Increase the weight by 5 kg," based on the user A's past training data. The device displays this advice to the user to help them with their next training.

[1796] Prompt Sentence Examples

[1797] "Please tell me how the app works, measuring heart rate in conjunction with a smartwatch and analyzing the data in real time."

[1798] "Please explain how to analyze recorded data of athletic form and generate feedback."

[1799] "Please tell me more about the system that generates personalized training advice."

[1800] The above is an embodiment of the present invention, and the system allows the user to achieve effective and safe training.

[1801] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1802] Step 1:

[1803] Input: The user puts on a wearable device (smartwatch) and launches the app on their smartphone.

[1804] How it works: The phone establishes a connection with the wearable device using Bluetooth. This connection uses the Bluetooth Low Energy protocol (BLE).

[1805] Output: The connection is established and the wearable device sends heart rate data to the smartphone every second.

[1806] Step 2:

[1807] Input: Heart rate data sent from a wearable device.

[1808] Operation: The device batches the received heart rate data at regular intervals (e.g., every 5 seconds) and sends it to the server.

[1809] Output: Batched heart rate data is sent to the server.

[1810] Step 3:

[1811] Input: Heart rate data received by the server from the device.

[1812] How it works: The server analyzes the received data with AI algorithms, first filtering out noise from the raw data, and then determining which training zone the user is in.

[1813] Output: The analysis results are generated, and training zone information is derived. Messages such as "Good", "Increase load", and "Decrease load" are also generated.

[1814] Step 4:

[1815] Input: Parse results and messages generated by the server.

[1816] Operation: The server sends the analysis results and a message to the terminal.

[1817] Output: The analysis results and messages are sent to the device and ready to be displayed in the smartphone app.

[1818] Step 5:

[1819] Input: Analysis results and messages sent from the server.

[1820] Operation: The device displays the analysis results and messages it receives on the screen, allowing users to check their training status in real time.

[1821] Output: The user is notified with a message such as "Good", "Please increase load", or "Please decrease load".

[1822] Step 6:

[1823] Input: The user records their exercise form using their smartphone camera.

[1824] How it works: The device receives the recording data, applies a compression algorithm to optimize the file size, and sends it to the server.

[1825] Output: Compressed recording data is sent to the server.

[1826] Step 7:

[1827] Input: Recording data received by the server.

[1828] How it works: The server analyzes the recorded data using an AI algorithm, comparing it with the ideal form and evaluating the accuracy and safety of the user's form.

[1829] Output: As a result of the evaluation, feedback on areas for improvement and points to note is generated.

[1830] Step 8:

[1831] Input: Server-generated evaluation results and feedback.

[1832] Operation: The server sends the evaluation results and feedback to the device.

[1833] Output: Feedback is sent to the device, ready to be displayed to the user.

[1834] Step 9:

[1835] Input: Feedback sent by the server.

[1836] How it works: The device displays the feedback it receives on the screen, allowing the user to see areas of their exercise form that need improvement.

[1837] Output: The user is given specific feedback, such as "Stand up straighter."

[1838] Step 10:

[1839] Input: User's past training data, gender, age, experience.

[1840] How it works: The server uses this information to generate personalized advice for your next workout.

[1841] Output: Personalized training advice is generated.

[1842] Step 11:

[1843] Input: Server-generated personalized training advice.

[1844] Operation: The server sends advice to the terminal.

[1845] Output: The personalized advice is sent to the device and ready to be presented to the user.

[1846] Step 12:

[1847] Input: Personalized advice sent from the server.

[1848] How it works: The device displays the received advice on the screen and encourages the user to use it for their next workout.

[1849] Output: A specific piece of advice is displayed to the user, such as "Increase the weight by 5kg in your next session."

[1850] (Application example 1)

[1851] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1852] In autonomous vehicles, there are issues with insufficient management of driver health, maintaining posture while driving, and ensuring safety. Real-time monitoring of heart rate and exercise form is particularly important for drivers who drive for long periods of time, and there are concerns about health risks associated with continued improper monitoring. Furthermore, conventional systems lack personalized advice suited to individual drivers, making it difficult to ensure comfort and safety while driving.

[1853] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1854] In this invention, the server includes: means for measuring a heart rate in cooperation with a wearable device; means for receiving heart rate data in real time; means for analyzing the received heart rate data and determining whether the heart rate is within an effective exercise zone; means for notifying the driver of the determination result; means for issuing a warning if the heart rate is abnormally high to monitor the driver's health; means for recording the user's exercise form; means for analyzing the recorded data and evaluating accuracy and safety; means for providing feedback to the user on the evaluation result; means for analyzing the user's posture and exercise form in the vehicle and providing appropriate feedback; means for generating personalized training advice based on the user's past training data, gender, age, and experience; means for presenting the generated advice to the user; and means for providing personalized exercise and relaxation methods to maintain the driver's health. This improves health monitoring of drivers and passengers in autonomous vehicles, enabling a safer and more comfortable driving environment.

