system

Abstract

We provide the system. [Solution] A sensor device that acquires motion data of a person exercising, A calculation device that processes the aforementioned motion data and calculates the intensity of the movement, A visualization device that visually displays the calculated exercise intensity to the person performing the exercise, A feedback device that provides feedback to the person performing the exercise based on the intensity of the exercise, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, 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] In modern exercise, there are problems of excessive or insufficient exercise due to inappropriate exercise intensity. Also, in many cases, an individual cannot receive guidance from a professional trainer, making it difficult to know the appropriate exercise intensity. Furthermore, since means for grasping the effect of exercise in real time and obtaining immediate feedback are limited, the quality of exercise may be impaired.

Means for Solving the Problems

[0005] The present invention comprises a sensor device that acquires motion data of an exerciser, and a calculation device that processes the motion data and calculates the intensity of the exercise. Furthermore, it includes a visualization device that visually presents the calculated intensity of the exercise to the exerciser, and a feedback device that provides feedback to the exerciser. This makes it possible to present the optimal exercise load to each individual in real time and maintain an appropriate exercise intensity.

[0006] A "sensor device" is a device used to acquire motion data of a person performing an exercise, and includes data collection means such as cameras and motion sensors.

[0007] A "computational device" is a device that processes acquired motion data and calculates the intensity of exercise, and it analyzes the data using a computer processor or AI algorithm.

[0008] A "visualization device" is a device that visually presents the calculated exercise intensity to the person exercising, and uses displays or head-mounted displays to show the information.

[0009] A "feedback device" is a device that provides feedback to an exerciser based on the state of their movement, and transmits information using visual and auditory means. [Brief explanation of the drawing]

[0010] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0011] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0012] First, let's explain the terminology used in the following explanation.

[0013] 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.

[0014] 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.

[0015] 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.

[0016] 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.

[0017] 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."

[0018] [First Embodiment]

[0019] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0020] 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.

[0021] 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).

[0022] 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.

[0023] 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.

[0024] 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.

[0025] 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.

[0026] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0027] 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.

[0028] 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.

[0029] 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.

[0030] 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".

[0031] This invention provides a system that measures and visualizes exercise intensity in real time using smart glasses worn by the person exercising. Specifically, a camera, acting as a sensor device, captures the user's movements and transmits them to a computing device. A server receives this movement data, analyzes it using computer vision and AI technology, and calculates the exercise intensity. This exercise intensity data is then visually displayed to the user through the device's display.

[0032] For example, suppose a user is jogging and wearing smart glasses. The device constantly records the user's running pace and movements using its camera and sends the data to a server. The server calculates whether the movement is at an appropriate speed and posture, and how much exercise load is being applied. Based on this information, the server generates feedback such as "Your current pace is appropriate" or "Let's increase your speed a little," and sends it to the device.

[0033] The device displays feedback on its screen and can also provide voice instructions to the user. Based on these instructions, the user can adjust the intensity and form of their exercise. This allows the user to understand their optimal exercise state in real time, enabling safe and effective training.

[0034] This system also features voice commands and a gesture interface, allowing users to give instructions such as "increase intensity" or "take a short break" by voice. Based on these instructions, the server re-analyzes the exercise intensity and provides a individually optimized exercise plan. By maintaining this feedback loop, the system is expected to improve the quality of training.

[0035] The following describes the processing flow.

[0036] Step 1:

[0037] The device uses a camera as a sensor to capture the user's movements in real time. The movement data includes changes in posture and movement speed.

[0038] Step 2:

[0039] The device transmits captured motion data to a server using wireless communication. The data is then converted to an optimized format for analysis.

[0040] Step 3:

[0041] The server analyzes the received motion data using computer vision technology. Machine learning algorithms are used to classify the user's movements and calculate the exercise intensity.

[0042] Step 4:

[0043] The server generates feedback based on the calculated exercise intensity. Here, AI is used to create personalized advice and instructions tailored to each individual user.

[0044] Step 5:

[0045] The server sends the generated feedback to the terminal. The feedback sent includes text data and audio data.

[0046] Step 6:

[0047] The device visually displays the received feedback to the user. The display clearly shows exercise intensity and advice, and voice guidance is also available.

[0048] Step 7:

[0049] Based on the displayed feedback, the user adjusts their exercise speed and form. They can also give voice commands to the device as needed to check the system's response.

[0050] Step 8:

[0051] The terminal recognizes voice commands from the user and sends them back to the server for further data analysis. If requested by the user, it performs a process to partially adjust the exercise plan.

[0052] Step 9:

[0053] This series of processes is repeated throughout the workout, allowing the user to constantly improve their training based on the latest feedback.

[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] Conventional exercise support systems have struggled to effectively monitor users' exercise status in real time and provide appropriate feedback. In particular, they have difficulty providing individually optimized exercise plans based on voice commands and actions, lacking the immediacy and adaptability needed to improve users' exercise efficiency.

[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 acquiring motion information using imaging means, analyzing the exercise load using calculation means, and providing visual and audible feedback using notification means. This allows the user to understand the exercise load in real time and optimize their exercise plan by utilizing voice commands based on a generated AI model.

[0059] "Imaging means" refers to equipment or devices used to acquire motion information of a person in motion, and primarily has the function of capturing images or videos.

[0060] "Calculation means" refers to functions or devices that analyze acquired motion information and estimate the load of exercise, and that have data processing capabilities.

[0061] "Display means" refers to devices or functions that visually present the estimated exercise load to the user, conveying this information to the user via a screen or display.

[0062] "Notification means" refers to devices or functions that provide instructions to the user based on the exercise load, and provides feedback through voice or visual means.

[0063] A "generative AI model" is a model that uses artificial intelligence technology and is utilized for analyzing voice commands and individually optimizing movement plans.

[0064] "Voice commands" refer to a method by which a user uses their voice to operate or instruct a system, and the system analyzes those commands and responds accordingly.

[0065] An "exercise plan" refers to a plan set up to ensure that the user exercises efficiently and safely, and includes adjustments to movements and load.

[0066] This invention is an exercise support system that grasps the appropriate exercise load for the exerciser in real time and provides individually optimized feedback. The system uses smart glasses as a terminal and includes imaging means, including a camera for capturing the user's movements.

[0067] The server receives this operational information and performs analysis using a generated AI model. This analysis utilizes computer vision technology and machine learning algorithms, and includes the ability to accurately estimate the exercise load from the data. The analysis results are used to evaluate the exercise intensity and the appropriateness of the form, and feedback based on the results is generated by the server.

[0068] The generated feedback is sent to the terminal and presented to the user via display or audio output. The system also has the ability to accept voice commands from the user and dynamically adjust the motor plan. The user operates the system by providing specific instructions by voice.

[0069] This technology allows users to maintain an appropriate exercise pace, avoid excessive strain, and train efficiently. For example, it can be used to adjust pace or correct form during jogging. An example of a prompt message would be, "Please provide detailed instructions for each step of the exercise intensity analysis system using smart glasses."

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

[0071] Step 1:

[0072] The device uses the smart glasses' camera to capture the user's movements in real time. In this process, the input is the user's physical movements, and the output is video data. The camera captures the user's joint positions and movement patterns in detail, and generates video that reflects this information.

[0073] Step 2:

[0074] The terminal compresses the captured video data and transmits it to the server via wireless communication. The input is the video data obtained on the terminal side, and the output is the compressed data sent to the server. A specific compression algorithm is used to efficiently transfer the data.

[0075] Step 3:

[0076] The server decompresses the received compressed data and analyzes it as motion information. The input is compressed video data, and the output is analysis data that numerically represents joint positions and movement patterns. Using computer vision technology, it extracts motion-related features and inputs them into a generated AI model.

[0077] Step 4:

[0078] The server processes motion information using a generative AI model to estimate the exercise load. The input is the analyzed motion data, and the output is numerical information of the estimated exercise load. The AI ​​model calculates the optimal load level based on a pre-trained motion dataset.

[0079] Step 5:

[0080] The server generates feedback to provide to the user based on the estimated exercise load. The input is exercise load information, and the output is visual and audio feedback data. The generating AI model verbalizes advice tailored to the user's current situation, providing effective guidance.

[0081] Step 6:

[0082] The terminal displays feedback received from the server on its screen and communicates it to the user via voice. The input is feedback data from the server, and the output is the information presented to the user. The display provides visual messages, and speech synthesis technology generates the voice feedback.

[0083] Step 7:

[0084] The user adjusts their movements based on the feedback and sends new voice commands to the device. Input is the display and voice feedback, and output is the user's voice commands. The voice commands are sent to the server and incorporated into the next analysis cycle.

[0085] Step 8:

[0086] The server analyzes the user's voice commands and readjusts the motor plan. The input is the user's voice commands, and the output is the adjusted motor plan. The generative AI model continues the feedback loop by analyzing the command content and generating individually optimized suggestions.

[0087] (Application Example 1)

[0088] 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."

[0089] Modern commercial facilities require detailed analysis of customer behavior and the design of effective store layouts based on that analysis. However, traditional methods make it difficult to collect and analyze detailed customer behavior data in real time, which can lead to situations where optimal layout proposals are not made. Furthermore, there is a lack of means for employees to immediately visualize and utilize this data.

[0090] 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.

[0091] In this invention, the server includes sensor means for acquiring motion data of exercisers and customers, calculation means for processing the motion data and calculating the intensity of exercise and customer behavior, and visualization means for visually presenting the calculated information to exercisers and store staff. This enables real-time tracking of customer behavior in stores, allowing for optimal layout design, as well as improved operational efficiency and enhanced sales strategies.

[0092] "Sensor means" refers to a group of devices that accurately capture the movements of a person or customer, and includes devices such as cameras.

[0093] A "calculation means" is a device or program that calculates the intensity of exercise and customer behavior based on acquired motion data and performs the necessary analysis.

[0094] "Visualization means" refers to a device or display interface for visually showing the calculated exercise intensity or customer movements to the person exercising or store staff.

[0095] A "feedback mechanism" is a device or program that provides necessary information and instructions to individuals exercising or store staff, and adjusts their actions and layout accordingly.

[0096] A "recording device" is a device used to record the movements of a person exercising or a customer as video footage, and includes equipment such as cameras.

[0097] The system of this invention tracks the movements of exercisers or customers in real time and provides feedback by analyzing the intensity of exercise and customer behavior. The system includes sensor means, calculation means, visualization means, and feedback means.

[0098] The sensor system uses cameras and other imaging devices mounted on smart glasses to acquire motion data of people exercising or customers. It also transmits the video data acquired from the imaging devices to a server.

[0099] The server receives video data and analyzes the motion data using OpenCV and TENSORFLOW® as computational tools. Specifically, it calculates exercise intensity and customer movements through data processing, and then aggregates and analyzes this data using NumPy and Pandas.

[0100] For visualization, the display functions of displays and smart glasses are used. The calculated results are presented visually using visualization tools such as Matplotlib and Plotly, allowing information to be provided to users and store staff in real time.

[0101] The feedback mechanisms, such as voice instructions and gestures, provide information regarding adjusting exercise intensity and optimizing store layout. This allows users and store staff to adopt appropriate actions and strategies based on the situation.

[0102] For example, if the system detects that a customer is spending a long time in front of a particular product in a store, staff can use that information to change the product's location and conduct more efficient promotional activities.

[0103] An example of a prompt when using a generative AI model is: "To optimize customer movement within the store, please input customer behavior data into the in-store movement analysis AI and generate specific improvement suggestions regarding product placement."

[0104] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0105] Step 1:

[0106] The device uses a camera mounted on smart glasses to capture the movements of people exercising or customers, and acquires the video data. This video data is acquired in real time by sensor means and transmitted to a server.

[0107] Step 2:

[0108] The server receives the acquired video data and analyzes the motion data using OpenCV and TensorFlow as computational tools. Specifically, the server extracts motion features from the video and performs analysis to calculate the intensity of the movement and the movement of the customers. The input is video data, and the output is data on the intensity of the movement and the movement path of the customers.

[0109] Step 3:

[0110] The server aggregates the analysis results using NumPy and Pandas and analyzes the data. Here, data processing and calculations are performed to generate statistical information on exercise intensity and customer movement patterns. The input is the analyzed data, and the output is aggregated statistical data.

