system

Abstract

We provide the system. [Solution] Sensors for collecting motion data, A processing unit that generates and updates a virtual representation model using motion data acquired by the sensor, A mathematical analysis device that evaluates the operation of the generated virtual representation model and creates instruction information for improving its operation, A display device that displays the instruction information, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, the method 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 as a response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] An object of the present invention is to solve problems in effectively rehabilitating patients or elderly people with physical disabilities or injuries. Specifically, it addresses the difficulties in accurately reproducing movements, immediately providing expert feedback, and providing individualized rehabilitation plans.

Means for Solving the Problems

[0005] The present invention provides a rehabilitation support system that allows patients to receive accurate and expert feedback in real time while at home and carry out an individualized exercise regeneration plan. This system comprises a sensor for collecting motion data, a processing device for generating and updating a virtual representation model using the motion data acquired by the sensor, a mathematical analysis device for evaluating the operation of the generated virtual representation model and creating instruction information for motion improvement, and a display device for displaying the instruction information.

[0006] "Motion data" refers to information about a user's physical movements, expressed as numerical values ​​or signals.

[0007] A "sensor" is a device used to detect physical phenomena and acquire them as operational data.

[0008] A "virtual representation model" is a digital model generated on a computer that reproduces the user's actions.

[0009] A "processing device" is a device that receives operational data and generates and updates a virtual representation model based on that data.

[0010] A "mathematical analysis device" is a device used to evaluate the operation of a virtual representation model and generate appropriate instruction information.

[0011] "Instructional information" refers to information that expresses specific advice or instructions for correcting or improving actions.

[0012] A "display device" is a device used to visually present instruction information to a user.

[0013] A "Motor Rehabilitation Plan" is an individualized program designed to help users undergo appropriate rehabilitation. [Brief explanation of the drawing]

[0014] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] 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 a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

MODE FOR CARRYING OUT THE INVENTION

[0015] 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.

[0016] First, the terms used in the following description will be explained.

[0017] In the following embodiments, a labeled processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0018] In the following embodiments, a labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0019] In the following embodiments, a labeled storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0020] In the following embodiments, a labeled communication I / F (Interface) is an interface including a communication processor and an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark), and the like.

[0021] 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."

[0022] [First Embodiment]

[0023] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0024] 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.

[0025] 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).

[0026] 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.

[0027] 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.

[0028] 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.

[0029] 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.

[0030] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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".

[0035] This invention is an advanced system for rehabilitation support, typically consisting of three elements: a server, a terminal, and a user.

[0036] First, users wear wearable sensors while performing rehabilitation exercises in their daily lives. This allows various data related to their movements to be collected in real time. The terminal device is responsible for receiving the movement data collected from these sensors and transmitting that data to a server.

[0037] The server processes the received motion data to generate a virtual representation model of the user, or avatar. The server continuously updates the avatar's movements to reflect the user's actual actions. Furthermore, the server evaluates the model's movements through a mathematical analysis system to determine whether the user is performing appropriate movements during rehabilitation. Based on this evaluation, the server generates instructional information for improving the movements.

[0038] The generated instruction information is sent from the server to the terminal and visually presented to the user on the terminal's display. This allows the user to check their actions in real time and easily understand any necessary improvements.

[0039] As a concrete example, consider a situation where a user is undergoing knee rehabilitation. Data collected from sensors measures the degree of knee flexion and is sent to a server. Based on this information, the server constructs a virtual representation model and generates instruction information such as, "Your knee flexion is insufficient. Please bend it to the specified range." This information is then transmitted to the user in real time via a terminal. By adjusting their movements according to this feedback, the user can perform efficient and accurate rehabilitation.

[0040] Based on the above, the present invention is a system that improves the quality and motivation of rehabilitation by providing users with immediate feedback.

[0041] The following describes the processing flow.

[0042] Step 1:

[0043] The device receives motion data from wearable sensors worn by the user. The sensors measure information such as the user's body position and joint angles in real time, and collect this data and transmit it to the device.