[1855] "Wearable device" refers to a wearable device used to measure a user's heart rate and exercise data.

[1856] "Heart Rate Data" means a user's heart rate information measured through a wearable device.

[1857] "Means for receiving in real time" refers to equipment or a system that has the functionality to instantly receive heart rate data transmitted from a wearable device.

[1858] "Effective exercise zone" refers to a specific heart rate range that a user targets to maximize the benefits of their exercise.

[1859] "Measures to monitor the driver's health and warn the driver if their heart rate is abnormally high" refers to a system that warns the driver if their heart rate exceeds the normal range.

[1860] "Means for recording the user's exercise form" refers to a device for recording the user's exercise using a video camera or the like and collecting the data.

[1861] "Means for analyzing recorded data" refers to software or algorithms used to analyze recorded exercise form data and evaluate accuracy and safety.

[1862] "Means for analyzing posture and exercise form in the vehicle and providing appropriate feedback" refers to a system that analyzes the driver's posture and exercise form captured by an in-vehicle camera and provides appropriate advice and warnings based on the results.

[1863] "Past training data" refers to historical information about the training a user has done up to now.

[1864] "Personalized Training Advice" means training advice that is tailored to you based on your individual characteristics and past training data.

[1865] "Means for providing personalized exercise and relaxation methods" refers to a system that suggests exercise and relaxation methods that are individually suited to the user's needs.

[1866] This invention provides a system that uses a wearable device to measure a driver's heart rate in real time and monitors it on an in-car display or smartphone. It also uses a camera to record and analyze the driver's exercise form and posture, providing safe and appropriate feedback. It also generates personalized health advice for each driver, creating a comfortable driving environment.

[1867] System configuration

[1868] The system mainly consists of the following components:

[1869] 1. Wearable devices

[1870] Measure the driver's heart rate in real time.

[1871] 2. Device (smartphone or in-car display)

[1872] Connect to a wearable device via Bluetooth to receive heart rate data.

[1873] The received data is sent to the server.

[1874] Displays analysis results and feedback from the server.

[1875] 3. Camera

[1876] The system uses an in-car camera to record the driver's posture and exercise form.

[1877] The recording data is sent to the server.

[1878] 4. Server

[1879] The received heart rate data is analyzed using an AI algorithm to determine whether the driver is within an effective heart rate zone.

[1880] The system analyzes recorded data of the driver's exercise form to evaluate accuracy and safety.

[1881] Generate personalized health advice and send it to your device.

[1882] Program processing overview

[1883] Connecting wearable devices and measuring heart rate

[1884] The smartphone or in-car display launches an application and connects to the wearable device via Bluetooth. Once the connection is established, the wearable device transmits heart rate data every second. For example, a driver puts on the wearable device and launches the app on the in-car display. If pairing with the device is successful, heart rate data will be displayed in real time.

[1885] Real-time data analysis and notifications

[1886] The device sends the received heart rate data to a server. The server analyzes the data and determines whether the driver is within an effective heart rate zone. The analysis results are sent back to the device, and if the heart rate is abnormally high, a notification such as "Take a deep breath" is sent. For example, the server may determine that the driver is under stress based on the received heart rate data and provide a relaxation suggestion.

[1887] Recording and analyzing exercise form

[1888] Users use the in-car camera to record their posture and exercise form while driving. The recorded data is sent to a server and analyzed by an AI algorithm. The server then generates feedback such as "Please adjust your seat" based on this data and sends it to the device.

[1889] Generating personalized health advice

[1890] The server generates personalized health advice based on the driver's past health data, age, and experience. The generated advice is sent to the device and used as reference for the next driving session. For example, the server generates advice based on past data, such as "Please stretch for five minutes before your next drive."

[1891] Hardware and software used

[1892] Hardware: Smartwatches (wearable devices), smartphones, in-car displays, and in-car cameras.

[1893] Software: Python program, OpenCV, Bluetooth library, HTTP library.

[1894] Prompt Sentence Examples

[1895] "Monitor the driver's heart rate in real time and display a warning message on the display if the heart rate is high."

[1896] "Use a camera to record the driver's posture and provide feedback based on the analysis results."

[1897] The system will improve health monitoring for drivers and passengers in autonomous vehicles, enabling a safer and more comfortable driving environment.

[1898] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1899] Step 1:

[1900] Connect the wearable device and the terminal via Bluetooth.

[1901] Input: Bluetooth address of the wearable device

[1902] Output: Connection success or failure status

[1903] Specific operation: The user wears the smartwatch and launches the smartphone app or the app on the car display. The app will attempt to connect to the device via Bluetooth, and if the connection is successful, it will display the connection success status.