[0111] Step 4:

[0112] The terminal visually displays exercise intensity and customer movement information using visualization tools. By displaying aggregated results from the server on the screen, users or store staff can make immediate decisions based on that information. The input is aggregated statistical data, and the output is a visualized display.

[0113] Step 5:

[0114] The user uses feedback mechanisms to provide voice instructions and gesture input, adjusting exercise intensity and rearranging the store layout as needed. Based on these actions, the server re-analyzes the updated information and generates optimized feedback. The input is user instruction data, and the output is optimized feedback information.

[0115] 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.

[0116] This invention is a system that uses smart glasses worn by the user as a terminal to measure the intensity of exercise and recognize the user's emotions in real time. This system comprehensively analyzes the user's movements and emotional state during exercise and provides feedback to realize a more personalized training experience.

[0117] Specifically, the device uses a camera as a sensor to capture the user's movements. Furthermore, this camera analyzes the user's facial expressions to recognize their emotions. The data transmitted from the device is processed on a server, where computer vision technology and machine learning are used to calculate the intensity of the movement and the user's emotional state. Based on these analysis results, feedback that is best suited to the user's emotions is generated.

[0118] For example, if a user is feeling stressed while exercising, the emotion engine will recognize this, and the server will suggest an exercise plan to reduce stress. This feedback is displayed visually on the screen and also provided as voice assistance.

[0119] Furthermore, the emotion engine receives situation-appropriate data from the user's voice commands and gestures, allowing the entire system to adjust dynamically. For example, if a user desires "relaxing exercise," that instruction is sent to the server and analyzed along with the movement data. This determines a new exercise plan tailored to the user's mood.

[0120] This system can respond to emotional changes that could not be observed with conventional exercise feedback systems, enabling comprehensive training tailored to the user's physical and mental needs.

[0121] The following describes the processing flow.

[0122] Step 1:

[0123] The device uses a camera as a sensor to capture the user's movements and facial expressions in real time. Movement data and facial expression data are acquired.

[0124] Step 2:

[0125] The device sends the acquired motion data and facial expression data to the server. The data is then converted into a format suitable for analysis.

[0126] Step 3:

[0127] The server analyzes motion data using computer vision technology and machine learning algorithms to calculate the intensity of the movement. Facial expression data is analyzed by an emotion engine to recognize the user's emotional state.

[0128] Step 4:

[0129] The server generates feedback based on exercise intensity and the user's emotional state. If the user is feeling down, for example, it will incorporate exercise suggestions that promote relaxation.

[0130] Step 5:

[0131] The server sends the generated feedback to the terminal. The feedback is provided as visual information and audio guidance.

[0132] Step 6:

[0133] The device displays feedback on its screen and provides voice guidance to the user as needed. For example, it might display specific instructions such as, "Slightly reduce the intensity of your exercise and take some deep breaths."

[0134] Step 7:

[0135] The user adjusts the speed and direction of their movements based on the displayed feedback. They can also give further instructions to the device using voice commands if necessary.

[0136] Step 8:

[0137] The terminal analyzes voice commands from the user and sends them to the server for further analysis and adjustments. For example, if the user gives the command "make it easier," the emotional state is re-analyzed based on that request.

[0138] Step 9:

[0139] This feedback loop is repeated continuously throughout the exercise period, allowing the user to perform optimal training tailored to their own physical and emotional state.

[0140] (Example 2)

[0141] 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".

[0142] Conventional exercise feedback systems focus primarily on measuring exercise intensity, making it difficult to provide feedback that takes into account the user's emotional state during exercise. As a result, it was difficult to provide training plans that matched the user's mental state, and personalization was challenging. This invention aims to solve this problem by comprehensively analyzing the user's exercise and emotional state to provide more appropriate training feedback.

[0143] 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.

[0144] In this invention, the server includes information gathering means for acquiring motion data and facial expression data of a person exercising; analysis means for processing the motion data and facial expression data and simultaneously calculating the intensity of the exercise and the emotional state; and generation means for generating optimal feedback based on the calculated intensity of the exercise and the emotional state. This makes it possible to provide a comprehensive and personalized training experience that is tailored to the user's physical and emotional state.

[0145] "Information gathering means" refers to the general term for devices and technologies used to acquire motion data and facial expression data of people performing exercises.

[0146] "Analysis means" refers to devices and technologies that process acquired motion data and facial expression data to simultaneously calculate the intensity of movement and emotional state.

[0147] A "generating means" refers to a device or technology that formulates optimal feedback for the person exercising based on the calculated exercise intensity and emotional state.

[0148] "Display means" refers to devices or technologies for presenting generated feedback to a person in motion, either visually or audibly.

[0149] "Interaction means" refers to devices and technologies that receive voice commands and gestures from a person performing an action and then interact with them.

[0150] "Adjustment means" refers to devices and technologies that dynamically adapt analysis means and generation means based on interaction to optimize the movement plan.

[0151] A "generative AI model" is a type of artificial intelligence technology used to generate feedback in natural language based on the user's emotional state.

[0152] This invention relates to a system that measures the intensity of exercise and recognizes the user's emotions in real time by having the user wear smart glasses as a terminal. The aim of this system is to comprehensively analyze the user's movements and emotional state and provide appropriate feedback.

[0153] The device uses a camera built into smart glasses to capture the user's movements and facial expressions as a means of information gathering. It is also equipped with sensors to collect movement and facial expression data, which are then transmitted to a server via wireless communication. Wi-Fi and Bluetooth are used for communication. The server processes the received data using analytical tools. In this process, image processing libraries known as computer vision technologies (e.g., OpenCV) are used, and machine learning models (e.g., TensorFlow) are used to simultaneously analyze movement characteristics and emotional states.

[0154] The generating AI model generates optimal feedback based on the analysis results, and this feedback is provided to the user through a display mechanism. The feedback is displayed as visual information on the smart glasses' display and, if necessary, is also provided audibly via the voice assistance function.

[0155] Users can easily interact with the system using voice commands and gestures. For example, if a user gives a voice command to the device saying, "I want to do some relaxing exercise," that information is sent to the server, which then dynamically optimizes the exercise plan through the interaction mechanism. The exercise plan generated in this way is then provided individually based on feedback from a generating AI model.

[0156] An example of a prompt is, "If the user is feeling stressed, explain how to suggest an exercise plan to stabilize their emotions." In this form, the present invention provides a training experience that comprehensively supports the user's physical and emotional changes.

[0157] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0158] Step 1:

[0159] The device, worn by the user as smart glasses, uses built-in sensors to record the user's movements and facial expressions in real time. The input is a camera that captures the user's body movements and facial expressions, which are collected as motion data and facial expression data. The output is the recorded motion data and facial expression data.

[0160] Step 2:

[0161] The terminal transmits collected motion and facial expression data to the server via wireless communication. The input is the collected raw data, and the output is digital data transferred to the server. A communication protocol is used for this transfer, typically Bluetooth or Wi-Fi.

[0162] Step 3:

[0163] The server processes the received data using computer vision technology and machine learning algorithms. Inputs include motion data and facial expression data received wirelessly. For specific motions, computer vision technology (e.g., OpenCV) is used to extract motion features, and a machine learning model (e.g., TensorFlow) is used to identify emotional states. The output provides information on the intensity of the analyzed motion and the emotional state.

[0164] Step 4:

[0165] The server uses a generative AI model based on the analysis results to generate feedback that is best suited to the user's emotional state. The inputs are the analyzed exercise intensity and emotional state. Natural language processing using the generative AI model generates feedback that is easy for the user to understand. This feedback is tailored to the user's plan and is generated as an action plan or advice.

[0166] Step 5:

[0167] The server sends the generated feedback to the device, and the device displays the information on the smart glasses' display. The input is the generated feedback data, and the output is the interface information that the user receives through visual or voice assistance functions. Specifically, the feedback text is displayed on the screen, or voice guidance is played through the earphones.

[0168] Step 6:

[0169] The user inputs new instructions into the terminal using voice or gestures, and sends this to the server to adjust the feedback and motor plan. The input consists of new voice commands and gesture data from the user. The server receives this information and uses adjustment mechanisms to dynamically readjust the entire system and regenerate an optimized motor plan. The output is the updated feedback and motor plan.

[0170] (Application Example 2)

[0171] 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 device 14 will be referred to as the "terminal."

[0172] In industrial environments, workers often experience stress and fatigue due to long working hours and monotonous tasks, which can negatively impact work efficiency and safety. Therefore, there is a need for methods to improve work efficiency and safety by analyzing workers' emotional states in real time and providing appropriate feedback.

[0173] 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.

[0174] In this invention, the server includes sensor means for acquiring worker motion data, calculation means for processing the motion data and calculating the intensity of the movement, and analysis means for recognizing the worker's emotional state in an industrial environment and generating feedback. This makes it possible to improve work efficiency and ensure safety based on the worker's state.

[0175] A "sensor" is a device used to acquire motion data of a person moving, and is used to collect necessary information in a specific environment.

[0176] "Calculation means" refers to a device or mechanism for processing acquired motion data and calculating the intensity of movement, and is a device equipped with the function of analyzing and quantifying data.

[0177] A "visualization device" is a device used to visually present the calculated exercise intensity to the person performing the exercise, and is a device used to display information in an easy-to-understand manner.

[0178] A "feedback device" is a device that provides appropriate feedback to the person exercising based on the intensity of the exercise, and has the function of assisting the next action by outputting information.

[0179] An "analysis means" is a mechanism that recognizes the emotional state of workers in an industrial environment and generates feedback to improve work efficiency, and is a device that performs precise analysis using the acquired data.

[0180] The invention based on this application is a system that analyzes the actions and emotional states of workers in an industrial environment in real time and provides appropriate feedback. The system consists of sensor means, calculation means, visualization means, and analysis means.

[0181] The sensor system uses a camera mounted on a device such as smart glasses to acquire motion data from the worker. The data collected by the sensor system is transmitted to a server via a network. The server functions as a computing device, processing the received motion data to calculate the intensity of the movement. Next, the server uses an analysis device to recognize the emotional state based on the worker's facial expression data. Computer vision technology and machine learning models are used in this process.

[0182] Based on the analysis results, the server provides appropriate feedback to the worker through a digital display and audio output device. This feedback includes specific suggestions for stress reduction and efficiency improvement, tailored to the worker's condition.

[0183] In particular, the generative AI model contributes to generating personalized feedback by analyzing the worker's facial expressions and movements. An example of a prompt for this generative AI model would be, "Analyze the operator's current situation and generate the optimal work break plan."

[0184] For example, if a worker shows signs of fatigue, the server uses analysis tools to detect it and displays feedback on the screen such as, "We recommend taking a 5-minute break," along with voice assistance. In this way, the system improves work efficiency and the work environment.

[0185] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0186] Step 1:

[0187] The terminal captures the worker's movements with a camera and acquires raw video data. The input is the video data acquired by the camera, and the output is this data itself.

[0188] Step 2:

[0189] The terminal transmits the captured video data to the server via wireless communication. The input is the video data, and the output is the state in which the data has been transferred to the server.

[0190] Step 3:

[0191] The server analyzes the received video data using computer vision technology. Specifically, a motion detection algorithm extracts the worker's movements and facial expressions from the video data. The input to this process is video data, and the output is data containing extracted motion and facial features.

[0192] Step 4:

[0193] The server uses a generative AI model to estimate the worker's emotional state from motion and facial expression data. The input is extracted feature data, and by classifying the emotional state using machine learning, an emotional state label is obtained as output.

[0194] Step 5:

[0195] The server generates appropriate feedback for the worker based on their emotional state label. The prompt used is "Analyze the operator's current state and generate an optimal work break plan." A generative AI model is used to generate the feedback; the input is the emotional state label, and the output is the feedback message.

[0196] Step 6:

[0197] The server sends the generated feedback to the terminal. The input is the feedback message, and the output is that it is displayed or played back on the terminal.

[0198] Step 7:

[0199] The user (worker) receives visual and auditory feedback from the terminal and adjusts their actions based on it. The input is feedback information, and the output is the worker's actions that are expected to improve work efficiency or reduce stress.

[0200] 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.

[0201] 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 those described above. 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 shown 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.

[0202] 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.

