system

Abstract

We provide the system. [Solution] A means for receiving user information acquired from an image input device, A means for analyzing physical characteristics based on the aforementioned user information, The aforementioned analysis results are compared with an accumulated database of exercise-related fields, and a means is provided to recommend appropriate exercise activities. A means for presenting the aforementioned recommended results to the user via an output device, A system that includes this.

Description

Technical Field

[0004] , , , ,

[0005] , , , , ,

[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 that responds 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] Selecting appropriate sports activities or sports fields is difficult for many parents and customers. In particular, it is not easy to judge suitability based on individual physical characteristics without professional knowledge. Therefore, there is a need to develop a system that objectively analyzes physical characteristics and recommends appropriate sports fields.

Means for Solving the Problems

[0005] This invention solves the above problem by utilizing user information acquired from an image input device, analyzing physical characteristics based on that information, and comparing the analysis results with an existing database of exercise fields to recommend appropriate exercise activities. Furthermore, by presenting the recommendation results to the user via an output device, the invention provides a means for users to easily find suitable exercise activities.

[0006] An "image input device" is a device that acquires images of objects as digital data and inputs them into a system.

[0007] "User information" refers to image data and other related information that form the basis for the system's analysis and decision-making.

[0008] "Physical characteristics" refer to numerical data and shape information that indicate the anatomical and morphological properties of an object.

[0009] "Analysis" refers to the process of deriving specific results or information based on collected data.

[0010] A "database" refers to a collection of information where related information is systematically structured and stored in an easily accessible manner.

[0011] "Physical activity" refers to specific sports or any activity that involves physical movement.

[0012] An "output device" refers to a device that displays information from a system in a format that is understandable to humans.

[0013] "Accumulated data on the field of sports" refers to a collection of information that records the achievements and characteristics of past athletes.

[0014] "Reconciliation" refers to the process of comparing different pieces of information and confirming their relationships.

[0015] "Recommended Results" refers to the presentation of options that are considered optimal for the user, obtained through analysis and comparison.

Brief Description of Drawings

[0016] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which multiple emotions are mapped. [Figure 10] It shows an emotion map to which multiple emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. <H [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. <H [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when the emotion engine is combined. [Figure 14]It is a sequence diagram showing the processing flow of a data processing system in Application Example 2 when a sentiment engine is combined.

Embodiment for Implementing the Invention

[0017] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0018] First, the terms used in the following description will be explained.

[0019] In the following embodiments, a 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 CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), etc.

[0020] In the following embodiments, a numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0021] In the following embodiments, a 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 disk (e.g., hard disk), or magnetic tape, etc.

[0022] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0023] 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."

[0024] [First Embodiment]

[0025] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0026] 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.

[0027] 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).

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] 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".

[0037] This invention relates to a system for determining the suitability of a user for exercise activities based on their physical characteristics. This system mainly consists of three elements: an image input device, a server, and a user terminal.

[0038] First, the user uses an image input device to capture a full-body image of the target person. The captured image is then sent via the user's device to a server in the cloud. This process requires that the image resolution and format be prepared in advance.

[0039] The server uses advanced image processing algorithms to analyze the received images. This allows for a detailed analysis of the subject's physical characteristics, including their skeleton. Skeletal data and body type-related information are cross-referenced with a database of athletes collected in the past. The database used here contains physical data of successful athletes in various athletic fields.

[0040] Based on the analysis and matching results, the server generates a list of exercise activities most suitable for the user. This list indicates areas of exercise in which the user is most likely to succeed, based on statistical analysis results. The generated list is sent to the user's device and presented in a visually easy-to-understand format.

[0041] For example, if a user has options such as soccer, basketball, or track and field, the system can narrow down the choices based on how well the user's skeletal structure and body type match those of successful athletes. As a result, the user can find the exercise activity that best suits their aptitude and start participating efficiently.

[0042] In this manner, the present invention functions as a system that suggests suitable exercise activities based on the user's physical characteristics.

[0043] The following describes the processing flow.

[0044] Step 1:

[0045] The user uses an image input device to capture a full-body image of the subject. After capture, the image is uploaded to the application on the device.

[0046] Step 2:

[0047] The device receives the image data and verifies that the format and resolution are in a state where they can be analyzed. After verification, the image is sent to a server in the cloud.

[0048] Step 3:

[0049] The server receives the image data and executes an image recognition algorithm. This identifies the physical characteristics of the subject, particularly the skeletal structure, and extracts them as numerical data.

[0050] Step 4:

[0051] The server extracts physical characteristic data and compares it with a database of athletes. Statistical analysis techniques are then used to identify similar athletic disciplines.

[0052] Step 5:

[0053] The server generates a list of the most suitable exercise activities based on the analysis and matching results. This list includes exercise areas that are likely to match the user's aptitude.

[0054] Step 6:

[0055] The server sends a list of exercise activities it has generated to the terminal. The terminal then presents the list to the user in a visually easy-to-understand format.

[0056] Step 7:

[0057] The user reviews the exercise activities presented via their device and considers the options selected based on their suitability. If necessary, they can also investigate detailed information about the exercise activities.

[0058] (Example 1)

[0059] 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."

[0060] The objective of this invention is to efficiently propose the most suitable exercise activity based on the user's physical characteristics. Conventional technologies have found it difficult to analyze a user's physical characteristics in detail and provide highly accurate exercise suitability without requiring expert knowledge.

[0061] 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.

[0062] In this invention, the server includes a medium for receiving user information obtained from an image acquisition device, means for analyzing physical characteristics based on the user information, and means for performing analysis and matching using a generated AI model. This makes it possible to suggest appropriate exercise activities based on the user's physical characteristics.

[0063] An "image acquisition device" is an electronic device used to capture or collect visual information, including the physical characteristics of a user.

[0064] "User information" refers to data that describes the physical characteristics of the person in question, and includes image information and measurement information.

[0065] "Medium" refers to a means of communication for transmitting or receiving data, and this includes networks and internet connections.

[0066] "Physical characteristics" refers to the user's physical features such as height, body type, and skeletal structure.

[0067] "Analysis methods" refer to technical processes for extracting detailed physical characteristics based on user information, and this includes algorithms and software tools.

[0068] "Information set" refers to relevant data on the motor domain collected in the past, including the physical characteristics of the athletes involved in the sport.

[0069] A "generative AI model" is an artificial intelligence model that has been trained on a large amount of data and is used to perform complex analyses and predictions.

[0070] An "information output device" refers to a display device that provides users with analysis results and recommended information.

[0071] "Exercise activities" refer to sports or physical activities that are most suitable for each individual user.

[0072] "Proposal" refers to the process of indicating the optimal course of action based on the analysis results.

[0073] This invention is a system that proposes optimal exercise activities based on the user's physical characteristics. The system mainly consists of three elements: an image acquisition device, a server, and a user terminal.

[0074] Subject: User

[0075] The user first takes a full-body image of themselves or a specific person using an image acquisition device. This image provides detailed information about the user's physical characteristics and is necessary for analysis. The image is then transmitted to a server in the cloud via the user's terminal. The image data needs to be converted to an appropriate resolution and format.

[0076] Subject: Server

[0077] The server uses advanced image processing algorithms to analyze the received images. Specifically, it uses software such as OpenCV and TENSORFLOW® to extract physical characteristics such as the user's skeletal structure and body shape from the images. This information is then compared with a set of information containing physical characteristic data of successful exercise participants. A generative AI model is used in this comparison process to perform highly accurate similarity evaluations.

[0078] Subject: Server

[0079] The server generates a list of exercise activities best suited to the user based on the analysis and matching results. This list is presented in a ranked order, with the most likely exercise areas identified by the generating AI model. The results are then sent to the user's device and provided in a visually easy-to-understand format.

[0080] For example, if a user wants to know whether they are suited to soccer or basketball, the system evaluates whether the user's physical characteristics are similar to those of a soccer player and recommends appropriate exercise activities. Based on these results, the user can choose the sport that best suits their aptitude and start playing sports efficiently.

[0081] An example of a prompt for a generative AI model is: "Use deep learning to determine the athletic aptitude of a given person and generate a list of recommended athletic fields based on that."

[0082] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0083] Step 1:

[0084] The user uses an image acquisition device to capture a full-body image of the subject. This capture generates digital image data that captures the subject's skeleton and body shape. The captured images are pre-processed through a dedicated application to ensure optimal resolution and format. The input is image data from the camera, and the output is the formatted digital image data.

[0085] Step 2:

[0086] The user's device sends the formatted digital image data to a server in the cloud. Encryption technology is used in this process to ensure secure and rapid data transfer. The input is the formatted digital image data, and the output is the image data sent to the server.

[0087] Step 3:

[0088] The server analyzes the received image data using advanced image processing algorithms. Here, skeletal and body shape features are automatically extracted using deep learning techniques. Software used includes OpenCV and TensorFlow. The input is the image data sent to the server, and the output is the extracted physical feature data.

[0089] Step 4:

[0090] The server compares the extracted physical characteristic data with a stored set of information related to motor domains. A generative AI model is used to evaluate similarity. This algorithm determines which motor domains the subject has high aptitude in. The input is the extracted physical characteristic data, and the output is the similarity calculation result.

[0091] Step 5:

[0092] The server generates a list of exercise activities best suited to the user based on the similarity calculation results. This list is visually formatted for presentation to the user, taking statistical analysis results into consideration. The input is the similarity calculation result, and the output is a list of recommended exercise activities.

[0093] Step 6:

[0094] The user's device receives and displays a list of recommended exercise activities sent from the server. Based on this list, the user can select the exercise best suited to them and provide appropriate feedback. The input is the list of recommended exercise activities from the server, and the output is a visually formatted list of recommendations.

[0095] (Application Example 1)

[0096] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0097] In modern society, finding appropriate exercise activities based on individual physical characteristics is crucial for maintaining health and improving athletic performance. However, existing systems have only provided general information, lacking sufficient specific guidance and feedback based on individual characteristics. Furthermore, there has been a lack of concrete support for users to select appropriate exercises and practice them correctly.

[0098] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0099] In this invention, the server includes means for receiving user information acquired from an image acquisition device, means for analyzing physical characteristics, means for comparing the analysis results with a stored information source on movement ranges and recommending appropriate physical activities, and means for demonstrating exercise methods to the user via a robotic device and performing posture correction in real time. This makes it possible to support the selection of specific and effective exercise activities based on the physical characteristics of each individual user and their accurate implementation.

[0100] An "image acquisition device" is a device used to acquire a user's physical information, and includes cameras and other sensor devices.

[0101] "User information" refers to information about the physical characteristics of individual users, obtained in the form of images or other data.

[0102] "Physical characteristics" refer to specific physical features of the user, such as their skeletal structure, muscle arrangement, and build.

[0103] "Analysis" refers to performing a detailed analysis based on acquired information through specific algorithms and processes.

[0104] "Information sources related to the exercise domain" refers to a database that aggregates past data and success stories related to various exercise activities, and includes information based on specific criteria.

[0105] "Recommending physical activity" means suggesting appropriate exercise and fitness activities based on the user's physical characteristics.

[0106] A "robot device" is an automated mechanical device used to demonstrate exercise methods and provide real-time feedback to users.

[0107] "Posture correction" is a process of detecting errors in the user's body position and movement during exercise and adjusting it on the spot to bring it closer to the optimal form.

[0108] The system's program analyzes the user's physical characteristics, proposes optimal physical activity, and uses a robotic device to demonstrate exercise methods in real time and correct the user's posture.

[0109] The server first receives user information transmitted from the image acquisition device. The acquired information consists of images of the body taken using a camera or similar device. The received information is then used to analyze the user's physical characteristics using image processing and machine learning software such as OpenCV and TensorFlow. The skeletal structure and body size data revealed through the analysis are then compared with information sources on motor regions stored in a cloud-based database.

[0110] The terminal provides the user with recommended results received from the server. These recommendations include exercises and fitness activities best suited to the user. The robotic device also demonstrates the suggested exercises and supports the user in performing them correctly. During this process, the robotic device monitors the user's posture in real time and provides corrective instructions as needed.

[0111] As a concrete example, the server analyzes the arrangement of shoulder muscles from a full-body image of the user and recommends "shoulder exercises using dumbbells" by comparing it with a database. The robotic device demonstrates the correct form in front of the user, continuously monitors the user's posture while they perform the exercise, and provides real-time feedback to correct incorrect form.

[0112] An example of a prompt to a generating AI model might be: "We want to develop a system where a robot analyzes a user's physique and suggests the optimal exercise. Please output design ideas for an application that instructs fitness beginners on suitable exercises and how to do them." Using this prompt, it is also possible to have the AI ​​generate specific instruction content and feedback methods.

