system

Abstract

We provide the system. [Solution] The sensor is a means for collecting the biometric information and movement data of the person performing the exercise, A server performs exercise analysis based on the aforementioned biometric information and exercise data, and generates optimal exercise guidance; The terminal provides means for notifying the exerciser of the exercise instruction in real time, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] There is a problem that individual athletes cannot exercise effectively and safely without a dedicated instructor and cannot receive appropriate guidance to improve their athletic performance. This problem is particularly prominent in activities that require real-time feedback according to the athlete's condition and environment.

Means for Solving the Problems

[0005] The present invention includes means for a sensor to collect biometric information and motion data of an athlete, means for a server to perform motion analysis based on the biometric information and motion data and generate optimal motion guidance, and means for a terminal to notify the athlete of the motion guidance in real time, thereby providing individualized motion guidance tailored to the athlete and supporting efficient and safe exercise.

[0006] A "sensor" is a device used to acquire biometric information and movement data from a person's body during exercise.

[0007] "Biometric information" refers to data that indicates the physical condition of an exerciser, such as their heart rate and body temperature.

[0008] "Exercise data" refers to numerical values ​​that indicate the movements of an exerciser, such as their pace and acceleration.

[0009] A "server" is a processing device that analyzes collected biometric and exercise data and generates appropriate exercise guidance.

[0010] "Exercise analysis" is the process of evaluating exercise performance based on the exerciser's biometric information and exercise data, and identifying areas for improvement.

[0011] "Exercise instruction" refers to advice and suggestions provided in real time to improve the exerciser's physical abilities and ensure safe exercise.

[0012] A "terminal" is a device used to transmit exercise instructions sent from a server to the person performing the exercise. [Brief explanation of the drawing]

[0013] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6]It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Embodiments for Carrying Out the Invention

[0014] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0015] First, the language used in the following description will be explained.

[0016] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0017] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0018] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0019] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor and an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark), and the like.

[0020] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0021] [First Embodiment]

[0022] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0023] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0024] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0025] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0026] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0027] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0028] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0029] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0030] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0031] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0032] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0033] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0034] This invention relates to a system for providing personalized exercise guidance to individuals. The system consists of sensors, a server, and terminals.

[0035] On the device, sensors attached to the exerciser's body collect biometric information and exercise data in real time during exercise. This information includes the exerciser's heart rate, pace, and acceleration. The collected data is periodically transmitted to a server.

[0036] The server analyzes the received biometric and exercise data to perform an exercise analysis. This analysis evaluates the exerciser's form and heart rate variability, and generates advice necessary for safe and effective exercise. For example, if the heart rate exceeds the target range, the server will create guidance advising the exerciser to slow down. Furthermore, based on the analysis results, it can select music that suits the exerciser's condition.

[0037] The device notifies the user in real time of exercise guidance sent from the server. For example, the device can notify the user via voice, "Your heart rate is too high. Slow down." It also provides guidance on appropriate hydration timing.

[0038] Based on the provided guidance, users can adjust their behavior during exercise to perform it efficiently and safely. After completing their workout, users can review their exercise history and feedback in detail through the application on their device. This feedback provides useful information for planning their next workout and helps them achieve their goals.

[0039] The following describes the processing flow.

[0040] Step 1:

[0041] The device collects biometric information and exercise data from the exerciser in real time through sensors. This includes heart rate, pace, and acceleration.

[0042] Step 2:

[0043] The terminal sends the collected data to the server. The data is buffered at regular intervals and sent to the server efficiently.

[0044] Step 3:

[0045] The server analyzes the received data. This analysis evaluates whether the heart rate exceeds the set range, whether the pace is appropriate, and whether there are any abnormalities in the exercise form.

[0046] Step 4:

[0047] Based on the analysis results, the server generates exercise instructions for the user. These instructions include adjusting the pace, improving form, determining the appropriate timing for hydration, and selecting music suitable for the exercise.

[0048] Step 5:

[0049] The server sends the generated exercise instructions back to the terminal. The terminal prepares to notify the user of the received instructions.

[0050] Step 6:

[0051] The device provides exercise guidance to the user through notifications. These notifications are delivered via voice or text and include practical advice such as, "Your heart rate is high, so slow down."

[0052] Step 7:

[0053] Users adjust their exercise behavior based on the provided instructions, ensuring safe and efficient workouts. After completing their workout, users can use the app to review detailed feedback and use it to plan their next workout.

[0054] (Example 1)

[0055] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0056] For athletes, real-time exercise guidance is essential for safe and effective exercise. However, existing systems struggle to provide guidance tailored to individual needs and often result in delayed feedback. There is a need to solve this problem and improve athletes' health and performance.

[0057] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0058] In this invention, the server includes means for analyzing the exerciser's biometric information and exercise data acquired by sensors, means for generating safe and effective exercise guidance based on the analysis results, and means for transmitting the generated exercise guidance to the exerciser in real time through a presentation device. This makes it possible to provide immediate feedback tailored to the exerciser and to achieve safe and effective exercise.

[0059] A "sensor" is a device attached to a person's body during exercise to acquire physiological information and exercise data in real time.

[0060] A "data communication device" is a device or means for transmitting motion data acquired by a sensor to a data processing device.

[0061] A "data processing device" is a device that analyzes received physiological information and exercise data and generates appropriate exercise guidance.

[0062] A "presentation device" is a device used to transmit exercise instructions generated by a data processing device to the person performing the exercise.

[0063] "Motion analysis" is the process by which a data processing device evaluates the characteristics and movements of motion based on physiological information and motion data.

[0064] "Exercise guidance" refers to advice or instructions for safe and effective exercise provided to individuals based on the results of exercise analysis.

[0065] "Acoustic information" refers to music or audio information that is selected according to the exerciser's state during exercise, and that contributes to improving the exerciser's motivation and rhythm.

[0066] This invention provides a system that offers personalized exercise guidance to individuals, and consists of a sensor, a data communication device, a data processing device, and a presentation device. This system utilizes a generative AI model to enable real-time exercise analysis and guidance generation.

[0067] The device uses sensors to collect physiological information and exercise data from the exerciser's body. This data is acquired in real time from sensors such as heart rate sensors and accelerometers, and is periodically transmitted to the data processing unit via a data communication device. The data processing unit then uses a generative AI model to analyze the received data and evaluate the exerciser's exercise form and heart rate fluctuations. Based on this analysis, the data processing unit generates specific exercise guidance for the exerciser. For example, if the exerciser's heart rate exceeds the target range, the data processing unit generates guidance such as "you should slow down." Furthermore, based on the analysis results, it is also possible to select acoustic information that is appropriate for the exerciser's condition.

[0068] The display device transmits exercise guidance sent from the data processing unit to the exerciser in real time. This allows the user to appropriately adjust their actions during exercise, enabling them to exercise safely and efficiently. After completing their workout, the user can check their exercise history and the feedback they received via an application on their terminal, which can be used to plan their next workout.

[0069] As a concrete example, the prompt message is: "Analyze the exerciser's heart rate and pace data, and tell the generative AI model how to provide guidance if the target range is exceeded."

[0070] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0071] Step 1:

[0072] The device collects biometric information and exercise data from the user via sensors. Specifically, it obtains heart rate data from a heart rate sensor and acceleration data from an accelerometer. This data is used as input and recorded in real time. The device periodically aggregates this data and stores it in a data communication device.

[0073] Step 2:

[0074] The terminal uses a data communication device to transmit collected biometric and movement data to the server. Data is transferred using communication technologies such as Bluetooth and Wi-Fi. Input is data from the terminal, and output indicates the completion of the transfer to the server.

[0075] Step 3:

[0076] The server analyzes the received data. Specifically, it uses a generative AI model to analyze the exercise form and heart rate variability of the person exercising. Data is supplied to the server as input, and as a result of the data analysis, information that forms the basis for exercise guidance is obtained. This information becomes the output.

[0077] Step 4:

[0078] The server generates exercise guidance based on the analysis results. For example, if the heart rate exceeds the target range, it will create specific advice such as "you should slow down." The generating AI model processes the data and outputs detailed guidance content.

[0079] Step 5:

[0080] The server sends the generated exercise instructions to the terminal. The output from the server is the content of the exercise instructions, which is transmitted to the terminal via communication.

[0081] Step 6:

[0082] The terminal notifies the exerciser of the received instructions in real time. As input, it retrieves the instruction content received from the server and notifies the user visually or audibly using a display device. Output is presented to the exerciser as an audio message or screen display.

[0083] Step 7:

[0084] Users adjust their exercise behavior based on the instructions they receive. For example, they might slow down their pace after receiving a notification. After the exercise, users check the feedback on an application on their device. The input is the instructions and sensory feedback received during the exercise, and the output is a post-exercise review and adjustment plan for the next time.

[0085] (Application Example 1)

[0086] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0087] There is a need to provide technology that offers personalized exercise guidance in real time, supporting users in exercising safely and effectively. Furthermore, this technology needs to work in conjunction with robots that act as personal trainers to enable more comprehensive exercise guidance.

[0088] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0089] In this invention, the server includes means for a sensing device to acquire the user's physiological information and movement information, means for an information processing device to perform motion analysis based on the physiological information and movement information and create optimal exercise guidance, and means for a robot device to provide physical escort to the user during movement and transmit additional exercise guidance via voice. This makes it possible to provide advanced exercise guidance in real time through robotic escort.

[0090] A "sensing device" is a device used to acquire a user's physiological and movement information in real time.

[0091] An "information processing device" is a system that performs motion analysis based on acquired physiological and movement information to create optimal exercise guidance for the user.

[0092] A "display device" is an interface that transmits exercise instructions generated by an information processing device to the user in real time.

[0093] A "robot device" is a machine that provides physical support to the user during exercise and delivers additional exercise instructions via voice.

[0094] "Physiological information" refers to data related to the user's physical condition, such as heart rate, respiratory rate, and body temperature.

[0095] "Movement information" refers to data related to the user's movements, such as acceleration and location information.

[0096] "Motor analysis" is a process for evaluating a user's motor skills based on physiological and movement data, and for deriving appropriate guidance.

[0097] "Physical escort" refers to a robotic device actually moving in accordance with the user's movements and acting together with them.

[0098] "Communicating via voice" refers to an output method that uses voice, in addition to visual information, to directly instruct users.

[0099] The system for carrying out this invention consists of a sensing device, an information processing device, a display device, and a robotic device. This system safely and effectively provides personalized exercise guidance to users.

[0100] The server collects physiological information such as heart rate, acceleration, and location data, as well as movement information, in real time from sensing devices. This information is acquired via sensors attached to the user's body and transmitted to the server wirelessly. The information processing device uses a program developed in Python to analyze this data using NumPy and Pandas to evaluate exercise form and biological state.

[0101] Furthermore, a machine learning model is built using Sci-kit Learn, which learns the user's movement patterns and generates optimal instruction. The generated exercise instruction is then converted into speech using Google Cloud's Text-to-Speech API.

[0102] The display device transmits these voice instructions to the user in real time, helping the user take appropriate actions during exercise. A robotic device also operates as part of this system, providing physical support and, as needed, voice-guided exercise instructions to the user.

[0103] For example, if a user's heart rate becomes too high while jogging, the display device will provide voice advice such as, "Let's slow down." This advice is based on real-time analysis performed by the information processing device.

[0104] An example of a prompt to a generating AI model might be, "Please suggest effective guidance methods if your heart rate data exceeds the target range during jogging." This prompt asks the AI ​​model to provide appropriate guidance based on a specific scenario during exercise.

[0105] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0106] Step 1:

[0107] The server acquires biometric information such as heart rate, acceleration, and location, as well as movement information, from the user's sensing device. This information is transmitted in real time via wireless communication and stored on the server. The input is biometric information and movement information, and the output is the dataset to be analyzed.

[0108] Step 2:

[0109] The server analyzes the received data using a Python program. It performs preprocessing using NumPy and Pandas to format the data. Specifically, it imputes missing values ​​and normalizes the data to convert it into a format suitable for machine learning models. The input is a dataset of biometric and mobility information, and the output is the preprocessed dataset.

[0110] Step 3:

[0111] The server inputs pre-processed data into a machine learning model using Sci-kit Learn. This process analyzes the user's exercise patterns and calculates optimal exercise guidance. The input is a pre-processed dataset, and the output is guidelines and advice for exercise guidance.

[0112] Step 4:

[0113] The server converts the generated exercise instructions into speech using the Google Cloud Text-to-Speech API. This speech data is then converted into a format provided to the user. The input is exercise guidelines and advice, and the output is speech data.

[0114] Step 5:

[0115] The device transmits generated audio data to the user in real time. Using a display device and speaker, it provides timely advice to help the user take appropriate actions during exercise. The input is audio data, and the output is audio output to the user.