[0203] [Second Embodiment]

[0204] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0205] 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.

[0206] 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).

[0207] 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.

[0208] 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.

[0209] 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).

[0210] 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.

[0211] 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.

[0212] 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.

[0213] 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.

[0214] 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.

[0215] 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".

[0216] This invention provides a system that measures and visualizes exercise intensity in real time using smart glasses worn by the person exercising. Specifically, a camera, acting as a sensor device, captures the user's movements and transmits them to a computing device. A server receives this movement data, analyzes it using computer vision and AI technology, and calculates the exercise intensity. This exercise intensity data is then visually displayed to the user through the device's display.

[0217] For example, suppose a user is jogging and wearing smart glasses. The device constantly records the user's running pace and movements using its camera and sends the data to a server. The server calculates whether the movement is at an appropriate speed and posture, and how much exercise load is being applied. Based on this information, the server generates feedback such as "Your current pace is appropriate" or "Let's increase your speed a little," and sends it to the device.

[0218] The device displays feedback on its screen and can also provide voice instructions to the user. Based on these instructions, the user can adjust the intensity and form of their exercise. This allows the user to understand their optimal exercise state in real time, enabling safe and effective training.

[0219] This system also features voice commands and a gesture interface, allowing users to give instructions such as "increase intensity" or "take a short break" by voice. Based on these instructions, the server re-analyzes the exercise intensity and provides a individually optimized exercise plan. By maintaining this feedback loop, the system is expected to improve the quality of training.

[0220] The following describes the processing flow.

[0221] Step 1:

[0222] The device uses a camera as a sensor to capture the user's movements in real time. The movement data includes changes in posture and movement speed.

[0223] Step 2:

[0224] The device transmits captured motion data to a server using wireless communication. The data is then converted to an optimized format for analysis.

[0225] Step 3:

[0226] The server analyzes the received motion data using computer vision technology. Machine learning algorithms are used to classify the user's movements and calculate the exercise intensity.

[0227] Step 4:

[0228] The server generates feedback based on the calculated exercise intensity. Here, AI is used to create personalized advice and instructions tailored to each individual user.

[0229] Step 5:

[0230] The server sends the generated feedback to the terminal. The feedback sent includes text data and audio data.

[0231] Step 6:

[0232] The device visually displays the received feedback to the user. The display clearly shows exercise intensity and advice, and voice guidance is also available.

[0233] Step 7:

[0234] Based on the displayed feedback, the user adjusts their exercise speed and form. They can also give voice commands to the device as needed to check the system's response.

[0235] Step 8:

[0236] The terminal recognizes voice commands from the user and sends them back to the server for further data analysis. If requested by the user, it performs a process to partially adjust the exercise plan.

[0237] Step 9:

[0238] This series of processes is repeated throughout the workout, allowing the user to constantly improve their training based on the latest feedback.

[0239] (Example 1)

[0240] 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."

[0241] Conventional exercise support systems have struggled to effectively monitor users' exercise status in real time and provide appropriate feedback. In particular, they have difficulty providing individually optimized exercise plans based on voice commands and actions, lacking the immediacy and adaptability needed to improve users' exercise efficiency.

[0242] 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.

[0243] In this invention, the server includes means for acquiring motion information using imaging means, analyzing the exercise load using calculation means, and providing visual and audible feedback using notification means. This allows the user to understand the exercise load in real time and optimize their exercise plan by utilizing voice commands based on a generated AI model.

[0244] "Imaging means" refers to equipment or devices used to acquire motion information of a person in motion, and primarily has the function of capturing images or videos.

[0245] "Calculation means" refers to functions or devices that analyze acquired motion information and estimate the load of exercise, and that have data processing capabilities.

[0246] "Display means" refers to devices or functions that visually present the estimated exercise load to the user, conveying this information to the user via a screen or display.

[0247] "Notification means" refers to devices or functions that provide instructions to the user based on the exercise load, and provides feedback through voice or visual means.

[0248] A "generative AI model" is a model that uses artificial intelligence technology and is utilized for analyzing voice commands and individually optimizing movement plans.

[0249] "Voice commands" refer to a method by which a user uses their voice to operate or instruct a system, and the system analyzes those commands and responds accordingly.

[0250] An "exercise plan" refers to a plan set up to ensure that the user exercises efficiently and safely, and includes adjustments to movements and load.

[0251] This invention is an exercise support system that grasps the appropriate exercise load for the exerciser in real time and provides individually optimized feedback. The system uses smart glasses as a terminal and includes imaging means, including a camera for capturing the user's movements.

[0252] The server receives this operational information and performs analysis using a generated AI model. This analysis utilizes computer vision technology and machine learning algorithms, and includes the ability to accurately estimate the exercise load from the data. The analysis results are used to evaluate the exercise intensity and the appropriateness of the form, and feedback based on the results is generated by the server.

[0253] The generated feedback is sent to the terminal and presented to the user via display or audio output. The system also has the ability to accept voice commands from the user and dynamically adjust the motor plan. The user operates the system by providing specific instructions by voice.

[0254] This technology allows users to maintain an appropriate exercise pace, avoid excessive strain, and train efficiently. For example, it can be used to adjust pace or correct form during jogging. An example of a prompt message would be, "Please provide detailed instructions for each step of the exercise intensity analysis system using smart glasses."

[0255] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0256] Step 1:

[0257] The device uses the smart glasses' camera to capture the user's movements in real time. In this process, the input is the user's physical movements, and the output is video data. The camera captures the user's joint positions and movement patterns in detail, and generates video that reflects this information.

[0258] Step 2:

[0259] The terminal compresses the captured video data and transmits it to the server via wireless communication. The input is the video data obtained on the terminal side, and the output is the compressed data sent to the server. A specific compression algorithm is used to efficiently transfer the data.

[0260] Step 3:

[0261] The server decompresses the received compressed data and analyzes it as motion information. The input is compressed video data, and the output is analysis data that numerically represents joint positions and movement patterns. Using computer vision technology, it extracts motion-related features and inputs them into a generated AI model.

[0262] Step 4:

[0263] The server processes motion information using a generative AI model to estimate the exercise load. The input is the analyzed motion data, and the output is numerical information of the estimated exercise load. The AI ​​model calculates the optimal load level based on a pre-trained motion dataset.

[0264] Step 5:

[0265] The server generates feedback to provide to the user based on the estimated exercise load. The input is exercise load information, and the output is visual and audio feedback data. The generating AI model verbalizes advice tailored to the user's current situation, providing effective guidance.

[0266] Step 6:

[0267] The terminal displays feedback received from the server on its screen and communicates it to the user via voice. The input is feedback data from the server, and the output is the information presented to the user. The display provides visual messages, and speech synthesis technology generates the voice feedback.

[0268] Step 7:

[0269] The user adjusts their movements based on the feedback and sends new voice commands to the device. Input is the display and voice feedback, and output is the user's voice commands. The voice commands are sent to the server and incorporated into the next analysis cycle.

[0270] Step 8:

[0271] The server analyzes the user's voice commands and readjusts the motor plan. The input is the user's voice commands, and the output is the adjusted motor plan. The generative AI model continues the feedback loop by analyzing the command content and generating individually optimized suggestions.

[0272] (Application Example 1)

[0273] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0274] Modern commercial facilities require detailed analysis of customer behavior and the design of effective store layouts based on that analysis. However, traditional methods make it difficult to collect and analyze detailed customer behavior data in real time, which can lead to situations where optimal layout proposals are not made. Furthermore, there is a lack of means for employees to immediately visualize and utilize this data.

[0275] 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.

[0276] In this invention, the server includes sensor means for acquiring motion data of exercisers and customers, calculation means for processing the motion data and calculating the intensity of exercise and customer behavior, and visualization means for visually presenting the calculated information to exercisers and store staff. This enables real-time tracking of customer behavior in stores, allowing for optimal layout design, as well as improved operational efficiency and enhanced sales strategies.

[0277] "Sensor means" refers to a group of devices that accurately capture the movements of a person or customer, and includes devices such as cameras.

[0278] A "calculation means" is a device or program that calculates the intensity of exercise and customer behavior based on acquired motion data and performs the necessary analysis.

[0279] "Visualization means" refers to a device or display interface for visually showing the calculated exercise intensity or customer movements to the person exercising or store staff.

[0280] "Feedback means" refers to a device or program that provides necessary information and instructions to moving individuals or store staff and adjusts actions and layouts.

[0281] "Imaging device" refers to a device used to record the actions of moving individuals or customers as images, including equipment such as cameras.

[0282] The system of this invention provides feedback by tracking the actions of moving individuals or customers in real time and analyzing the intensity of movement and customer behavior. The system includes sensor means, computing means, visualization means, and feedback means.

[0283] The sensor means uses an imaging device such as a camera mounted on smart glasses to acquire the action data of moving individuals or customers. Also, the video data acquired from the imaging device is transmitted to a server.

[0284] The server receives the video data and analyzes the action data using OpenCV or TensorFlow as computing means. Specifically, the intensity of movement and customer trends are calculated through data processing, and they are aggregated and analyzed using NumPy or Pandas.

[0285] As visualization means, the display function of a display or smart glasses is used. The calculated results are visually presented using visualization tools such as Matplotlib or Plotly, and information can be provided to users or store staff in real time.

[0286] The feedback means provides information regarding the adjustment of movement intensity and the optimization of store layout through voice instructions, gestures, etc. This enables users or store staff to adopt appropriate actions and strategies according to the situation.

[0287] For example, if the system detects that a customer is spending a long time in front of a particular product in a store, staff can use that information to change the product's location and conduct more efficient promotional activities.

[0288] An example of a prompt when using a generative AI model is: "To optimize customer movement within the store, please input customer behavior data into the in-store movement analysis AI and generate specific improvement suggestions regarding product placement."

[0289] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0290] Step 1:

[0291] The device uses a camera mounted on smart glasses to capture the movements of people exercising or customers, and acquires the video data. This video data is acquired in real time by sensor means and transmitted to a server.

[0292] Step 2:

[0293] The server receives the acquired video data and analyzes the motion data using OpenCV and TensorFlow as computational tools. Specifically, the server extracts motion features from the video and performs analysis to calculate the intensity of the movement and the movement of the customers. The input is video data, and the output is data on the intensity of the movement and the movement path of the customers.

[0294] Step 3:

[0295] The server aggregates the analysis results using NumPy and Pandas and analyzes the data. Here, data processing and calculations are performed to generate statistical information on exercise intensity and customer movement patterns. The input is the analyzed data, and the output is aggregated statistical data.

[0296] Step 4:

[0297] The terminal visually displays exercise intensity and customer movement information using visualization tools. By displaying aggregated results from the server on the screen, users or store staff can make immediate decisions based on that information. The input is aggregated statistical data, and the output is a visualized display.

[0298] Step 5:

[0299] The user uses feedback mechanisms to provide voice instructions and gesture input, adjusting exercise intensity and rearranging the store layout as needed. Based on these actions, the server re-analyzes the updated information and generates optimized feedback. The input is user instruction data, and the output is optimized feedback information.

[0300] 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.

[0301] This invention is a system that uses smart glasses worn by the user as a terminal to measure the intensity of exercise and recognize the user's emotions in real time. This system comprehensively analyzes the user's movements and emotional state during exercise and provides feedback to realize a more personalized training experience.

[0302] Specifically, the device uses a camera as a sensor to capture the user's movements. Furthermore, this camera analyzes the user's facial expressions to recognize their emotions. The data transmitted from the device is processed on a server, where computer vision technology and machine learning are used to calculate the intensity of the movement and the user's emotional state. Based on these analysis results, feedback that is best suited to the user's emotions is generated.

[0303] For example, when a user is exercising and feeling stressed, the emotion engine recognizes this, and the server proposes an exercise plan to reduce stress. Such feedback is visually displayed on the display and also provided as voice assistance.

[0304] Furthermore, based on the user's voice commands and gestures, data according to the situation is fed back to the emotion engine, and the entire system is dynamically adjusted. As an example, when the user desires a "relaxed exercise", the instruction is sent to the server and analyzed together with the motion data. Thereby, a new exercise plan according to the user's mood is determined.

[0305] This system can handle changes in emotions that were not observable in conventional exercise feedback systems, enabling comprehensive training according to the physical and mental needs of the user.