[1904] Step 2:

[1905] Heart rate data is acquired in real time and sent to the device.

[1906] Input: Heart rate data from a wearable device

[1907] Output: Real-time heart rate data displayed on the device

[1908] What it does: The device sends heart rate data every second, and the application analyzes this data and displays it in real time.

[1909] Step 3:

[1910] The received heart rate data is sent to the server.

[1911] Input: Heart rate data received by the device

[1912] Output: Heart rate data sent to the server

[1913] Specific operation: The smartphone or in-car display sends the received heart rate data to the server at regular intervals using an HTTP POST request.

[1914] Step 4:

[1915] The server analyzes the heart rate data using an AI algorithm.

[1916] Input: Heart rate data received by the server

[1917] Output: Determining whether your heart rate is within the effective exercise zone

[1918] How it works: The server inputs the received heart rate data into an AI algorithm for real-time analysis, and determines whether the driver is in an effective exercise zone based on the analysis results.

[1919] Step 5:

[1920] The server sends the analysis results to the device and notifies the user.

[1921] Input: Analysis result notification data

[1922] Output: A notification message displayed on the terminal.

[1923] Specific operation: The server sends the analysis results to the device, and based on the results, a message such as "Please take a deep breath" is displayed.

[1924] Step 6:

[1925] The camera records the driver's exercise form.

[1926] Input: Camera image

[1927] Output: Video data of recorded exercise form

[1928] Specific operation: A camera installed inside the vehicle records the driver's posture and exercise form and acquires the video data.

[1929] Step 7:

[1930] The recorded data is sent to a server and form analysis is performed.

[1931] Input: Recorded video data

[1932] Output: Feedback of analysis results

[1933] How it works: The recorded data is sent to a server, which then analyzes it using an AI algorithm. Based on the analysis results, feedback such as "Please adjust your seat" is generated.

[1934] Step 8:

[1935] A server generates personalized health advice.

[1936] Input: Past training data, gender, age, experience

[1937] Output: Health advice data

[1938] How it works: The server generates personalized health advice based on the driver's past data, such as "Try doing five minutes of stretching before your next drive."

[1939] Step 9:

[1940] The generated advice is sent to the terminal and presented to the driver.

[1941] Input: Health advice data

[1942] Output: Health advice displayed on the device

[1943] Specific operation: The server sends the generated health advice to the device, and the driver can check the advice on the device.

[1944] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1945] This invention provides a system that uses a wearable device to measure heart rate and perform real-time monitoring and analysis on devices such as smartphones. It also uses a camera to record and analyze the user's exercise form, providing feedback to encourage correct form and safe training. It also generates personalized training advice for each user and uses an emotion engine that recognizes the user's emotions to achieve optimal training.

[1946] System configuration

[1947] The system mainly consists of the following components:

[1948] 1. Wearable devices (smartwatches, etc.)

[1949] Measures the user's heart rate in real time.

[1950] 2. Device (smartphone)

[1951] Connect to a wearable device via Bluetooth to receive heart rate data.

[1952] The received data is sent to the server.

[1953] Displays analysis results and feedback from the server.

[1954] The built-in camera records the user's exercise form.

[1955] 3. Server

[1956] The received heart rate data is analyzed using an AI algorithm to determine whether you are within an effective training zone.

[1957] The user's exercise form is analyzed based on the recorded data to evaluate accuracy and safety.

[1958] Generates personalized training advice based on the user's past training data, gender, age, and experience.

[1959] An emotion engine is used to analyze user emotion data and reflect it in training optimization.

[1960] Program processing and specific examples

[1961] Connecting wearable devices and measuring heart rate

[1962] Device: The user's smartphone runs a dedicated app and connects to the wearable device via Bluetooth. Once the connection is established, the smartwatch sends heart rate data every second.

[1963] Example: User A puts on a wearable device and launches the smartphone app. Once pairing with the device is successful, heart rate data is sent to the smartphone every second.

[1964] Real-time data analysis and notifications

[1965] Device: Sends the received heart rate data to the server.

[1966] Server: Analyzes the data with AI algorithms to determine whether the user is in an effective training zone.

[1967] Server: Based on the analysis results, evaluates whether the user is in the target zone. If the user is in the fat burning zone, it generates a message saying "You're in the right zone." If the heart rate is too low, it generates a notification saying "Increase your load."

[1968] Device: Display the notification to the user.

[1969] Example: The server determines that user A is in the fat burning zone based on the received heart rate data and sends a message saying "Appropriate" to the device. The device displays this message to the user.

[1970] Recording and analyzing exercise form

[1971] User: Use a smartphone to record your training form. Adjust the camera position and angle to capture your entire form.

[1972] Device: Start recording mode and record your training form. The recorded data is temporarily saved in the cache.