[0116] Step 6:

[0117] The robotic device also provides physical guidance to the user and offers additional exercise instruction via voice as needed. The robotic device further enhances real-time exercise support by operating based on instructions from the server. Inputs are the user's movement data and instructions from the server, while outputs are physical guidance and voice instruction.

[0118] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0119] This invention provides a system that offers personalized exercise guidance to individuals, and incorporates an emotion engine. The system consists of sensors, a server, and terminals.

[0120] Sensors attached to the device collect biometric information and exercise data from the exerciser in real time during exercise. This data includes heart rate, pace, acceleration, and information necessary to estimate the user's emotions, such as facial expressions and voice tone. The collected data is transmitted to a server.

[0121] The server analyzes the received data. In particular, it uses an emotion engine to recognize the user's emotional state from their biometric information. By analyzing the emotional state, it can estimate the user's motivation and stress level during exercise. Based on this information, the server generates specially tailored exercise guidance. For example, if the user is feeling tense, the server can suggest exercises and music that have a relaxing effect.

[0122] Furthermore, the server selects music that matches the user's emotional state, providing music that enhances the enjoyment and effectiveness of exercise. This music selection is optimized for each individual user based on analysis by the emotion engine.

[0123] The device notifies the user in real time of exercise instructions and selected music sent from the server. This allows the user to continue exercising safely and effectively while receiving feedback that fits their emotional state. For example, when the user is feeling stressed, calming music can be played along with advice such as, "Take a deep breath and slow down."

[0124] Users can adjust their behavior during exercise based on the provided guidance and receive detailed feedback after the exercise is completed. This allows users to understand their emotional state and use that information to plan their next exercise session. This invention optimizes exercise according to individual emotional states and supports the exerciser in achieving their goals.

[0125] The following describes the processing flow.

[0126] Step 1:

[0127] The device collects biometric information, exercise data, and data necessary for emotion estimation from the exerciser in real time through sensors. This includes heart rate, pace, acceleration, and facial expression information.

[0128] Step 2:

[0129] The terminal sends the collected data to the server. The data is divided into optimal packets according to the broadcast conditions and then transmitted.

[0130] Step 3:

[0131] The server analyzes the received data and evaluates fluctuations in heart rate and acceleration. It also uses an emotion engine to recognize the user's emotional state from biometric information.

[0132] Step 4:

[0133] The server generates personalized exercise guidance based on analysis of exercise data and emotional state. This guidance may include advice on appropriate pacing and relaxation.

[0134] Step 5:

[0135] The server selects music that matches the user's emotional state. This music is chosen to promote motivation and relaxation, taking into account the progress of the exercise and the user's emotional state.

[0136] Step 6:

[0137] The server packages the generated exercise instructions and selected music for transmission to the terminal.

[0138] Step 7:

[0139] The device notifies the user in real time, via voice or text, of exercise instructions and music received from the server. For example, it might advise, "Your mind is tense. Take a deep breath," and then play relaxing music.

[0140] Step 8:

[0141] Users adjust their exercise behavior based on the provided advice, ensuring safe and efficient workouts. They also enjoy a more comfortable exercise experience while listening to music tailored to their emotional state.

[0142] Step 9:

[0143] After completing an exercise session, users can view detailed exercise data and emotional feedback through an application on their device. This allows them to evaluate their progress and use the information to plan their next exercise session.

[0144] (Example 2)

[0145] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0146] In modern society, effective exercise is sought after for maintaining personal health and managing stress. However, conventional exercise instruction systems do not provide guidance tailored to the emotional state and individual needs of the exerciser, making it difficult to obtain effective feedback and maintain sustained motivation. Furthermore, the lack of individualized music and exercise instruction means that the exerciser does not receive the optimal exercise experience.

[0147] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0148] In this invention, the server includes means for recognizing emotional states using an AI model generated based on physical information and activity data, means for generating optimized activity guidance based on the emotional state, and means for selecting personalized music that matches the emotional state. This makes it possible to provide exercisers with personalized exercise guidance and music, thereby maximizing the effects of exercise, reducing stress, and maintaining motivation.

[0149] A "sensor" is a device used to acquire physical information and activity data from a person exercising.

[0150] "Physical information" refers to biological information acquired during exercise, such as data on the exerciser's heart rate, body movements, and posture.

[0151] "Activity data" refers to information about the content and intensity of exercise, such as the exerciser's acceleration and pace.

[0152] A "server" is a computer system that receives information sent from sensors and uses generated AI models to perform analysis and provide guidance.

[0153] A "generative AI model" is an artificial intelligence technology used to analyze collected data and generate information about the emotional state of an exerciser and optimal exercise guidance.

[0154] "Emotional state" is an indicator that shows the psychological state of an athlete, and includes stress levels and motivation levels.

[0155] "Activity guidance" refers to specific instructions and advice provided to help athletes exercise efficiently and safely.

[0156] "Personalized music" refers to music selected according to the emotional state of the person exercising and optimized to enhance the effectiveness of the exercise.

[0157] A "terminal" is a device used to transmit exercise instructions and music from a server to the person performing the exercise.

[0158] This invention relates to a system that enhances the effectiveness of exercise and maintains motivation by providing exercise guidance and music optimized for the exerciser. This system mainly consists of sensors, a server, and terminals.

[0159] Sensors play a role in acquiring physical information and activity data from the exerciser, collecting this information via a device worn by the wearer. Specifically, this data includes heart rate, acceleration, pace, facial expressions, and voice tone. By aggregating this data, it becomes possible to accurately understand the physiological and psychological state of the exerciser during exercise.

[0160] The server receives data from sensors and analyzes it using a generative AI model. This model recognizes the exerciser's emotional state and generates exercise instructions and music tailored to their individual needs. This process allows the exerciser to receive feedback appropriate to their emotional state at any given time. For example, if the exerciser is feeling tense, they can be offered relaxation-enhancing exercise instructions and calming music.

[0161] The device notifies the user in real time of exercise instructions and music transmitted from the server. In this way, the user can adjust their exercise based on the feedback they receive and obtain an optimal exercise experience. The device is also equipped with data transmission and notification functions, and transmits information through communication with the server.

[0162] By using this system, users can optimize their exercise behavior and use the feedback afterward to plan their next workout. This allows for more effective maintenance of personal health and stress management.

[0163] A concrete example of its use is that by inputting the prompt message "What combination of exercise and music should be suggested when the user's stress level is high?" into the server, the AI ​​can provide optimal exercise guidance and music selection, and present it to the user in real time.

[0164] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0165] Step 1:

[0166] The device acquires real-time physical information and activity data such as heart rate, acceleration, pace, facial expressions, and voice tone through sensors attached to the exerciser. The input consists of various biometric data collected by the sensors, and this data is transmitted directly to the server, enabling accurate analysis.

[0167] Step 2:

[0168] The server receives biometric and activity data transmitted from the terminal. This input data is fed into a generative AI model to analyze the user's real-time emotional state. The generative AI model used here utilizes machine learning algorithms to recognize emotional states such as tension, relaxation, and stress from the user's heart rate, voice tone, etc. The output is the analysis results regarding the user's emotional state.

[0169] Step 3:

[0170] The server generates exercise instructions optimized for the user's current state, based on the results of emotion analysis. This process uses the results of emotion analysis as input, and utilizes a generation AI model to devise a set of relaxing exercises and workouts. Specific exercise instructions are provided as output.

[0171] Step 4:

[0172] In parallel, the server selects music that matches the user's emotional state. In this step, the results of the emotion analysis obtained earlier are used as input data, and a suitable song is selected for the user through a generative AI model. The output is music optimized to give the user a better exercise experience.

[0173] Step 5:

[0174] The device notifies the user in real time of exercise instructions and selected music received from the server. At this stage, the input is the data of exercise instructions and music sent from the server, and by dynamically providing this to the user, the user can perform the exercise based on the feedback. The output is the stream of specific instructions and music provided to the user during the exercise.

[0175] (Application Example 2)

[0176] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0177] There is a need to maximize the effectiveness of exercise and improve the motivation of exercisers by analyzing their psychological state in real time during exercise and providing personalized feedback tailored to that state. However, current systems are limited to analysis and guidance based on general biometric information, making it difficult to provide feedback that takes emotional states into account. This leads to a problem where appropriate exercise support cannot be provided when exercisers are experiencing stress or fatigue.

[0178] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0179] In this invention, the server includes means for collecting biometric information, exercise data, and acoustic data of an exerciser; means for performing exercise analysis through biometric information, exercise data, and emotion recognition to generate optimal exercise guidance based on the exerciser's psychological state; and means for determining audio information appropriate to the exerciser's emotional state and providing it through a terminal. This makes it possible to provide exercise guidance and audio information tailored to the individual emotional state of the exerciser, thereby enhancing the effectiveness of exercise while maintaining the exerciser's motivation.

[0180] A "sensor" is a device that collects biological information, movement data, and acoustic data from inside or on the surface of a person's body during exercise.

[0181] "Biometric information" refers to numerical values ​​and signals that indicate the physical state of an exerciser, such as heart rate, respiratory rate, and body temperature.

[0182] "Motion data" refers to data that indicates the physical state of a person's movement, such as acceleration, velocity, and position.

[0183] "Acoustic data" refers to data that captures audio signals and ambient sounds, and is used to estimate the emotions of a person performing an action.

[0184] A "server" is a computer system that analyzes collected data and generates exercise guidance and audio information optimized for the user.

[0185] "Motion analysis" is the process of analyzing a person's movement and form based on sensor data, and then evaluating it using a specific algorithm.

[0186] "Emotion recognition" is a technology that estimates a person's emotions and psychological state by analyzing biometric information and acoustic data.

[0187] "Audio information" refers to music or audio messages selected according to the psychological state of the person exercising.

[0188] A "terminal" is a device that provides real-time feedback to the person exercising and has the function of outputting information and providing interactive responses.

[0189] This invention is a system for providing personalized exercise guidance to exercisers in real time. This system mainly consists of three elements: a sensor, a server, and a terminal.

[0190] Hardware and data acquisition

[0191] First, the "sensor" collects the exerciser's "biometric information" and "exercise data." This sensor measures heart rate, respiratory rate, acceleration data, acoustic data, etc., enabling estimation of the user's emotional state. The collected data is then transmitted to the "server."

[0192] Data analysis and feedback generation

[0193] Next, the server analyzes the received data. The exerciser's movements are evaluated through "exercise analysis," and "emotion recognition" is performed based on the obtained information. In particular, a generative AI model is used to estimate the user's psychological state from their biometric information, and exercise optimization is performed. This makes it possible to dynamically adjust the exercise intensity and content according to the exerciser's emotional state.

[0194] Furthermore, this "server" also has the function of generating appropriate "audio information" to promote the psychological stability of the exerciser. In this process, music and audio messages selected according to the exerciser's emotional state are used.

[0195] Feedback and Interaction

[0196] The "terminal" is responsible for notifying the user of generated exercise instructions and audio information in real time. This allows the user to perform effective and safe exercise while receiving instructions and music that are tailored to their current emotional state.

[0197] Specific example

[0198] For example, if a user is feeling tense or anxious, the server might recommend "taking deep breaths and doing stretches" and instruct them to select and play relaxing classical music from their device.

[0199] Example of a prompt

[0200] "Design a method to help users relax by suggesting appropriate fitness guidance and music when they feel anxious."

[0201] In this way, individually optimized exercise guidance is provided according to the emotional state of the exerciser, resulting in improved exercise performance and user experience.

[0202] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0203] Step 1:

[0204] The user attaches sensors to their body before starting exercise. This prepares the sensors to collect biometric information and exercise data in real time.

[0205] Step 2:

[0206] The "sensor" collects heart rate, respiratory rate, acceleration data, and acoustic data from the "user" while they are active. This collected data is sent to the "server" as information necessary for emotion estimation.

[0207] Step 3:

[0208] The "server" receives the collected data and first performs "motion analysis." It analyzes acceleration data to evaluate the type and intensity of the user's movements. At this time, it also determines whether improvement in exercise form is necessary. The output is the analysis results based on the motion data.

[0209] Step 4:

[0210] Next, the "server" uses a generative AI model to analyze "biometric information" and "acoustic data" to perform "emotion recognition" and "psychological state estimation." It estimates the user's stress and motivation levels from the input data and obtains the emotional state as output.

[0211] Step 5:

[0212] Based on the emotional state estimated by the "server," optimized exercise guidance is generated. This guidance adjusts the type and intensity of exercise according to the emotional state. The generated output is personalized exercise guidance.

[0213] Step 6:

[0214] Furthermore, the "server" uses an AI model to generate and select the "audio information" that is optimal for the user's psychological state. It utilizes the results of emotion analysis as input and selects music that promotes relaxation or concentration as output.

[0215] Step 7:

[0216] The "terminal" receives exercise instructions and audio information sent from the "server" and notifies the "user" of this information in real time. Specifically, this includes music playback and audio instruction messages.