[0306] The processing flow will be described below.

[0307] Step 1:

[0308] The terminal uses a camera as a sensor device to capture the user's exercise movements and expressions in real time. Motion data and expression data are acquired.

[0309] Step 2:

[0310] The terminal sends the acquired motion data and expression data to the server. The data is converted into a format suitable for analysis.

[0311] Step 3:

[0312] The server analyzes the motion data using computer vision technology and machine learning algorithms to calculate the intensity of the exercise. The expression data is analyzed by the emotion engine to recognize the user's emotional state.

[0313] Step 4:

[0314] The server generates feedback based on exercise intensity and the user's emotional state. If the user is feeling down, for example, it will incorporate exercise suggestions that promote relaxation.

[0315] Step 5:

[0316] The server sends the generated feedback to the terminal. The feedback is provided as visual information and audio guidance.

[0317] Step 6:

[0318] The device displays feedback on its screen and provides voice guidance to the user as needed. For example, it might display specific instructions such as, "Slightly reduce the intensity of your exercise and take some deep breaths."

[0319] Step 7:

[0320] The user adjusts the speed and direction of their movements based on the displayed feedback. They can also give further instructions to the device using voice commands if necessary.

[0321] Step 8:

[0322] The terminal analyzes voice commands from the user and sends them to the server for further analysis and adjustments. For example, if the user gives the command "make it easier," the emotional state is re-analyzed based on that request.

[0323] Step 9:

[0324] This feedback loop is repeated continuously throughout the exercise period, allowing the user to perform optimal training tailored to their own physical and emotional state.

[0325] (Example 2)

[0326] 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".

[0327] Conventional exercise feedback systems focus primarily on measuring exercise intensity, making it difficult to provide feedback that takes into account the user's emotional state during exercise. As a result, it was difficult to provide training plans that matched the user's mental state, and personalization was challenging. This invention aims to solve this problem by comprehensively analyzing the user's exercise and emotional state to provide more appropriate training feedback.

[0328] 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.

[0329] In this invention, the server includes information gathering means for acquiring motion data and facial expression data of a person exercising; analysis means for processing the motion data and facial expression data and simultaneously calculating the intensity of the exercise and the emotional state; and generation means for generating optimal feedback based on the calculated intensity of the exercise and the emotional state. This makes it possible to provide a comprehensive and personalized training experience that is tailored to the user's physical and emotional state.

[0330] "Information gathering means" refers to the general term for devices and technologies used to acquire motion data and facial expression data of people performing exercises.

[0331] "Analysis means" refers to devices and technologies that process acquired motion data and facial expression data to simultaneously calculate the intensity of movement and emotional state.

[0332] A "generating means" refers to a device or technology that formulates optimal feedback for the person exercising based on the calculated exercise intensity and emotional state.

[0333] "Display means" refers to devices or technologies for presenting generated feedback to a person in motion, either visually or audibly.

[0334] "Interaction means" refers to devices and technologies that receive voice commands and gestures from a person performing an action and then interact with them.

[0335] "Adjustment means" refers to devices and technologies that dynamically adapt analysis means and generation means based on interaction to optimize the movement plan.

[0336] A "generative AI model" is a type of artificial intelligence technology used to generate feedback in natural language based on the user's emotional state.

[0337] This invention relates to a system that measures the intensity of exercise and recognizes the user's emotions in real time by having the user wear smart glasses as a terminal. The aim of this system is to comprehensively analyze the user's movements and emotional state and provide appropriate feedback.

[0338] The device uses a camera built into smart glasses to capture the user's movements and facial expressions as a means of information gathering. It is also equipped with sensors to collect movement and facial expression data, which are then transmitted to a server via wireless communication. Wi-Fi and Bluetooth are used for communication. The server processes the received data using analytical tools. In this process, image processing libraries known as computer vision technologies (e.g., OpenCV) are used, and machine learning models (e.g., TensorFlow) are used to simultaneously analyze movement characteristics and emotional states.

[0339] The generating AI model generates optimal feedback based on the analysis results, and this feedback is provided to the user through a display mechanism. The feedback is displayed as visual information on the smart glasses' display and, if necessary, is also provided audibly via the voice assistance function.

[0340] Users can easily interact with the system using voice commands and gestures. For example, if a user gives a voice command to the device saying, "I want to do some relaxing exercise," that information is sent to the server, which then dynamically optimizes the exercise plan through the interaction mechanism. The exercise plan generated in this way is then provided individually based on feedback from a generating AI model.

[0341] An example of a prompt is, "If the user is feeling stressed, explain how to suggest an exercise plan to stabilize their emotions." In this form, the present invention provides a training experience that comprehensively supports the user's physical and emotional changes.

[0342] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0343] Step 1:

[0344] The device, worn by the user as smart glasses, uses built-in sensors to record the user's movements and facial expressions in real time. The input is a camera that captures the user's body movements and facial expressions, which are collected as motion data and facial expression data. The output is the recorded motion data and facial expression data.

[0345] Step 2:

[0346] The terminal transmits collected motion and facial expression data to the server via wireless communication. The input is the collected raw data, and the output is digital data transferred to the server. A communication protocol is used for this transfer, typically Bluetooth or Wi-Fi.

[0347] Step 3:

[0348] The server processes the received data using computer vision technology and machine learning algorithms. Inputs include motion data and facial expression data received wirelessly. For specific motions, computer vision technology (e.g., OpenCV) is used to extract motion features, and a machine learning model (e.g., TensorFlow) is used to identify emotional states. The output provides information on the intensity of the analyzed motion and the emotional state.

[0349] Step 4:

[0350] The server uses a generative AI model based on the analysis results to generate feedback that is best suited to the user's emotional state. The inputs are the analyzed exercise intensity and emotional state. Natural language processing using the generative AI model generates feedback that is easy for the user to understand. This feedback is tailored to the user's plan and is generated as an action plan or advice.

[0351] Step 5:

[0352] The server sends the generated feedback to the device, and the device displays the information on the smart glasses' display. The input is the generated feedback data, and the output is the interface information that the user receives through visual or voice assistance functions. Specifically, the feedback text is displayed on the screen, or voice guidance is played through the earphones.

[0353] Step 6:

[0354] The user inputs new instructions into the terminal using voice or gestures, and sends this to the server to adjust the feedback and motor plan. The input consists of new voice commands and gesture data from the user. The server receives this information and uses adjustment mechanisms to dynamically readjust the entire system and regenerate an optimized motor plan. The output is the updated feedback and motor plan.

[0355] (Application Example 2)

[0356] 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."

[0357] In industrial environments, workers often experience stress and fatigue due to long working hours and monotonous tasks, which can negatively impact work efficiency and safety. Therefore, there is a need for methods to improve work efficiency and safety by analyzing workers' emotional states in real time and providing appropriate feedback.

[0358] 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.

[0359] In this invention, the server includes sensor means for acquiring worker motion data, calculation means for processing the motion data and calculating the intensity of the movement, and analysis means for recognizing the worker's emotional state in an industrial environment and generating feedback. This makes it possible to improve work efficiency and ensure safety based on the worker's state.

[0360] A "sensor" is a device used to acquire motion data of a person moving, and is used to collect necessary information in a specific environment.

[0361] "Calculation means" refers to a device or mechanism for processing acquired motion data and calculating the intensity of movement, and is a device equipped with the function of analyzing and quantifying data.

[0362] A "visualization device" is a device used to visually present the calculated exercise intensity to the person performing the exercise, and is a device used to display information in an easy-to-understand manner.

[0363] A "feedback device" is a device that provides appropriate feedback to the person exercising based on the intensity of the exercise, and has the function of assisting the next action by outputting information.

[0364] An "analysis means" is a mechanism that recognizes the emotional state of workers in an industrial environment and generates feedback to improve work efficiency, and is a device that performs precise analysis using the acquired data.

[0365] The invention based on this application is a system that analyzes the actions and emotional states of workers in an industrial environment in real time and provides appropriate feedback. The system consists of sensor means, calculation means, visualization means, and analysis means.

[0366] The sensor system uses a camera mounted on a device such as smart glasses to acquire motion data from the worker. The data collected by the sensor system is transmitted to a server via a network. The server functions as a computing device, processing the received motion data to calculate the intensity of the movement. Next, the server uses an analysis device to recognize the emotional state based on the worker's facial expression data. Computer vision technology and machine learning models are used in this process.

[0367] Based on the analysis results, the server provides appropriate feedback to the worker through a digital display and audio output device. This feedback includes specific suggestions for stress reduction and efficiency improvement, tailored to the worker's condition.

[0368] In particular, the generative AI model contributes to generating personalized feedback by analyzing the worker's facial expressions and movements. An example of a prompt for this generative AI model would be, "Analyze the operator's current situation and generate the optimal work break plan."

[0369] For example, if a worker shows signs of fatigue, the server uses analysis tools to detect it and displays feedback on the screen such as, "We recommend taking a 5-minute break," along with voice assistance. In this way, the system improves work efficiency and the work environment.

[0370] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0371] Step 1:

[0372] The terminal captures the worker's movements with a camera and acquires raw video data. The input is the video data acquired by the camera, and the output is this data itself.

[0373] Step 2:

[0374] The terminal transmits the captured video data to the server via wireless communication. The input is the video data, and the output is the state in which the data has been transferred to the server.

[0375] Step 3:

[0376] The server analyzes the received video data using computer vision technology. Specifically, a motion detection algorithm extracts the worker's movements and facial expressions from the video data. The input to this process is video data, and the output is data containing extracted motion and facial features.

[0377] Step 4:

[0378] The server uses a generative AI model to estimate the worker's emotional state from motion and facial expression data. The input is extracted feature data, and by classifying the emotional state using machine learning, an emotional state label is obtained as output.

[0379] Step 5:

[0380] The server generates appropriate feedback for the worker based on their emotional state label. The prompt used is "Analyze the operator's current state and generate an optimal work break plan." A generative AI model is used to generate the feedback; the input is the emotional state label, and the output is the feedback message.

[0381] Step 6:

[0382] The server sends the generated feedback to the terminal. The input is the feedback message, and the output is that it is displayed or played back on the terminal.

[0383] Step 7:

[0384] The user (worker) receives visual and auditory feedback from the terminal and adjusts their actions based on it. The input is feedback information, and the output is the worker's actions that are expected to improve work efficiency or reduce stress.

[0385] 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.

[0386] 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 those described above. 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 shown 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.

[0387] 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.

[0388] [Third Embodiment]

[0389] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0390] 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.

[0391] 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).

[0392] 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.

[0393] 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.

[0394] 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).

[0395] 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.

[0396] 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.

[0397] 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.

[0398] 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.

[0399] 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.

[0400] 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".

[0401] This invention provides a system that measures and visualizes exercise intensity in real time using smart glasses worn by the person exercising. Specifically, a camera, acting as a sensor device, captures the user's movements and transmits them to a computing device. A server receives this movement data, analyzes it using computer vision and AI technology, and calculates the exercise intensity. This exercise intensity data is then visually displayed to the user through the device's display.

[0402] For example, suppose a user is jogging and wearing smart glasses. The device constantly records the user's running pace and movements using its camera and sends the data to a server. The server calculates whether the movement is at an appropriate speed and posture, and how much exercise load is being applied. Based on this information, the server generates feedback such as "Your current pace is appropriate" or "Let's increase your speed a little," and sends it to the device.

[0403] The device displays feedback on its screen and can also provide voice instructions to the user. Based on these instructions, the user can adjust the intensity and form of their exercise. This allows the user to understand their optimal exercise state in real time, enabling safe and effective training.

[0404] This system also features voice commands and a gesture interface, allowing users to give instructions such as "increase intensity" or "take a short break" by voice. Based on these instructions, the server re-analyzes the exercise intensity and provides a individually optimized exercise plan. By maintaining this feedback loop, the system is expected to improve the quality of training.

[0405] The following describes the processing flow.

[0406] Step 1:

[0407] The device uses a camera as a sensor to capture the user's movements in real time. The movement data includes changes in posture and movement speed.

[0408] Step 2:

[0409] The device transmits captured motion data to a server using wireless communication. The data is then converted to an optimized format for analysis.