[0113] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0114] Step 1:

[0115] The user takes a full-body image using an image acquisition device. The input is a JPEG image file acquired by the camera. The output is image data ready to be sent to the server. The user sends this image to the server via their device.

[0116] Step 2:

[0117] The server receives image data sent from the user terminal. The input is the image data sent from the user terminal. The received image data is processed using OpenCV for resolution adjustment and image format conversion. The output is image data formatted into a parseable format.

[0118] Step 3:

[0119] The server analyzes the user's physical characteristics using image processing algorithms. The input is formatted image data. This data is then fed into a skeleton detection model using TensorFlow to extract the user's skeletal structure and body shape data. The output is digital data that reflects the physical characteristics.

[0120] Step 4:

[0121] The server matches the analysis results against a cloud database and recommends appropriate exercise activities. The input is data representing the user's physical characteristics. This data is matched with a database of accumulated athletes to identify the optimal exercise activity. The output is a list of recommended results.

[0122] Step 5:

[0123] The terminal displays recommended results received from the server to the user. The input is a list of recommended results from the server. The output is a list of recommended exercise activities displayed on the user terminal's screen. The user can then select an exercise based on this information.

[0124] Step 6:

[0125] Based on the selected movement, the robotic device begins demonstrating the movement method. The input is information about the movement activity selected by the user. The output is a demonstration of the movement by the robotic device. The robot demonstrates the appropriate form and movements in front of the user, and the user begins to move according to those movements.

[0126] Step 7:

[0127] The robotic device monitors the user's posture in real time during exercise. The input is the user's movement information captured by the robot's sensors. The output is feedback information for posture correction. Based on this, the robot instructs the user to make appropriate posture corrections, maximizing the effectiveness of the exercise. This entire process improves the quality of the user's exercise activity.

[0128] 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.

[0129] This invention combines an emotional engine with a system that recommends exercise activities based on the user's physical characteristics. This system mainly consists of an image input device, a server, a terminal, and an emotional engine.

[0130] First, the user takes a full-body image of the target person using an image input device and uploads it to the terminal. This image is used to identify the user's physical characteristics. The terminal then sends the captured image to a server in the cloud.

[0131] Upon receiving image data, the server uses advanced image processing algorithms to analyze physical characteristics. This includes measuring skeletal structure and body shape. The analysis results are then compared against a database of characteristics of past successful athletes. This allows the system to statistically determine and list the most suitable exercise activities for the user.

[0132] Furthermore, this invention utilizes an emotion engine to analyze the user's emotional state in real time. This emotion engine acquires biometric data such as the user's facial expressions and heart rate using facial recognition technology and biosensors. Based on the analysis results, the recommendations for exercise activities are adjusted. For example, if it is estimated that the user is feeling stressed, the list will prioritize exercises with relaxing effects.

[0133] As a concrete example, when a user uses this system, they first take an image of their body and send it to the server. Then, the emotion engine analyzes the user's real-time emotional state, and the server takes this emotional data into consideration to select an appropriate exercise activity. In this way, exercise selection optimized for the user's physical characteristics and emotional state becomes possible, leading to more effective health management.

[0134] This invention is a system that provides personalized suggestions in selecting appropriate exercise activities by considering not only the user's physical aptitude but also their psychological state.

[0135] The following describes the processing flow.

[0136] Step 1:

[0137] The user uses an image input device to capture a full-body image of the subject. The captured image is uploaded to the system's application via the user's terminal.

[0138] Step 2:

[0139] After the device receives image data, it checks its format and resolution and securely transmits it to a server in the cloud.

[0140] Step 3:

[0141] The server processes the received images and uses image analysis algorithms to analyze the subject's skeletal structure and body shape in detail. The numerical data obtained here includes bone length and body proportions.

[0142] Step 4:

[0143] The server analyzes the physical characteristics data and compares it with a database containing physical data from past successful athletes. This comparison statistically derives the most suitable exercise activities for the individual.

[0144] Step 5:

[0145] On the other hand, the user's device activates an emotion engine and captures the user's facial expressions with its camera. Additionally, biometric data is acquired from sensors such as a heart rate sensor as needed.

[0146] Step 6:

[0147] The device's emotion engine analyzes the user's emotional state from acquired facial expression data and biometric data, and sends the results to the server.

[0148] Step 7:

[0149] Based on the emotional data received by the server, the system optimizes recommendations for exercise activities. For example, if the analysis indicates that the user is feeling stressed, exercises that help reduce stress will be recommended.

[0150] Step 8:

[0151] The server generates a final recommendation list and sends it to the terminal. The terminal then presents this list to the user in a visually easy-to-understand format.

[0152] Step 9:

[0153] Users review a list of suggested exercise activities and select those that match their interests and goals. They can also learn more about the exercises and how to perform them if needed.

[0154] (Example 2)

[0155] 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".

[0156] In modern society, there is a growing demand for personalized exercise programs that take into account not only individual physical characteristics but also emotional states. However, systems that can adequately address this are not yet readily available, making it difficult to provide exercise programs optimized for each user.

[0157] 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.

[0158] In this invention, the server includes means for receiving user information obtained from an image input device, means for analyzing physical data based on the user information, and emotion analysis means for analyzing the user's emotional state in real time. This makes it possible to recommend optimal exercise activities that take into account both physical characteristics and emotional state.

[0159] An "image input device" is a device used to acquire video information of a user's body.

[0160] "User information" refers to information including physical characteristics and other related data acquired through an image input device.

[0161] "Physical data" refers to data on physical characteristics such as skeletal structure and body type, which are analyzed from user information.

[0162] A "field of activity" is a field that includes various techniques and methodologies related to physical activity and exercise.

[0163] A "database" is a collection of information that systematically stores and makes searchable successful case studies and data accumulated in the past.

[0164] "Emotional analysis tools" are functions that analyze, investigate, and evaluate the emotional state of users in real time.

[0165] An "output device" is a device used to present recommended results or information to the user.

[0166] "Adjustment" refers to the process of optimizing the proposed exercise activity based on the analysis results.

[0167]

[0168] This invention is a system that proposes optimal exercise activities for the user, and utilizes an image input device, a server, a terminal, and an emotion analysis system. Specifically, this system is implemented in the following manner.

[0169] First, the user takes a full-body image of themselves using an image input device. This image provides the basic information necessary for analyzing the user's physical data. The image is uploaded to a server in the cloud via the terminal. The server uses the OpenCV library in Python to analyze the image and extract the user's skeletal structure and body shape.

[0170] The server uses this physical data to compare it with a database of past successful activity areas. This database contains a large number of exercise examples, allowing for the efficient listing of exercise activities optimized for individual physical characteristics.

[0171] Next, biosensors are attached to the device to analyze the user's emotional state in real time. This emotional analysis utilizes facial recognition technology and Google's MediaPipe library. The device then sends the resulting emotional data to a server.

[0172] The server takes the user's emotional state into account and adjusts and selects the optimal exercise activity based on the analysis of physical data. For example, if the user is feeling stressed, suggestions for yoga or meditation, which are effective in relieving stress, will be prioritized.

[0173] Ultimately, the server sends a list of recommended exercise activities to the terminal, which then displays this to the user. For example, by entering a prompt such as, "Please recommend an effective yoga session to relieve fatigue after work," it is possible to suggest exercise activities tailored to the user. This allows the user to receive exercise selections optimized for their physical characteristics and psychological state, enabling more effective health management.

[0174] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0175] Step 1:

[0176] The user takes a full-body image of themselves using an image input device. The input is video data of the user's body. The device can upload this video data to a server in the cloud. Once the video data is uploaded, the server receives it for further analysis.

[0177] Step 2:

[0178] The server performs image analysis using the Python OpenCV library based on the received video data. The input is the image data itself. From this data, the server identifies the user's skeletal structure and body shape and outputs it as body data. This analysis process is an important step in generating basic information for matching with the database.

[0179] Step 3:

[0180] The server matches the analyzed physical data against a database associated with past successful activity areas. The input is the analyzed physical data, and the output is a list of optimal exercise activities for the user. The server uses statistical methods to select the optimal exercise and generates the results.

[0181] Step 4:

[0182] The device collects the user's emotional state in real time via biosensors. The input is emotional data from facial recognition technology and biosensors. The device sends this data to a server and requests emotional analysis. This process evaluates the emotions based on the user's heart rate and facial expressions and generates an emotional state report as output.

[0183] Step 5:

[0184] The server receives the results of the emotion analysis and adjusts the list of exercise activities based on them. The input is a report of the emotional state, and the output is the final adjusted list of exercise activities. The server optimizes this list, highlighting suggestions that are appropriate for the emotional state, such as exercises that have a high relaxation effect.

[0185] Step 6:

[0186] The server sends a final list of adjusted exercise activities to the terminal. The terminal displays this to the user and provides specific exercise suggestions. As a concrete example, the AI ​​model generates a prompt message such as, "Please tell me an effective yoga session to relieve fatigue after work," and then presents the user with the most suitable exercise.

[0187] (Application Example 2)

[0188] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0189] Conventional exercise recommendation systems propose exercise activities based solely on an individual's physical characteristics, failing to consider their psychological state, which makes it difficult to recommend the most suitable exercise. In particular, when emotional state significantly impacts exercise achievement, adjustments that reflect individual emotions are needed.

[0190] 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.

[0191] In this invention, the server includes means for receiving personal information obtained from an image acquisition device, means for analyzing physical attributes based on the personal information, and means for evaluating the individual's emotional state in real time using an emotion analysis device and adjusting the recommendation results. This makes it possible to propose optimal exercise activities that take into account the individual's physical attributes and emotional state.

[0192] An "image acquisition device" is a device used to capture an individual's physical characteristics, and mainly refers to cameras and sensor devices.

[0193] "Personal information" refers to data obtained from an image acquisition device, including information about an individual's physical characteristics and attributes.

[0194] "Physical attributes" refer to physical characteristics and traits, such as an individual's body type and skeletal structure.

[0195] The "information set in the motor domain" refers to a database related to various exercise activities, and in particular, includes information on the physical attributes of successful exercise practitioners.

[0196] An "emotion analysis device" is a device used to evaluate an individual's emotional state, analyzing emotions in real time using facial recognition technology and biosensors.

[0197] A "communication device" is a device used to transmit adjusted exercise suggestions to an individual, and generally refers to a display or speaker.

[0198] In this invention, the user first takes a full-body image of themselves using an image acquisition device, specifically a camera. The captured image is transmitted by the terminal to a server in the cloud. The server receives this image data and uses image processing software to analyze the user's physical attributes. In this process, image processing libraries such as OpenCV are utilized. The analysis results are compared with a set of information regarding the motor domain, specifically with the physical attribute data of past successful exercise practitioners.

[0199] Subsequently, the server uses an emotion analysis device to evaluate the user's emotional state in real time. This emotion analysis utilizes a system that combines facial recognition technology and biosensors. Using a facial recognition API, the emotional state is measured based on the user's facial expression data and heart rate. Based on the emotional state, the recommendations for exercise activity are adjusted.

[0200] The adjusted recommendations are presented to the user via a communication device. At this stage, the user receives feedback on the most suitable exercise suggestions using a display or speaker. For example, if the user is feeling stressed, the server will prioritize recommending exercises with relaxing effects, and the user will receive guidance from their smartphone or a robot.

[0201] An example of a prompt that uses a generative AI model to create an exercise plan is: "Recognize the user's emotional state and design a relaxing exercise plan. Highlight the user's stress levels as a key point when formulating suggestions." This prompt allows the AI ​​model to generate exercise suggestions optimized for each individual user.

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

[0203] Step 1:

[0204] The user takes a full-body image using an image acquisition device. This image is transferred to the terminal and used as input data for transmission to the server. The terminal must maintain a stable network connection until the captured image data is sent to the server.

[0205] Step 2:

[0206] The server receives whole-body image data transmitted from the terminal. The server analyzes this data using image processing software to identify the user's physical attributes. Specifically, it analyzes the skeleton and body shape using libraries such as OpenCV and compares this with a set of information regarding range of motion. As output, candidate exercises suitable for the user are generated.

[0207] Step 3:

[0208] The server uses an emotion analysis device to evaluate the user's emotional state in real time. This process uses data obtained from facial recognition technology and biosensors as input. A facial recognition API is used to analyze the user's facial expressions and heart rate. This allows the server to understand the user's emotional state and adjust the recommended exercise activities accordingly. The output is a list of exercise suggestions that take the emotional state into account.