[0217] Step 8:

[0218] The user performs exercises based on the exercise guidance and audio information provided by the device and experiences the effects. In this step, the user maximizes the effects of exercise by continuing to exercise in a way that fits their emotional state.

[0219] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0220] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0221] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0222] [Second Embodiment]

[0223] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0224] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0225] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0226] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0227] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0228] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0229] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0230] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0231] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0232] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0233] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0234] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0235] This invention relates to a system for providing personalized exercise guidance to individuals. The system consists of sensors, a server, and terminals.

[0236] On the device, sensors attached to the exerciser's body collect biometric information and exercise data in real time during exercise. This information includes the exerciser's heart rate, pace, and acceleration. The collected data is periodically transmitted to a server.

[0237] The server analyzes the received biometric and exercise data to perform an exercise analysis. This analysis evaluates the exerciser's form and heart rate variability, and generates advice necessary for safe and effective exercise. For example, if the heart rate exceeds the target range, the server will create guidance advising the exerciser to slow down. Furthermore, based on the analysis results, it can select music that suits the exerciser's condition.

[0238] The device notifies the user in real time of exercise guidance sent from the server. For example, the device can notify the user via voice, "Your heart rate is too high. Slow down." It also provides guidance on appropriate hydration timing.

[0239] Based on the provided guidance, users can adjust their behavior during exercise to perform it efficiently and safely. After completing their workout, users can review their exercise history and feedback in detail through the application on their device. This feedback provides useful information for planning their next workout and helps them achieve their goals.

[0240] The following describes the processing flow.

[0241] Step 1:

[0242] The device collects biometric information and exercise data from the exerciser in real time through sensors. This includes heart rate, pace, and acceleration.

[0243] Step 2:

[0244] The terminal sends the collected data to the server. The data is buffered at regular intervals and sent to the server efficiently.

[0245] Step 3:

[0246] The server analyzes the received data. This analysis evaluates whether the heart rate exceeds the set range, whether the pace is appropriate, and whether there are any abnormalities in the exercise form.

[0247] Step 4:

[0248] Based on the analysis results, the server generates exercise instructions for the user. These instructions include adjusting the pace, improving form, determining the appropriate timing for hydration, and selecting music suitable for the exercise.

[0249] Step 5:

[0250] The server sends the generated exercise instructions back to the terminal. The terminal prepares to notify the user of the received instructions.

[0251] Step 6:

[0252] The device provides exercise guidance to the user through notifications. These notifications are delivered via voice or text and include practical advice such as, "Your heart rate is high, so slow down."

[0253] Step 7:

[0254] Users adjust their exercise behavior based on the provided instructions, ensuring safe and efficient workouts. After completing their workout, users can use the app to review detailed feedback and use it to plan their next workout.

[0255] (Example 1)

[0256] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0257] For athletes, real-time exercise guidance is essential for safe and effective exercise. However, existing systems struggle to provide guidance tailored to individual needs and often result in delayed feedback. There is a need to solve this problem and improve athletes' health and performance.

[0258] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0259] In this invention, the server includes means for analyzing the exerciser's biometric information and exercise data acquired by sensors, means for generating safe and effective exercise guidance based on the analysis results, and means for transmitting the generated exercise guidance to the exerciser in real time through a presentation device. This makes it possible to provide immediate feedback tailored to the exerciser and to achieve safe and effective exercise.

[0260] A "sensor" is a device attached to a person's body during exercise to acquire physiological information and exercise data in real time.

[0261] A "data communication device" is a device or means for transmitting motion data acquired by a sensor to a data processing device.

[0262] A "data processing device" is a device that analyzes received physiological information and exercise data and generates appropriate exercise guidance.

[0263] A "presentation device" is a device used to transmit exercise instructions generated by a data processing device to the person performing the exercise.

[0264] "Motion analysis" is the process by which a data processing device evaluates the characteristics and movements of motion based on physiological information and motion data.

[0265] "Exercise guidance" refers to advice or instructions for safe and effective exercise provided to individuals based on the results of exercise analysis.

[0266] "Acoustic information" refers to music or audio information that is selected according to the exerciser's state during exercise, and that contributes to improving the exerciser's motivation and rhythm.

[0267] This invention provides a system that offers personalized exercise guidance to individuals, and consists of a sensor, a data communication device, a data processing device, and a presentation device. This system utilizes a generative AI model to enable real-time exercise analysis and guidance generation.

[0268] The device uses sensors to collect physiological information and exercise data from the exerciser's body. This data is acquired in real time from sensors such as heart rate sensors and accelerometers, and is periodically transmitted to the data processing unit via a data communication device. The data processing unit then uses a generative AI model to analyze the received data and evaluate the exerciser's exercise form and heart rate fluctuations. Based on this analysis, the data processing unit generates specific exercise guidance for the exerciser. For example, if the exerciser's heart rate exceeds the target range, the data processing unit generates guidance such as "you should slow down." Furthermore, based on the analysis results, it is also possible to select acoustic information that is appropriate for the exerciser's condition.

[0269] The display device transmits exercise guidance sent from the data processing unit to the exerciser in real time. This allows the user to appropriately adjust their actions during exercise, enabling them to exercise safely and efficiently. After completing their workout, the user can check their exercise history and the feedback they received via an application on their terminal, which can be used to plan their next workout.

[0270] As a concrete example, the prompt message is: "Analyze the exerciser's heart rate and pace data, and tell the generative AI model how to provide guidance if the target range is exceeded."

[0271] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0272] Step 1:

[0273] The device collects biometric information and exercise data from the user via sensors. Specifically, it obtains heart rate data from a heart rate sensor and acceleration data from an accelerometer. This data is used as input and recorded in real time. The device periodically aggregates this data and stores it in a data communication device.

[0274] Step 2:

[0275] The terminal uses a data communication device to transmit collected biometric and movement data to the server. Data is transferred using communication technologies such as Bluetooth and Wi-Fi. Input is data from the terminal, and output indicates the completion of the transfer to the server.

[0276] Step 3:

[0277] The server analyzes the received data. Specifically, it uses a generative AI model to analyze the exercise form and heart rate variability of the person exercising. Data is supplied to the server as input, and as a result of the data analysis, information that forms the basis for exercise guidance is obtained. This information becomes the output.

[0278] Step 4:

[0279] The server generates exercise guidance based on the analysis results. For example, if the heart rate exceeds the target range, it will create specific advice such as "you should slow down." The generating AI model processes the data and outputs detailed guidance content.

[0280] Step 5:

[0281] The server sends the generated exercise instructions to the terminal. The output from the server is the content of the exercise instructions, which is transmitted to the terminal via communication.

[0282] Step 6:

[0283] The terminal notifies the exerciser of the received guidance in real time. As input, it obtains the guidance content received from the server and notifies the user visually or audibly using a presentation device. The output is presented to the exerciser as an audio message or a screen display.

[0284] Step 7:

[0285] The user adjusts their actions during exercise based on the received guidance. For example, upon receiving the notification, they slow down their pace and continue exercising. After exercise, the user checks the feedback on the application on the terminal. The input is the guidance received during exercise and the sensory feedback, and the output is the reflection after exercise and the adjustment plan for the next time.

[0286] (Application Example 1)

[0287] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0288] There is a need to provide a technology for providing individualized exercise guidance in real time to assist users in exercising safely and effectively. Also, this technology needs to cooperate with a robot having the role of a personal trainer to enable more comprehensive exercise guidance.

[0289] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0290] In this invention, the server includes means for the sensing device to obtain the user's physiological information and movement information, means for the information processing device to perform exercise analysis based on the physiological information and movement information and create optimal exercise guidance, and means for the robot device to provide physical accompaniment to the user during operation and transmit additional exercise guidance by voice. Thereby, it becomes possible to provide advanced exercise guidance in real time by the accompaniment of the robot.

[0291] A "sensing device" is a device used to acquire a user's physiological and movement information in real time.

[0292] An "information processing device" is a system that performs motion analysis based on acquired physiological and movement information to create optimal exercise guidance for the user.

[0293] A "display device" is an interface that transmits exercise instructions generated by an information processing device to the user in real time.

[0294] A "robot device" is a machine that provides physical support to the user during exercise and delivers additional exercise instructions via voice.

[0295] "Physiological information" refers to data related to the user's physical condition, such as heart rate, respiratory rate, and body temperature.

[0296] "Movement information" refers to data related to the user's movements, such as acceleration and location information.

[0297] "Motor analysis" is a process for evaluating a user's motor skills based on physiological and movement data, and for deriving appropriate guidance.

[0298] "Physical escort" refers to a robotic device actually moving in accordance with the user's movements and acting together with them.

[0299] "Communicating via voice" refers to an output method that uses voice, in addition to visual information, to directly instruct users.

[0300] The system for carrying out this invention consists of a sensing device, an information processing device, a display device, and a robotic device. This system safely and effectively provides personalized exercise guidance to users.

[0301] The server collects physiological information such as heart rate, acceleration, and position information, as well as movement information from the sensing device in real time. This information is obtained via sensors attached to the user's body and transmitted to the server via wireless communication. The information processing device analyzes this data using a program developed in Python, leveraging NumPy and Pandas, to evaluate the movement form and biological state.

[0302] Furthermore, a machine learning model is constructed using Sci-kit Learn, which learns the user's movement pattern and generates optimal guidance. The generated movement guidance is vocalized using Google Cloud's Text-to-Speech API.

[0303] The display device conveys these voice instructions to the user in real time, assisting the user in taking appropriate actions during exercise. The robotic device also operates as part of this system, providing physical accompaniment and, if necessary, offering voice-based movement guidance to the user.

[0304] As a specific example, when the user's heart rate rises too high during jogging, the display device advises "Slow down the pace" audibly. This advice is based on the analysis performed by the information processing device in real time.

[0305] As an example of a prompt sentence for the generation AI model, something like "Please propose an effective guidance method when the heart rate data exceeds the target range during jogging" can be considered. This prompt sentence requests appropriate guidance from the AI model based on a specific scenario during exercise.

[0306] The flow of the specific process in Application Example 1 will be described using Figure 12.

[0307] Step 1:

[0308] The server acquires biometric information such as heart rate, acceleration, and location, as well as movement information, from the user's sensing device. This information is transmitted in real time via wireless communication and stored on the server. The input is biometric information and movement information, and the output is the dataset to be analyzed.

[0309] Step 2:

[0310] The server analyzes the received data using a Python program. It performs preprocessing using NumPy and Pandas to format the data. Specifically, it imputes missing values ​​and normalizes the data to convert it into a format suitable for machine learning models. The input is a dataset of biometric and mobility information, and the output is the preprocessed dataset.

[0311] Step 3:

[0312] The server inputs pre-processed data into a machine learning model using Sci-kit Learn. This process analyzes the user's exercise patterns and calculates optimal exercise guidance. The input is a pre-processed dataset, and the output is guidelines and advice for exercise guidance.

[0313] Step 4:

[0314] The server converts the generated exercise instructions into speech using the Google Cloud Text-to-Speech API. This speech data is then converted into a format provided to the user. The input is exercise guidelines and advice, and the output is speech data.

[0315] Step 5:

[0316] The device transmits generated audio data to the user in real time. Using a display device and speaker, it provides timely advice to help the user take appropriate actions during exercise. The input is audio data, and the output is audio output to the user.

[0317] Step 6:

[0318] The robotic device also provides physical guidance to the user and offers additional exercise instruction via voice as needed. The robotic device further enhances real-time exercise support by operating based on instructions from the server. Inputs are the user's movement data and instructions from the server, while outputs are physical guidance and voice instruction.

[0319] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0320] This invention provides a system that offers personalized exercise guidance to individuals, and incorporates an emotion engine. The system consists of sensors, a server, and terminals.

[0321] Sensors attached to the device collect biometric information and exercise data from the exerciser in real time during exercise. This data includes heart rate, pace, acceleration, and information necessary to estimate the user's emotions, such as facial expressions and voice tone. The collected data is transmitted to a server.

[0322] The server analyzes the received data. In particular, it uses an emotion engine to recognize the user's emotional state from their biometric information. By analyzing the emotional state, it can estimate the user's motivation and stress level during exercise. Based on this information, the server generates specially tailored exercise guidance. For example, if the user is feeling tense, the server can suggest exercises and music that have a relaxing effect.

[0323] Furthermore, the server selects music that matches the user's emotional state, providing music that enhances the enjoyment and effectiveness of exercise. This music selection is optimized for each individual user based on analysis by the emotion engine.

[0324] The device notifies the user in real time of exercise instructions and selected music sent from the server. This allows the user to continue exercising safely and effectively while receiving feedback that fits their emotional state. For example, when the user is feeling stressed, calming music can be played along with advice such as, "Take a deep breath and slow down."

[0325] Users can adjust their behavior during exercise based on the provided guidance and receive detailed feedback after the exercise is completed. This allows users to understand their emotional state and use that information to plan their next exercise session. This invention optimizes exercise according to individual emotional states and supports the exerciser in achieving their goals.