[1973] Device: After the training session is over, the recording data is sent to the server.

[1974] Server: Adds the received recording data to the analysis queue. Analyzes the form using an AI image recognition algorithm and compares it with the ideal form.

[1975] Server: Based on the analysis results, it identifies areas of form that need improvement or attention. For example, it generates feedback such as "Please stand up straighter" or "Your knee angle is too shallow."

[1976] Terminal: Feedback is displayed to the user, who can then improve the form based on the feedback.

[1977] Example: User A sets up a smartphone to record squat training. The recorded data is sent to a server and analyzed by an AI algorithm. The server generates feedback such as "Please straighten your back more" and sends it to the device. The device then displays this feedback to the user.

[1978] Generate personalized training advice

[1979] Server: Generates personalized advice based on the user's past training data, gender, age, and experience.

[1980] Server: Sends the generated advice to the terminal.

[1981] Device: Provides advice to users and encourages them to use it for their next workout.

[1982] Example: The server generates advice for the next training session, such as "Increase the weight by 5 kg," based on the user A's past training data. The device displays this advice to the user to help them with their next training.

[1983] Introducing an emotion engine and analyzing emotion data

[1984] Device: Equipped with a camera and sensors to capture emotional data during training. This data is sent to the server in real time.

[1985] Server: Analyzes the received emotional data using an emotion engine to understand the user's emotional state.

[1986] Server: Adjusts training intensity and recommended actions based on emotional data. For example, if a user feels fatigued or stressed, the server generates advice to reduce training intensity.

[1987] Terminal: Presents the generated advice to the user.

[1988] Example: The server analyzes the emotion data received in real time and determines that User A is tired. Based on this, it generates advice such as "Please do light exercise in your next training session" and sends it to the device. The device then displays this advice to the user.

[1989] The above is an embodiment of the present invention that combines an emotion engine, allowing users to train effectively and safely and receive personalized feedback that takes into account their emotional state during training.

[1990] The processing flow will be explained below.

[1991] Step 1:

[1992] Device: Launch the dedicated app on the user's smartphone and open the Bluetooth settings. Once the app is launched, a screen will appear to detect wearable devices (such as smartwatches).

[1993] Step 2:

[1994] Device: Detects the smartwatch's Bluetooth and sends a pairing request. The user enters the device's pairing code and approves the pairing.

[1995] Step 3:

[1996] Device: Once paired successfully, the smartwatch will begin to maintain connection with the wearable device and begin transmitting heart rate data.

[1997] Step 4:

[1998] Terminal: Receives heart rate data from the wearable device every second and sends the received heart rate data to the server.

[1999] Step 5:

[2000] Server: Analyzes the received heart rate data using AI algorithms to determine whether the user is in an effective training zone (e.g., fat burning zone, cardiovascular zone, or maximum effort zone).

[2001] Step 6:

[2002] Server: Based on the analysis results, evaluates whether the user is in the target zone. If the user is in the fat burning zone, it generates a message saying "You're in the right zone." If the heart rate is too low, it generates a notification saying "Increase your load."

[2003] Step 7:

[2004] Server: Returns the evaluation result and a notification message to the terminal.

[2005] Step 8:

[2006] Terminal: Displays the evaluation results and notification messages from the server to the user. The user continues training based on the displayed information.

[2007] Step 9:

[2008] User: Use a smartphone to record your training form. Adjust the camera position and angle to capture your entire form.

[2009] Step 10:

[2010] Device: Start recording mode and record your training form. The recorded data is temporarily saved in the cache.

[2011] Step 11:

[2012] Device: After the training session is over, the recording data is sent to the server.

[2013] Step 12:

[2014] Server: Adds the received recording data to the analysis queue. Analyzes the form using an AI image recognition algorithm and compares it with the ideal form.

[2015] Step 13:

[2016] Server: Based on the analysis results, it identifies areas of form that need improvement or attention. For example, it generates feedback such as "Please stand up straighter" or "Your knee angle is too shallow."

[2017] Step 14:

[2018] Server: Sends the generated feedback back to the device.

[2019] Step 15:

[2020] Terminal: Feedback is displayed to the user, who can then improve the form based on the feedback.

[2021] Step 16:

[2022] Server: Retrieves user gender, age, experience, and past training data from the database.

[2023] Step 17:

[2024] Server: Based on this data, it recalculates optimal training intensity and goals and generates personalized training advice, such as "Increase the weight by 5 kg in your next training session" or "Take a rest next time."

[2025] Step 18:

[2026] Server: Sends the generated advice to the terminal.

[2027] Step 19:

[2028] On the device: Providing advice to users and encouraging them to use it for their next training session.

[2029] Step 20:

[2030] User: Use cameras and sensors to capture emotional data during training. The cameras and sensors capture the user's facial expressions and physical reactions, monitoring their emotional state in real time.