[0209] Step 4:

[0210] The final exercise suggestions are presented to the user via a communication device. The server transmits the processed exercise suggestion data to a display or speaker, providing visual and audible feedback. The user receives the presented exercise suggestions and can then begin exercising based on them.

[0211] Step 5:

[0212] In creating exercise plans using a generative AI model, prompts are generated on the server side and sent to the AI. For example, the prompt "Recognize the user's emotional state and design a relaxing exercise plan. Emphasize whether the user is stressed as a key point when formulating suggestions" is input to the AI ​​model. The output is a specific exercise plan generated based on the prompt.

[0213] 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.

[0214] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0215] 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.

[0216] [Second Embodiment]

[0217] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0218] 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.

[0219] 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).

[0220] 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.

[0221] 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.

[0222] 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).

[0223] 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.

[0224] 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.

[0225] 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.

[0226] 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.

[0227] 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.

[0228] 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".

[0229] This invention relates to a system for determining the suitability of a user for exercise activities based on their physical characteristics. This system mainly consists of three elements: an image input device, a server, and a user terminal.

[0230] First, the user uses an image input device to capture a full-body image of the target person. The captured image is then sent via the user's device to a server in the cloud. This process requires that the image resolution and format be prepared in advance.

[0231] The server uses advanced image processing algorithms to analyze the received images. This allows for a detailed analysis of the subject's physical characteristics, including their skeleton. Skeletal data and body type-related information are cross-referenced with a database of athletes collected in the past. The database used here contains physical data of successful athletes in various athletic fields.

[0232] Based on the analysis and matching results, the server generates a list of exercise activities most suitable for the user. This list indicates areas of exercise in which the user is most likely to succeed, based on statistical analysis results. The generated list is sent to the user's device and presented in a visually easy-to-understand format.

[0233] For example, if a user has options such as soccer, basketball, or track and field, the system can narrow down the choices based on how well the user's skeletal structure and body type match those of successful athletes. As a result, the user can find the exercise activity that best suits their aptitude and start participating efficiently.

[0234] In this manner, the present invention functions as a system that suggests suitable exercise activities based on the user's physical characteristics.

[0235] The following describes the processing flow.

[0236] Step 1:

[0237] The user uses an image input device to capture a full-body image of the subject. After capture, the image is uploaded to the application on the device.

[0238] Step 2:

[0239] The device receives the image data and verifies that the format and resolution are in a state where they can be analyzed. After verification, the image is sent to a server in the cloud.

[0240] Step 3:

[0241] The server receives the image data and executes an image recognition algorithm. This identifies the physical characteristics of the subject, particularly the skeletal structure, and extracts them as numerical data.

[0242] Step 4:

[0243] The server extracts physical characteristic data and compares it with a database of athletes. Statistical analysis techniques are then used to identify similar athletic disciplines.

[0244] Step 5:

[0245] The server generates a list of the most suitable exercise activities based on the analysis and matching results. This list includes exercise areas that are likely to match the user's aptitude.

[0246] Step 6:

[0247] The server sends a list of exercise activities it has generated to the terminal. The terminal then presents the list to the user in a visually easy-to-understand format.

[0248] Step 7:

[0249] The user reviews the exercise activities presented via their device and considers the options selected based on their suitability. If necessary, they can also investigate detailed information about the exercise activities.

[0250] (Example 1)

[0251] 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."

[0252] The objective of this invention is to efficiently propose the most suitable exercise activity based on the user's physical characteristics. Conventional technologies have found it difficult to analyze a user's physical characteristics in detail and provide highly accurate exercise suitability without requiring expert knowledge.

[0253] 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.

[0254] In this invention, the server includes a medium for receiving user information obtained from an image acquisition device, means for analyzing physical characteristics based on the user information, and means for performing analysis and matching using a generated AI model. This makes it possible to suggest appropriate exercise activities based on the user's physical characteristics.

[0255] An "image acquisition device" is an electronic device used to capture or collect visual information, including the physical characteristics of a user.

[0256] "User information" refers to data that describes the physical characteristics of the person in question, and includes image information and measurement information.

[0257] "Medium" refers to a means of communication for transmitting or receiving data, and this includes networks and internet connections.

[0258] "Physical characteristics" refers to the user's physical features such as height, body type, and skeletal structure.

[0259] "Analysis methods" refer to technical processes for extracting detailed physical characteristics based on user information, and this includes algorithms and software tools.

[0260] "Information set" refers to relevant data on the motor domain collected in the past, including the physical characteristics of the athletes involved in the sport.

[0261] A "generative AI model" is an artificial intelligence model that has been trained on a large amount of data and is used to perform complex analyses and predictions.

[0262] An "information output device" refers to a display device that provides users with analysis results and recommended information.

[0263] "Exercise activities" refer to sports or physical activities that are most suitable for each individual user.

[0264] "Proposal" refers to the process of indicating the optimal course of action based on the analysis results.

[0265] This invention is a system that proposes optimal exercise activities based on the user's physical characteristics. The system mainly consists of three elements: an image acquisition device, a server, and a user terminal.

[0266] Subject: User

[0267] The user first takes a full-body image of themselves or a specific person using an image acquisition device. This image provides detailed information about the user's physical characteristics and is necessary for analysis. The image is then transmitted to a server in the cloud via the user's terminal. The image data needs to be converted to an appropriate resolution and format.

[0268] Subject: Server

[0269] The server uses advanced image processing algorithms to analyze the received images. Specifically, it uses software such as OpenCV and TensorFlow to extract physical characteristics such as the user's skeleton and body shape from the images. This information is then compared with a set of information containing physical characteristic data of successful exercise participants. A generative AI model is used in this comparison process to perform highly accurate similarity evaluations.

[0270] Subject: Server

[0271] The server generates a list of exercise activities best suited to the user based on the analysis and matching results. This list is presented in a ranked order, with the most likely exercise areas identified by the generating AI model. The results are then sent to the user's device and provided in a visually easy-to-understand format.

[0272] For example, if a user wants to know whether they are suited to soccer or basketball, the system evaluates whether the user's physical characteristics are similar to those of a soccer player and recommends appropriate exercise activities. Based on these results, the user can choose the sport that best suits their aptitude and start playing sports efficiently.

[0273] An example of a prompt for a generative AI model is: "Use deep learning to determine the athletic aptitude of a given person and generate a list of recommended athletic fields based on that."

[0274] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0275] Step 1:

[0276] The user uses an image acquisition device to capture a full-body image of the subject. This capture generates digital image data that captures the subject's skeleton and body shape. The captured images are pre-processed through a dedicated application to ensure optimal resolution and format. The input is image data from the camera, and the output is the formatted digital image data.

[0277] Step 2:

[0278] The user's device sends the formatted digital image data to a server in the cloud. Encryption technology is used in this process to ensure secure and rapid data transfer. The input is the formatted digital image data, and the output is the image data sent to the server.

[0279] Step 3:

[0280] The server analyzes the received image data using advanced image processing algorithms. Here, the features of the skeleton and body shape are automatically extracted by deep learning technology. Software such as OpenCV and TensorFlow is used. The input is the image data sent to the server, and the output is the extracted physical feature data.

[0281] Step 4:

[0282] The server compares the extracted physical feature data with the set of information related to the accumulated exercise areas. A generative AI model is used to evaluate the similarity. This algorithm determines in which exercise field the target person has high suitability. The input is the extracted physical feature data, and the output is the calculation result of the similarity.

[0283] Step 5:

[0284] The server generates a list of exercise activities most suitable for the user based on the calculation result of the similarity. This list is visually formatted for presentation to the user considering the statistical analysis results. The input is the calculation result of the similarity, and the output is the recommended list of exercise activities.

[0285] Step 6:

[0286] The user's terminal receives and displays the recommended list of exercise activities sent from the server. The user can select the exercise most suitable for themselves based on this list and send appropriate feedback. The input is the recommended list of exercise activities from the server, and the output is the visually formatted recommended list.

[0287] (Application Example 1)

[0288] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0289] In modern society, finding appropriate exercise activities based on individual physical characteristics is crucial for maintaining health and improving athletic performance. However, existing systems have only provided general information, lacking sufficient specific guidance and feedback based on individual characteristics. Furthermore, there has been a lack of concrete support for users to select appropriate exercises and practice them correctly.

[0290] 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.

[0291] In this invention, the server includes means for receiving user information acquired from an image acquisition device, means for analyzing physical characteristics, means for comparing the analysis results with a stored information source on movement ranges and recommending appropriate physical activities, and means for demonstrating exercise methods to the user via a robotic device and performing posture correction in real time. This makes it possible to support the selection of specific and effective exercise activities based on the physical characteristics of each individual user and their accurate implementation.

[0292] An "image acquisition device" is a device used to acquire a user's physical information, and includes cameras and other sensor devices.

[0293] "User information" refers to information about the physical characteristics of individual users, obtained in the form of images or other data.

[0294] "Physical characteristics" refer to specific physical features of the user, such as their skeletal structure, muscle arrangement, and build.

[0295] "Analysis" refers to performing a detailed analysis based on acquired information through specific algorithms and processes.

[0296] "Information sources related to the exercise domain" refers to a database that aggregates past data and success stories related to various exercise activities, and includes information based on specific criteria.

[0297] "Recommending physical activity" means suggesting appropriate exercise and fitness activities based on the user's physical characteristics.

[0298] A "robot device" is an automated mechanical device used to demonstrate exercise methods and provide real-time feedback to users.

[0299] "Posture correction" is a process of detecting errors in the user's body position and movement during exercise and adjusting it on the spot to bring it closer to the optimal form.

[0300] The system's program analyzes the user's physical characteristics, proposes optimal physical activity, and uses a robotic device to demonstrate exercise methods in real time and correct the user's posture.

[0301] The server first receives user information transmitted from the image acquisition device. The acquired information consists of images of the body taken using a camera or similar device. The received information is then used to analyze the user's physical characteristics using image processing and machine learning software such as OpenCV and TensorFlow. The skeletal structure and body size data revealed through the analysis are then compared with information sources on motor regions stored in a cloud-based database.

[0302] The terminal provides the user with recommended results received from the server. These recommendations include exercises and fitness activities best suited to the user. The robotic device also demonstrates the suggested exercises and supports the user in performing them correctly. During this process, the robotic device monitors the user's posture in real time and provides corrective instructions as needed.

[0303] As a specific example, assume that the server analyzes the arrangement of shoulder muscles from the user's full-body image and recommends "shoulder exercises using dumbbells" by comparing it with a database. The robot device demonstrates the correct form in front of the user, continuously monitors the posture while the user is performing the exercise, and provides real-time feedback to correct incorrect forms.

[0304] As an example of a prompt sentence for the generative AI model, something like "I want to develop a system where a robot analyzes the user's physique and proposes optimal exercises. Please output design ideas for an application that guides exercises suitable for beginners in fitness and how to do them." can be considered. Using this prompt sentence, it is also possible to have the AI generate specific guidance content and feedback methods.

[0305] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0306] Step 1:

[0307] The user takes a full-body image using an image acquisition device. The input is a JPEG format image file acquired by the camera. The output is image data in a ready-to-transmit format to the server. The user sends this image to the server via their terminal.

[0308] Step 2:

[0309] The server receives the image data transmitted from the user terminal. The input is the image data sent from the user terminal. The received image data is adjusted in resolution and converted in image format using OpenCV. The output is image data formatted into an analyzable form.

[0310] Step 3:

[0311] The server analyzes the user's physical characteristics using image processing algorithms. The input is formatted image data. This data is then fed into a skeleton detection model using TensorFlow to extract the user's skeletal structure and body shape data. The output is digital data that reflects the physical characteristics.

[0312] Step 4:

[0313] The server matches the analysis results against a cloud database and recommends appropriate exercise activities. The input is data representing the user's physical characteristics. This data is matched with a database of accumulated athletes to identify the optimal exercise activity. The output is a list of recommended results.

[0314] Step 5:

[0315] The terminal displays recommended results received from the server to the user. The input is a list of recommended results from the server. The output is a list of recommended exercise activities displayed on the user terminal's screen. The user can then select an exercise based on this information.

[0316] Step 6:

[0317] Based on the selected movement, the robotic device begins demonstrating the movement method. The input is information about the movement activity selected by the user. The output is a demonstration of the movement by the robotic device. The robot demonstrates the appropriate form and movements in front of the user, and the user begins to move according to those movements.

[0318] Step 7:

[0319] The robotic device monitors the user's posture in real time during exercise. The input is the user's movement information captured by the robot's sensors. The output is feedback information for posture correction. Based on this, the robot instructs the user to make appropriate posture corrections, maximizing the effectiveness of the exercise. This entire process improves the quality of the user's exercise activity.