[0326] The following describes the processing flow.

[0327] Step 1:

[0328] The device collects biometric information, exercise data, and data necessary for emotion estimation from the exerciser in real time through sensors. This includes heart rate, pace, acceleration, and facial expression information.

[0329] Step 2:

[0330] The terminal sends the collected data to the server. The data is divided into optimal packets according to the broadcast conditions and then transmitted.

[0331] Step 3:

[0332] The server analyzes the received data and evaluates fluctuations in heart rate and acceleration. It also uses an emotion engine to recognize the user's emotional state from biometric information.

[0333] Step 4:

[0334] The server generates personalized exercise guidance based on analysis of exercise data and emotional state. This guidance may include advice on appropriate pacing and relaxation.

[0335] Step 5:

[0336] The server selects music that matches the user's emotional state. This music is chosen to promote motivation and relaxation, taking into account the progress of the exercise and the user's emotional state.

[0337] Step 6:

[0338] The server packages the generated exercise instructions and selected music for transmission to the terminal.

[0339] Step 7:

[0340] The device notifies the user in real time, via voice or text, of exercise instructions and music received from the server. For example, it might advise, "Your mind is tense. Take a deep breath," and then play relaxing music.

[0341] Step 8:

[0342] Users adjust their exercise behavior based on the provided advice, ensuring safe and efficient workouts. They also enjoy a more comfortable exercise experience while listening to music tailored to their emotional state.

[0343] Step 9:

[0344] After completing an exercise session, users can view detailed exercise data and emotional feedback through an application on their device. This allows them to evaluate their progress and use the information to plan their next exercise session.

[0345] (Example 2)

[0346] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0347] In modern society, effective exercise is sought after for maintaining personal health and managing stress. However, conventional exercise instruction systems do not provide guidance tailored to the emotional state and individual needs of the exerciser, making it difficult to obtain effective feedback and maintain sustained motivation. Furthermore, the lack of individualized music and exercise instruction means that the exerciser does not receive the optimal exercise experience.

[0348] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0349] In this invention, the server includes means for recognizing emotional states using an AI model generated based on physical information and activity data, means for generating optimized activity guidance based on the emotional state, and means for selecting personalized music that matches the emotional state. This makes it possible to provide exercisers with personalized exercise guidance and music, thereby maximizing the effects of exercise, reducing stress, and maintaining motivation.

[0350] A "sensor" is a device used to acquire physical information and activity data from a person exercising.

[0351] "Physical information" refers to biological information acquired during exercise, such as data on the exerciser's heart rate, body movements, and posture.

[0352] "Activity data" refers to information about the content and intensity of exercise, such as the exerciser's acceleration and pace.

[0353] A "server" is a computer system that receives information sent from sensors and uses generated AI models to perform analysis and provide guidance.

[0354] A "generative AI model" is an artificial intelligence technology used to analyze collected data and generate information about the emotional state of an exerciser and optimal exercise guidance.

[0355] "Emotional state" is an indicator that shows the psychological state of an athlete, and includes stress levels and motivation levels.

[0356] "Activity guidance" refers to specific instructions and advice provided to help athletes exercise efficiently and safely.

[0357] "Personalized music" refers to music selected according to the emotional state of the person exercising and optimized to enhance the effectiveness of the exercise.

[0358] A "terminal" is a device used to transmit exercise instructions and music from a server to the person performing the exercise.

[0359] This invention relates to a system that enhances the effectiveness of exercise and maintains motivation by providing exercise guidance and music optimized for the exerciser. This system mainly consists of sensors, a server, and terminals.

[0360] Sensors play a role in acquiring physical information and activity data from the exerciser, collecting this information via a device worn by the wearer. Specifically, this data includes heart rate, acceleration, pace, facial expressions, and voice tone. By aggregating this data, it becomes possible to accurately understand the physiological and psychological state of the exerciser during exercise.

[0361] The server receives data from sensors and analyzes it using a generative AI model. This model recognizes the exerciser's emotional state and generates exercise instructions and music tailored to their individual needs. This process allows the exerciser to receive feedback appropriate to their emotional state at any given time. For example, if the exerciser is feeling tense, they can be offered relaxation-enhancing exercise instructions and calming music.

[0362] The device notifies the user in real time of exercise instructions and music transmitted from the server. In this way, the user can adjust their exercise based on the feedback they receive and obtain an optimal exercise experience. The device is also equipped with data transmission and notification functions, and transmits information through communication with the server.

[0363] By using this system, users can optimize their exercise behavior and use the feedback afterward to plan their next workout. This allows for more effective maintenance of personal health and stress management.

[0364] A concrete example of its use is that by inputting the prompt message "What combination of exercise and music should be suggested when the user's stress level is high?" into the server, the AI ​​can provide optimal exercise guidance and music selection, and present it to the user in real time.

[0365] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0366] Step 1:

[0367] The device acquires real-time physical information and activity data such as heart rate, acceleration, pace, facial expressions, and voice tone through sensors attached to the exerciser. The input consists of various biometric data collected by the sensors, and this data is transmitted directly to the server, enabling accurate analysis.

[0368] Step 2:

[0369] The server receives biometric and activity data transmitted from the terminal. This input data is fed into a generative AI model to analyze the user's real-time emotional state. The generative AI model used here utilizes machine learning algorithms to recognize emotional states such as tension, relaxation, and stress from the user's heart rate, voice tone, etc. The output is the analysis results regarding the user's emotional state.

[0370] Step 3:

[0371] The server generates exercise instructions optimized for the user's current state, based on the results of emotion analysis. This process uses the results of emotion analysis as input, and utilizes a generation AI model to devise a set of relaxing exercises and workouts. Specific exercise instructions are provided as output.

[0372] Step 4:

[0373] In parallel, the server selects music that matches the user's emotional state. In this step, the results of the emotion analysis obtained earlier are used as input data, and a suitable song is selected for the user through a generative AI model. The output is music optimized to give the user a better exercise experience.

[0374] Step 5:

[0375] The device notifies the user in real time of exercise instructions and selected music received from the server. At this stage, the input is the data of exercise instructions and music sent from the server, and by dynamically providing this to the user, the user can perform the exercise based on the feedback. The output is the stream of specific instructions and music provided to the user during the exercise.

[0376] (Application Example 2)

[0377] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0378] There is a need to maximize the effectiveness of exercise and improve the motivation of exercisers by analyzing their psychological state in real time during exercise and providing personalized feedback tailored to that state. However, current systems are limited to analysis and guidance based on general biometric information, making it difficult to provide feedback that takes emotional states into account. This leads to a problem where appropriate exercise support cannot be provided when exercisers are experiencing stress or fatigue.

[0379] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0380] In this invention, the server includes means for collecting biometric information, exercise data, and acoustic data of an exerciser; means for performing exercise analysis through biometric information, exercise data, and emotion recognition to generate optimal exercise guidance based on the exerciser's psychological state; and means for determining audio information appropriate to the exerciser's emotional state and providing it through a terminal. This makes it possible to provide exercise guidance and audio information tailored to the individual emotional state of the exerciser, thereby enhancing the effectiveness of exercise while maintaining the exerciser's motivation.

[0381] A "sensor" is a device that collects biological information, movement data, and acoustic data from inside or on the surface of a person's body during exercise.

[0382] "Biometric information" refers to numerical values ​​and signals that indicate the physical state of an exerciser, such as heart rate, respiratory rate, and body temperature.

[0383] "Motion data" refers to data that indicates the physical state of a person's movement, such as acceleration, velocity, and position.

[0384] "Acoustic data" refers to data that captures audio signals and ambient sounds, and is used to estimate the emotions of a person performing an action.

[0385] A "server" is a computer system that analyzes collected data and generates exercise guidance and audio information optimized for the user.

[0386] "Motion analysis" is the process of analyzing a person's movement and form based on sensor data, and then evaluating it using a specific algorithm.

[0387] "Emotion recognition" is a technology that estimates a person's emotions and psychological state by analyzing biometric information and acoustic data.

[0388] "Audio information" refers to music or audio messages selected according to the psychological state of the person exercising.

[0389] A "terminal" is a device that provides real-time feedback to the person exercising and has the function of outputting information and providing interactive responses.

[0390] This invention is a system for providing personalized exercise guidance to exercisers in real time. This system mainly consists of three elements: a sensor, a server, and a terminal.

[0391] Hardware and data acquisition

[0392] First, the "sensor" collects the exerciser's "biometric information" and "exercise data." This sensor measures heart rate, respiratory rate, acceleration data, acoustic data, etc., enabling estimation of the user's emotional state. The collected data is then transmitted to the "server."

[0393] Data analysis and feedback generation

[0394] Next, the server analyzes the received data. The exerciser's movements are evaluated through "exercise analysis," and "emotion recognition" is performed based on the obtained information. In particular, a generative AI model is used to estimate the user's psychological state from their biometric information, and exercise optimization is performed. This makes it possible to dynamically adjust the exercise intensity and content according to the exerciser's emotional state.

[0395] Furthermore, this "server" also has the function of generating appropriate "audio information" to promote the psychological stability of the exerciser. In this process, music and audio messages selected according to the exerciser's emotional state are used.

[0396] Feedback and Interaction

[0397] The "terminal" is responsible for notifying the user of generated exercise instructions and audio information in real time. This allows the user to perform effective and safe exercise while receiving instructions and music that are tailored to their current emotional state.

[0398] Specific example

[0399] For example, if a user is feeling tense or anxious, the server might recommend "taking deep breaths and doing stretches" and instruct them to select and play relaxing classical music from their device.

[0400] Example of a prompt

[0401] "Design a method to help users relax by suggesting appropriate fitness guidance and music when they feel anxious."

[0402] In this way, individually optimized exercise guidance is provided according to the emotional state of the exerciser, resulting in improved exercise performance and user experience.

[0403] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0404] Step 1:

[0405] The user attaches sensors to their body before starting exercise. This prepares the sensors to collect biometric information and exercise data in real time.

[0406] Step 2:

[0407] The "sensor" collects heart rate, respiratory rate, acceleration data, and acoustic data from the "user" while they are active. This collected data is sent to the "server" as information necessary for emotion estimation.

[0408] Step 3:

[0409] The "server" receives the collected data and first performs "motion analysis." It analyzes acceleration data to evaluate the type and intensity of the user's movements. At this time, it also determines whether improvement in exercise form is necessary. The output is the analysis results based on the motion data.

[0410] Step 4:

[0411] Next, the "server" uses a generative AI model to analyze "biometric information" and "acoustic data" to perform "emotion recognition" and "psychological state estimation." It estimates the user's stress and motivation levels from the input data and obtains the emotional state as output.

[0412] Step 5:

[0413] Based on the emotional state estimated by the "server," optimized exercise guidance is generated. This guidance adjusts the type and intensity of exercise according to the emotional state. The generated output is personalized exercise guidance.

[0414] Step 6:

[0415] Furthermore, the "server" uses an AI model to generate and select the "audio information" that is optimal for the user's psychological state. It utilizes the results of emotion analysis as input and selects music that promotes relaxation or concentration as output.

[0416] Step 7:

[0417] The "terminal" receives exercise instructions and audio information sent from the "server" and notifies the "user" of this information in real time. Specifically, this includes music playback and audio instruction messages.

[0418] Step 8:

[0419] The user performs exercises based on the exercise guidance and audio information provided by the device and experiences the effects. In this step, the user maximizes the effects of exercise by continuing to exercise in a way that fits their emotional state.

[0420] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0421] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0422] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0423] [Third Embodiment]

[0424] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0425] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0426] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0427] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0428] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0429] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0430] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0431] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0432] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0433] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0434] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0435] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0436] This invention relates to a system for providing personalized exercise guidance to individuals. The system consists of sensors, a server, and terminals.

[0437] On the device, sensors attached to the exerciser's body collect biometric information and exercise data in real time during exercise. This information includes the exerciser's heart rate, pace, and acceleration. The collected data is periodically transmitted to a server.

[0438] The server analyzes the received biometric and exercise data to perform an exercise analysis. This analysis evaluates the exerciser's form and heart rate variability, and generates advice necessary for safe and effective exercise. For example, if the heart rate exceeds the target range, the server will create guidance advising the exerciser to slow down. Furthermore, based on the analysis results, it can select music that suits the exerciser's condition.

[0439] The device notifies the user in real time of exercise guidance sent from the server. For example, the device can notify the user via voice, "Your heart rate is too high. Slow down." It also provides guidance on appropriate hydration timing.

[0440] Based on the provided guidance, users can adjust their behavior during exercise to perform it efficiently and safely. After completing their workout, users can review their exercise history and feedback in detail through the application on their device. This feedback provides useful information for planning their next workout and helps them achieve their goals.

[0441] The following describes the processing flow.