[0410] Step 3:

[0411] The server analyzes the received motion data using computer vision technology. Machine learning algorithms are used to classify the user's movements and calculate the exercise intensity.

[0412] Step 4:

[0413] The server generates feedback based on the calculated exercise intensity. Here, AI is used to create personalized advice and instructions tailored to each individual user.

[0414] Step 5:

[0415] The server sends the generated feedback to the terminal. The feedback sent includes text data and audio data.

[0416] Step 6:

[0417] The device visually displays the received feedback to the user. The display clearly shows exercise intensity and advice, and voice guidance is also available.

[0418] Step 7:

[0419] Based on the displayed feedback, the user adjusts their exercise speed and form. They can also give voice commands to the device as needed to check the system's response.

[0420] Step 8:

[0421] The terminal recognizes voice commands from the user and sends them back to the server for further data analysis. If requested by the user, it performs a process to partially adjust the exercise plan.

[0422] Step 9:

[0423] This series of processes is repeated throughout the workout, allowing the user to constantly improve their training based on the latest feedback.

[0424] (Example 1)

[0425] 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."

[0426] Conventional exercise support systems have struggled to effectively monitor users' exercise status in real time and provide appropriate feedback. In particular, they have difficulty providing individually optimized exercise plans based on voice commands and actions, lacking the immediacy and adaptability needed to improve users' exercise efficiency.

[0427] 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.

[0428] In this invention, the server includes means for acquiring motion information using imaging means, analyzing the exercise load using calculation means, and providing visual and audible feedback using notification means. This allows the user to understand the exercise load in real time and optimize their exercise plan by utilizing voice commands based on a generated AI model.

[0429] "Imaging means" refers to equipment or devices used to acquire motion information of a person in motion, and primarily has the function of capturing images or videos.

[0430] "Calculation means" refers to functions or devices that analyze acquired motion information and estimate the load of exercise, and that have data processing capabilities.

[0431] "Display means" refers to devices or functions that visually present the estimated exercise load to the user, conveying this information to the user via a screen or display.

[0432] "Notification means" refers to devices or functions that provide instructions to the user based on the exercise load, and provides feedback through voice or visual means.

[0433] A "generative AI model" is a model that uses artificial intelligence technology and is utilized for analyzing voice commands and individually optimizing movement plans.

[0434] "Voice commands" refer to a method by which a user uses their voice to operate or instruct a system, and the system analyzes those commands and responds accordingly.

[0435] An "exercise plan" refers to a plan set up to ensure that the user exercises efficiently and safely, and includes adjustments to movements and load.

[0436] This invention is an exercise support system that grasps the appropriate exercise load for the exerciser in real time and provides individually optimized feedback. The system uses smart glasses as a terminal and includes imaging means, including a camera for capturing the user's movements.

[0437] The server receives this operational information and performs analysis using a generated AI model. This analysis utilizes computer vision technology and machine learning algorithms, and includes the ability to accurately estimate the exercise load from the data. The analysis results are used to evaluate the exercise intensity and the appropriateness of the form, and feedback based on the results is generated by the server.

[0438] The generated feedback is sent to the terminal and presented to the user via display or audio output. The system also has the ability to accept voice commands from the user and dynamically adjust the motor plan. The user operates the system by providing specific instructions by voice.

[0439] This technology allows users to maintain an appropriate exercise pace, avoid excessive strain, and train efficiently. For example, it can be used to adjust pace or correct form during jogging. An example of a prompt message would be, "Please provide detailed instructions for each step of the exercise intensity analysis system using smart glasses."

[0440] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0441] Step 1:

[0442] The device uses the smart glasses' camera to capture the user's movements in real time. In this process, the input is the user's physical movements, and the output is video data. The camera captures the user's joint positions and movement patterns in detail, and generates video that reflects this information.

[0443] Step 2:

[0444] The terminal compresses the captured video data and transmits it to the server via wireless communication. The input is the video data obtained on the terminal side, and the output is the compressed data sent to the server. A specific compression algorithm is used to efficiently transfer the data.

[0445] Step 3:

[0446] The server decompresses the received compressed data and analyzes it as motion information. The input is compressed video data, and the output is analysis data that numerically represents joint positions and movement patterns. Using computer vision technology, it extracts motion-related features and inputs them into a generated AI model.

[0447] Step 4:

[0448] The server processes motion information using a generative AI model to estimate the exercise load. The input is the analyzed motion data, and the output is numerical information of the estimated exercise load. The AI ​​model calculates the optimal load level based on a pre-trained motion dataset.

[0449] Step 5:

[0450] The server generates feedback to provide to the user based on the estimated exercise load. The input is exercise load information, and the output is visual and audio feedback data. The generating AI model verbalizes advice tailored to the user's current situation, providing effective guidance.

[0451] Step 6:

[0452] The terminal displays feedback received from the server on its screen and communicates it to the user via voice. The input is feedback data from the server, and the output is the information presented to the user. The display provides visual messages, and speech synthesis technology generates the voice feedback.

[0453] Step 7:

[0454] The user adjusts their movements based on the feedback and sends new voice commands to the device. Input is the display and voice feedback, and output is the user's voice commands. The voice commands are sent to the server and incorporated into the next analysis cycle.

[0455] Step 8:

[0456] The server analyzes the user's voice commands and readjusts the motor plan. The input is the user's voice commands, and the output is the adjusted motor plan. The generative AI model continues the feedback loop by analyzing the command content and generating individually optimized suggestions.

[0457] (Application Example 1)

[0458] 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."

[0459] Modern commercial facilities require detailed analysis of customer behavior and the design of effective store layouts based on that analysis. However, traditional methods make it difficult to collect and analyze detailed customer behavior data in real time, which can lead to situations where optimal layout proposals are not made. Furthermore, there is a lack of means for employees to immediately visualize and utilize this data.

[0460] 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.

[0461] In this invention, the server includes sensor means for acquiring motion data of exercisers and customers, calculation means for processing the motion data and calculating the intensity of exercise and customer behavior, and visualization means for visually presenting the calculated information to exercisers and store staff. This enables real-time tracking of customer behavior in stores, allowing for optimal layout design, as well as improved operational efficiency and enhanced sales strategies.

[0462] "Sensor means" refers to a group of devices that accurately capture the movements of a person or customer, and includes devices such as cameras.

[0463] A "calculation means" is a device or program that calculates the intensity of exercise and customer behavior based on acquired motion data and performs the necessary analysis.

[0464] "Visualization means" refers to a device or display interface for visually showing the calculated exercise intensity or customer movements to the person exercising or store staff.

[0465] A "feedback mechanism" is a device or program that provides necessary information and instructions to individuals exercising or store staff, and adjusts their actions and layout accordingly.

[0466] A "recording device" is a device used to record the movements of a person exercising or a customer as video footage, and includes equipment such as cameras.

[0467] The system of this invention tracks the movements of exercisers or customers in real time and provides feedback by analyzing the intensity of exercise and customer behavior. The system includes sensor means, calculation means, visualization means, and feedback means.

[0468] The sensor system uses cameras and other imaging devices mounted on smart glasses to acquire motion data of people exercising or customers. It also transmits the video data acquired from the imaging devices to a server.

[0469] The server receives video data and analyzes the motion data using OpenCV or TensorFlow as computational tools. Specifically, it calculates exercise intensity and customer movements through data processing, and then aggregates and analyzes this data using NumPy or Pandas.

[0470] For visualization, the display functions of displays and smart glasses are used. The calculated results are presented visually using visualization tools such as Matplotlib and Plotly, allowing information to be provided to users and store staff in real time.

[0471] The feedback mechanisms, such as voice instructions and gestures, provide information regarding adjusting exercise intensity and optimizing store layout. This allows users and store staff to adopt appropriate actions and strategies based on the situation.

[0472] For example, if the system detects that a customer is spending a long time in front of a particular product in a store, staff can use that information to change the product's location and conduct more efficient promotional activities.

[0473] An example of a prompt when using a generative AI model is: "To optimize customer movement within the store, please input customer behavior data into the in-store movement analysis AI and generate specific improvement suggestions regarding product placement."

[0474] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0475] Step 1:

[0476] The device uses a camera mounted on smart glasses to capture the movements of people exercising or customers, and acquires the video data. This video data is acquired in real time by sensor means and transmitted to a server.

[0477] Step 2:

[0478] The server receives the acquired video data and analyzes the motion data using OpenCV and TensorFlow as computational tools. Specifically, the server extracts motion features from the video and performs analysis to calculate the intensity of the movement and the movement of the customers. The input is video data, and the output is data on the intensity of the movement and the movement path of the customers.

[0479] Step 3:

[0480] The server aggregates the analysis results using NumPy and Pandas and analyzes the data. Here, data processing and calculations are performed to generate statistical information on exercise intensity and customer movement patterns. The input is the analyzed data, and the output is aggregated statistical data.

[0481] Step 4:

[0482] The terminal visually displays exercise intensity and customer movement information using visualization tools. By displaying aggregated results from the server on the screen, users or store staff can make immediate decisions based on that information. The input is aggregated statistical data, and the output is a visualized display.

[0483] Step 5:

[0484] The user uses feedback mechanisms to provide voice instructions and gesture input, adjusting exercise intensity and rearranging the store layout as needed. Based on these actions, the server re-analyzes the updated information and generates optimized feedback. The input is user instruction data, and the output is optimized feedback information.

[0485] 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.

[0486] This invention is a system that uses smart glasses worn by the user as a terminal to measure the intensity of exercise and recognize the user's emotions in real time. This system comprehensively analyzes the user's movements and emotional state during exercise and provides feedback to realize a more personalized training experience.

[0487] Specifically, the device uses a camera as a sensor to capture the user's movements. Furthermore, this camera analyzes the user's facial expressions to recognize their emotions. The data transmitted from the device is processed on a server, where computer vision technology and machine learning are used to calculate the intensity of the movement and the user's emotional state. Based on these analysis results, feedback that is best suited to the user's emotions is generated.

[0488] For example, if a user is feeling stressed while exercising, the emotion engine will recognize this, and the server will suggest an exercise plan to reduce stress. This feedback is displayed visually on the screen and also provided as voice assistance.

[0489] Furthermore, the emotion engine receives situation-appropriate data from the user's voice commands and gestures, allowing the entire system to adjust dynamically. For example, if a user desires "relaxing exercise," that instruction is sent to the server and analyzed along with the movement data. This determines a new exercise plan tailored to the user's mood.

[0490] This system can respond to emotional changes that could not be observed with conventional exercise feedback systems, enabling comprehensive training tailored to the user's physical and mental needs.

[0491] The following describes the processing flow.

[0492] Step 1:

[0493] The device uses a camera as a sensor to capture the user's movements and facial expressions in real time. Movement data and facial expression data are acquired.

[0494] Step 2:

[0495] The device sends the acquired motion data and facial expression data to the server. The data is then converted into a format suitable for analysis.

[0496] Step 3:

[0497] The server analyzes motion data using computer vision technology and machine learning algorithms to calculate the intensity of the movement. Facial expression data is analyzed by an emotion engine to recognize the user's emotional state.

[0498] Step 4:

[0499] The server generates feedback based on exercise intensity and the user's emotional state. If the user is feeling down, for example, it will incorporate exercise suggestions that promote relaxation.

[0500] Step 5:

[0501] The server sends the generated feedback to the terminal. The feedback is provided as visual information and audio guidance.

[0502] Step 6:

[0503] The device displays feedback on its screen and provides voice guidance to the user as needed. For example, it might display specific instructions such as, "Slightly reduce the intensity of your exercise and take some deep breaths."

[0504] Step 7:

[0505] The user adjusts the speed and direction of their movements based on the displayed feedback. They can also give further instructions to the device using voice commands if necessary.

[0506] Step 8:

[0507] The terminal analyzes voice commands from the user and sends them to the server for further analysis and adjustments. For example, if the user gives the command "make it easier," the emotional state is re-analyzed based on that request.

[0508] Step 9:

[0509] This feedback loop is repeated continuously throughout the exercise period, allowing the user to perform optimal training tailored to their own physical and emotional state.

[0510] (Example 2)

[0511] 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."

[0512] Conventional exercise feedback systems focus primarily on measuring exercise intensity, making it difficult to provide feedback that takes into account the user's emotional state during exercise. As a result, it was difficult to provide training plans that matched the user's mental state, and personalization was challenging. This invention aims to solve this problem by comprehensively analyzing the user's exercise and emotional state to provide more appropriate training feedback.