[0320] 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.

[0321] This invention combines an emotional engine with a system that recommends exercise activities based on the user's physical characteristics. This system mainly consists of an image input device, a server, a terminal, and an emotional engine.

[0322] First, the user takes a full-body image of the target person using an image input device and uploads it to the terminal. This image is used to identify the user's physical characteristics. The terminal then sends the captured image to a server in the cloud.

[0323] Upon receiving image data, the server uses advanced image processing algorithms to analyze physical characteristics. This includes measuring skeletal structure and body shape. The analysis results are then compared against a database of characteristics of past successful athletes. This allows the system to statistically determine and list the most suitable exercise activities for the user.

[0324] Furthermore, this invention utilizes an emotion engine to analyze the user's emotional state in real time. This emotion engine acquires biometric data such as the user's facial expressions and heart rate using facial recognition technology and biosensors. Based on the analysis results, the recommendations for exercise activities are adjusted. For example, if it is estimated that the user is feeling stressed, the list will prioritize exercises with relaxing effects.

[0325] As a concrete example, when a user uses this system, they first take an image of their body and send it to the server. Then, the emotion engine analyzes the user's real-time emotional state, and the server takes this emotional data into consideration to select an appropriate exercise activity. In this way, exercise selection optimized for the user's physical characteristics and emotional state becomes possible, leading to more effective health management.

[0326] This invention is a system that provides personalized suggestions in selecting appropriate exercise activities by considering not only the user's physical aptitude but also their psychological state.

[0327] The following describes the processing flow.

[0328] Step 1:

[0329] The user uses an image input device to capture a full-body image of the subject. The captured image is uploaded to the system's application via the user's terminal.

[0330] Step 2:

[0331] After the device receives image data, it checks its format and resolution and securely transmits it to a server in the cloud.

[0332] Step 3:

[0333] The server processes the received images and uses image analysis algorithms to analyze the subject's skeletal structure and body shape in detail. The numerical data obtained here includes bone length and body proportions.

[0334] Step 4:

[0335] The server analyzes the physical characteristics data and compares it with a database containing physical data from past successful athletes. This comparison statistically derives the most suitable exercise activities for the individual.

[0336] Step 5:

[0337] On the other hand, the user's device activates an emotion engine and captures the user's facial expressions with its camera. Additionally, biometric data is acquired from sensors such as a heart rate sensor as needed.

[0338] Step 6:

[0339] The device's emotion engine analyzes the user's emotional state from acquired facial expression data and biometric data, and sends the results to the server.

[0340] Step 7:

[0341] Based on the emotional data received by the server, the system optimizes recommendations for exercise activities. For example, if the analysis indicates that the user is feeling stressed, exercises that help reduce stress will be recommended.

[0342] Step 8:

[0343] The server generates a final recommendation list and sends it to the terminal. The terminal then presents this list to the user in a visually easy-to-understand format.

[0344] Step 9:

[0345] Users review a list of suggested exercise activities and select those that match their interests and goals. They can also learn more about the exercises and how to perform them if needed.

[0346] (Example 2)

[0347] 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".

[0348] In modern society, there is a growing demand for personalized exercise programs that take into account not only individual physical characteristics but also emotional states. However, systems that can adequately address this are not yet readily available, making it difficult to provide exercise programs optimized for each user.

[0349] 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.

[0350] In this invention, the server includes means for receiving user information obtained from an image input device, means for analyzing physical data based on the user information, and emotion analysis means for analyzing the user's emotional state in real time. This makes it possible to recommend optimal exercise activities that take into account both physical characteristics and emotional state.

[0351] An "image input device" is a device used to acquire video information of a user's body.

[0352] "User information" refers to information including physical characteristics and other related data acquired through an image input device.

[0353] "Physical data" refers to data on physical characteristics such as skeletal structure and body type, which are analyzed from user information.

[0354] A "field of activity" is a field that includes various techniques and methodologies related to physical activity and exercise.

[0355] A "database" is a collection of information that systematically stores and makes searchable successful case studies and data accumulated in the past.

[0356] "Emotional analysis tools" are functions that analyze, investigate, and evaluate the emotional state of users in real time.

[0357] An "output device" is a device used to present recommended results or information to the user.

[0358] "Adjustment" refers to the process of optimizing the proposed exercise activity based on the analysis results.

[0359]

[0360] This invention is a system that proposes optimal exercise activities for the user, and utilizes an image input device, a server, a terminal, and an emotion analysis system. Specifically, this system is implemented in the following manner.

[0361] First, the user takes a full-body image of themselves using an image input device. This image provides the basic information necessary for analyzing the user's physical data. The image is uploaded to a server in the cloud via the terminal. The server uses the OpenCV library in Python to analyze the image and extract the user's skeletal structure and body shape.

[0362] The server uses this physical data to compare it with a database of past successful activity areas. This database contains a large number of exercise examples, allowing for the efficient listing of exercise activities optimized for individual physical characteristics.

[0363] Next, biosensors are attached to the device to analyze the user's emotional state in real time. This emotional analysis utilizes facial recognition technology and Google's MediaPipe library. The device then sends the resulting emotional data to a server.

[0364] The server takes the user's emotional state into account and adjusts and selects the optimal exercise activity based on the analysis of physical data. For example, if the user is feeling stressed, suggestions for yoga or meditation, which are effective in relieving stress, will be prioritized.

[0365] Ultimately, the server sends a list of recommended exercise activities to the terminal, which then displays this to the user. For example, by entering a prompt such as, "Please recommend an effective yoga session to relieve fatigue after work," it is possible to suggest exercise activities tailored to the user. This allows the user to receive exercise selections optimized for their physical characteristics and psychological state, enabling more effective health management.

[0366] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0367] Step 1:

[0368] The user takes a full-body image of themselves using an image input device. The input is video data of the user's body. The device can upload this video data to a server in the cloud. Once the video data is uploaded, the server receives it for further analysis.

[0369] Step 2:

[0370] The server performs image analysis using the Python OpenCV library based on the received video data. The input is the image data itself. From this data, the server identifies the user's skeletal structure and body shape and outputs it as body data. This analysis process is an important step in generating basic information for matching with the database.

[0371] Step 3:

[0372] The server matches the analyzed physical data against a database associated with past successful activity areas. The input is the analyzed physical data, and the output is a list of optimal exercise activities for the user. The server uses statistical methods to select the optimal exercise and generates the results.

[0373] Step 4:

[0374] The device collects the user's emotional state in real time via biosensors. The input is emotional data from facial recognition technology and biosensors. The device sends this data to a server and requests emotional analysis. This process evaluates the emotions based on the user's heart rate and facial expressions and generates an emotional state report as output.

[0375] Step 5:

[0376] The server receives the results of the emotion analysis and adjusts the list of exercise activities based on them. The input is a report of the emotional state, and the output is the final adjusted list of exercise activities. The server optimizes this list, highlighting suggestions that are appropriate for the emotional state, such as exercises that have a high relaxation effect.

[0377] Step 6:

[0378] The server sends a final list of adjusted exercise activities to the terminal. The terminal displays this to the user and provides specific exercise suggestions. As a concrete example, the AI ​​model generates a prompt message such as, "Please tell me an effective yoga session to relieve fatigue after work," and then presents the user with the most suitable exercise.

[0379] (Application Example 2)

[0380] 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."

[0381] Conventional exercise recommendation systems propose exercise activities based solely on an individual's physical characteristics, failing to consider their psychological state, which makes it difficult to recommend the most suitable exercise. In particular, when emotional state significantly impacts exercise achievement, adjustments that reflect individual emotions are needed.

[0382] 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.

[0383] In this invention, the server includes means for receiving personal information obtained from an image acquisition device, means for analyzing physical attributes based on the personal information, and means for evaluating the individual's emotional state in real time using an emotion analysis device and adjusting the recommendation results. This makes it possible to propose optimal exercise activities that take into account the individual's physical attributes and emotional state.

[0384] An "image acquisition device" is a device used to capture an individual's physical characteristics, and mainly refers to cameras and sensor devices.

[0385] "Personal information" refers to data obtained from an image acquisition device, including information about an individual's physical characteristics and attributes.

[0386] "Physical attributes" refer to physical characteristics and traits, such as an individual's body type and skeletal structure.

[0387] The "information set in the motor domain" refers to a database related to various exercise activities, and in particular, includes information on the physical attributes of successful exercise practitioners.

[0388] An "emotion analysis device" is a device used to evaluate an individual's emotional state, analyzing emotions in real time using facial recognition technology and biosensors.

[0389] A "communication device" is a device used to transmit adjusted exercise suggestions to an individual, and generally refers to a display or speaker.

[0390] In this invention, the user first takes a full-body image of themselves using an image acquisition device, specifically a camera. The captured image is transmitted by the terminal to a server in the cloud. The server receives this image data and uses image processing software to analyze the user's physical attributes. In this process, image processing libraries such as OpenCV are utilized. The analysis results are compared with a set of information regarding the motor domain, specifically with the physical attribute data of past successful exercise practitioners.

[0391] Subsequently, the server uses an emotion analysis device to evaluate the user's emotional state in real time. This emotion analysis utilizes a system that combines facial recognition technology and biosensors. Using a facial recognition API, the emotional state is measured based on the user's facial expression data and heart rate. Based on the emotional state, the recommendations for exercise activity are adjusted.

[0392] The adjusted recommendations are presented to the user via a communication device. At this stage, the user receives feedback on the most suitable exercise suggestions using a display or speaker. For example, if the user is feeling stressed, the server will prioritize recommending exercises with relaxing effects, and the user will receive guidance from their smartphone or a robot.

[0393] An example of a prompt that uses a generative AI model to create an exercise plan is: "Recognize the user's emotional state and design a relaxing exercise plan. Highlight the user's stress levels as a key point when formulating suggestions." This prompt allows the AI ​​model to generate exercise suggestions optimized for each individual user.

[0394] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0395] Step 1:

[0396] The user takes a full-body image using an image acquisition device. This image is transferred to the terminal and used as input data for transmission to the server. The terminal must maintain a stable network connection until the captured image data is sent to the server.

[0397] Step 2:

[0398] The server receives whole-body image data transmitted from the terminal. The server analyzes this data using image processing software to identify the user's physical attributes. Specifically, it analyzes the skeleton and body shape using libraries such as OpenCV and compares this with a set of information regarding range of motion. As output, candidate exercises suitable for the user are generated.

[0399] Step 3:

[0400] The server uses an emotion analysis device to evaluate the user's emotional state in real time. This process uses data obtained from facial recognition technology and biosensors as input. A facial recognition API is used to analyze the user's facial expressions and heart rate. This allows the server to understand the user's emotional state and adjust the recommended exercise activities accordingly. The output is a list of exercise suggestions that take the emotional state into account.

[0401] Step 4:

[0402] The final exercise suggestions are presented to the user via a communication device. The server transmits the processed exercise suggestion data to a display or speaker, providing visual and audible feedback. The user receives the presented exercise suggestions and can then begin exercising based on them.

[0403] Step 5:

[0404] In creating exercise plans using a generative AI model, prompts are generated on the server side and sent to the AI. For example, the prompt "Recognize the user's emotional state and design a relaxing exercise plan. Emphasize whether the user is stressed as a key point when formulating suggestions" is input to the AI ​​model. The output is a specific exercise plan generated based on the prompt.

[0405] 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.

[0406] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0407] 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.

[0408] [Third Embodiment]

[0409] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0410] 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.

[0411] 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).

[0412] 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.

[0413] 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.

[0414] 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).

[0415] 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.

[0416] 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.

[0417] 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.

[0418] 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.

[0419] 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.

[0420] 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".

[0421] This invention relates to a system for determining the suitability of a user for exercise activities based on their physical characteristics. This system mainly consists of three elements: an image input device, a server, and a user terminal.

[0422] First, the user uses an image input device to capture a full-body image of the target person. The captured image is then sent via the user's device to a server in the cloud. This process requires that the image resolution and format be prepared in advance.

[0423] The server uses advanced image processing algorithms to analyze the received images. This allows for a detailed analysis of the subject's physical characteristics, including their skeleton. Skeletal data and body type-related information are cross-referenced with a database of athletes collected in the past. The database used here contains physical data of successful athletes in various athletic fields.

[0424] Based on the analysis and matching results, the server generates a list of exercise activities most suitable for the user. This list indicates areas of exercise in which the user is most likely to succeed, based on statistical analysis results. The generated list is sent to the user's device and presented in a visually easy-to-understand format.