[0442] Step 1:

[0443] The device collects biometric information and exercise data from the exerciser in real time through sensors. This includes heart rate, pace, and acceleration.

[0444] Step 2:

[0445] The terminal sends the collected data to the server. The data is buffered at regular intervals and sent to the server efficiently.

[0446] Step 3:

[0447] The server analyzes the received data. This analysis evaluates whether the heart rate exceeds the set range, whether the pace is appropriate, and whether there are any abnormalities in the exercise form.

[0448] Step 4:

[0449] Based on the analysis results, the server generates exercise instructions for the user. These instructions include adjusting the pace, improving form, determining the appropriate timing for hydration, and selecting music suitable for the exercise.

[0450] Step 5:

[0451] The server sends the generated exercise instructions back to the terminal. The terminal prepares to notify the user of the received instructions.

[0452] Step 6:

[0453] The device provides exercise guidance to the user through notifications. These notifications are delivered via voice or text and include practical advice such as, "Your heart rate is high, so slow down."

[0454] Step 7:

[0455] Users adjust their exercise behavior based on the provided instructions, ensuring safe and efficient workouts. After completing their workout, users can use the app to review detailed feedback and use it to plan their next workout.

[0456] (Example 1)

[0457] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0458] For athletes, real-time exercise guidance is essential for safe and effective exercise. However, existing systems struggle to provide guidance tailored to individual needs and often result in delayed feedback. There is a need to solve this problem and improve athletes' health and performance.

[0459] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0460] In this invention, the server includes means for analyzing the exerciser's biometric information and exercise data acquired by sensors, means for generating safe and effective exercise guidance based on the analysis results, and means for transmitting the generated exercise guidance to the exerciser in real time through a presentation device. This makes it possible to provide immediate feedback tailored to the exerciser and to achieve safe and effective exercise.

[0461] A "sensor" is a device attached to a person's body during exercise to acquire physiological information and exercise data in real time.

[0462] A "data communication device" is a device or means for transmitting motion data acquired by a sensor to a data processing device.

[0463] A "data processing device" is a device that analyzes received physiological information and exercise data and generates appropriate exercise guidance.

[0464] A "presentation device" is a device used to transmit exercise instructions generated by a data processing device to the person performing the exercise.

[0465] "Motion analysis" is the process by which a data processing device evaluates the characteristics and movements of motion based on physiological information and motion data.

[0466] "Exercise guidance" refers to advice or instructions for safe and effective exercise provided to individuals based on the results of exercise analysis.

[0467] "Acoustic information" refers to music or audio information that is selected according to the exerciser's state during exercise, and that contributes to improving the exerciser's motivation and rhythm.

[0468] This invention provides a system that offers personalized exercise guidance to individuals, and consists of a sensor, a data communication device, a data processing device, and a presentation device. This system utilizes a generative AI model to enable real-time exercise analysis and guidance generation.

[0469] The device uses sensors to collect physiological information and exercise data from the exerciser's body. This data is acquired in real time from sensors such as heart rate sensors and accelerometers, and is periodically transmitted to the data processing unit via a data communication device. The data processing unit then uses a generative AI model to analyze the received data and evaluate the exerciser's exercise form and heart rate fluctuations. Based on this analysis, the data processing unit generates specific exercise guidance for the exerciser. For example, if the exerciser's heart rate exceeds the target range, the data processing unit generates guidance such as "you should slow down." Furthermore, based on the analysis results, it is also possible to select acoustic information that is appropriate for the exerciser's condition.

[0470] The display device transmits exercise guidance sent from the data processing unit to the exerciser in real time. This allows the user to appropriately adjust their actions during exercise, enabling them to exercise safely and efficiently. After completing their workout, the user can check their exercise history and the feedback they received via an application on their terminal, which can be used to plan their next workout.

[0471] As a concrete example, the prompt message is: "Analyze the exerciser's heart rate and pace data, and tell the generative AI model how to provide guidance if the target range is exceeded."

[0472] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0473] Step 1:

[0474] The device collects biometric information and exercise data from the user via sensors. Specifically, it obtains heart rate data from a heart rate sensor and acceleration data from an accelerometer. This data is used as input and recorded in real time. The device periodically aggregates this data and stores it in a data communication device.

[0475] Step 2:

[0476] The terminal uses a data communication device to transmit collected biometric and movement data to the server. Data is transferred using communication technologies such as Bluetooth and Wi-Fi. Input is data from the terminal, and output indicates the completion of the transfer to the server.

[0477] Step 3:

[0478] The server analyzes the received data. Specifically, it uses a generative AI model to analyze the exercise form and heart rate variability of the person exercising. Data is supplied to the server as input, and as a result of the data analysis, information that forms the basis for exercise guidance is obtained. This information becomes the output.

[0479] Step 4:

[0480] The server generates exercise guidance based on the analysis results. For example, if the heart rate exceeds the target range, it will create specific advice such as "you should slow down." The generating AI model processes the data and outputs detailed guidance content.

[0481] Step 5:

[0482] The server sends the generated exercise instructions to the terminal. The output from the server is the content of the exercise instructions, which is transmitted to the terminal via communication.

[0483] Step 6:

[0484] The terminal notifies the exerciser of the received instructions in real time. As input, it retrieves the instruction content received from the server and notifies the user visually or audibly using a display device. Output is presented to the exerciser as an audio message or screen display.

[0485] Step 7:

[0486] Users adjust their exercise behavior based on the instructions they receive. For example, they might slow down their pace after receiving a notification. After the exercise, users check the feedback on an application on their device. The input is the instructions and sensory feedback received during the exercise, and the output is a post-exercise review and adjustment plan for the next time.

[0487] (Application Example 1)

[0488] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0489] There is a need to provide technology that offers personalized exercise guidance in real time, supporting users in exercising safely and effectively. Furthermore, this technology needs to work in conjunction with robots that act as personal trainers to enable more comprehensive exercise guidance.

[0490] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0491] In this invention, the server includes means for a sensing device to acquire the user's physiological information and movement information, means for an information processing device to perform motion analysis based on the physiological information and movement information and create optimal exercise guidance, and means for a robot device to provide physical escort to the user during movement and transmit additional exercise guidance via voice. This makes it possible to provide advanced exercise guidance in real time through robotic escort.

[0492] A "sensing device" is a device used to acquire a user's physiological and movement information in real time.

[0493] An "information processing device" is a system that performs motion analysis based on acquired physiological and movement information to create optimal exercise guidance for the user.

[0494] A "display device" is an interface that transmits exercise instructions generated by an information processing device to the user in real time.

[0495] A "robot device" is a machine that provides physical support to the user during exercise and delivers additional exercise instructions via voice.

[0496] "Physiological information" refers to data related to the user's physical condition, such as heart rate, respiratory rate, and body temperature.

[0497] "Movement information" refers to data related to the user's movements, such as acceleration and location information.

[0498] "Motor analysis" is a process for evaluating a user's motor skills based on physiological and movement data, and for deriving appropriate guidance.

[0499] "Physical escort" refers to a robotic device actually moving in accordance with the user's movements and acting together with them.

[0500] "Communicating via voice" refers to an output method that uses voice, in addition to visual information, to directly instruct users.

[0501] The system for carrying out this invention consists of a sensing device, an information processing device, a display device, and a robotic device. This system safely and effectively provides personalized exercise guidance to users.

[0502] The server collects physiological information such as heart rate, acceleration, and location data, as well as movement information, in real time from sensing devices. This information is acquired via sensors attached to the user's body and transmitted to the server wirelessly. The information processing device uses a program developed in Python to analyze this data using NumPy and Pandas to evaluate exercise form and biological state.

[0503] Furthermore, a machine learning model is built using Sci-kit Learn, which learns the user's movement patterns and generates optimal instruction. The generated exercise instruction is then converted into speech using Google Cloud's Text-to-Speech API.

[0504] The display device transmits these voice instructions to the user in real time, helping the user take appropriate actions during exercise. A robotic device also operates as part of this system, providing physical support and, as needed, voice-guided exercise instructions to the user.

[0505] For example, if a user's heart rate becomes too high while jogging, the display device will provide voice advice such as, "Let's slow down." This advice is based on real-time analysis performed by the information processing device.

[0506] An example of a prompt to a generating AI model might be, "Please suggest effective guidance methods if your heart rate data exceeds the target range during jogging." This prompt asks the AI ​​model to provide appropriate guidance based on a specific scenario during exercise.

[0507] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0508] Step 1:

[0509] The server acquires biometric information such as heart rate, acceleration, and location, as well as movement information, from the user's sensing device. This information is transmitted in real time via wireless communication and stored on the server. The input is biometric information and movement information, and the output is the dataset to be analyzed.

[0510] Step 2:

[0511] The server analyzes the received data using a Python program. It performs preprocessing using NumPy and Pandas to format the data. Specifically, it imputes missing values ​​and normalizes the data to convert it into a format suitable for machine learning models. The input is a dataset of biometric and mobility information, and the output is the preprocessed dataset.

[0512] Step 3:

[0513] The server inputs pre-processed data into a machine learning model using Sci-kit Learn. This process analyzes the user's exercise patterns and calculates optimal exercise guidance. The input is a pre-processed dataset, and the output is guidelines and advice for exercise guidance.

[0514] Step 4:

[0515] The server converts the generated exercise instructions into speech using the Google Cloud Text-to-Speech API. This speech data is then converted into a format provided to the user. The input is exercise guidelines and advice, and the output is speech data.

[0516] Step 5:

[0517] The device transmits generated audio data to the user in real time. Using a display device and speaker, it provides timely advice to help the user take appropriate actions during exercise. The input is audio data, and the output is audio output to the user.

[0518] Step 6:

[0519] The robotic device also provides physical guidance to the user and offers additional exercise instruction via voice as needed. The robotic device further enhances real-time exercise support by operating based on instructions from the server. Inputs are the user's movement data and instructions from the server, while outputs are physical guidance and voice instruction.

[0520] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0521] This invention provides a system that offers personalized exercise guidance to individuals, and incorporates an emotion engine. The system consists of sensors, a server, and terminals.

[0522] Sensors attached to the device collect biometric information and exercise data from the user in real time during exercise. This data includes heart rate, pace, acceleration, and information necessary to estimate the user's emotions, such as facial expressions and voice tone. The collected data is transmitted to a server.

[0523] The server analyzes the received data. In particular, it uses an emotion engine to recognize the user's emotional state from their biometric information. By analyzing the emotional state, it can estimate the user's motivation and stress level during exercise. Based on this information, the server generates specially tailored exercise guidance. For example, if the user is feeling tense, the server can suggest exercises and music that have a relaxing effect.

[0524] Furthermore, the server selects music that matches the user's emotional state, providing music that enhances the enjoyment and effectiveness of exercise. This music selection is optimized for each individual user based on analysis by the emotion engine.

[0525] The device notifies the user in real time of exercise instructions and selected music sent from the server. This allows the user to continue exercising safely and effectively while receiving feedback that fits their emotional state. For example, when the user is feeling stressed, calming music can be played along with advice such as, "Take a deep breath and slow down."

[0526] Users can adjust their behavior during exercise based on the provided guidance and receive detailed feedback after the exercise is completed. This allows users to understand their emotional state and use that information to plan their next exercise session. This invention optimizes exercise according to individual emotional states and supports the exerciser in achieving their goals.

[0527] The following describes the processing flow.

[0528] Step 1:

[0529] The device collects biometric information, exercise data, and data necessary for emotion estimation from the exerciser in real time through sensors. This includes heart rate, pace, acceleration, and facial expression information.

[0530] Step 2:

[0531] The terminal sends the collected data to the server. The data is divided into optimal packets according to the broadcast conditions and then transmitted.

[0532] Step 3:

[0533] The server analyzes the received data and evaluates fluctuations in heart rate and acceleration. It also uses an emotion engine to recognize the user's emotional state from biometric information.

[0534] Step 4:

[0535] The server generates personalized exercise guidance based on analysis of exercise data and emotional state. This guidance may include advice on appropriate pacing and relaxation.

[0536] Step 5:

[0537] The server selects music that matches the user's emotional state. This music is chosen to promote motivation and relaxation, taking into account the progress of the exercise and the user's emotional state.

[0538] Step 6:

[0539] The server packages the generated exercise instructions and selected music for transmission to the terminal.

[0540] Step 7:

[0541] The device notifies the user in real time, via voice or text, of exercise instructions and music received from the server. For example, it might advise, "Your mind is tense. Take a deep breath," and then play relaxing music.

[0542] Step 8:

[0543] Users adjust their exercise behavior based on the provided advice, ensuring safe and efficient workouts. They also enjoy a more comfortable exercise experience while listening to music tailored to their emotional state.

[0544] Step 9:

[0545] After completing an exercise session, users can view detailed exercise data and emotional feedback through an application on their device. This allows them to evaluate their progress and use the information to plan their next exercise session.