[2031] Step 21:

[2032] Terminal: Transmits acquired emotion data to the server in real time.

[2033] Step 22:

[2034] Server: The emotion engine analyzes the received emotion data and identifies the user's emotional state. The emotion engine uses AI technology to determine emotions such as stress, joy, and fatigue from the user's facial expressions and physical reactions.

[2035] Step 23:

[2036] Server: Adjusts training intensity and recommended actions based on emotional data. For example, if a user feels fatigued or stressed, the server generates advice to reduce training intensity.

[2037] Step 24:

[2038] Server: Sends tailored feedback and advice to devices.

[2039] Step 25:

[2040] Terminal: Presents the generated advice to the user.

[2041] Examples:

[2042] Example 1: The server analyzes emotion data received in real time and determines that user A is tired. Based on this, it generates advice such as "Please do light exercise in your next training session" and sends it to the device. The device then displays this advice to the user.

[2043] Example 2: The emotion engine detects that user B is feeling joy or satisfaction during training. Based on this information, the server generates an encouraging message such as "Keep it up!" and sends it to the device. The device then displays the generated message to the user, further increasing their motivation.

[2044] The above is an embodiment of the present invention that combines an emotion engine, allowing users to train effectively and safely and receive personalized feedback that takes into account their emotional state during training.

[2045] Example 2

[2046] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[2047] While conventional fitness systems using wearable devices can monitor users' biometric information such as heart rate in real time, they do not support recording and analysis of exercise form or providing personalized training advice. Furthermore, there is no training feedback that takes into account the user's emotional state. In particular, it is difficult to correct incorrect exercise form or provide advice tailored to individual needs, resulting in safety and effectiveness issues.

[2048] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes a means for measuring biometric information in cooperation with the wearable device, a means for receiving the measured biometric data in real time, a means for analyzing the received biometric data and determining whether the user is within an effective exercise zone, a means for notifying the user of the determination result, a means for recording the user's exercise movements, a means for analyzing the recorded data and evaluating accuracy and safety, a means for feeding back the evaluation result to the user, a means for generating personalized exercise advice based on the user's past exercise data and attribute data, and a means for presenting the generated advice to the user. This makes it possible to monitor biometric information in real time, as well as analyze exercise form and provide safe and effective personalized advice.

[2049] A "wearable device" is an electronic device that can be worn on the user's body and used to measure biometric information.

[2050] "Biometric information" refers to data about the user's physical condition, such as heart rate, body temperature, and blood pressure.

[2051] "Biometric data" refers to the numerical values ​​and indicators of biometric information measured by a wearable device.

[2052] "Real-time" is a time concept that refers to data being collected and processed almost immediately.

[2053] "Exercise movements" refers to movements or movements performed by a user, including training and exercise.

[2054] "Recorded data" refers to video data of exercise movements captured using a recording device such as a camera.

[2055] "Analysis" refers to the activity of processing received data, transforming it into a meaningful form, and deriving results.

[2056] "Accuracy" refers to the degree to which a user's athletic movements match the ideal form and movements they are aiming for.

[2057] "Safety" refers to the suitability of the device to prevent injury or other hazards when a user performs an exercise.

[2058] "Feedback" refers to the evaluation and advice the system provides to the user.

[2059] "Personalized exercise advice" refers to exercise advice and instructions that are optimized based on a user's individual characteristics and past data.

[2060] "Attribute data" refers to data that indicates a user's individual characteristics, such as age, gender, and experience.

[2061] This invention is a system that uses wearable devices and terminals such as smartphones to monitor and analyze a user's biometric information and exercise movements in real time, allowing the user to train appropriately and receive safer and more effective training advice.

[2062] Hardware and software used

[2063] 1. Wearable devices (e.g., smartwatches): Wearable devices measure the user's biometric information, such as heart rate, and transmit it to a smartphone via Bluetooth.

[2064] 2. Device (e.g., smartphone): The smartphone runs a dedicated app and receives biometric data sent from the wearable device. The received data is sent to a server in real time. The smartphone also uses a camera to record the user's exercise movements.

[2065] 3. Server: The server analyzes the received biometric data and recorded exercise data. It uses AI algorithms to determine whether the user is within the effective exercise zone, evaluate the accuracy and safety of the exercise form, and generate personalized training advice based on the user's past data and attributes.

[2066] Explanation of program processing

[2067] Measurement and transmission of biometric data

[2068] 1. The device launches the dedicated app and connects to the wearable device via Bluetooth. Once the connection is established, the smartwatch sends heart rate data every second.

[2069] Example: A user wears a smartwatch and launches an app on their smartphone. Once the app successfully pairs with the smartwatch, heart rate data is sent to the smartphone every second. For example, data such as "Heart rate: 75, Time: 10:00:01" is continuously sent.