[0425] For example, if a user has options such as soccer, basketball, or track and field, the system can narrow down the choices based on how well the user's skeletal structure and body type match those of successful athletes. As a result, the user can find the exercise activity that best suits their aptitude and start participating efficiently.

[0426] In this manner, the present invention functions as a system that suggests suitable exercise activities based on the user's physical characteristics.

[0427] The following describes the processing flow.

[0428] Step 1:

[0429] The user uses an image input device to capture a full-body image of the subject. After capture, the image is uploaded to the application on the device.

[0430] Step 2:

[0431] The device receives the image data and verifies that the format and resolution are in a state where they can be analyzed. After verification, the image is sent to a server in the cloud.

[0432] Step 3:

[0433] The server receives the image data and executes an image recognition algorithm. This identifies the physical characteristics of the subject, particularly the skeletal structure, and extracts them as numerical data.

[0434] Step 4:

[0435] The server extracts physical characteristic data and compares it with a database of athletes. Statistical analysis techniques are then used to identify similar athletic disciplines.

[0436] Step 5:

[0437] The server generates a list of the most suitable exercise activities based on the analysis and matching results. This list includes exercise areas that are likely to match the user's aptitude.

[0438] Step 6:

[0439] The server sends a list of exercise activities it has generated to the terminal. The terminal then presents the list to the user in a visually easy-to-understand format.

[0440] Step 7:

[0441] The user reviews the exercise activities presented via their device and considers the options selected based on their suitability. If necessary, they can also investigate detailed information about the exercise activities.

[0442] (Example 1)

[0443] 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."

[0444] The objective of this invention is to efficiently propose the most suitable exercise activity based on the user's physical characteristics. Conventional technologies have found it difficult to analyze a user's physical characteristics in detail and provide highly accurate exercise suitability without requiring expert knowledge.

[0445] 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.

[0446] In this invention, the server includes a medium for receiving user information obtained from an image acquisition device, means for analyzing physical characteristics based on the user information, and means for performing analysis and matching using a generated AI model. This makes it possible to suggest appropriate exercise activities based on the user's physical characteristics.

[0447] An "image acquisition device" is an electronic device used to capture or collect visual information, including the physical characteristics of a user.

[0448] "User information" refers to data that describes the physical characteristics of the person in question, and includes image information and measurement information.

[0449] "Medium" refers to a means of communication for transmitting or receiving data, and this includes networks and internet connections.

[0450] "Physical characteristics" refers to the user's physical features such as height, body type, and skeletal structure.

[0451] "Analysis methods" refer to technical processes for extracting detailed physical characteristics based on user information, and this includes algorithms and software tools.

[0452] "Information set" refers to relevant data on the motor domain collected in the past, including the physical characteristics of the athletes involved in the sport.

[0453] A "generative AI model" is an artificial intelligence model that has been trained on a large amount of data and is used to perform complex analyses and predictions.

[0454] An "information output device" refers to a display device that provides users with analysis results and recommended information.

[0455] "Exercise activities" refer to sports or physical activities that are most suitable for each individual user.

[0456] "Proposal" refers to the process of indicating the optimal course of action based on the analysis results.

[0457] This invention is a system that proposes optimal exercise activities based on the user's physical characteristics. The system mainly consists of three elements: an image acquisition device, a server, and a user terminal.

[0458] Subject: User

[0459] The user first takes a full-body image of themselves or a specific person using an image acquisition device. This image provides detailed information about the user's physical characteristics and is necessary for analysis. The image is then transmitted to a server in the cloud via the user's terminal. The image data needs to be converted to an appropriate resolution and format.

[0460] Subject: Server

[0461] The server uses advanced image processing algorithms to analyze the received images. Specifically, it uses software such as OpenCV and TensorFlow to extract physical characteristics such as the user's skeleton and body shape from the images. This information is then compared with a set of information containing physical characteristic data of successful exercise participants. A generative AI model is used in this comparison process to perform highly accurate similarity evaluations.

[0462] Subject: Server

[0463] The server generates a list of exercise activities best suited to the user based on the analysis and matching results. This list is presented in a ranked order, with the most likely exercise areas identified by the generating AI model. The results are then sent to the user's device and provided in a visually easy-to-understand format.

[0464] For example, if a user wants to know whether they are suited to soccer or basketball, the system evaluates whether the user's physical characteristics are similar to those of a soccer player and recommends appropriate exercise activities. Based on these results, the user can choose the sport that best suits their aptitude and start playing sports efficiently.

[0465] An example of a prompt for a generative AI model is: "Use deep learning to determine the athletic aptitude of a given person and generate a list of recommended athletic fields based on that."

[0466] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0467] Step 1:

[0468] The user uses an image acquisition device to capture a full-body image of the subject. This capture generates digital image data that captures the subject's skeleton and body shape. The captured images are pre-processed through a dedicated application to ensure optimal resolution and format. The input is image data from the camera, and the output is the formatted digital image data.

[0469] Step 2:

[0470] The user's device sends the formatted digital image data to a server in the cloud. Encryption technology is used in this process to ensure secure and rapid data transfer. The input is the formatted digital image data, and the output is the image data sent to the server.

[0471] Step 3:

[0472] The server analyzes the received image data using advanced image processing algorithms. Here, skeletal and body shape features are automatically extracted using deep learning techniques. Software used includes OpenCV and TensorFlow. The input is the image data sent to the server, and the output is the extracted physical feature data.

[0473] Step 4:

[0474] The server compares the extracted physical characteristic data with a stored set of information related to motor domains. A generative AI model is used to evaluate similarity. This algorithm determines which motor domains the subject has high aptitude in. The input is the extracted physical characteristic data, and the output is the similarity calculation result.

[0475] Step 5:

[0476] The server generates a list of exercise activities best suited to the user based on the similarity calculation results. This list is visually formatted for presentation to the user, taking statistical analysis results into consideration. The input is the similarity calculation result, and the output is a list of recommended exercise activities.

[0477] Step 6:

[0478] The user's device receives and displays a list of recommended exercise activities sent from the server. Based on this list, the user can select the exercise best suited to them and provide appropriate feedback. The input is the list of recommended exercise activities from the server, and the output is a visually formatted list of recommendations.

[0479] (Application Example 1)

[0480] 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."

[0481] In modern society, finding appropriate exercise activities based on individual physical characteristics is crucial for maintaining health and improving athletic performance. However, existing systems have only provided general information, lacking sufficient specific guidance and feedback based on individual characteristics. Furthermore, there has been a lack of concrete support for users to select appropriate exercises and practice them correctly.

[0482] 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.

[0483] In this invention, the server includes means for receiving user information acquired from an image acquisition device, means for analyzing physical characteristics, means for comparing the analysis results with a stored information source on movement ranges and recommending appropriate physical activities, and means for demonstrating exercise methods to the user via a robotic device and performing posture correction in real time. This makes it possible to support the selection of specific and effective exercise activities based on the physical characteristics of each individual user and their accurate implementation.

[0484] An "image acquisition device" is a device used to acquire a user's physical information, and includes cameras and other sensor devices.

[0485] "User information" refers to information about the physical characteristics of individual users, obtained in the form of images or other data.

[0486] "Physical characteristics" refer to specific physical features of the user, such as their skeletal structure, muscle arrangement, and build.

[0487] "Analysis" refers to performing a detailed analysis based on acquired information through specific algorithms and processes.

[0488] "Information sources related to the exercise domain" refers to a database that aggregates past data and success stories related to various exercise activities, and includes information based on specific criteria.

[0489] "Recommending physical activity" means suggesting appropriate exercise and fitness activities based on the user's physical characteristics.

[0490] A "robot device" is an automated mechanical device used to demonstrate exercise methods and provide real-time feedback to users.

[0491] "Posture correction" is a process of detecting errors in the user's body position and movement during exercise and adjusting it on the spot to bring it closer to the optimal form.

[0492] The system's program analyzes the user's physical characteristics, proposes optimal physical activity, and uses a robotic device to demonstrate exercise methods in real time and correct the user's posture.

[0493] The server first receives user information transmitted from the image acquisition device. The acquired information consists of images of the body taken using a camera or similar device. The received information is then used to analyze the user's physical characteristics using image processing and machine learning software such as OpenCV and TensorFlow. The skeletal structure and body size data revealed through the analysis are then compared with information sources on motor regions stored in a cloud-based database.

[0494] The terminal provides the user with recommended results received from the server. These recommendations include exercises and fitness activities best suited to the user. The robotic device also demonstrates the suggested exercises and supports the user in performing them correctly. During this process, the robotic device monitors the user's posture in real time and provides corrective instructions as needed.

[0495] As a concrete example, the server analyzes the arrangement of shoulder muscles from a full-body image of the user and recommends "shoulder exercises using dumbbells" by comparing it with a database. The robotic device demonstrates the correct form in front of the user, continuously monitors the user's posture while they perform the exercise, and provides real-time feedback to correct incorrect form.

[0496] An example of a prompt to a generating AI model might be: "We want to develop a system where a robot analyzes a user's physique and suggests the optimal exercise. Please output design ideas for an application that instructs fitness beginners on suitable exercises and how to do them." Using this prompt, it is also possible to have the AI ​​generate specific instruction content and feedback methods.

[0497] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0498] Step 1:

[0499] The user takes a full-body image using an image acquisition device. The input is a JPEG image file acquired by the camera. The output is image data ready to be sent to the server. The user sends this image to the server via their device.

[0500] Step 2:

[0501] The server receives image data sent from the user terminal. The input is the image data sent from the user terminal. The received image data is processed using OpenCV for resolution adjustment and image format conversion. The output is image data formatted into a parseable format.

[0502] Step 3:

[0503] The server analyzes the user's physical characteristics using image processing algorithms. The input is formatted image data. This data is then fed into a skeleton detection model using TensorFlow to extract the user's skeletal structure and body shape data. The output is digital data that reflects the physical characteristics.

[0504] Step 4:

[0505] The server matches the analysis results against a cloud database and recommends appropriate exercise activities. The input is data representing the user's physical characteristics. This data is matched with a database of accumulated athletes to identify the optimal exercise activity. The output is a list of recommended results.

[0506] Step 5:

[0507] The terminal displays recommended results received from the server to the user. The input is a list of recommended results from the server. The output is a list of recommended exercise activities displayed on the user terminal's screen. The user can then select an exercise based on this information.

[0508] Step 6:

[0509] Based on the selected movement, the robotic device begins demonstrating the movement method. The input is information about the movement activity selected by the user. The output is a demonstration of the movement by the robotic device. The robot demonstrates the appropriate form and movements in front of the user, and the user begins to move according to those movements.

[0510] Step 7:

[0511] The robotic device monitors the user's posture in real time during exercise. The input is the user's movement information captured by the robot's sensors. The output is feedback information for posture correction. Based on this, the robot instructs the user to make appropriate posture corrections, maximizing the effectiveness of the exercise. This entire process improves the quality of the user's exercise activity.

[0512] 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.

[0513] This invention combines an emotional engine with a system that recommends exercise activities based on the user's physical characteristics. This system mainly consists of an image input device, a server, a terminal, and an emotional engine.

[0514] First, the user takes a full-body image of the target person using an image input device and uploads it to the terminal. This image is used to identify the user's physical characteristics. The terminal then sends the captured image to a server in the cloud.

[0515] Upon receiving image data, the server uses advanced image processing algorithms to analyze physical characteristics. This includes measuring skeletal structure and body shape. The analysis results are then compared against a database of characteristics of past successful athletes. This allows the system to statistically determine and list the most suitable exercise activities for the user.

[0516] Furthermore, this invention utilizes an emotion engine to analyze the user's emotional state in real time. This emotion engine acquires biometric data such as the user's facial expressions and heart rate using facial recognition technology and biosensors. Based on the analysis results, the recommendations for exercise activities are adjusted. For example, if it is estimated that the user is feeling stressed, the list will prioritize exercises with relaxing effects.

[0517] As a concrete example, when a user uses this system, they first take an image of their body and send it to the server. Then, the emotion engine analyzes the user's real-time emotional state, and the server takes this emotional data into consideration to select an appropriate exercise activity. In this way, exercise selection optimized for the user's physical characteristics and emotional state becomes possible, leading to more effective health management.

[0518] This invention is a system that provides personalized suggestions in selecting appropriate exercise activities by considering not only the user's physical aptitude but also their psychological state.

[0519] The following describes the processing flow.

[0520] Step 1:

[0521] The user uses an image input device to capture a full-body image of the subject. The captured image is uploaded to the system's application via the user's terminal.

[0522] Step 2:

[0523] After the device receives image data, it checks its format and resolution and securely transmits it to a server in the cloud.