[0044] Step 2:

[0045] The terminal sends the received operational data to the server. The data is converted to the appropriate format and transferred to the server in real time via a secure communication path.

[0046] Step 3:

[0047] The server receives operational data sent from the terminal. After receiving the data, it checks its integrity and filters out any abnormal data as needed.

[0048] Step 4:

[0049] The server generates and updates a virtual representation model based on the received data. An avatar is created to reproduce the user's current actions in real time.

[0050] Step 5:

[0051] The server's mathematical analysis unit analyzes the behavior of the virtual representation model. The analysis evaluates whether it matches the correct behavioral pattern and, if necessary, generates instructions for behavioral improvement.

[0052] Step 6:

[0053] The server sends the generated instruction information to the terminal. This information specifically indicates how the user's actions should be improved.

[0054] Step 7:

[0055] The terminal displays instruction information sent from the server on its display device. Through the terminal screen, users can visually see areas for improvement and guidance for correcting their actions.

[0056] Step 8:

[0057] Based on feedback received through the device, users can modify their rehabilitation movements. This allows users to learn the correct movements and continue the rehabilitation program.

[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] Conventional rehabilitation support systems have challenges in providing efficient rehabilitation due to delayed feedback on user movements and abstract improvement instructions. Furthermore, it has been difficult to provide exercise regeneration plans tailored to individual users.

[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 sensor means for collecting motion information, processing means for generating and updating a virtual representation model, and analysis means for creating instruction information for motion improvement. This makes it possible to evaluate the user's movements in real time, provide specific improvement instructions, and visually present individual motion recovery plans.

[0063] "Motion information" refers to data about the user's body movements, which is collected in real time by sensors.

[0064] A "sensor" is a device used to detect user actions and acquire information.

[0065] A "virtual representation model" is a 3D representation model generated based on user actions, and it is intended to mimic the user's actual actions.

[0066] "Processing means" refers to a computing device or software for generating and updating a virtual representation model using acquired operational information.

[0067] "Analysis means" refers to a device or system for evaluating the generated virtual representation model and creating instructional information for improving its operation.

[0068] "Instructional information" refers to information that includes specific instructions and guidelines to improve user behavior.

[0069] A "display means" is a device that visually presents instructional information to the user and provides it in a format that is easy for the user to understand.

[0070] A "prompt message generation means" is a means that has the function of generating specific instructions and information to provide feedback to the user.

[0071] This invention is a system for supporting rehabilitation and includes three elements: a user, a terminal, and a server. First, the user wears wearable sensors on their body to collect motion information through daily activities. This includes, for example, walking and joint movements.

[0072] Next, the terminal receives operational information from this sensor via Bluetooth or Wi-Fi and transmits it to the server. The terminal also displays the feedback received from the server, making it a crucial device for real-time visualization for the user. Mobile devices or tablets are commonly used as display devices.

[0073] The server constructs a virtual representation model of the user using a generated AI model based on the received motion information. This model is represented as a 3D model that digitally mimics the user's actual movements. The server then performs mathematical analysis to evaluate the movements of this virtual model and generate instructions for improving the movements. In particular, it verifies whether the joint angles and movement speeds conform to standard ranges.

[0074] The generated instruction information is sent to the terminal as a prompt and presented visually to the user. This allows the user to quickly receive specific guidance to improve their actions.

[0075] For example, if a user is undergoing knee rehabilitation, the sensor measures the knee's movement in detail and sends the data to a server. The server updates a virtual representation model based on the received data and generates an instruction such as, "Your knee is not bending enough. The target is 90 degrees." This instruction is communicated to the user in real time via the terminal.

[0076] An example of a prompt message might be, "Evaluate the user's rehabilitation progress based on the latest activity data and generate necessary instructions."

[0077] This invention enables users to receive efficient and effective rehabilitation through real-time feedback while following an individualized exercise regeneration plan.

[0078] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0079] Step 1: The user puts on a wearable sensor and begins rehabilitation. The sensor collects the user's movement information in real time and transmits it to the terminal. The input is the user's physical movements, and the output is movement information data. This data includes joint angles and acceleration, among other things.