[2070] Real-time data analysis

[2071] 1. The device sends the received heart rate data to the server.

[2072] 2. The server uses an AI algorithm to analyze your heart rate data and determine whether you are within an effective exercise zone.

[2073] Example: The server processes data such as "Heart rate: 135, Time: 10:05:01" and determines that the user is in the fat burning zone. Based on this, it generates a notification message saying "Appropriate" and sends it to the device. The device then displays this message to the user.

[2074] Recording and analyzing exercise form

[2075] 1. The user uses a smartphone to record their exercise movements, adjusting the camera position and angle to capture their entire form.

[2076] 2. The device will start recording mode and record your training form, temporarily saving the recorded data in the cache.

[2077] 3. After the training session ends, the device sends the recorded data to the server.

[2078] 4. The server analyzes the received recording data and uses an AI image recognition algorithm to analyze the user's form. It compares the user's form with the ideal form and identifies areas for improvement.

[2079] Example: A user records a squat and sends the recorded data to a server. The server generates a feedback message saying "Please stand up straighter" and sends it to the device. The device displays this feedback to the user.

[2080] Generate personalized training advice

[2081] 1. The server generates personalized training advice based on the user's past training data, gender, age, and experience, and sends it to the device.

[2082] 2. The device displays the advice to the user.

[2083] Example: Based on the user's training data, the server generates advice such as "Increase the weight by 5 kg in your next training session." The device notifies the user of this advice and displays it on the training guide screen.

[2084] Prompt Sentence Examples

[2085] "Today's training session was very effective as it kept my heart rate stable and allowed me to train in the fat burning zone. Next time, I'll try increasing the intensity a bit and extending the duration by 5 minutes."

[2086] The above is an embodiment of the invention. This system allows users to monitor their biological information in real time, analyze their exercise form, and perform safe and effective training.

[2087] The flow of the identification process in the second embodiment will be described with reference to FIG.

[2088] Step 1:

[2089] Connecting wearable devices and measuring heart rate

[2090] The device launches a dedicated app and connects to the wearable device via Bluetooth. Once the connection is established, the smartwatch sends heart rate data every second.

[2091] Input: The user puts on the smartwatch and launches the app on their smartphone.

[2092] Data processing: The app pairs with the wearable device and a connection is established.

[2093] Output: Heart rate data is sent from the smartwatch to the smartphone every second, and is simultaneously displayed in real time on the app screen.

[2094] Specific operation: The user wears the smartwatch and launches the smartphone app. Once paired with the device, heart rate data will be sent to the smartphone every second and displayed on the real-time monitoring screen within the app.

[2095] Step 2:

[2096] Real-time data analysis and notifications

[2097] The terminal transmits the received heart rate data to the server.

[2098] The server uses AI algorithms to analyze heart rate data in real time to determine whether the user is in an effective exercise zone, and generates a message based on the analysis results and sends it to the device.

[2099] Input: Heart rate data sent from the device (e.g., "Heart rate: 135, Time: 10:05:01").

[2100] Data processing: The server analyzes the heart rate data and determines whether you are in an effective exercise zone (e.g., fat burning zone).

[2101] Output: The judgment result (e.g., "Appropriate") is sent as a message to the terminal.

[2102] Specific operation: The server processes the received heart rate data and confirms that the user is in the fat burning zone. It generates a notification message saying "Suitable" and sends it to the device. The device then displays this message to the user in a pop-up.

[2103] Step 3:

[2104] Recording and analyzing exercise form

[2105] Users use their smartphones to record their exercise movements, adjusting the camera position and angle to capture their entire form.

[2106] The device will enter recording mode and record your training form. The recorded data will be temporarily saved in the cache.

[2107] After the training session ends, the terminal transmits the recorded data to the server.

[2108] The server analyzes the received recording data and uses an AI image recognition algorithm to analyze the user's form, comparing it with the ideal form and identifying areas for improvement.

[2109] Input: Video data (e.g., video of squats).

[2110] Data processing: The server analyzes the recorded data using AI image recognition algorithms and evaluates the user's form.

[2111] Output: The analysis results (e.g., "Please stand up straighter") are sent to the device as a feedback message.

[2112] How it works: The user records their squats and the recorded data is sent to the server. The server then uses an AI algorithm to analyze the data, generates a feedback message saying "Please straighten your back more," and sends it to the device. The device then displays this feedback to the user.

[2113] Step 4:

[2114] Generate personalized training advice

[2115] The server generates personalized training advice based on the user's past training data, gender, age, and experience, and sends it to the device.

[2116] The device displays the advice to the user.

[2117] Input: User's past training data and demographic data (e.g., age, gender, experience).

[2118] Data processing: The server uses this data to generate advice tailored to your individual needs.