[0546] (Example 2)

[0547] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0548] In modern society, effective exercise is sought after for maintaining personal health and managing stress. However, conventional exercise instruction systems do not provide guidance tailored to the emotional state and individual needs of the exerciser, making it difficult to obtain effective feedback and maintain sustained motivation. Furthermore, the lack of individualized music and exercise instruction means that the exerciser does not receive the optimal exercise experience.

[0549] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0550] In this invention, the server includes means for recognizing emotional states using an AI model generated based on physical information and activity data, means for generating optimized activity guidance based on the emotional state, and means for selecting personalized music that matches the emotional state. This makes it possible to provide exercisers with personalized exercise guidance and music, thereby maximizing the effects of exercise, reducing stress, and maintaining motivation.

[0551] A "sensor" is a device used to acquire physical information and activity data from a person exercising.

[0552] "Physical information" refers to biological information acquired during exercise, such as data on the exerciser's heart rate, body movements, and posture.

[0553] "Activity data" refers to information about the content and intensity of exercise, such as the exerciser's acceleration and pace.

[0554] A "server" is a computer system that receives information sent from sensors and uses generated AI models to perform analysis and provide guidance.

[0555] A "generative AI model" is an artificial intelligence technology used to analyze collected data and generate information about the emotional state of an exerciser and optimal exercise guidance.

[0556] "Emotional state" is an indicator that shows the psychological state of an athlete, and includes stress levels and motivation levels.

[0557] "Activity guidance" refers to specific instructions and advice provided to help athletes exercise efficiently and safely.

[0558] "Personalized music" refers to music selected according to the emotional state of the person exercising and optimized to enhance the effectiveness of the exercise.

[0559] A "terminal" is a device used to transmit exercise instructions and music from a server to the person performing the exercise.

[0560] This invention relates to a system that enhances the effectiveness of exercise and maintains motivation by providing exercise guidance and music optimized for the exerciser. This system mainly consists of sensors, a server, and terminals.

[0561] Sensors play a role in acquiring physical information and activity data from the exerciser, collecting this information via a device worn by the wearer. Specifically, this data includes heart rate, acceleration, pace, facial expressions, and voice tone. By aggregating this data, it becomes possible to accurately understand the physiological and psychological state of the exerciser during exercise.

[0562] The server receives data from sensors and analyzes it using a generative AI model. This model recognizes the exerciser's emotional state and generates exercise instructions and music tailored to their individual needs. This process allows the exerciser to receive feedback appropriate to their emotional state at any given time. For example, if the exerciser is feeling tense, they can be offered relaxation-enhancing exercise instructions and calming music.

[0563] The device notifies the user in real time of exercise instructions and music transmitted from the server. In this way, the user can adjust their exercise based on the feedback they receive and obtain an optimal exercise experience. The device is also equipped with data transmission and notification functions, and transmits information through communication with the server.

[0564] By using this system, users can optimize their exercise behavior and use the feedback afterward to plan their next workout. This allows for more effective maintenance of personal health and stress management.

[0565] A concrete example of its use is that by inputting the prompt message "What combination of exercise and music should be suggested when the user's stress level is high?" into the server, the AI ​​can provide optimal exercise guidance and music selection, and present it to the user in real time.

[0566] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0567] Step 1:

[0568] The device acquires real-time physical information and activity data such as heart rate, acceleration, pace, facial expressions, and voice tone through sensors attached to the exerciser. The input consists of various biometric data collected by the sensors, and this data is transmitted directly to the server, enabling accurate analysis.

[0569] Step 2:

[0570] The server receives biometric and activity data transmitted from the terminal. This input data is fed into a generative AI model to analyze the user's real-time emotional state. The generative AI model used here utilizes machine learning algorithms to recognize emotional states such as tension, relaxation, and stress from the user's heart rate, voice tone, etc. The output is the analysis results regarding the user's emotional state.

[0571] Step 3:

[0572] The server generates exercise instructions optimized for the user's current state, based on the results of emotion analysis. This process uses the results of emotion analysis as input, and utilizes a generation AI model to devise a set of relaxing exercises and workouts. Specific exercise instructions are provided as output.

[0573] Step 4:

[0574] In parallel, the server selects music that matches the user's emotional state. In this step, the results of the emotion analysis obtained earlier are used as input data, and a suitable song is selected for the user through a generative AI model. The output is music optimized to give the user a better exercise experience.

[0575] Step 5:

[0576] The device notifies the user in real time of exercise instructions and selected music received from the server. At this stage, the input is the data of exercise instructions and music sent from the server, and by dynamically providing this to the user, the user can perform the exercise based on the feedback. The output is the stream of specific instructions and music provided to the user during the exercise.

[0577] (Application Example 2)

[0578] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0579] There is a need to maximize the effectiveness of exercise and improve the motivation of exercisers by analyzing their psychological state in real time during exercise and providing personalized feedback tailored to that state. However, current systems are limited to analysis and guidance based on general biometric information, making it difficult to provide feedback that takes emotional states into account. This leads to a problem where appropriate exercise support cannot be provided when exercisers are experiencing stress or fatigue.

[0580] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0581] In this invention, the server includes means for collecting biometric information, exercise data, and acoustic data of an exerciser; means for performing exercise analysis through biometric information, exercise data, and emotion recognition to generate optimal exercise guidance based on the exerciser's psychological state; and means for determining audio information appropriate to the exerciser's emotional state and providing it through a terminal. This makes it possible to provide exercise guidance and audio information tailored to the individual emotional state of the exerciser, thereby enhancing the effectiveness of exercise while maintaining the exerciser's motivation.

[0582] A "sensor" is a device that collects biological information, movement data, and acoustic data from inside or on the surface of a person's body during exercise.

[0583] "Biometric information" refers to numerical values ​​and signals that indicate the physical state of an exerciser, such as heart rate, respiratory rate, and body temperature.

[0584] "Motion data" refers to data that indicates the physical state of a person's movement, such as acceleration, velocity, and position.

[0585] "Acoustic data" refers to data that captures audio signals and ambient sounds, and is used to estimate the emotions of a person performing an action.

[0586] A "server" is a computer system that analyzes collected data and generates exercise guidance and audio information optimized for the user.

[0587] "Motion analysis" is the process of analyzing a person's movement and form based on sensor data, and evaluating it through a specific algorithm.

[0588] "Emotion recognition" is a technology that estimates a person's emotions and psychological state by analyzing biometric information and acoustic data.

[0589] "Audio information" refers to music or audio messages selected according to the psychological state of the person exercising.

[0590] A "terminal" is a device that provides real-time feedback to the person exercising and has the function of outputting information and providing interactive responses.

[0591] This invention is a system for providing personalized exercise guidance to exercisers in real time. This system mainly consists of three elements: a sensor, a server, and a terminal.

[0592] Hardware and data acquisition

[0593] First, the "sensor" collects the exerciser's "biometric information" and "exercise data." This sensor measures heart rate, respiratory rate, acceleration data, acoustic data, etc., enabling estimation of the user's emotional state. The collected data is then transmitted to the "server."

[0594] Data analysis and feedback generation

[0595] Next, the server analyzes the received data. The exerciser's movements are evaluated through "exercise analysis," and "emotion recognition" is performed based on the obtained information. In particular, a generative AI model is used to estimate the user's psychological state from their biometric information, and exercise optimization is performed. This makes it possible to dynamically adjust the exercise intensity and content according to the exerciser's emotional state.

[0596] Furthermore, this "server" also has the function of generating appropriate "audio information" to promote the psychological stability of the exerciser. In this process, music and audio messages selected according to the exerciser's emotional state are used.

[0597] Feedback and Interaction

[0598] The "terminal" is responsible for notifying the user of generated exercise instructions and audio information in real time. This allows the user to perform effective and safe exercise while receiving instructions and music that are tailored to their current emotional state.

[0599] Specific example

[0600] For example, if a user is feeling tense or anxious, the server might recommend "taking deep breaths and doing stretches" and instruct them to select and play relaxing classical music from their device.

[0601] Example of a prompt

[0602] "Design a method to help users relax by suggesting appropriate fitness guidance and music when they feel anxious."

[0603] In this way, individually optimized exercise guidance is provided according to the emotional state of the exerciser, resulting in improved exercise performance and user experience.

[0604] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0605] Step 1:

[0606] The user attaches sensors to their body before starting exercise. This prepares the sensors to collect biometric information and exercise data in real time.

[0607] Step 2:

[0608] The "sensor" collects heart rate, respiratory rate, acceleration data, and acoustic data from the "user" while they are active. This collected data is sent to the "server" as information necessary for emotion estimation.

[0609] Step 3:

[0610] The "server" receives the collected data and first performs "motion analysis." It analyzes acceleration data to evaluate the type and intensity of the user's movements. At this time, it also determines whether improvement in exercise form is necessary. The output is the analysis results based on the motion data.

[0611] Step 4:

[0612] Next, the "server" uses a generative AI model to analyze "biometric information" and "acoustic data" to perform "emotion recognition" and "psychological state estimation." It estimates the user's stress and motivation levels from the input data and obtains the emotional state as output.

[0613] Step 5:

[0614] Based on the emotional state estimated by the "server," optimized exercise guidance is generated. This guidance adjusts the type and intensity of exercise according to the emotional state. The generated output is personalized exercise guidance.

[0615] Step 6:

[0616] Furthermore, the "server" uses an AI model to generate and select the "audio information" that is optimal for the user's psychological state. It utilizes the results of emotion analysis as input and selects music that promotes relaxation or concentration as output.

[0617] Step 7:

[0618] The "terminal" receives exercise instructions and audio information sent from the "server" and notifies the "user" of this information in real time. Specifically, this includes music playback and audio instruction messages.

[0619] Step 8:

[0620] The user performs exercises based on the exercise guidance and audio information provided by the device and experiences the effects. In this step, the user maximizes the effects of exercise by continuing to exercise in a way that fits their emotional state.

[0621] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0622] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0623] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0624] [Fourth Embodiment]

[0625] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0626] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0627] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0628] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0629] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0630] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0631] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0632] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0633] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0634] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0635] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0636] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0637] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0638] This invention relates to a system for providing personalized exercise guidance to individuals. The system consists of sensors, a server, and terminals.

[0639] On the device, sensors attached to the exerciser's body collect biometric information and exercise data in real time during exercise. This information includes the exerciser's heart rate, pace, and acceleration. The collected data is periodically transmitted to a server.

[0640] The server analyzes the received biometric and exercise data to perform an exercise analysis. This analysis evaluates the exerciser's form and heart rate variability, and generates advice necessary for safe and effective exercise. For example, if the heart rate exceeds the target range, the server will create guidance advising the exerciser to slow down. Furthermore, based on the analysis results, it can select music that suits the exerciser's condition.

[0641] The device notifies the user in real time of exercise guidance sent from the server. For example, the device can notify the user via voice, "Your heart rate is too high. Slow down." It also provides guidance on appropriate hydration timing.

[0642] Based on the provided guidance, users can adjust their behavior during exercise to perform it efficiently and safely. After completing their workout, users can review their exercise history and feedback in detail through the application on their device. This feedback provides useful information for planning their next workout and helps them achieve their goals.

[0643] The following describes the processing flow.

[0644] Step 1:

[0645] The device collects biometric information and exercise data from the exerciser in real time through sensors. This includes heart rate, pace, and acceleration.

[0646] Step 2:

[0647] The terminal sends the collected data to the server. The data is buffered at regular intervals and sent to the server efficiently.

[0648] Step 3:

[0649] The server analyzes the received data. This analysis evaluates whether the heart rate exceeds the set range, whether the pace is appropriate, and whether there are any abnormalities in the exercise form.

[0650] Step 4:

[0651] Based on the analysis results, the server generates exercise instructions for the user. These instructions include adjusting the pace, improving form, determining the appropriate timing for hydration, and selecting music suitable for the exercise.

[0652] Step 5:

[0653] The server sends the generated exercise instructions back to the terminal. The terminal prepares to notify the user of the received instructions.

[0654] Step 6:

[0655] The device provides exercise guidance to the user through notifications. These notifications are delivered via voice or text and include practical advice such as, "Your heart rate is high, so slow down."

[0656] Step 7:

[0657] Users adjust their exercise behavior based on the provided instructions, ensuring safe and efficient workouts. After completing their workout, users can use the app to review detailed feedback and use it to plan their next workout.

[0658] (Example 1)

[0659] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0660] For athletes, real-time exercise guidance is essential for safe and effective exercise. However, existing systems struggle to provide guidance tailored to individual needs and often result in delayed feedback. There is a need to solve this problem and improve athletes' health and performance.

[0661] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0662] In this invention, the server includes means for analyzing the exerciser's biometric information and exercise data acquired by sensors, means for generating safe and effective exercise guidance based on the analysis results, and means for transmitting the generated exercise guidance to the exerciser in real time through a presentation device. This makes it possible to provide immediate feedback tailored to the exerciser and to achieve safe and effective exercise.