[0080] Step 2: The terminal receives motion information from the sensor. The terminal formats this data and adapts it to the communication protocol in order to send it to the server. The input is raw data from the sensor, and the output is formatted data ready to be sent to the server.

[0081] Step 3: The server analyzes the behavioral information received from the terminal. Using a generative AI model, it generates a virtual representation model of the user and simulates their behavior. The input is the formatted data received from the terminal, and the output is the virtual representation model. The server removes noise from the data and captures the behavioral trends.

[0082] Step 4: The server performs mathematical analysis based on the virtual representation model to evaluate whether its operation meets the criteria. The input is the virtual representation model, and the output is the operation evaluation result. Specifically, it detects items that fall outside the standard range and identifies areas for improvement.

[0083] Step 5: The server generates instruction information for improving the operation based on the performance evaluation results. This instruction includes adjustments to the degree of knee flexion and movement speed. The input is the performance evaluation results, and the output is specific instruction information.

[0084] Step 6: The terminal presents the user with the instruction information received from the server. A display device is used to visualize the feedback in a way that is easy for the user to understand. The input is the instruction information from the server, and the output is the visualized feedback.

[0085] Step 7: The user adjusts their movements based on the feedback provided by the device. The user can improve their movements by performing rehabilitation according to the real-time information. This involves adjusting specific body movements based on the feedback.

[0086] (Application Example 1)

[0087] 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."

[0088] While the operation of factory machinery demands improved efficiency and precision, malfunctions and errors can lead to decreased productivity and the accumulation of errors. Conventional methods simply follow pre-set operation plans, lacking real-time feedback and making it difficult to correct operational accuracy on the spot. Therefore, there is a need for a feedback system that enables immediate correction of operations.

[0089] 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.

[0090] In this invention, the server includes means for using a measuring device to collect operational data, information processing means for generating a virtual representation structure using the operational data acquired by the measuring device, and analysis means for evaluating the operation of the generated virtual representation structure and creating instructions for operational correction. This makes it possible to evaluate the operation of a work machine in real time and immediately issue correction instructions when necessary.

[0091] "Motion data" refers to information related to the movement of an object collected from a measuring device, and includes physical parameters such as velocity, position, angle, and acceleration.

[0092] A "measuring device" is a device used to acquire motion data of an object, and includes components such as sensors.

[0093] A "virtual representation structure" is a model generated based on collected motion data, which visually or mathematically represents the motion of an object.

[0094] An "information processing device" is a computing device that generates and updates virtual representation structures and executes programs for processing operational data.

[0095] An "analysis device" is a device that evaluates the operation of a virtual representation structure and generates instructions for improving its operation.

[0096] A "display media device" is a device used to visually present the instructions generated by an analysis device, and includes screens and displays.

[0097] A "feedback mechanism" is a function that analyzes operational data in real time and immediately outputs instructions for correcting the operation.

[0098] A "structural connection point" is a link between multiple elements that make up an object, and it is the part that controls rotation and angle during movement.

[0099] To implement this invention, a dedicated program is first installed on the terminal, and a measuring device is used to collect motion data. The measuring device uses an IMU (Inertial Measurement Unit) sensor, etc., and can collect motion speed, position, angle, acceleration, etc. in real time. The collected data is transmitted to the server via the terminal.

[0100] The server generates a virtual representation structure from the received operational data and updates the virtual model based on this structure. The Python programming language is used for this information processing, and data analysis libraries such as NumPy and Pandas are employed. Additionally, Tensorflow® and PyTorch are sometimes used to build machine learning models.

[0101] The virtual representation structure is evaluated for operation by an analysis device on the server, and instructions for operation improvement are generated. These instructions are presented to the user in real time through a display media device. Examples of display media devices include displays and head-mounted displays.

[0102] As a concrete example, when a factory machine assembles parts, a terminal reads the operation data and sends it to a server. The server analyzes this data, generates a virtual representation structure, evaluates the operation, and immediately generates instructions such as, "The part is out of position; please make corrections," if necessary. This allows the machine to correct its operation and improve accuracy and efficiency.