[0524] Step 3:

[0525] The server processes the received images and uses image analysis algorithms to analyze the subject's skeletal structure and body shape in detail. The numerical data obtained here includes bone length and body proportions.

[0526] Step 4:

[0527] The server analyzes the physical characteristics data and compares it with a database containing physical data from past successful athletes. This comparison statistically derives the most suitable exercise activities for the individual.

[0528] Step 5:

[0529] On the other hand, the user's device activates an emotion engine and captures the user's facial expressions with its camera. Additionally, biometric data is acquired from sensors such as a heart rate sensor as needed.

[0530] Step 6:

[0531] The device's emotion engine analyzes the user's emotional state from acquired facial expression data and biometric data, and sends the results to the server.

[0532] Step 7:

[0533] Based on the emotional data received by the server, the system optimizes recommendations for exercise activities. For example, if the analysis indicates that the user is feeling stressed, exercises that help reduce stress will be recommended.

[0534] Step 8:

[0535] The server generates a final recommendation list and sends it to the terminal. The terminal then presents this list to the user in a visually easy-to-understand format.

[0536] Step 9:

[0537] Users review a list of suggested exercise activities and select those that match their interests and goals. They can also learn more about the exercises and how to perform them if needed.

[0538] (Example 2)

[0539] 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."

[0540] In modern society, there is a growing demand for personalized exercise programs that take into account not only individual physical characteristics but also emotional states. However, systems that can adequately address this are not yet readily available, making it difficult to provide exercise programs optimized for each user.

[0541] 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.

[0542] In this invention, the server includes means for receiving user information obtained from an image input device, means for analyzing physical data based on the user information, and emotion analysis means for analyzing the user's emotional state in real time. This makes it possible to recommend optimal exercise activities that take into account both physical characteristics and emotional state.

[0543] An "image input device" is a device used to acquire video information of a user's body.

[0544] "User information" refers to information including physical characteristics and other related data acquired through an image input device.

[0545] "Physical data" refers to data on physical characteristics such as skeletal structure and body type, which are analyzed from user information.

[0546] A "field of activity" is a field that includes various techniques and methodologies related to physical activity and exercise.

[0547] A "database" is a collection of information that systematically stores and makes searchable successful case studies and data accumulated in the past.

[0548] "Emotional analysis tools" are functions that analyze, investigate, and evaluate the emotional state of users in real time.

[0549] An "output device" is a device used to present recommended results or information to the user.

[0550] "Adjustment" refers to the process of optimizing the proposed exercise activity based on the analysis results.

[0551]

[0552] This invention is a system that proposes optimal exercise activities for the user, and utilizes an image input device, a server, a terminal, and an emotion analysis system. Specifically, this system is implemented in the following manner.

[0553] First, the user takes a full-body image of themselves using an image input device. This image provides the basic information necessary for analyzing the user's physical data. The image is uploaded to a server in the cloud via the terminal. The server uses the OpenCV library in Python to analyze the image and extract the user's skeletal structure and body shape.

[0554] The server uses this physical data to compare it with a database of past successful activity areas. This database contains a large number of exercise examples, allowing for the efficient listing of exercise activities optimized for individual physical characteristics.

[0555] Next, biosensors are attached to the device to analyze the user's emotional state in real time. This emotional analysis utilizes facial recognition technology and Google's MediaPipe library. The device then sends the resulting emotional data to a server.

[0556] The server takes the user's emotional state into account and adjusts and selects the optimal exercise activity based on the analysis of physical data. For example, if the user is feeling stressed, suggestions for yoga or meditation, which are effective in relieving stress, will be prioritized.

[0557] Ultimately, the server sends a list of recommended exercise activities to the terminal, which then displays this to the user. For example, by entering a prompt such as, "Please recommend an effective yoga session to relieve fatigue after work," it is possible to suggest exercise activities tailored to the user. This allows the user to receive exercise selections optimized for their physical characteristics and psychological state, enabling more effective health management.

[0558] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0559] Step 1:

[0560] The user takes a full-body image of themselves using an image input device. The input is video data of the user's body. The device can upload this video data to a server in the cloud. Once the video data is uploaded, the server receives it for further analysis.

[0561] Step 2:

[0562] The server performs image analysis using the Python OpenCV library based on the received video data. The input is the image data itself. From this data, the server identifies the user's skeletal structure and body shape and outputs it as body data. This analysis process is an important step in generating basic information for matching with the database.

[0563] Step 3:

[0564] The server matches the analyzed physical data against a database associated with past successful activity areas. The input is the analyzed physical data, and the output is a list of optimal exercise activities for the user. The server uses statistical methods to select the optimal exercise and generates the results.

[0565] Step 4:

[0566] The device collects the user's emotional state in real time via biosensors. The input is emotional data from facial recognition technology and biosensors. The device sends this data to a server and requests emotional analysis. This process evaluates the emotions based on the user's heart rate and facial expressions and generates an emotional state report as output.

[0567] Step 5:

[0568] The server receives the results of the emotion analysis and adjusts the list of exercise activities based on them. The input is a report of the emotional state, and the output is the final adjusted list of exercise activities. The server optimizes this list, highlighting suggestions that are appropriate for the emotional state, such as exercises that have a high relaxation effect.

[0569] Step 6:

[0570] The server sends a final list of adjusted exercise activities to the terminal. The terminal displays this to the user and provides specific exercise suggestions. As a concrete example, the AI ​​model generates a prompt message such as, "Please tell me an effective yoga session to relieve fatigue after work," and then presents the user with the most suitable exercise.

[0571] (Application Example 2)

[0572] 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."

[0573] Conventional exercise recommendation systems propose exercise activities based solely on an individual's physical characteristics, failing to consider their psychological state, which makes it difficult to recommend the most suitable exercise. In particular, when emotional state significantly impacts exercise achievement, adjustments that reflect individual emotions are needed.

[0574] 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.

[0575] In this invention, the server includes means for receiving personal information obtained from an image acquisition device, means for analyzing physical attributes based on the personal information, and means for evaluating the individual's emotional state in real time using an emotion analysis device and adjusting the recommendation results. This makes it possible to propose optimal exercise activities that take into account the individual's physical attributes and emotional state.

[0576] An "image acquisition device" is a device used to capture an individual's physical characteristics, and mainly refers to cameras and sensor devices.

[0577] "Personal information" refers to data obtained from an image acquisition device, including information about an individual's physical characteristics and attributes.

[0578] "Physical attributes" refer to physical characteristics and traits, such as an individual's body type and skeletal structure.

[0579] The "information set in the motor domain" refers to a database related to various exercise activities, and in particular, includes information on the physical attributes of successful exercise practitioners.

[0580] An "emotion analysis device" is a device used to evaluate an individual's emotional state, analyzing emotions in real time using facial recognition technology and biosensors.

[0581] A "communication device" is a device used to transmit adjusted exercise suggestions to an individual, and generally refers to a display or speaker.

[0582] In this invention, the user first takes a full-body image of themselves using an image acquisition device, specifically a camera. The captured image is transmitted by the terminal to a server in the cloud. The server receives this image data and uses image processing software to analyze the user's physical attributes. In this process, image processing libraries such as OpenCV are utilized. The analysis results are compared with a set of information regarding the motor domain, specifically with the physical attribute data of past successful exercise practitioners.

[0583] Subsequently, the server uses an emotion analysis device to evaluate the user's emotional state in real time. This emotion analysis utilizes a system that combines facial recognition technology and biosensors. Using a facial recognition API, the emotional state is measured based on the user's facial expression data and heart rate. Based on the emotional state, the recommendations for exercise activity are adjusted.

[0584] The adjusted recommendations are presented to the user via a communication device. At this stage, the user receives feedback on the most suitable exercise suggestions using a display or speaker. For example, if the user is feeling stressed, the server will prioritize recommending exercises with relaxing effects, and the user will receive guidance from their smartphone or a robot.

[0585] An example of a prompt that uses a generative AI model to create an exercise plan is: "Recognize the user's emotional state and design a relaxing exercise plan. Highlight the user's stress levels as a key point when formulating suggestions." This prompt allows the AI ​​model to generate exercise suggestions optimized for each individual user.

[0586] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0587] Step 1:

[0588] The user takes a full-body image using an image acquisition device. This image is transferred to the terminal and used as input data for transmission to the server. The terminal must maintain a stable network connection until the captured image data is sent to the server.

[0589] Step 2:

[0590] The server receives whole-body image data transmitted from the terminal. The server analyzes this data using image processing software to identify the user's physical attributes. Specifically, it analyzes the skeleton and body shape using libraries such as OpenCV and compares this with a set of information regarding range of motion. As output, candidate exercises suitable for the user are generated.

[0591] Step 3:

[0592] The server uses an emotion analysis device to evaluate the user's emotional state in real time. This process uses data obtained from facial recognition technology and biosensors as input. A facial recognition API is used to analyze the user's facial expressions and heart rate. This allows the server to understand the user's emotional state and adjust the recommended exercise activities accordingly. The output is a list of exercise suggestions that take the emotional state into account.

[0593] Step 4:

[0594] The final exercise suggestions are presented to the user via a communication device. The server transmits the processed exercise suggestion data to a display or speaker, providing visual and audible feedback. The user receives the presented exercise suggestions and can then begin exercising based on them.

[0595] Step 5:

[0596] In creating exercise plans using a generative AI model, prompts are generated on the server side and sent to the AI. For example, the prompt "Recognize the user's emotional state and design a relaxing exercise plan. Emphasize whether the user is stressed as a key point when formulating suggestions" is input to the AI ​​model. The output is a specific exercise plan generated based on the prompt.

[0597] 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.

[0598] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0599] 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.

[0600] [Fourth Embodiment]

[0601] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0602] 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.

[0603] 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).

[0604] 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.

[0605] 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.

[0606] 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).

[0607] 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.

[0608] 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.

[0609] 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.

[0610] 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.

[0611] 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.

[0612] 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.

[0613] 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".

[0614] This invention relates to a system for determining the suitability of a user for exercise activities based on their physical characteristics. This system mainly consists of three elements: an image input device, a server, and a user terminal.

[0615] First, the user uses an image input device to capture a full-body image of the target person. The captured image is then sent via the user's device to a server in the cloud. This process requires that the image resolution and format be prepared in advance.

[0616] The server uses advanced image processing algorithms to analyze the received images. This allows for a detailed analysis of the subject's physical characteristics, including their skeleton. Skeletal data and body type-related information are cross-referenced with a database of athletes collected in the past. The database used here contains physical data of successful athletes in various athletic fields.

[0617] Based on the analysis and matching results, the server generates a list of exercise activities most suitable for the user. This list indicates areas of exercise in which the user is most likely to succeed, based on statistical analysis results. The generated list is sent to the user's device and presented in a visually easy-to-understand format.

[0618] For example, if a user has options such as soccer, basketball, or track and field, the system can narrow down the choices based on how well the user's skeletal structure and body type match those of successful athletes. As a result, the user can find the exercise activity that best suits their aptitude and start participating efficiently.

[0619] In this manner, the present invention functions as a system that suggests suitable exercise activities based on the user's physical characteristics.

[0620] The following describes the processing flow.

[0621] Step 1:

[0622] The user uses an image input device to capture a full-body image of the subject. After capture, the image is uploaded to the application on the device.

[0623] Step 2:

[0624] The device receives the image data and verifies that the format and resolution are in a state where they can be analyzed. After verification, the image is sent to a server in the cloud.

[0625] Step 3:

[0626] The server receives the image data and executes an image recognition algorithm. This identifies the physical characteristics of the subject, particularly the skeletal structure, and extracts them as numerical data.

[0627] Step 4:

[0628] The server extracts physical characteristic data and compares it with a database of athletes. Statistical analysis techniques are then used to identify similar athletic disciplines.

[0629] Step 5:

[0630] The server generates a list of the most suitable exercise activities based on the analysis and matching results. This list includes exercise areas that are likely to match the user's aptitude.

[0631] Step 6:

[0632] The server sends a list of exercise activities it has generated to the terminal. The terminal then presents the list to the user in a visually easy-to-understand format.

[0633] Step 7:

[0634] The user reviews the exercise activities presented via their device and considers the options selected based on their suitability. If necessary, they can also investigate detailed information about the exercise activities.

[0635] (Example 1)

[0636] 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".

[0637] The objective of this invention is to efficiently propose the most suitable exercise activity based on the user's physical characteristics. Conventional technologies have found it difficult to analyze a user's physical characteristics in detail and provide highly accurate exercise suitability without requiring expert knowledge.