[0513] 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.

[0514] In this invention, the server includes information gathering means for acquiring motion data and facial expression data of a person exercising; analysis means for processing the motion data and facial expression data and simultaneously calculating the intensity of the exercise and the emotional state; and generation means for generating optimal feedback based on the calculated intensity of the exercise and the emotional state. This makes it possible to provide a comprehensive and personalized training experience that is tailored to the user's physical and emotional state.

[0515] "Information gathering means" refers to the general term for devices and technologies used to acquire motion data and facial expression data of people performing exercises.

[0516] "Analysis means" refers to devices and technologies that process acquired motion data and facial expression data to simultaneously calculate the intensity of movement and emotional state.

[0517] A "generating means" refers to a device or technology that formulates optimal feedback for the person exercising based on the calculated exercise intensity and emotional state.

[0518] "Display means" refers to devices or technologies for presenting generated feedback to a person in motion, either visually or audibly.

[0519] "Interaction means" refers to devices and technologies that receive voice commands and gestures from a person performing an action and then interact with them.

[0520] "Adjustment means" refers to devices and technologies that dynamically adapt analysis means and generation means based on interaction to optimize the movement plan.

[0521] A "generative AI model" is a type of artificial intelligence technology used to generate feedback in natural language based on the user's emotional state.

[0522] This invention relates to a system that measures the intensity of exercise and recognizes the user's emotions in real time by having the user wear smart glasses as a terminal. The aim of this system is to comprehensively analyze the user's movements and emotional state and provide appropriate feedback.

[0523] The device uses a camera built into smart glasses to capture the user's movements and facial expressions as a means of information gathering. It is also equipped with sensors to collect movement and facial expression data, which are then transmitted to a server via wireless communication. Wi-Fi and Bluetooth are used for communication. The server processes the received data using analytical tools. In this process, image processing libraries known as computer vision technologies (e.g., OpenCV) are used, and machine learning models (e.g., TensorFlow) are used to simultaneously analyze movement characteristics and emotional states.

[0524] The generating AI model generates optimal feedback based on the analysis results, and this feedback is provided to the user through a display mechanism. The feedback is displayed as visual information on the smart glasses' display and, if necessary, is also provided audibly via the voice assistance function.

[0525] Users can easily interact with the system using voice commands and gestures. For example, if a user gives a voice command to the device saying, "I want to do some relaxing exercise," that information is sent to the server, which then dynamically optimizes the exercise plan through the interaction mechanism. The exercise plan generated in this way is then provided individually based on feedback from a generating AI model.

[0526] An example of a prompt is, "If the user is feeling stressed, explain how to suggest an exercise plan to stabilize their emotions." In this form, the present invention provides a training experience that comprehensively supports the user's physical and emotional changes.

[0527] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0528] Step 1:

[0529] The device, worn by the user as smart glasses, uses built-in sensors to record the user's movements and facial expressions in real time. The input is a camera that captures the user's body movements and facial expressions, which are collected as motion data and facial expression data. The output is the recorded motion data and facial expression data.

[0530] Step 2:

[0531] The terminal transmits collected motion and facial expression data to the server via wireless communication. The input is the collected raw data, and the output is digital data transferred to the server. A communication protocol is used for this transfer, typically Bluetooth or Wi-Fi.

[0532] Step 3:

[0533] The server processes the received data using computer vision technology and machine learning algorithms. Inputs include motion data and facial expression data received wirelessly. For specific motions, computer vision technology (e.g., OpenCV) is used to extract motion features, and a machine learning model (e.g., TensorFlow) is used to identify emotional states. The output provides information on the intensity of the analyzed motion and the emotional state.

[0534] Step 4:

[0535] The server uses a generative AI model based on the analysis results to generate feedback that is best suited to the user's emotional state. The inputs are the analyzed exercise intensity and emotional state. Natural language processing using the generative AI model generates feedback that is easy for the user to understand. This feedback is tailored to the user's plan and is generated as an action plan or advice.

[0536] Step 5:

[0537] The server sends the generated feedback to the device, and the device displays the information on the smart glasses' display. The input is the generated feedback data, and the output is the interface information that the user receives through visual or voice assistance functions. Specifically, the feedback text is displayed on the screen, or voice guidance is played through the earphones.

[0538] Step 6:

[0539] The user inputs new instructions into the terminal using voice or gestures, and sends this to the server to adjust the feedback and motor plan. The input consists of new voice commands and gesture data from the user. The server receives this information and uses adjustment mechanisms to dynamically readjust the entire system and regenerate an optimized motor plan. The output is the updated feedback and motor plan.

[0540] (Application Example 2)

[0541] 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."

[0542] In industrial environments, workers often experience stress and fatigue due to long working hours and monotonous tasks, which can negatively impact work efficiency and safety. Therefore, there is a need for methods to improve work efficiency and safety by analyzing workers' emotional states in real time and providing appropriate feedback.

[0543] 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.

[0544] In this invention, the server includes sensor means for acquiring worker motion data, calculation means for processing the motion data and calculating the intensity of the movement, and analysis means for recognizing the worker's emotional state in an industrial environment and generating feedback. This makes it possible to improve work efficiency and ensure safety based on the worker's state.

[0545] A "sensor" is a device used to acquire motion data of a person moving, and is used to collect necessary information in a specific environment.

[0546] "Calculation means" refers to a device or mechanism for processing acquired motion data and calculating the intensity of movement, and is a device equipped with the function of analyzing and quantifying data.

[0547] A "visualization device" is a device used to visually present the calculated exercise intensity to the person performing the exercise, and is a device used to display information in an easy-to-understand manner.

[0548] A "feedback device" is a device that provides appropriate feedback to the person exercising based on the intensity of the exercise, and has the function of assisting the next action by outputting information.

[0549] An "analysis means" is a mechanism that recognizes the emotional state of workers in an industrial environment and generates feedback to improve work efficiency, and is a device that performs precise analysis using the acquired data.

[0550] The invention based on this application is a system that analyzes the actions and emotional states of workers in an industrial environment in real time and provides appropriate feedback. The system consists of sensor means, calculation means, visualization means, and analysis means.

[0551] The sensor system uses a camera mounted on a device such as smart glasses to acquire motion data from the worker. The data collected by the sensor system is transmitted to a server via a network. The server functions as a computing device, processing the received motion data to calculate the intensity of the movement. Next, the server uses an analysis device to recognize the emotional state based on the worker's facial expression data. Computer vision technology and machine learning models are used in this process.

[0552] Based on the analysis results, the server provides appropriate feedback to the worker through a digital display and audio output device. This feedback includes specific suggestions for stress reduction and efficiency improvement, tailored to the worker's condition.

[0553] In particular, the generative AI model contributes to generating personalized feedback by analyzing the worker's facial expressions and movements. An example of a prompt for this generative AI model would be, "Analyze the operator's current situation and generate the optimal work break plan."

[0554] For example, if a worker shows signs of fatigue, the server uses analysis tools to detect it and displays feedback on the screen such as, "We recommend taking a 5-minute break," along with voice assistance. In this way, the system improves work efficiency and the work environment.

[0555] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0556] Step 1:

[0557] The terminal captures the worker's movements with a camera and acquires raw video data. The input is the video data acquired by the camera, and the output is this data itself.

[0558] Step 2:

[0559] The terminal transmits the captured video data to the server via wireless communication. The input is the video data, and the output is the state in which the data has been transferred to the server.

[0560] Step 3:

[0561] The server analyzes the received video data using computer vision technology. Specifically, a motion detection algorithm extracts the worker's movements and facial expressions from the video data. The input to this process is video data, and the output is data containing extracted motion and facial features.

[0562] Step 4:

[0563] The server uses a generative AI model to estimate the worker's emotional state from motion and facial expression data. The input is extracted feature data, and by classifying the emotional state using machine learning, an emotional state label is obtained as output.

[0564] Step 5:

[0565] The server generates appropriate feedback for the worker based on their emotional state label. The prompt used is "Analyze the operator's current state and generate an optimal work break plan." A generative AI model is used to generate the feedback; the input is the emotional state label, and the output is the feedback message.

[0566] Step 6:

[0567] The server sends the generated feedback to the terminal. The input is the feedback message, and the output is that it is displayed or played back on the terminal.

[0568] Step 7:

[0569] The user (worker) receives visual and auditory feedback from the terminal and adjusts their actions based on it. The input is feedback information, and the output is the worker's actions that are expected to improve work efficiency or reduce stress.

[0570] 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.

[0571] 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 those described above. 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 shown 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.

[0572] 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.

[0573] [Fourth Embodiment]

[0574] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0575] 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.

[0576] 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).

[0577] 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.

[0578] 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.

[0579] 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).

[0580] 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.

[0581] 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.

[0582] 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.

[0583] 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.

[0584] 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.

[0585] 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.

[0586] 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".

[0587] This invention provides a system that measures and visualizes exercise intensity in real time using smart glasses worn by the person exercising. Specifically, a camera, acting as a sensor device, captures the user's movements and transmits them to a computing device. A server receives this movement data, analyzes it using computer vision and AI technology, and calculates the exercise intensity. This exercise intensity data is then visually displayed to the user through the device's display.

[0588] For example, suppose a user is jogging and wearing smart glasses. The device constantly records the user's running pace and movements using its camera and sends the data to a server. The server calculates whether the movement is at an appropriate speed and posture, and how much exercise load is being applied. Based on this information, the server generates feedback such as "Your current pace is appropriate" or "Let's increase your speed a little," and sends it to the device.

[0589] The device displays feedback on its screen and can also provide voice instructions to the user. Based on these instructions, the user can adjust the intensity and form of their exercise. This allows the user to understand their optimal exercise state in real time, enabling safe and effective training.

[0590] This system also features voice commands and a gesture interface, allowing users to give instructions such as "increase intensity" or "take a short break" by voice. Based on these instructions, the server re-analyzes the exercise intensity and provides a individually optimized exercise plan. By maintaining this feedback loop, the system is expected to improve the quality of training.

[0591] The following describes the processing flow.

[0592] Step 1:

[0593] The device uses a camera as a sensor to capture the user's movements in real time. The movement data includes changes in posture and movement speed.

[0594] Step 2:

[0595] The device transmits captured motion data to a server using wireless communication. The data is then converted to an optimized format for analysis.

[0596] Step 3:

[0597] The server analyzes the received motion data using computer vision technology. Machine learning algorithms are used to classify the user's movements and calculate the exercise intensity.

[0598] Step 4:

[0599] The server generates feedback based on the calculated exercise intensity. Here, AI is used to create personalized advice and instructions tailored to each individual user.

[0600] Step 5:

[0601] The server sends the generated feedback to the terminal. The feedback sent includes text data and audio data.

[0602] Step 6:

[0603] The device visually displays the received feedback to the user. The display clearly shows exercise intensity and advice, and voice guidance is also available.

[0604] Step 7:

[0605] Based on the displayed feedback, the user adjusts their exercise speed and form. They can also give voice commands to the device as needed to check the system's response.

[0606] Step 8:

[0607] The terminal recognizes voice commands from the user and sends them back to the server for further data analysis. If requested by the user, it performs a process to partially adjust the exercise plan.

[0608] Step 9:

[0609] This series of processes is repeated throughout the workout, allowing the user to constantly improve their training based on the latest feedback.

[0610] (Example 1)

[0611] 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".

[0612] Conventional exercise support systems have struggled to effectively monitor users' exercise status in real time and provide appropriate feedback. In particular, they have difficulty providing individually optimized exercise plans based on voice commands and actions, lacking the immediacy and adaptability needed to improve users' exercise efficiency.

[0613] 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.

[0614] In this invention, the server includes means for acquiring motion information using imaging means, analyzing the exercise load using calculation means, and providing visual and audible feedback using notification means. This allows the user to understand the exercise load in real time and optimize their exercise plan by utilizing voice commands based on a generated AI model.

[0615] "Imaging means" refers to equipment or devices used to acquire motion information of a person in motion, and primarily has the function of capturing images or videos.

[0616] "Calculation means" refers to functions or devices that analyze acquired motion information and estimate the load of exercise, and that have data processing capabilities.