[0103] An example of a prompt message for the generating AI model is: "Based on the operation data of the work machine, perform a real-time operation evaluation and generate any necessary correction instructions."

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

[0105] Step 1:

[0106] The terminal collects operational data using a measuring device. This measuring device, for example, uses an IMU sensor to measure data such as speed, position, angle, and acceleration in real time. This collected data is then transmitted directly to the server. The input is raw data from the measuring device, and the output is the data transmission to the server.

[0107] Step 2:

[0108] The server receives behavioral data from the terminal and generates a virtual representation structure. Here, Python is used, and libraries such as NumPy and Pandas are employed to analyze the data and build a model that visually represents the object's behavior. The input is behavioral data sent from the terminal, and the output is a virtual representation structure. This model performs appropriate calculations based on the data and is then abstracted.

[0109] Step 3:

[0110] The server performs performance evaluation on the generated virtual representation structure and uses an analysis device to identify areas for improvement. This process involves, for example, using machine learning algorithms to compare the results against predefined behavioral patterns. The input is the virtual representation structure, and the output is instruction information for performance improvement.

[0111] Step 4:

[0112] The server generates instructions for improving the operation using a generated AI model based on the analysis results. In this process, the prompt "Generate and present appropriate correction instructions based on the operation data" is used to prompt the AI ​​to generate part of the feedback. The input is the analysis results, and the output is the instruction information.

[0113] Step 5:

[0114] The user receives instructions for improving the operation from the server through the terminal's display media device. They also receive visual feedback using a display or head-mounted display. The input is instruction information, and the output is visual feedback. This allows the user to appropriately correct the operation.

[0115] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0116] This invention is a system that integrates motion data and emotional data to provide more effective rehabilitation support. This system is built around a server, terminals, and users, and by incorporating an emotion engine, it evaluates the user's emotional state and improves the quality of rehabilitation.

[0117] When a user undergoes rehabilitation, the device first acquires motion data through wearable sensors. In addition, the device is equipped with a camera and microphone, which are used to collect emotional data from the user's facial expressions and voice. This emotional data is analyzed in real time by an emotion engine and output as the user's emotional state.

[0118] The collected motion and emotion data are transmitted to the server via the terminal. The server generates and updates a virtual representation model based on the motion data, and further analyzes the emotion data to understand the user's psychological state. Based on the information thus obtained, the mathematical analysis device evaluates both the user's motion and emotion, and creates instructional information for motion improvement and psychological support.

[0119] Instructions are presented to the user via the terminal's display. In addition to feedback on performance improvements, messages that consider the user's emotions are also displayed. For example, if the user is tired, a message such as "Take a short rest and refresh yourself" is provided to maintain the user's motivation.

[0120] For example, if the emotion engine detects that a user's expression is gloomy during knee rehabilitation, the server will display instructions regarding movement along with supportive messages aimed at improving their emotions. By following these instructions, the user can adjust their movements and improve their mental state, thereby maximizing the effectiveness of their rehabilitation.

[0121] Thus, the present invention is a system that supports effective and continuous rehabilitation by approaching it from both the functional and emotional aspects.

[0122] The following describes the processing flow.

[0123] Step 1:

[0124] The device receives motion data from wearable sensors worn by the user. The sensors collect information such as the user's body position and joint angles, and transmit this data in real time.

[0125] Step 2:

[0126] The device uses a camera and microphone to capture the user's facial expressions and voice, collecting emotional data. This includes changes in facial expressions and tone of voice.

[0127] Step 3:

[0128] The device sends collected behavioral and emotional data to the server. Data transmission is performed via a secure protocol.

[0129] Step 4:

[0130] The server generates a virtual representation model based on the received motion data and reproduces the user's current movements in real time.

[0131] Step 5:

[0132] The server uses an emotion engine to analyze emotional data and assess the user's psychological state. For example, it can identify feelings of fatigue or stress.

[0133] Step 6:

[0134] The server comprehensively analyzes behavioral and emotional data and uses mathematical analysis tools to generate instructions for improving the user's behavior and psychological support messages.