[0638] 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.

[0639] In this invention, the server includes a medium for receiving user information obtained from an image acquisition device, means for analyzing physical characteristics based on the user information, and means for performing analysis and matching using a generated AI model. This makes it possible to suggest appropriate exercise activities based on the user's physical characteristics.

[0640] An "image acquisition device" is an electronic device used to capture or collect visual information, including the physical characteristics of a user.

[0641] "User information" refers to data that describes the physical characteristics of the person in question, and includes image information and measurement information.

[0642] "Medium" refers to a means of communication for transmitting or receiving data, and this includes networks and internet connections.

[0643] "Physical characteristics" refers to the user's physical features such as height, body type, and skeletal structure.

[0644] "Analysis methods" refer to technical processes for extracting detailed physical characteristics based on user information, and this includes algorithms and software tools.

[0645] "Information set" refers to relevant data on the motor domain collected in the past, including the physical characteristics of the athletes involved in the sport.

[0646] A "generative AI model" is an artificial intelligence model that has been trained on a large amount of data and is used to perform complex analyses and predictions.

[0647] An "information output device" refers to a display device that provides users with analysis results and recommended information.

[0648] "Exercise activities" refer to sports or physical activities that are most suitable for each individual user.

[0649] "Proposal" refers to the process of indicating the optimal course of action based on the analysis results.

[0650] This invention is a system that proposes optimal exercise activities based on the user's physical characteristics. The system mainly consists of three elements: an image acquisition device, a server, and a user terminal.

[0651] Subject: User

[0652] The user first takes a full-body image of themselves or a specific person using an image acquisition device. This image provides detailed information about the user's physical characteristics and is necessary for analysis. The image is then transmitted to a server in the cloud via the user's terminal. The image data needs to be converted to an appropriate resolution and format.

[0653] Subject: Server

[0654] The server uses advanced image processing algorithms to analyze the received images. Specifically, it uses software such as OpenCV and TensorFlow to extract physical characteristics such as the user's skeleton and body shape from the images. This information is then compared with a set of information containing physical characteristic data of successful exercise participants. A generative AI model is used in this comparison process to perform highly accurate similarity evaluations.

[0655] Subject: Server

[0656] The server generates a list of exercise activities best suited to the user based on the analysis and matching results. This list is presented in a ranked order, with the most likely exercise areas identified by the generating AI model. The results are then sent to the user's device and provided in a visually easy-to-understand format.

[0657] For example, if a user wants to know whether they are suited to soccer or basketball, the system evaluates whether the user's physical characteristics are similar to those of a soccer player and recommends appropriate exercise activities. Based on these results, the user can choose the sport that best suits their aptitude and start playing sports efficiently.

[0658] An example of a prompt for a generative AI model is: "Use deep learning to determine the athletic aptitude of a given person and generate a list of recommended athletic fields based on that."

[0659] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0660] Step 1:

[0661] The user uses an image acquisition device to capture a full-body image of the subject. This capture generates digital image data that captures the subject's skeleton and body shape. The captured images are pre-processed through a dedicated application to ensure optimal resolution and format. The input is image data from the camera, and the output is the formatted digital image data.

[0662] Step 2:

[0663] The user's device sends the formatted digital image data to a server in the cloud. Encryption technology is used in this process to ensure secure and rapid data transfer. The input is the formatted digital image data, and the output is the image data sent to the server.

[0664] Step 3:

[0665] The server analyzes the received image data using advanced image processing algorithms. Here, skeletal and body shape features are automatically extracted using deep learning techniques. Software used includes OpenCV and TensorFlow. The input is the image data sent to the server, and the output is the extracted physical feature data.

[0666] Step 4:

[0667] The server compares the extracted physical characteristic data with a stored set of information related to motor domains. A generative AI model is used to evaluate similarity. This algorithm determines which motor domains the subject has high aptitude in. The input is the extracted physical characteristic data, and the output is the similarity calculation result.

[0668] Step 5:

[0669] The server generates a list of exercise activities best suited to the user based on the similarity calculation results. This list is visually formatted for presentation to the user, taking statistical analysis results into consideration. The input is the similarity calculation result, and the output is a list of recommended exercise activities.

[0670] Step 6:

[0671] The user's device receives and displays a list of recommended exercise activities sent from the server. Based on this list, the user can select the exercise best suited to them and provide appropriate feedback. The input is the list of recommended exercise activities from the server, and the output is a visually formatted list of recommendations.

[0672] (Application Example 1)

[0673] 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".

[0674] In modern society, finding appropriate exercise activities based on individual physical characteristics is crucial for maintaining health and improving athletic performance. However, existing systems have only provided general information, lacking sufficient specific guidance and feedback based on individual characteristics. Furthermore, there has been a lack of concrete support for users to select appropriate exercises and practice them correctly.

[0675] 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.

[0676] In this invention, the server includes means for receiving user information acquired from an image acquisition device, means for analyzing physical characteristics, means for comparing the analysis results with a stored information source on movement ranges and recommending appropriate physical activities, and means for demonstrating exercise methods to the user via a robotic device and performing posture correction in real time. This makes it possible to support the selection of specific and effective exercise activities based on the physical characteristics of each individual user and their accurate implementation.

[0677] An "image acquisition device" is a device used to acquire a user's physical information, and includes cameras and other sensor devices.

[0678] "User information" refers to information about the physical characteristics of individual users, obtained in the form of images or other data.

[0679] "Physical characteristics" refer to specific physical features of the user, such as their skeletal structure, muscle arrangement, and build.

[0680] "Analysis" refers to performing a detailed analysis based on acquired information through specific algorithms and processes.

[0681] "Information sources related to the exercise domain" refers to a database that aggregates past data and success stories related to various exercise activities, and includes information based on specific criteria.

[0682] "Recommending physical activity" means suggesting appropriate exercise and fitness activities based on the user's physical characteristics.

[0683] A "robot device" is an automated mechanical device used to demonstrate exercise methods and provide real-time feedback to users.

[0684] "Posture correction" is a process of detecting errors in the user's body position and movement during exercise and adjusting it on the spot to bring it closer to the optimal form.

[0685] The system's program analyzes the user's physical characteristics, proposes optimal physical activity, and uses a robotic device to demonstrate exercise methods in real time and correct the user's posture.

[0686] The server first receives user information transmitted from the image acquisition device. The acquired information consists of images of the body taken using a camera or similar device. The received information is then used to analyze the user's physical characteristics using image processing and machine learning software such as OpenCV and TensorFlow. The skeletal structure and body size data revealed through the analysis are then compared with information sources on motor regions stored in a cloud-based database.

[0687] The terminal provides the user with recommended results received from the server. These recommendations include exercises and fitness activities best suited to the user. The robotic device also demonstrates the suggested exercises and supports the user in performing them correctly. During this process, the robotic device monitors the user's posture in real time and provides corrective instructions as needed.

[0688] As a concrete example, the server analyzes the arrangement of shoulder muscles from a full-body image of the user and recommends "shoulder exercises using dumbbells" by comparing it with a database. The robotic device demonstrates the correct form in front of the user, continuously monitors the user's posture while they perform the exercise, and provides real-time feedback to correct incorrect form.

[0689] An example of a prompt to a generating AI model might be: "We want to develop a system where a robot analyzes a user's physique and suggests the optimal exercise. Please output design ideas for an application that instructs fitness beginners on suitable exercises and how to do them." Using this prompt, it is also possible to have the AI ​​generate specific instruction content and feedback methods.

[0690] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0691] Step 1:

[0692] The user takes a full-body image using an image acquisition device. The input is a JPEG image file acquired by the camera. The output is image data ready to be sent to the server. The user sends this image to the server via their device.

[0693] Step 2:

[0694] The server receives image data sent from the user terminal. The input is the image data sent from the user terminal. The received image data is processed using OpenCV for resolution adjustment and image format conversion. The output is image data formatted into a parseable format.

[0695] Step 3:

[0696] The server analyzes the user's physical characteristics using image processing algorithms. The input is formatted image data. This data is then fed into a skeleton detection model using TensorFlow to extract the user's skeletal structure and body shape data. The output is digital data that reflects the physical characteristics.

[0697] Step 4:

[0698] The server matches the analysis results against a cloud database and recommends appropriate exercise activities. The input is data representing the user's physical characteristics. This data is matched with a database of accumulated athletes to identify the optimal exercise activity. The output is a list of recommended results.

[0699] Step 5:

[0700] The terminal displays recommended results received from the server to the user. The input is a list of recommended results from the server. The output is a list of recommended exercise activities displayed on the user terminal's screen. The user can then select an exercise based on this information.

[0701] Step 6:

[0702] Based on the selected movement, the robotic device begins demonstrating the movement method. The input is information about the movement activity selected by the user. The output is a demonstration of the movement by the robotic device. The robot demonstrates the appropriate form and movements in front of the user, and the user begins to move according to those movements.

[0703] Step 7:

[0704] The robotic device monitors the user's posture in real time during exercise. The input is the user's movement information captured by the robot's sensors. The output is feedback information for posture correction. Based on this, the robot instructs the user to make appropriate posture corrections, maximizing the effectiveness of the exercise. This entire process improves the quality of the user's exercise activity.

[0705] 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.

[0706] This invention combines an emotional engine with a system that recommends exercise activities based on the user's physical characteristics. This system mainly consists of an image input device, a server, a terminal, and an emotional engine.

[0707] First, the user takes a full-body image of the target person using an image input device and uploads it to the terminal. This image is used to identify the user's physical characteristics. The terminal then sends the captured image to a server in the cloud.

[0708] Upon receiving image data, the server uses advanced image processing algorithms to analyze physical characteristics. This includes measuring skeletal structure and body shape. The analysis results are then compared against a database of characteristics of past successful athletes. This allows the system to statistically determine and list the most suitable exercise activities for the user.

[0709] Furthermore, this invention utilizes an emotion engine to analyze the user's emotional state in real time. This emotion engine acquires biometric data such as the user's facial expressions and heart rate using facial recognition technology and biosensors. Based on the analysis results, the recommendations for exercise activities are adjusted. For example, if it is estimated that the user is feeling stressed, the list will prioritize exercises with relaxing effects.

[0710] As a concrete example, when a user uses this system, they first take an image of their body and send it to the server. Then, the emotion engine analyzes the user's real-time emotional state, and the server takes this emotional data into consideration to select an appropriate exercise activity. In this way, exercise selection optimized for the user's physical characteristics and emotional state becomes possible, leading to more effective health management.

[0711] This invention is a system that provides personalized suggestions in selecting appropriate exercise activities by considering not only the user's physical aptitude but also their psychological state.

[0712] The following describes the processing flow.

[0713] Step 1:

[0714] The user uses an image input device to capture a full-body image of the subject. The captured image is uploaded to the system's application via the user's terminal.

[0715] Step 2:

[0716] After the device receives image data, it checks its format and resolution and securely transmits it to a server in the cloud.

[0717] Step 3:

[0718] The server processes the received images and uses image analysis algorithms to analyze the subject's skeletal structure and body shape in detail. The numerical data obtained here includes bone length and body proportions.

[0719] Step 4:

[0720] The server analyzes the physical characteristics data and compares it with a database containing physical data from past successful athletes. This comparison statistically derives the most suitable exercise activities for the individual.

[0721] Step 5:

[0722] On the other hand, the user's device activates an emotion engine and captures the user's facial expressions with its camera. Additionally, biometric data is acquired from sensors such as a heart rate sensor as needed.

[0723] Step 6:

[0724] The device's emotion engine analyzes the user's emotional state from acquired facial expression data and biometric data, and sends the results to the server.

[0725] Step 7:

[0726] Based on the emotional data received by the server, the system optimizes recommendations for exercise activities. For example, if the analysis indicates that the user is feeling stressed, exercises that help reduce stress will be recommended.

[0727] Step 8:

[0728] The server generates a final recommendation list and sends it to the terminal. The terminal then presents this list to the user in a visually easy-to-understand format.

[0729] Step 9:

[0730] Users review a list of suggested exercise activities and select those that match their interests and goals. They can also learn more about the exercises and how to perform them if needed.

[0731] (Example 2)

[0732] 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".

[0733] In modern society, there is a growing demand for personalized exercise programs that take into account not only individual physical characteristics but also emotional states. However, systems that can adequately address this are not yet readily available, making it difficult to provide exercise programs optimized for each user.

[0734] 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.

[0735] In this invention, the server includes means for receiving user information obtained from an image input device, means for analyzing physical data based on the user information, and emotion analysis means for analyzing the user's emotional state in real time. This makes it possible to recommend optimal exercise activities that take into account both physical characteristics and emotional state.