[0617] "Display means" refers to devices or functions that visually present the estimated exercise load to the user, conveying this information to the user via a screen or display.

[0618] "Notification means" refers to devices or functions that provide instructions to the user based on the exercise load, and provides feedback through voice or visual means.

[0619] A "generative AI model" is a model that uses artificial intelligence technology and is utilized for analyzing voice commands and individually optimizing movement plans.

[0620] "Voice commands" refer to a method by which a user uses their voice to operate or instruct a system, and the system analyzes those commands and responds accordingly.

[0621] An "exercise plan" refers to a plan set up to ensure that the user exercises efficiently and safely, and includes adjustments to movements and load.

[0622] This invention is an exercise support system that grasps the appropriate exercise load for the exerciser in real time and provides individually optimized feedback. The system uses smart glasses as a terminal and includes imaging means, including a camera for capturing the user's movements.

[0623] The server receives this operational information and performs analysis using a generated AI model. This analysis utilizes computer vision technology and machine learning algorithms, and includes the ability to accurately estimate the exercise load from the data. The analysis results are used to evaluate the exercise intensity and the appropriateness of the form, and feedback based on the results is generated by the server.

[0624] The generated feedback is sent to the terminal and presented to the user via display or audio output. The system also has the ability to accept voice commands from the user and dynamically adjust the motor plan. The user operates the system by providing specific instructions by voice.

[0625] This technology allows users to maintain an appropriate exercise pace, avoid excessive strain, and train efficiently. For example, it can be used to adjust pace or correct form during jogging. An example of a prompt message would be, "Please provide detailed instructions for each step of the exercise intensity analysis system using smart glasses."

[0626] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0627] Step 1:

[0628] The device uses the smart glasses' camera to capture the user's movements in real time. In this process, the input is the user's physical movements, and the output is video data. The camera captures the user's joint positions and movement patterns in detail, and generates video that reflects this information.

[0629] Step 2:

[0630] The terminal compresses the captured video data and transmits it to the server via wireless communication. The input is the video data obtained on the terminal side, and the output is the compressed data sent to the server. A specific compression algorithm is used to efficiently transfer the data.

[0631] Step 3:

[0632] The server decompresses the received compressed data and analyzes it as motion information. The input is compressed video data, and the output is analysis data that numerically represents joint positions and movement patterns. Using computer vision technology, it extracts motion-related features and inputs them into a generated AI model.

[0633] Step 4:

[0634] The server processes motion information using a generative AI model to estimate the exercise load. The input is the analyzed motion data, and the output is numerical information of the estimated exercise load. The AI ​​model calculates the optimal load level based on a pre-trained motion dataset.

[0635] Step 5:

[0636] The server generates feedback to provide to the user based on the estimated exercise load. The input is exercise load information, and the output is visual and audio feedback data. The generating AI model verbalizes advice tailored to the user's current situation, providing effective guidance.

[0637] Step 6:

[0638] The terminal displays feedback received from the server on its screen and communicates it to the user via voice. The input is feedback data from the server, and the output is the information presented to the user. The display provides visual messages, and speech synthesis technology generates the voice feedback.

[0639] Step 7:

[0640] The user adjusts their movements based on the feedback and sends new voice commands to the device. Input is the display and voice feedback, and output is the user's voice commands. The voice commands are sent to the server and incorporated into the next analysis cycle.

[0641] Step 8:

[0642] The server analyzes the user's voice commands and readjusts the motor plan. The input is the user's voice commands, and the output is the adjusted motor plan. The generative AI model continues the feedback loop by analyzing the command content and generating individually optimized suggestions.

[0643] (Application Example 1)

[0644] 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".

[0645] Modern commercial facilities require detailed analysis of customer behavior and the design of effective store layouts based on that analysis. However, traditional methods make it difficult to collect and analyze detailed customer behavior data in real time, which can lead to situations where optimal layout proposals are not made. Furthermore, there is a lack of means for employees to immediately visualize and utilize this data.

[0646] 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.

[0647] In this invention, the server includes sensor means for acquiring motion data of exercisers and customers, calculation means for processing the motion data and calculating the intensity of exercise and customer behavior, and visualization means for visually presenting the calculated information to exercisers and store staff. This enables real-time tracking of customer behavior in stores, allowing for optimal layout design, as well as improved operational efficiency and enhanced sales strategies.

[0648] "Sensor means" refers to a group of devices that accurately capture the movements of a person or customer, and includes devices such as cameras.

[0649] A "calculation means" is a device or program that calculates the intensity of exercise and customer behavior based on acquired motion data and performs the necessary analysis.

[0650] "Visualization means" refers to a device or display interface for visually showing the calculated exercise intensity or customer movements to the person exercising or store staff.

[0651] A "feedback mechanism" is a device or program that provides necessary information and instructions to individuals exercising or store staff, and adjusts their actions and layout accordingly.

[0652] A "recording device" is a device used to record the movements of a person exercising or a customer as video footage, and includes equipment such as cameras.

[0653] The system of this invention tracks the movements of exercisers or customers in real time and provides feedback by analyzing the intensity of exercise and customer behavior. The system includes sensor means, calculation means, visualization means, and feedback means.

[0654] The sensor system uses cameras and other imaging devices mounted on smart glasses to acquire motion data of people exercising or customers. It also transmits the video data acquired from the imaging devices to a server.

[0655] The server receives video data and analyzes the motion data using OpenCV or TensorFlow as computational tools. Specifically, it calculates exercise intensity and customer movements through data processing, and then aggregates and analyzes this data using NumPy or Pandas.

[0656] For visualization, the display functions of displays and smart glasses are used. The calculated results are presented visually using visualization tools such as Matplotlib and Plotly, allowing information to be provided to users and store staff in real time.

[0657] The feedback mechanisms, such as voice instructions and gestures, provide information regarding adjusting exercise intensity and optimizing store layout. This allows users and store staff to adopt appropriate actions and strategies based on the situation.

[0658] For example, if the system detects that a customer is spending a long time in front of a particular product in a store, staff can use that information to change the product's location and conduct more efficient promotional activities.

[0659] An example of a prompt when using a generative AI model is: "To optimize customer movement within the store, please input customer behavior data into the in-store movement analysis AI and generate specific improvement suggestions regarding product placement."

[0660] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0661] Step 1:

[0662] The device uses a camera mounted on smart glasses to capture the movements of people exercising or customers, and acquires the video data. This video data is acquired in real time by sensor means and transmitted to a server.

[0663] Step 2:

[0664] The server receives the acquired video data and analyzes the motion data using OpenCV and TensorFlow as computational tools. Specifically, the server extracts motion features from the video and performs analysis to calculate the intensity of the movement and the movement of the customers. The input is video data, and the output is data on the intensity of the movement and the movement path of the customers.

[0665] Step 3:

[0666] The server aggregates the analysis results using NumPy and Pandas and analyzes the data. Here, data processing and calculations are performed to generate statistical information on exercise intensity and customer movement patterns. The input is the analyzed data, and the output is aggregated statistical data.

[0667] Step 4:

[0668] The terminal visually displays exercise intensity and customer movement information using visualization tools. By displaying aggregated results from the server on the screen, users or store staff can make immediate decisions based on that information. The input is aggregated statistical data, and the output is a visualized display.

[0669] Step 5:

[0670] The user uses feedback mechanisms to provide voice instructions and gesture input, adjusting exercise intensity and rearranging the store layout as needed. Based on these actions, the server re-analyzes the updated information and generates optimized feedback. The input is user instruction data, and the output is optimized feedback information.

[0671] 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.

[0672] This invention is a system that uses smart glasses worn by the user as a terminal to measure the intensity of exercise and recognize the user's emotions in real time. This system comprehensively analyzes the user's movements and emotional state during exercise and provides feedback to realize a more personalized training experience.

[0673] Specifically, the device uses a camera as a sensor to capture the user's movements. Furthermore, this camera analyzes the user's facial expressions to recognize their emotions. The data transmitted from the device is processed on a server, where computer vision technology and machine learning are used to calculate the intensity of the movement and the user's emotional state. Based on these analysis results, feedback that is best suited to the user's emotions is generated.

[0674] For example, if a user is feeling stressed while exercising, the emotion engine will recognize this, and the server will suggest an exercise plan to reduce stress. This feedback is displayed visually on the screen and also provided as voice assistance.

[0675] Furthermore, the emotion engine receives situation-appropriate data from the user's voice commands and gestures, allowing the entire system to adjust dynamically. For example, if a user desires "relaxing exercise," that instruction is sent to the server and analyzed along with the movement data. This determines a new exercise plan tailored to the user's mood.

[0676] This system can respond to emotional changes that could not be observed with conventional exercise feedback systems, enabling comprehensive training tailored to the user's physical and mental needs.

[0677] The following describes the processing flow.

[0678] Step 1:

[0679] The device uses a camera as a sensor to capture the user's movements and facial expressions in real time. Movement data and facial expression data are acquired.

[0680] Step 2:

[0681] The device sends the acquired motion data and facial expression data to the server. The data is then converted into a format suitable for analysis.

[0682] Step 3:

[0683] The server analyzes motion data using computer vision technology and machine learning algorithms to calculate the intensity of the movement. Facial expression data is analyzed by an emotion engine to recognize the user's emotional state.

[0684] Step 4:

[0685] The server generates feedback based on exercise intensity and the user's emotional state. If the user is feeling down, for example, it will incorporate exercise suggestions that promote relaxation.

[0686] Step 5:

[0687] The server sends the generated feedback to the terminal. The feedback is provided as visual information and audio guidance.

[0688] Step 6:

[0689] The device displays feedback on its screen and provides voice guidance to the user as needed. For example, it might display specific instructions such as, "Slightly reduce the intensity of your exercise and take some deep breaths."

[0690] Step 7:

[0691] The user adjusts the speed and direction of their movements based on the displayed feedback. They can also give further instructions to the device using voice commands if necessary.

[0692] Step 8:

[0693] The terminal analyzes voice commands from the user and sends them to the server for further analysis and adjustments. For example, if the user gives the command "make it easier," the emotional state is re-analyzed based on that request.

[0694] Step 9:

[0695] This feedback loop is repeated continuously throughout the exercise period, allowing the user to perform optimal training tailored to their own physical and emotional state.

[0696] (Example 2)

[0697] 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".

[0698] Conventional exercise feedback systems focus primarily on measuring exercise intensity, making it difficult to provide feedback that takes into account the user's emotional state during exercise. As a result, it was difficult to provide training plans that matched the user's mental state, and personalization was challenging. This invention aims to solve this problem by comprehensively analyzing the user's exercise and emotional state to provide more appropriate training feedback.

[0699] 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.

[0700] In this invention, the server includes information gathering means for acquiring motion data and facial expression data of a person exercising; analysis means for processing the motion data and facial expression data and simultaneously calculating the intensity of the exercise and the emotional state; and generation means for generating optimal feedback based on the calculated intensity of the exercise and the emotional state. This makes it possible to provide a comprehensive and personalized training experience that is tailored to the user's physical and emotional state.

[0701] "Information gathering means" refers to the general term for devices and technologies used to acquire motion data and facial expression data of people performing exercises.

[0702] "Analysis means" refers to devices and technologies that process acquired motion data and facial expression data to simultaneously calculate the intensity of movement and emotional state.

[0703] A "generating means" refers to a device or technology that formulates optimal feedback for the person exercising based on the calculated exercise intensity and emotional state.

[0704] "Display means" refers to devices or technologies for presenting generated feedback to a person in motion, either visually or audibly.

[0705] "Interaction means" refers to devices and technologies that receive voice commands and gestures from a person performing an action and then interact with them.

[0706] "Adjustment means" refers to devices and technologies that dynamically adapt analysis means and generation means based on interaction to optimize the movement plan.

[0707] A "generative AI model" is a type of artificial intelligence technology used to generate feedback in natural language based on the user's emotional state.

[0708] This invention relates to a system that measures the intensity of exercise and recognizes the user's emotions in real time by having the user wear smart glasses as a terminal. The aim of this system is to comprehensively analyze the user's movements and emotional state and provide appropriate feedback.