[2119] Output: Generated advice (e.g. "Increase the weight by 5kg in your next workout") is sent to the device.

[2120] Specific operation: Based on the user's training data, the server generates advice such as "Increase the weight by 5 kg in your next training session." The device notifies the user of this advice and displays it on the training guide screen.

[2121] Step 5:

[2122] Introducing an emotion engine and analyzing emotion data

[2123] The device uses cameras and sensors to capture emotional data while the user is training, and transmits this data to a server in real time.

[2124] The server analyzes the received emotional data using an emotion engine to understand the user's emotional state and adjusts training intensity and recommended actions as needed.

[2125] Input: Emotion data obtained in real time (e.g., facial expression data from a facial recognition sensor).

[2126] Data processing: The server uses an emotion engine to analyze the emotion data and identify the user's emotional state.

[2127] Output: Advice generated based on the results of sentiment analysis (e.g., "Do light exercise during your next workout") is sent to the device.

[2128] Specific operation: The server analyzes the user's emotional data and determines that the user is tired. It generates advice such as "Try to do lighter exercise next time" and sends it to the device. The device notifies the user of this advice and displays it on the home screen.

[2129] (Application example 2)

[2130] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[2131] While conventional training monitoring systems provide functions such as measuring heart rate and analyzing exercise form, they lack a mechanism for providing optimal training advice that takes into account the user's emotional state. Furthermore, there are limitations to generating personalized advice using past training data, making it difficult to provide effective feedback.

[2132] The identification processing by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for measuring heart rate in cooperation with the wearable device, means for receiving measured heart rate data in real time, means for analyzing the received heart rate data and determining whether the user is within an effective training zone, means for notifying the user of the determination result, means for acquiring user emotion data, and means for analyzing the emotion data and adjusting training intensity and recommended actions. This makes it possible to provide effective and personalized feedback that simultaneously takes into account the user's heart rate and emotional state.

[2133] A "wearable device" is a device that can be worn by a user to measure biometric information such as heart rate and share information with other devices.

[2134] "Heart rate" refers to the number of times the heart beats within a certain period of time, and is a biometric indicator generally measured in beats per minute.

[2135] "Real-time reception" is a function that means receiving measured data almost simultaneously without delay.

[2136] "Training zone" refers to a range of exercise intensity based on heart rate and other biometric information to achieve a specific exercise effect.

[2137] "Notification means" refers to the method or device for communicating analysis results or other information to the user.

[2138] "Emotional data" refers to information about a user's emotional state derived from facial expressions, tone of voice, behavior, etc.

[2139] An "emotion engine" refers to a software algorithm that analyzes emotional data and understands the user's emotional state.

[2140] "Exercise form" refers to the body posture and movement method used by a user when performing a particular exercise.

[2141] "Recorded data" refers to video files captured by a camera, which can be used to analyze the user's exercise form.

[2142] "Feedback" refers to the information and comments that the system provides to the user regarding analysis results and advice.

[2143] "Personalized Training Advice" refers to training recommendations generated based on a user's individual historical data, gender, age, and experience.

[2144] This invention is a system that uses a wearable device to measure a user's heart rate and receives and analyzes the data in real time on a smartphone or other device. It also records and analyzes the user's exercise form with a camera, provides personalized training advice, and uses an emotion engine to recognize the user's emotional state and optimize training.

[2145] The detailed configuration and usage of this system will be explained below.

[2146] Hardware and Software Configuration

[2147] 1. Wearable devices:

[2148] Function: Real-time heart rate measurement

[2149] Example: Apple Watch, Fitbit, etc.

[2150] 2. Device (smartphone or smart glasses):

[2151] Functions: Receive heart rate data, record exercise form, and display feedback

[2152] Examples: Android / iOS devices, Google Glass, Microsoft HoloLens, etc.

[2153] Software: Dedicated app (e.g., developed with Unity)

[2154] 3. Server:

[2155] Functions: Analyzing data, generating feedback, generating training advice, analyzing emotional data

[2156] Example: Cloud platforms (AWS, Google Cloud, etc.)

[2157] Software: AI algorithms (TensorFlow, PyTorch), emotion engines (Azure Emotion API, IBM Watson Tone Analyzer)

[2158] System Operation

[2159] Connecting wearable devices and measuring heart rate

[2160] Users launch a dedicated application on their smartphone or smart glasses and connect to the wearable device via Bluetooth. Once the connection is established, the wearable device transmits heart rate data every second. The device receives this data in real time and sends it to a cloud server.

[2161] Real-time data analysis and notifications

[2162] The cloud server uses an AI algorithm to analyze the received heart rate data and determine whether the user is in an effective training zone. For example, if the heart rate is within the fat-burning zone, a notification stating "Appropriate" is generated and sent to the device. The user can then view this notification on their device.