[0736] An "image input device" is a device used to acquire video information of a user's body.

[0737] "User information" refers to information including physical characteristics and other related data acquired through an image input device.

[0738] "Physical data" refers to data on physical characteristics such as skeletal structure and body type, which are analyzed from user information.

[0739] A "field of activity" is a field that includes various techniques and methodologies related to physical activity and exercise.

[0740] A "database" is a collection of information that systematically stores and makes searchable successful case studies and data accumulated in the past.

[0741] "Emotional analysis tools" are functions that analyze, investigate, and evaluate the emotional state of users in real time.

[0742] An "output device" is a device used to present recommended results or information to the user.

[0743] "Adjustment" refers to the process of optimizing the proposed exercise activity based on the analysis results.

[0744]

[0745] This invention is a system that proposes optimal exercise activities for the user, and utilizes an image input device, a server, a terminal, and an emotion analysis system. Specifically, this system is implemented in the following manner.

[0746] First, the user takes a full-body image of themselves using an image input device. This image provides the basic information necessary for analyzing the user's physical data. The image is uploaded to a server in the cloud via the terminal. The server uses the OpenCV library in Python to analyze the image and extract the user's skeletal structure and body shape.

[0747] The server uses this physical data to compare it with a database of past successful activity areas. This database contains a large number of exercise examples, allowing for the efficient listing of exercise activities optimized for individual physical characteristics.

[0748] Next, biosensors are attached to the device to analyze the user's emotional state in real time. This emotional analysis utilizes facial recognition technology and Google's MediaPipe library. The device then sends the resulting emotional data to a server.

[0749] The server takes the user's emotional state into account and adjusts and selects the optimal exercise activity based on the analysis of physical data. For example, if the user is feeling stressed, suggestions for yoga or meditation, which are effective in relieving stress, will be prioritized.

[0750] Ultimately, the server sends a list of recommended exercise activities to the terminal, which then displays this to the user. For example, by entering a prompt such as, "Please recommend an effective yoga session to relieve fatigue after work," it is possible to suggest exercise activities tailored to the user. This allows the user to receive exercise selections optimized for their physical characteristics and psychological state, enabling more effective health management.

[0751] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0752] Step 1:

[0753] The user takes a full-body image of themselves using an image input device. The input is video data of the user's body. The device can upload this video data to a server in the cloud. Once the video data is uploaded, the server receives it for further analysis.

[0754] Step 2:

[0755] The server performs image analysis using the Python OpenCV library based on the received video data. The input is the image data itself. From this data, the server identifies the user's skeletal structure and body shape and outputs it as body data. This analysis process is an important step in generating basic information for matching with the database.

[0756] Step 3:

[0757] The server matches the analyzed physical data against a database associated with past successful activity areas. The input is the analyzed physical data, and the output is a list of optimal exercise activities for the user. The server uses statistical methods to select the optimal exercise and generates the results.

[0758] Step 4:

[0759] The device collects the user's emotional state in real time via biosensors. The input is emotional data from facial recognition technology and biosensors. The device sends this data to a server and requests emotional analysis. This process evaluates the emotions based on the user's heart rate and facial expressions and generates an emotional state report as output.

[0760] Step 5:

[0761] The server receives the results of the emotion analysis and adjusts the list of exercise activities based on them. The input is a report of the emotional state, and the output is the final adjusted list of exercise activities. The server optimizes this list, highlighting suggestions that are appropriate for the emotional state, such as exercises that have a high relaxation effect.

[0762] Step 6:

[0763] The server sends a final list of adjusted exercise activities to the terminal. The terminal displays this to the user and provides specific exercise suggestions. As a concrete example, the AI ​​model generates a prompt message such as, "Please tell me an effective yoga session to relieve fatigue after work," and then presents the user with the most suitable exercise.

[0764] (Application Example 2)

[0765] 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".

[0766] Conventional exercise recommendation systems propose exercise activities based solely on an individual's physical characteristics, failing to consider their psychological state, which makes it difficult to recommend the most suitable exercise. In particular, when emotional state significantly impacts exercise achievement, adjustments that reflect individual emotions are needed.

[0767] 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.

[0768] In this invention, the server includes means for receiving personal information obtained from an image acquisition device, means for analyzing physical attributes based on the personal information, and means for evaluating the individual's emotional state in real time using an emotion analysis device and adjusting the recommendation results. This makes it possible to propose optimal exercise activities that take into account the individual's physical attributes and emotional state.

[0769] An "image acquisition device" is a device used to capture an individual's physical characteristics, and mainly refers to cameras and sensor devices.

[0770] "Personal information" refers to data obtained from an image acquisition device, including information about an individual's physical characteristics and attributes.

[0771] "Physical attributes" refer to physical characteristics and traits, such as an individual's body type and skeletal structure.

[0772] The "information set in the motor domain" refers to a database related to various exercise activities, and in particular, includes information on the physical attributes of successful exercise practitioners.

[0773] An "emotion analysis device" is a device used to evaluate an individual's emotional state, analyzing emotions in real time using facial recognition technology and biosensors.

[0774] A "communication device" is a device used to transmit adjusted exercise suggestions to an individual, and generally refers to a display or speaker.

[0775] In this invention, the user first takes a full-body image of themselves using an image acquisition device, specifically a camera. The captured image is transmitted by the terminal to a server in the cloud. The server receives this image data and uses image processing software to analyze the user's physical attributes. In this process, image processing libraries such as OpenCV are utilized. The analysis results are compared with a set of information regarding the motor domain, specifically with the physical attribute data of past successful exercise practitioners.

[0776] Subsequently, the server uses an emotion analysis device to evaluate the user's emotional state in real time. This emotion analysis utilizes a system that combines facial recognition technology and biosensors. Using a facial recognition API, the emotional state is measured based on the user's facial expression data and heart rate. Based on the emotional state, the recommendations for exercise activity are adjusted.

[0777] The adjusted recommendations are presented to the user via a communication device. At this stage, the user receives feedback on the most suitable exercise suggestions using a display or speaker. For example, if the user is feeling stressed, the server will prioritize recommending exercises with relaxing effects, and the user will receive guidance from their smartphone or a robot.

[0778] An example of a prompt that uses a generative AI model to create an exercise plan is: "Recognize the user's emotional state and design a relaxing exercise plan. Highlight the user's stress levels as a key point when formulating suggestions." This prompt allows the AI ​​model to generate exercise suggestions optimized for each individual user.

[0779] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0780] Step 1:

[0781] The user takes a full-body image using an image acquisition device. This image is transferred to the terminal and used as input data for transmission to the server. The terminal must maintain a stable network connection until the captured image data is sent to the server.

[0782] Step 2:

[0783] The server receives whole-body image data transmitted from the terminal. The server analyzes this data using image processing software to identify the user's physical attributes. Specifically, it analyzes the skeleton and body shape using libraries such as OpenCV and compares this with a set of information regarding range of motion. As output, candidate exercises suitable for the user are generated.

[0784] Step 3:

[0785] The server uses an emotion analysis device to evaluate the user's emotional state in real time. This process uses data obtained from facial recognition technology and biosensors as input. A facial recognition API is used to analyze the user's facial expressions and heart rate. This allows the server to understand the user's emotional state and adjust the recommended exercise activities accordingly. The output is a list of exercise suggestions that take the emotional state into account.

[0786] Step 4:

[0787] The final exercise suggestions are presented to the user via a communication device. The server transmits the processed exercise suggestion data to a display or speaker, providing visual and audible feedback. The user receives the presented exercise suggestions and can then begin exercising based on them.

[0788] Step 5:

[0789] In creating exercise plans using a generative AI model, prompts are generated on the server side and sent to the AI. For example, the prompt "Recognize the user's emotional state and design a relaxing exercise plan. Emphasize whether the user is stressed as a key point when formulating suggestions" is input to the AI ​​model. The output is a specific exercise plan generated based on the prompt.

[0790] 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.

[0791] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0792] 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.

[0793] 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.

[0794] 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.

[0795] 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.

[0796] 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.

[0797] 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.

[0798] 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."

[0799] 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.

[0800] 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.

[0801] 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.

[0802] 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.

[0803] 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.

[0804] 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.

[0805] 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.

[0806] 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.

[0807] 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.

[0808] 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.

[0809] 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.

[0810] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0811] The following is further disclosed regarding the embodiments described above.

[0812] (Claim 1)

[0813] A means for receiving user information acquired from an image input device,

[0814] A means for analyzing physical characteristics based on the aforementioned user information,

[0815] The aforementioned analysis results are compared with an accumulated database of exercise-related fields, and a means is provided to recommend appropriate exercise activities.

[0816] A means for presenting the aforementioned recommended results to the user via an output device,

[0817] A system that includes this.

[0818] (Claim 2)

[0819] The system according to claim 1, wherein the user information includes image information of the body.

[0820] (Claim 3)

[0821] The system according to claim 1, wherein the database relating to the aforementioned athletic field includes physical characteristic data of successful athletes.

[0822] "Example 1"

[0823] (Claim 1)

[0824] A medium for receiving user information obtained from an image acquisition device,

[0825] A means for analyzing physical characteristics based on the aforementioned user information,

[0826] A means for recommending appropriate motor activities by comparing the analyzed features with a collection of information related to the accumulated motor domain,

[0827] A means for providing the aforementioned exercise activity recommendation through an information output device,

[0828] A means of performing analysis and matching using a generative AI model,

[0829] A system that includes this.

[0830] (Claim 2)

[0831] The system according to claim 1, wherein the user information includes image information showing physical characteristics.

[0832] (Claim 3)

[0833] The system according to claim 1, wherein the information set related to the exercise domain includes physical characteristic data of a successful exercise participant.

[0834] "Application Example 1"

[0835] (Claim 1)

[0836] A means for receiving user information acquired from an image acquisition device,

[0837] A means for analyzing physical characteristics based on the aforementioned user information,

[0838] The aforementioned analysis results are compared with accumulated information sources on motor ranges, and a means is provided to recommend appropriate physical activity.

[0839] Means for providing the aforementioned recommended results to the user via a display device,

[0840] A means of demonstrating exercise methods to the user via a robotic device and performing posture correction in real time,

[0841] A system that includes this.

[0842] (Claim 2)

[0843] The system according to claim 1, wherein the user information includes image information of the body.

[0844] (Claim 3)

[0845] The system according to claim 1, wherein the information source relating to the exercise domain includes physical characteristics data of successful athletes.

[0846] "Example 2 of combining an emotion engine"

[0847] (Claim 1)

[0848] A means for receiving user information obtained from an image input device,

[0849] A means for analyzing physical data based on the aforementioned user information,

[0850] The aforementioned analysis results are compared with a database of accumulated activity fields, and a means is provided to recommend appropriate physical activity.

[0851] An emotion analysis method that analyzes the user's emotional state in real time,

[0852] Taking into consideration the results of the emotion analysis, means for adjusting the recommended physical activity,

[0853] A means for presenting the aforementioned recommended results to the user via an output device,

[0854] A system that includes this.

[0855] (Claim 2)

[0856] The system according to claim 1, wherein the user information includes video information of the body.

[0857] (Claim 3)

[0858] The system according to claim 1, wherein the database relating to the activity field includes physical data of successful participants.

[0859] "Application example 2 of combining emotional engines"

[0860] (Claim 1)

[0861] A means of receiving personal information obtained from an image acquisition device,

[0862] A means for analyzing physical attributes based on the aforementioned personal information,

[0863] The analysis results are compared with an accumulated collection of information on motor domains, and a means is provided to recommend appropriate motor activities.

[0864] A means for evaluating an individual's emotional state in real time using an emotion analysis device and adjusting the recommendation results accordingly,

[0865] A means for presenting the adjusted recommendation results to an individual via a communication device,

[0866] A system that includes this.

[0867] (Claim 2)

[0868] The system according to claim 1, which uses image information of the body as the aforementioned personal information.

[0869] (Claim 3)

[0870] The system according to claim 1, wherein the set of information relating to the exercise domain includes physical attribute information of a successful exercise practitioner. [Explanation of Symbols]

[0871] 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 means for receiving user information acquired from an image input device, A means for analyzing physical characteristics based on the aforementioned user information, The aforementioned analysis results are compared with an accumulated database of exercise-related fields, and a means is provided to recommend appropriate exercise activities. A means for presenting the aforementioned recommended results to the user via an output device, A system that includes this.

2. The system according to claim 1, wherein the user information includes image information of the body.

3. The system according to claim 1, wherein the database relating to the aforementioned athletic field includes physical characteristic data of successful athletes.

Images