[0663] A "sensor" is a device attached to a person's body during exercise to acquire physiological information and exercise data in real time.

[0664] A "data communication device" is a device or means for transmitting motion data acquired by a sensor to a data processing device.

[0665] A "data processing device" is a device that analyzes received physiological information and exercise data and generates appropriate exercise guidance.

[0666] A "presentation device" is a device used to transmit exercise instructions generated by a data processing device to the person performing the exercise.

[0667] "Motion analysis" is the process by which a data processing device evaluates the characteristics and movements of motion based on physiological information and motion data.

[0668] "Exercise guidance" refers to advice or instructions for safe and effective exercise provided to individuals based on the results of exercise analysis.

[0669] "Acoustic information" refers to music or audio information that is selected according to the exerciser's state during exercise, and that contributes to improving the exerciser's motivation and rhythm.

[0670] This invention provides a system that offers personalized exercise guidance to individuals, and consists of a sensor, a data communication device, a data processing device, and a presentation device. This system utilizes a generative AI model to enable real-time exercise analysis and guidance generation.

[0671] The device uses sensors to collect physiological information and exercise data from the exerciser's body. This data is acquired in real time from sensors such as heart rate sensors and accelerometers, and is periodically transmitted to the data processing unit via a data communication device. The data processing unit then uses a generative AI model to analyze the received data and evaluate the exerciser's exercise form and heart rate fluctuations. Based on this analysis, the data processing unit generates specific exercise guidance for the exerciser. For example, if the exerciser's heart rate exceeds the target range, the data processing unit generates guidance such as "you should slow down." Furthermore, based on the analysis results, it is also possible to select acoustic information that is appropriate for the exerciser's condition.

[0672] The display device transmits exercise guidance sent from the data processing unit to the exerciser in real time. This allows the user to appropriately adjust their actions during exercise, enabling them to exercise safely and efficiently. After completing their workout, the user can check their exercise history and the feedback they received via an application on their terminal, which can be used to plan their next workout.

[0673] As a concrete example, the prompt message is: "Analyze the exerciser's heart rate and pace data, and tell the generative AI model how to provide guidance if the target range is exceeded."

[0674] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0675] Step 1:

[0676] The device collects biometric information and exercise data from the user via sensors. Specifically, it obtains heart rate data from a heart rate sensor and acceleration data from an accelerometer. This data is used as input and recorded in real time. The device periodically aggregates this data and stores it in a data communication device.

[0677] Step 2:

[0678] The terminal uses a data communication device to transmit collected biometric and movement data to the server. Data is transferred using communication technologies such as Bluetooth and Wi-Fi. Input is data from the terminal, and output indicates the completion of the transfer to the server.

[0679] Step 3:

[0680] The server analyzes the received data. Specifically, it uses a generative AI model to analyze the exercise form and heart rate variability of the person exercising. Data is supplied to the server as input, and as a result of the data analysis, information that forms the basis for exercise guidance is obtained. This information becomes the output.

[0681] Step 4:

[0682] The server generates exercise guidance based on the analysis results. For example, if the heart rate exceeds the target range, it will create specific advice such as "you should slow down." The generating AI model processes the data and outputs detailed guidance content.

[0683] Step 5:

[0684] The server sends the generated exercise instructions to the terminal. The output from the server is the content of the exercise instructions, which is transmitted to the terminal via communication.

[0685] Step 6:

[0686] The terminal notifies the exerciser of the received instructions in real time. As input, it retrieves the instruction content received from the server and notifies the user visually or audibly using a display device. Output is presented to the exerciser as an audio message or screen display.

[0687] Step 7:

[0688] Users adjust their exercise behavior based on the instructions they receive. For example, they might slow down their pace after receiving a notification. After the exercise, users check the feedback on an application on their device. The input is the instructions and sensory feedback received during the exercise, and the output is a post-exercise review and adjustment plan for the next time.

[0689] (Application Example 1)

[0690] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0691] There is a need to provide technology that offers personalized exercise guidance in real time, supporting users in exercising safely and effectively. Furthermore, this technology needs to work in conjunction with robots that act as personal trainers to enable more comprehensive exercise guidance.

[0692] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0693] In this invention, the server includes means for a sensing device to acquire the user's physiological information and movement information, means for an information processing device to perform motion analysis based on the physiological information and movement information and create optimal exercise guidance, and means for a robot device to provide physical escort to the user during movement and transmit additional exercise guidance via voice. This makes it possible to provide advanced exercise guidance in real time through robotic escort.

[0694] A "sensing device" is a device used to acquire a user's physiological and movement information in real time.

[0695] An "information processing device" is a system that performs motion analysis based on acquired physiological and movement information to create optimal exercise guidance for the user.

[0696] A "display device" is an interface that transmits exercise instructions generated by an information processing device to the user in real time.

[0697] A "robot device" is a machine that provides physical support to the user during exercise and delivers additional exercise instructions via voice.

[0698] "Physiological information" refers to data related to the user's physical condition, such as heart rate, respiratory rate, and body temperature.

[0699] "Movement information" refers to data related to the user's movements, such as acceleration and location information.

[0700] "Motor analysis" is a process for evaluating a user's motor skills based on physiological and movement data, and for deriving appropriate guidance.

[0701] "Physical escort" refers to a robotic device actually moving in accordance with the user's movements and acting together with them.

[0702] "Communicating via voice" refers to an output method that uses voice, in addition to visual information, to directly instruct users.

[0703] The system for carrying out this invention consists of a sensing device, an information processing device, a display device, and a robotic device. This system safely and effectively provides personalized exercise guidance to users.

[0704] The server collects physiological information such as heart rate, acceleration, and location data, as well as movement information, in real time from sensing devices. This information is acquired via sensors attached to the user's body and transmitted to the server wirelessly. The information processing device uses a program developed in Python to analyze this data using NumPy and Pandas to evaluate exercise form and biological state.

[0705] Furthermore, a machine learning model is built using Sci-kit Learn, which learns the user's movement patterns and generates optimal instruction. The generated exercise instruction is then converted into speech using Google Cloud's Text-to-Speech API.

[0706] The display device transmits these voice instructions to the user in real time, helping the user take appropriate actions during exercise. A robotic device also operates as part of this system, providing physical support and, as needed, voice-guided exercise instructions to the user.

[0707] For example, if a user's heart rate becomes too high while jogging, the display device will provide voice advice such as, "Let's slow down." This advice is based on real-time analysis performed by the information processing device.

[0708] An example of a prompt to a generating AI model might be, "Please suggest effective guidance methods if your heart rate data exceeds the target range during jogging." This prompt asks the AI ​​model to provide appropriate guidance based on a specific scenario during exercise.

[0709] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0710] Step 1:

[0711] The server acquires biometric information such as heart rate, acceleration, and location, as well as movement information, from the user's sensing device. This information is transmitted in real time via wireless communication and stored on the server. The input is biometric information and movement information, and the output is the dataset to be analyzed.

[0712] Step 2:

[0713] The server analyzes the received data using a Python program. It performs preprocessing using NumPy and Pandas to format the data. Specifically, it imputes missing values ​​and normalizes the data to convert it into a format suitable for machine learning models. The input is a dataset of biometric and mobility information, and the output is the preprocessed dataset.

[0714] Step 3:

[0715] The server inputs pre-processed data into a machine learning model using Sci-kit Learn. This process analyzes the user's exercise patterns and calculates optimal exercise guidance. The input is a pre-processed dataset, and the output is guidelines and advice for exercise guidance.

[0716] Step 4:

[0717] The server converts the generated exercise instructions into speech using the Google Cloud Text-to-Speech API. This speech data is then converted into a format provided to the user. The input is exercise guidelines and advice, and the output is speech data.

[0718] Step 5:

[0719] The device transmits generated audio data to the user in real time. Using a display device and speaker, it provides timely advice to help the user take appropriate actions during exercise. The input is audio data, and the output is audio output to the user.

[0720] Step 6:

[0721] The robotic device also provides physical guidance to the user and offers additional exercise instruction via voice as needed. The robotic device further enhances real-time exercise support by operating based on instructions from the server. Inputs are the user's movement data and instructions from the server, while outputs are physical guidance and voice instruction.

[0722] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0723] This invention provides a system that offers personalized exercise guidance to individuals, and incorporates an emotion engine. The system consists of sensors, a server, and terminals.

[0724] Sensors attached to the device collect biometric information and exercise data from the user in real time during exercise. This data includes heart rate, pace, acceleration, and information necessary to estimate the user's emotions, such as facial expressions and voice tone. The collected data is transmitted to a server.

[0725] The server analyzes the received data. In particular, it uses an emotion engine to recognize the user's emotional state from their biometric information. By analyzing the emotional state, it can estimate the user's motivation and stress level during exercise. Based on this information, the server generates specially tailored exercise guidance. For example, if the user is feeling tense, the server can suggest exercises and music that have a relaxing effect.

[0726] Furthermore, the server selects music that matches the user's emotional state, providing music that enhances the enjoyment and effectiveness of exercise. This music selection is optimized for each individual user based on analysis by the emotion engine.

[0727] The device notifies the user in real time of exercise instructions and selected music sent from the server. This allows the user to continue exercising safely and effectively while receiving feedback that fits their emotional state. For example, when the user is feeling stressed, calming music can be played along with advice such as, "Take a deep breath and slow down."

[0728] Users can adjust their behavior during exercise based on the provided guidance and receive detailed feedback after the exercise is completed. This allows users to understand their emotional state and use that information to plan their next exercise session. This invention optimizes exercise according to individual emotional states and supports the exerciser in achieving their goals.

[0729] The following describes the processing flow.

[0730] Step 1:

[0731] The device collects biometric information, exercise data, and data necessary for emotion estimation from the exerciser in real time through sensors. This includes heart rate, pace, acceleration, and facial expression information.

[0732] Step 2:

[0733] The terminal sends the collected data to the server. The data is divided into optimal packets according to the broadcast conditions and then transmitted.

[0734] Step 3:

[0735] The server analyzes the received data and evaluates fluctuations in heart rate and acceleration. It also uses an emotion engine to recognize the user's emotional state from biometric information.

[0736] Step 4:

[0737] The server generates personalized exercise guidance based on analysis of exercise data and emotional state. This guidance may include advice on appropriate pacing and relaxation.

[0738] Step 5:

[0739] The server selects music that matches the user's emotional state. This music is chosen to promote motivation and relaxation, taking into account the progress of the exercise and the user's emotional state.

[0740] Step 6:

[0741] The server packages the generated exercise instructions and selected music for transmission to the terminal.

[0742] Step 7:

[0743] The device notifies the user in real time, via voice or text, of exercise instructions and music received from the server. For example, it might advise, "Your mind is tense. Take a deep breath," and then play relaxing music.

[0744] Step 8:

[0745] Users adjust their exercise behavior based on the provided advice, ensuring safe and efficient workouts. They also enjoy a more comfortable exercise experience while listening to music tailored to their emotional state.

[0746] Step 9:

[0747] After completing an exercise session, users can view detailed exercise data and emotional feedback through an application on their device. This allows them to evaluate their progress and use the information to plan their next exercise session.

[0748] (Example 2)

[0749] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0750] In modern society, effective exercise is sought after for maintaining personal health and managing stress. However, conventional exercise instruction systems do not provide guidance tailored to the emotional state and individual needs of the exerciser, making it difficult to obtain effective feedback and maintain sustained motivation. Furthermore, the lack of individualized music and exercise instruction means that the exerciser does not receive the optimal exercise experience.

[0751] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0752] In this invention, the server includes means for recognizing emotional states using an AI model generated based on physical information and activity data, means for generating optimized activity guidance based on the emotional state, and means for selecting personalized music that matches the emotional state. This makes it possible to provide exercisers with personalized exercise guidance and music, thereby maximizing the effects of exercise, reducing stress, and maintaining motivation.

[0753] A "sensor" is a device used to acquire physical information and activity data from a person exercising.

[0754] "Physical information" refers to biological information acquired during exercise, such as data on the exerciser's heart rate, body movements, and posture.

[0755] "Activity data" refers to information about the content and intensity of exercise, such as the exerciser's acceleration and pace.

[0756] A "server" is a computer system that receives information sent from sensors and uses generated AI models to perform analysis and provide guidance.

[0757] A "generative AI model" is an artificial intelligence technology used to analyze collected data and generate information about the emotional state of an exerciser and optimal exercise guidance.

[0758] "Emotional state" is an indicator that shows the psychological state of an athlete, and includes stress levels and motivation levels.

[0759] "Activity guidance" refers to specific instructions and advice provided to help athletes exercise efficiently and safely.

[0760] "Personalized music" refers to music selected according to the emotional state of the person exercising and optimized to enhance the effectiveness of the exercise.

[0761] A "terminal" is a device used to transmit exercise instructions and music from a server to the person performing the exercise.