[0709] The device uses a camera built into smart glasses to capture the user's movements and facial expressions as a means of information gathering. It is also equipped with sensors to collect movement and facial expression data, which are then transmitted to a server via wireless communication. Wi-Fi and Bluetooth are used for communication. The server processes the received data using analytical tools. In this process, image processing libraries known as computer vision technologies (e.g., OpenCV) are used, and machine learning models (e.g., TensorFlow) are used to simultaneously analyze movement characteristics and emotional states.

[0710] The generating AI model generates optimal feedback based on the analysis results, and this feedback is provided to the user through a display mechanism. The feedback is displayed as visual information on the smart glasses' display and, if necessary, is also provided audibly via the voice assistance function.

[0711] Users can easily interact with the system using voice commands and gestures. For example, if a user gives a voice command to the device saying, "I want to do some relaxing exercise," that information is sent to the server, which then dynamically optimizes the exercise plan through the interaction mechanism. The exercise plan generated in this way is then provided individually based on feedback from a generating AI model.

[0712] An example of a prompt is, "If the user is feeling stressed, explain how to suggest an exercise plan to stabilize their emotions." In this form, the present invention provides a training experience that comprehensively supports the user's physical and emotional changes.

[0713] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0714] Step 1:

[0715] The device, worn by the user as smart glasses, uses built-in sensors to record the user's movements and facial expressions in real time. The input is a camera that captures the user's body movements and facial expressions, which are collected as motion data and facial expression data. The output is the recorded motion data and facial expression data.

[0716] Step 2:

[0717] The terminal transmits collected motion and facial expression data to the server via wireless communication. The input is the collected raw data, and the output is digital data transferred to the server. A communication protocol is used for this transfer, typically Bluetooth or Wi-Fi.

[0718] Step 3:

[0719] The server processes the received data using computer vision technology and machine learning algorithms. Inputs include motion data and facial expression data received wirelessly. For specific motions, computer vision technology (e.g., OpenCV) is used to extract motion features, and a machine learning model (e.g., TensorFlow) is used to identify emotional states. The output provides information on the intensity of the analyzed motion and the emotional state.

[0720] Step 4:

[0721] The server uses a generative AI model based on the analysis results to generate feedback that is best suited to the user's emotional state. The inputs are the analyzed exercise intensity and emotional state. Natural language processing using the generative AI model generates feedback that is easy for the user to understand. This feedback is tailored to the user's plan and is generated as an action plan or advice.

[0722] Step 5:

[0723] The server sends the generated feedback to the device, and the device displays the information on the smart glasses' display. The input is the generated feedback data, and the output is the interface information that the user receives through visual or voice assistance functions. Specifically, the feedback text is displayed on the screen, or voice guidance is played through the earphones.

[0724] Step 6:

[0725] The user inputs new instructions into the terminal using voice or gestures, and sends this to the server to adjust the feedback and motor plan. The input consists of new voice commands and gesture data from the user. The server receives this information and uses adjustment mechanisms to dynamically readjust the entire system and regenerate an optimized motor plan. The output is the updated feedback and motor plan.

[0726] (Application Example 2)

[0727] 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".

[0728] In industrial environments, workers often experience stress and fatigue due to long working hours and monotonous tasks, which can negatively impact work efficiency and safety. Therefore, there is a need for methods to improve work efficiency and safety by analyzing workers' emotional states in real time and providing appropriate feedback.

[0729] 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.

[0730] In this invention, the server includes sensor means for acquiring worker motion data, calculation means for processing the motion data and calculating the intensity of the movement, and analysis means for recognizing the worker's emotional state in an industrial environment and generating feedback. This makes it possible to improve work efficiency and ensure safety based on the worker's state.

[0731] A "sensor" is a device used to acquire motion data of a person moving, and is used to collect necessary information in a specific environment.

[0732] "Calculation means" refers to a device or mechanism for processing acquired motion data and calculating the intensity of movement, and is a device equipped with the function of analyzing and quantifying data.

[0733] A "visualization device" is a device used to visually present the calculated exercise intensity to the person performing the exercise, and is a device used to display information in an easy-to-understand manner.

[0734] A "feedback device" is a device that provides appropriate feedback to the person exercising based on the intensity of the exercise, and has the function of assisting the next action by outputting information.

[0735] An "analysis means" is a mechanism that recognizes the emotional state of workers in an industrial environment and generates feedback to improve work efficiency, and is a device that performs precise analysis using the acquired data.

[0736] The invention based on this application is a system that analyzes the actions and emotional states of workers in an industrial environment in real time and provides appropriate feedback. The system consists of sensor means, calculation means, visualization means, and analysis means.

[0737] The sensor system uses a camera mounted on a device such as smart glasses to acquire motion data from the worker. The data collected by the sensor system is transmitted to a server via a network. The server functions as a computing device, processing the received motion data to calculate the intensity of the movement. Next, the server uses an analysis device to recognize the emotional state based on the worker's facial expression data. Computer vision technology and machine learning models are used in this process.

[0738] Based on the analysis results, the server provides appropriate feedback to the worker through a digital display and audio output device. This feedback includes specific suggestions for stress reduction and efficiency improvement, tailored to the worker's condition.

[0739] In particular, the generative AI model contributes to generating personalized feedback by analyzing the worker's facial expressions and movements. An example of a prompt for this generative AI model would be, "Analyze the operator's current situation and generate the optimal work break plan."

[0740] For example, if a worker shows signs of fatigue, the server uses analysis tools to detect it and displays feedback on the screen such as, "We recommend taking a 5-minute break," along with voice assistance. In this way, the system improves work efficiency and the work environment.

[0741] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0742] Step 1:

[0743] The terminal captures the worker's movements with a camera and acquires raw video data. The input is the video data acquired by the camera, and the output is this data itself.

[0744] Step 2:

[0745] The terminal transmits the captured video data to the server via wireless communication. The input is the video data, and the output is the state in which the data has been transferred to the server.

[0746] Step 3:

[0747] The server analyzes the received video data using computer vision technology. Specifically, a motion detection algorithm extracts the worker's movements and facial expressions from the video data. The input to this process is video data, and the output is data containing extracted motion and facial features.

[0748] Step 4:

[0749] The server uses a generative AI model to estimate the worker's emotional state from motion and facial expression data. The input is extracted feature data, and by classifying the emotional state using machine learning, an emotional state label is obtained as output.

[0750] Step 5:

[0751] The server generates appropriate feedback for the worker based on their emotional state label. The prompt used is "Analyze the operator's current state and generate an optimal work break plan." A generative AI model is used to generate the feedback; the input is the emotional state label, and the output is the feedback message.

[0752] Step 6:

[0753] The server sends the generated feedback to the terminal. The input is the feedback message, and the output is that it is displayed or played back on the terminal.

[0754] Step 7:

[0755] The user (worker) receives visual and auditory feedback from the terminal and adjusts their actions based on it. The input is feedback information, and the output is the worker's actions that are expected to improve work efficiency or reduce stress.

[0756] 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.

[0757] 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 those described above. 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 shown 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.

[0758] 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.

[0759] 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.

[0760] 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.

[0761] 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.

[0762] 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.

[0763] 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.

[0764] 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."

[0765] 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.

[0766] 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.

[0767] 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.

[0768] 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.

[0769] 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.

[0770] 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.

[0771] 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.

[0772] 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.

[0773] 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.

[0774] 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.

[0775] 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.

[0776] 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 as being incorporated by reference.

[0777] The following is further disclosed regarding the embodiments described above.

[0778] (Claim 1)

[0779] A sensor device that acquires motion data of a person exercising,

[0780] A calculation device that processes the aforementioned motion data and calculates the intensity of the movement,

[0781] A visualization device that visually displays the calculated exercise intensity to the person performing the exercise,

[0782] A feedback device that provides feedback to the person performing the exercise based on the intensity of the exercise,

[0783] A system that includes this.

[0784] (Claim 2)

[0785] The system according to claim 1, wherein the sensor device includes a camera that captures the movements of a person exercising.

[0786] (Claim 3)

[0787] The system according to claim 1, wherein the feedback device includes means for receiving operations from a person exercising using voice commands or gestures and adjusting the exercise plan.

[0788] "Example 1"

[0789] (Claim 1)

[0790] An imaging means for acquiring motion information of a person performing an exercise,

[0791] A calculation means for analyzing the aforementioned motion information and estimating the load of the exercise,

[0792] A display means for visually presenting the estimated exercise load to the person performing the exercise,

[0793] A notification means that provides instructions to the person performing the exercise based on the load of the exercise,

[0794] A means for analyzing voice commands from a user using a generative AI model and optimizing the motor plan,

[0795] A system that includes this.

[0796] (Claim 2)

[0797] The system according to claim 1, wherein the imaging means includes an image acquisition device for capturing images of the movements of a person exercising.

[0798] (Claim 3)

[0799] The system according to claim 1, wherein the notification means includes means for receiving operations from a person performing an exercise using voice commands or an action interface and adjusting the exercise plan.

[0800] "Application Example 1"

[0801] (Claim 1)

[0802] A sensor means for acquiring motion data of a person exercising,

[0803] A calculation means that processes the aforementioned motion data and calculates the intensity of the movement,

[0804] A visualization means for visually presenting the calculated exercise intensity to the person performing the exercise,

[0805] A feedback means that provides feedback to the person performing the exercise based on the intensity of the exercise,

[0806] A data collection method for acquiring customer behavior data and analyzing customer trends,

[0807] Analysis and visualization methods for optimizing and visualizing customer movement patterns,

[0808] A system that includes this.

[0809] (Claim 2)

[0810] The system according to claim 1, wherein the sensor means includes a camera for capturing images of the movements of a person or customer.

[0811] (Claim 3)

[0812] The system according to claim 1, wherein the feedback means includes means for receiving operations from a person exercising using voice commands or gestures and adjusting the exercise plan or store layout.

[0813] "Example 2 of combining an emotion engine"

[0814] (Claim 1)

[0815] Information gathering means for acquiring motion data and facial expression data of a person exercising,

[0816] An analysis means that processes the aforementioned motion data and facial expression data and simultaneously calculates the intensity of the movement and the emotional state,

[0817] A generation means that generates optimal feedback based on the calculated exercise intensity and emotional state,

[0818] A display means for visually and audibly presenting the aforementioned feedback to the person performing the exercise,

[0819] An interaction means that receives new instructions from the person performing the exercise using voice commands or gestures based on the aforementioned feedback,

[0820] The adjustment means dynamically adjusts the analysis means and generation means through the aforementioned interaction to optimize the movement plan,

[0821] A system that includes this.

[0822] (Claim 2)

[0823] The system according to claim 1, wherein the information gathering means includes means for analyzing the facial expressions of a person exercising and recognizing their emotional state.

[0824] (Claim 3)

[0825] The system according to claim 1, wherein the generation means includes a generative AI model that generates feedback in natural language based on the user's emotional state.

[0826] "Application example 2 when combining with an emotional engine"

[0827] (Claim 1)

[0828] A sensor means for acquiring motion data of a person exercising,

[0829] A calculation means that processes the aforementioned motion data and calculates the intensity of the movement,

[0830] A visualization means for visually presenting the calculated exercise intensity to the person performing the exercise,

[0831] A feedback means that provides feedback to the person performing the exercise based on the intensity of the exercise,

[0832] An analytical means for recognizing the emotional state of workers in an industrial environment and generating feedback to improve work efficiency,

[0833] A system that includes this.

[0834] (Claim 2)

[0835] The system according to claim 1, wherein the sensor means includes a camera for capturing images of the movements of a person who is moving.

[0836] (Claim 3)

[0837] The system according to claim 1, wherein the feedback means includes means for receiving operations from a person exercising using voice commands or gestures and adjusting the exercise plan. [Explanation of Symbols]

[0838] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A sensor device that acquires motion data of a person exercising, A calculation device that processes the aforementioned motion data and calculates the intensity of the movement, A visualization device that visually displays the calculated exercise intensity to the person performing the exercise, A feedback device that provides feedback to the person performing the exercise based on the intensity of the exercise, A system that includes this.

2. The system according to claim 1, wherein the sensor device includes a camera that captures the movements of a person exercising.

3. The system according to claim 1, wherein the feedback device includes means for receiving operations from a person performing an exercise using voice commands or gestures and adjusting the exercise plan.

Images