[2163] Recording and analyzing exercise form

[2164] When a user performs a specific exercise, the device's camera is used to record their exercise form. The recorded data is temporarily stored on the device and then sent to a cloud server after the workout is completed. The server analyzes the recorded data and uses an AI image recognition algorithm to compare it with ideal form and generate feedback.

[2165] Generate personalized training advice

[2166] The cloud server generates personalized training advice based on the user's past training data, gender, age, and experience, and sends the advice to the user's device, where it can be used as a reference for the next training session.

[2167] Introducing an emotion engine and analyzing emotion data

[2168] During training, the device's camera and sensors collect the user's emotional data. The collected data is sent to a cloud server where it is analyzed by the emotion engine. Based on the analysis results, if the user feels fatigued or stressed, advice to reduce the intensity of training is generated. This is also sent to the device and can be viewed by the user.

[2169] Examples of concrete examples and prompts

[2170] Specific examples

[2171] User A puts on a smartwatch at the gym and launches the smartphone app. The app automatically pairs with the smartwatch and sends heart rate data to the server. The server analyzes the heart rate data, determines that User A is in the fat burning zone, and sends a notification that the user is in the "appropriate" zone. User A can check this notification on his smartphone. When doing squat training, the smartphone camera can record his exercise form and send it to the server, allowing him to receive feedback such as "straighten your back more."

[2172] Prompt Sentence Examples

[2173] "Generate next training advice based on the user's heart rate and exercise data. Output in the following format:

[2174] User's past training data: 30 minutes of running, average heart rate 140 bpm, weight training: 50 kg

[2175] Suggestion: Next time you train, increase your running time to 35 minutes and use 55kg.

[2176] As described above, the present invention provides an optimal training environment for users by linking a wearable device with a terminal and a server.

[2177] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[2178] Step 1:

[2179] The user puts on the wearable device and launches the dedicated smartphone app. The app automatically pairs with the wearable device via Bluetooth and starts transmitting heart rate data. The input is the heart rate data from the wearable device, and the output is the heart rate data received in real time.

[2180] Step 2:

[2181] The device (smartphone) sends the received heart rate data to a cloud server. The cloud server uses an AI algorithm to analyze the heart rate data and determine whether the user is within an effective training zone. The input is the heart rate data sent from the device, and the output is the analysis result (training zone status).

[2182] Step 3:

[2183] The server sends a notification to the user's device based on the analysis results. The device displays this notification in real time, providing feedback to the user. The input is the analysis results from the server, and the output is a notification message displayed on the device. A specific example of this is the notification "Inside the fat burning zone."

[2184] Step 4:

[2185] When a user exercises, the device's camera is used to record the user's exercise form. The recorded data is temporarily stored on the device. The input is the video data captured by the camera, and the output is the saved recorded data.

[2186] Step 5:

[2187] After the exercise session, the device sends the recorded data to a cloud server. The cloud server analyzes this data using an AI image recognition algorithm to evaluate the accuracy and safety of the user's exercise form. The input is the recorded data sent from the device, and the output is the analysis result (evaluation of exercise form).

[2188] Step 6:

[2189] The server generates feedback for improving the form based on the analysis results and sends it to the terminal. The terminal displays this feedback to the user. The input is the analysis results from the server, and the output is a feedback message displayed on the terminal. As a specific example, feedback such as "Please stand up straighter" is displayed.

[2190] Step 7:

[2191] The server generates personalized training advice based on the user's past training data, gender, age, and experience. The generated advice is sent to the terminal and presented to the user. The input is the user's past data and the server's analysis results, and the output is the advice presented to the user.

[2192] Step 8:

[2193] During training, the device's camera and sensors capture user emotion data. The emotion data is sent to a cloud server and analyzed by the emotion engine. The input is the emotion data captured by the camera and sensors, and the output is the emotion analysis results by the server.

[2194] Step 9:

[2195] The server generates advice to adjust training intensity and recommended actions based on the emotion analysis results and sends it to the device. The device then displays this advice to the user. The input is the emotion analysis results, and the output is the adjustment advice presented to the user. A specific example of this behavior is the display of advice such as "Please do light exercise in your next training session."

[2196] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[2197] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[2198] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[2199] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping...

Claims

1. A means for measuring a heart rate in cooperation with a wearable device; means for receiving measured heart rate data in real time; a means for analyzing the received heart rate data to determine whether the user is within an effective training zone; and a means for notifying the result of the judgment; A system including:

2. a means for recording a user's exercise form; A means of analyzing the recorded data and assessing its accuracy and safety; A means of providing feedback on the evaluation results to users; The system of claim 1 further comprising:

3. A means to generate personalized training advice based on the user's past training data, gender, age, and experience; a means for presenting the generated advice to a user; The system of claim 1 further comprising:

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