[0762] This invention relates to a system that enhances the effectiveness of exercise and maintains motivation by providing exercise guidance and music optimized for the exerciser. This system mainly consists of sensors, a server, and terminals.

[0763] Sensors play a role in acquiring physical information and activity data from the exerciser, collecting this information via a device worn by the wearer. Specifically, this data includes heart rate, acceleration, pace, facial expressions, and voice tone. By aggregating this data, it becomes possible to accurately understand the physiological and psychological state of the exerciser during exercise.

[0764] The server receives data from sensors and analyzes it using a generative AI model. This model recognizes the exerciser's emotional state and generates exercise instructions and music tailored to their individual needs. This process allows the exerciser to receive feedback appropriate to their emotional state at any given time. For example, if the exerciser is feeling tense, they can be offered relaxation-enhancing exercise instructions and calming music.

[0765] The device notifies the user in real time of exercise instructions and music transmitted from the server. In this way, the user can adjust their exercise based on the feedback they receive and obtain an optimal exercise experience. The device is also equipped with data transmission and notification functions, and transmits information through communication with the server.

[0766] By using this system, users can optimize their exercise behavior and use the feedback afterward to plan their next workout. This allows for more effective maintenance of personal health and stress management.

[0767] A concrete example of its use is that by inputting the prompt message "What combination of exercise and music should be suggested when the user's stress level is high?" into the server, the AI ​​can provide optimal exercise guidance and music selection, and present it to the user in real time.

[0768] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0769] Step 1:

[0770] The device acquires real-time physical information and activity data such as heart rate, acceleration, pace, facial expressions, and voice tone through sensors attached to the exerciser. The input consists of various biometric data collected by the sensors, and this data is transmitted directly to the server, enabling accurate analysis.

[0771] Step 2:

[0772] The server receives biometric and activity data transmitted from the terminal. This input data is fed into a generative AI model to analyze the user's real-time emotional state. The generative AI model used here utilizes machine learning algorithms to recognize emotional states such as tension, relaxation, and stress from the user's heart rate, voice tone, etc. The output is the analysis results regarding the user's emotional state.

[0773] Step 3:

[0774] The server generates exercise instructions optimized for the user's current state, based on the results of emotion analysis. This process uses the results of emotion analysis as input, and utilizes a generation AI model to devise a set of relaxing exercises and workouts. Specific exercise instructions are provided as output.

[0775] Step 4:

[0776] In parallel, the server selects music that matches the user's emotional state. In this step, the results of the emotion analysis obtained earlier are used as input data, and a suitable song is selected for the user through a generative AI model. The output is music optimized to give the user a better exercise experience.

[0777] Step 5:

[0778] The device notifies the user in real time of exercise instructions and selected music received from the server. At this stage, the input is the data of exercise instructions and music sent from the server, and by dynamically providing this to the user, the user can perform the exercise based on the feedback. The output is the stream of specific instructions and music provided to the user during the exercise.

[0779] (Application Example 2)

[0780] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0781] There is a need to maximize the effectiveness of exercise and improve the motivation of exercisers by analyzing their psychological state in real time during exercise and providing personalized feedback tailored to that state. However, current systems are limited to analysis and guidance based on general biometric information, making it difficult to provide feedback that takes emotional states into account. This leads to a problem where appropriate exercise support cannot be provided when exercisers are experiencing stress or fatigue.

[0782] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0783] In this invention, the server includes means for collecting biometric information, exercise data, and acoustic data of an exerciser; means for performing exercise analysis through biometric information, exercise data, and emotion recognition to generate optimal exercise guidance based on the exerciser's psychological state; and means for determining audio information appropriate to the exerciser's emotional state and providing it through a terminal. This makes it possible to provide exercise guidance and audio information tailored to the individual emotional state of the exerciser, thereby enhancing the effectiveness of exercise while maintaining the exerciser's motivation.

[0784] A "sensor" is a device that collects biological information, movement data, and acoustic data from inside or on the surface of a person's body during exercise.

[0785] "Biometric information" refers to numerical values ​​and signals that indicate the physical state of an exerciser, such as heart rate, respiratory rate, and body temperature.

[0786] "Motion data" refers to data that indicates the physical state of a person's movement, such as acceleration, velocity, and position.

[0787] "Acoustic data" refers to data that captures audio signals and ambient sounds, and is used to estimate the emotions of a person performing an action.

[0788] A "server" is a computer system that analyzes collected data and generates exercise guidance and audio information optimized for the user.

[0789] "Motion analysis" is the process of analyzing a person's movement and form based on sensor data, and evaluating it through a specific algorithm.

[0790] "Emotion recognition" is a technology that estimates a person's emotions and psychological state by analyzing biometric information and acoustic data.

[0791] "Audio information" refers to music or audio messages selected according to the psychological state of the person exercising.

[0792] A "terminal" is a device that provides real-time feedback to the person exercising and has the function of outputting information and providing interactive responses.

[0793] This invention is a system for providing personalized exercise guidance to exercisers in real time. This system mainly consists of three elements: a sensor, a server, and a terminal.

[0794] Hardware and data acquisition

[0795] First, the "sensor" collects the exerciser's "biometric information" and "exercise data." This sensor measures heart rate, respiratory rate, acceleration data, acoustic data, etc., enabling estimation of the user's emotional state. The collected data is then transmitted to the "server."

[0796] Data analysis and feedback generation

[0797] Next, the server analyzes the received data. The exerciser's movements are evaluated through "exercise analysis," and "emotion recognition" is performed based on the obtained information. In particular, a generative AI model is used to estimate the user's psychological state from their biometric information, and exercise optimization is performed. This makes it possible to dynamically adjust the exercise intensity and content according to the exerciser's emotional state.

[0798] Furthermore, this "server" also has the function of generating appropriate "audio information" to promote the psychological stability of the exerciser. In this process, music and audio messages selected according to the exerciser's emotional state are used.

[0799] Feedback and Interaction

[0800] The "terminal" is responsible for notifying the user of generated exercise instructions and audio information in real time. This allows the user to perform effective and safe exercise while receiving instructions and music that are tailored to their current emotional state.

[0801] Specific example

[0802] For example, if a user is feeling tense or anxious, the server might recommend "taking deep breaths and doing stretches" and instruct them to select and play relaxing classical music from their device.

[0803] Example of a prompt

[0804] "Design a method to help users relax by suggesting appropriate fitness guidance and music when they feel anxious."

[0805] In this way, individually optimized exercise guidance is provided according to the emotional state of the exerciser, resulting in improved exercise performance and user experience.

[0806] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0807] Step 1:

[0808] The user attaches sensors to their body before starting exercise. This prepares the sensors to collect biometric information and exercise data in real time.

[0809] Step 2:

[0810] The "sensor" collects heart rate, respiratory rate, acceleration data, and acoustic data from the "user" while they are active. This collected data is sent to the "server" as information necessary for emotion estimation.

[0811] Step 3:

[0812] The "server" receives the collected data and first performs "motion analysis." It analyzes acceleration data to evaluate the type and intensity of the user's movements. At this time, it also determines whether improvement in exercise form is necessary. The output is the analysis results based on the motion data.

[0813] Step 4:

[0814] Next, the "server" uses a generative AI model to analyze "biometric information" and "acoustic data" to perform "emotion recognition" and "psychological state estimation." It estimates the user's stress and motivation levels from the input data and obtains the emotional state as output.

[0815] Step 5:

[0816] Based on the emotional state estimated by the "server," optimized exercise guidance is generated. This guidance adjusts the type and intensity of exercise according to the emotional state. The generated output is personalized exercise guidance.

[0817] Step 6:

[0818] Furthermore, the "server" uses an AI model to generate and select the "audio information" that is optimal for the user's psychological state. It utilizes the results of emotion analysis as input and selects music that promotes relaxation or concentration as output.

[0819] Step 7:

[0820] The "terminal" receives exercise instructions and audio information sent from the "server" and notifies the "user" of this information in real time. Specifically, this includes music playback and audio instruction messages.

[0821] Step 8:

[0822] The user performs exercises based on the exercise guidance and audio information provided by the device and experiences the effects. In this step, the user maximizes the effects of exercise by continuing to exercise in a way that fits their emotional state.

[0823] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0824] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0825] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0826] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0827] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0828] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0829] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0830] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0831] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0832] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0833] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0834] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0835] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0836] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0837] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0838] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0839] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0840] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0841] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0842] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0843] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0844] The following is further disclosed regarding the embodiments described above.

[0845] (Claim 1)

[0846] The sensor is a means for collecting the biometric information and movement data of the person performing the exercise,

[0847] A server performs exercise analysis based on the aforementioned biometric information and exercise data, and generates optimal exercise guidance;

[0848] The terminal provides means for notifying the exerciser of the exercise instruction in real time,

[0849] A system that includes this.

[0850] (Claim 2)

[0851] The system according to claim 1, wherein the server performs motion analysis based on acceleration data in the motion analysis.

[0852] (Claim 3)

[0853] The system according to claim 1, wherein the server selects appropriate music based on the biometric information and provides it to the exerciser through the terminal.

[0854] "Example 1"

[0855] (Claim 1)

[0856] The sensor is a means for acquiring the biometric information and movement data of the person performing the exercise,

[0857] The data communication device includes means for transmitting the biological information and motion data to the data processing device,

[0858] A data processing device performs exercise analysis based on the aforementioned biological information and exercise data, and generates safe and effective exercise guidance.

[0859] The display device includes means for transmitting the exercise instruction to the exerciser in real time,

[0860] A system that includes this.

[0861] (Claim 2)

[0862] The system according to claim 1, wherein the data processing device performs motion posture analysis based on acceleration data in the motion analysis.

[0863] (Claim 3)

[0864] The system according to claim 1, wherein the data processing device selects appropriate acoustic information based on the biological information and provides it to the person exercising through the presentation device.

[0865] "Application Example 1"

[0866] (Claim 1)

[0867] The sensing device is a means for acquiring the user's physiological information and movement information,

[0868] The information processing device performs motion analysis based on the physiological information and movement information and provides means for creating optimal exercise guidance.

[0869] The display device provides means for transmitting the exercise instruction to the user in real time,

[0870] A robotic device provides physical support to the user during operation and delivers additional exercise instructions via voice.

[0871] The robotic device includes means for providing instruction to the user regarding exercise form based on the voice guidance,

[0872] A system that includes this.

[0873] (Claim 2)

[0874] The system according to claim 1, wherein the information processing device performs motion analysis based on motion data in the motion analysis.

[0875] (Claim 3)

[0876] The system according to claim 1, wherein the information processing device selects an appropriate sound based on the physiological information and supplies it to the user through the display device.

[0877] "Example 2 of combining an emotion engine"

[0878] (Claim 1)

[0879] A means by which a sensor acquires the physical information and activity data of an exerciser,

[0880] A server uses an AI model generated based on the aforementioned physical information and activity data to recognize the emotional state and generate optimized activity guidance.

[0881] A means by which the server selects personalized music based on the aforementioned emotional state so that the activity can be carried out enjoyably and effectively,

[0882] The terminal provides means for notifying the exerciser in real time of the activity guidance and selected music,

[0883] A system that includes this.

[0884] (Claim 2)

[0885] The system according to claim 1, wherein the server performs motion form analysis based on acceleration data in the activity data.

[0886] (Claim 3)

[0887] The system according to claim 1, wherein the terminal provides feedback corresponding to the emotional state of the person exercising during exercise.

[0888] "Application example 2 when combining with an emotional engine"

[0889] (Claim 1)

[0890] The sensor is a means for collecting biometric information and movement data of the person performing the exercise, as well as acoustic data.

[0891] A server performs exercise analysis through the aforementioned biometric information, exercise data, and emotion recognition, and generates optimal exercise guidance based on the psychological state of the exerciser.

[0892] A means by which an acoustic selection program determines audio information appropriate to the emotional state of the person performing the action and provides it through the terminal,

[0893] The terminal provides means for notifying the exerciser of the exercise instruction and audio information in real time,

[0894] A system that includes this.

[0895] (Claim 2)

[0896] The system according to claim 1, wherein the server dynamically adjusts the exercise intensity based on the emotional state of the person exercising through bio-emotion analysis.

[0897] (Claim 3)

[0898] The system according to claim 1, wherein the server optimizes audio information for promoting the psychological stability of the exerciser using a generative AI model. [Explanation of symbols]

[0899] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. The sensor is a means for collecting the biometric information and movement data of the person performing the exercise, A server performs exercise analysis based on the aforementioned biometric information and exercise data, and generates optimal exercise guidance; The terminal provides means for notifying the exerciser of the exercise instruction in real time, A system that includes this.

2. The system according to claim 1, wherein the server performs motion form analysis based on acceleration data in the motion analysis.

3. The system according to claim 1, wherein the server selects appropriate music based on the biometric information and provides it to the exerciser through the terminal.

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