[0135] Step 7:

[0136] Instructions sent from the server are received by the terminal and displayed to the user on the display device. In addition to instructions to correct actions, the user can also receive advice tailored to their emotions.

[0137] Step 8:

[0138] Users adjust their rehabilitation exercises based on feedback from their devices and also receive emotional support through advice on feelings. This improves the effectiveness of the rehabilitation.

[0139] (Example 2)

[0140] 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".

[0141] Traditional rehabilitation support systems often focus on physical improvements based on movement data, and do not adequately consider the user's emotional state or psychological aspects. Therefore, there is a need for a new system that can maintain user motivation and enable more effective and continuous rehabilitation.

[0142] 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.

[0143] In this invention, the server includes means for a measuring device for collecting motion data, means for analyzing the emotional state using the motion data, voice, and image data acquired by the measuring device, and means for a computing device that evaluates the operation of the generated virtual representation model and creates instruction information regarding motion improvement and psychological support. This makes it possible to manage the user's motion and emotions in an integrated manner and support rehabilitation from various aspects.

[0144] "Motion data" refers to information about the user's body movements and forms the basis of physical assessment in rehabilitation.

[0145] A "measuring device" is a device used to acquire user motion data, and generally refers to a device that includes sensors and cameras.

[0146] "Audio and image data" refers to information obtained from audio and images used to analyze the user's emotional state.

[0147] "Analysis means for analyzing emotional states" refers to technologies and devices that use audio and image data to analyze a user's emotions and psychological state.

[0148] A "virtual representation model" refers to a model that virtually reproduces a user's movements based on motion data.

[0149] A "computational device" refers to a device or system that processes data on actions and emotions and generates instructional information.

[0150] "Instructional information" refers to specific guidance and messages provided to users for the purpose of improving their performance or providing psychological support.

[0151] "Presentation means" refers to devices or methods for showing instructional information to a user visually or audibly.

[0152] This invention provides a system that integrates user actions and emotions to offer more effective rehabilitation support. The system consists primarily of a server, a terminal, and the user. When the user performs rehabilitation, the terminal acquires action data using wearable sensors. The terminal is also equipped with a camera and microphone, which collect emotional data from the user's facial expressions and voice.

[0153] The terminal transmits collected behavioral and emotional data to the server. The server uses specific analysis software to generate a virtual representation model based on the behavioral data and analyzes the emotional data using an emotion engine. Based on the analysis results, the mathematical analysis device evaluates the user's behavior and emotional state and creates instructional information for behavioral improvement and psychological support.

[0154] The terminal uses the instruction information received from the server to provide feedback to the user via the display device. Specifically, this includes instructions indicating actions the user should take to improve their condition, as well as messages to support their emotions. For example, if the user is tired, a message such as "Take a short rest and refresh yourself" might be displayed.

[0155] For example, if a user undergoing knee rehabilitation is determined to be in a negative emotional state based on their facial expressions, the server may generate a supportive message encouraging positive emotions along with instructions for action.

[0156] An example of a prompt message for the generating AI model would be: "Based on the facial expression and voice data of a user undergoing rehabilitation, determine their emotional state and generate instructional information to provide movement improvement and psychological support."

[0157] Thus, the present invention makes it possible to provide comprehensive rehabilitation support that takes into account not only the user's actions but also their emotional state.

[0158] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0159] Step 1:

[0160] When a user begins rehabilitation, the device collects data on physical movements in real time via wearable sensors. This data includes the user's limb movements, joint angles, and posture. The input is the user's physical movements, and the output is the generated data of these movements.

[0161] Step 2:

[0162] The device collects facial expression and voice data using its built-in camera and microphone. It uses facial expression analysis and voice analysis to understand the user's emotional state in real time. The input consists of the user's facial expressions and voice, and the output is generated as emotional data.

[0163] Step 3:

[0164] The device sends collected behavioral and emotional data to the server. This data transfer enables smooth data analysis on the server. The input consists of behavioral and emotional data, and the output is the data sent to the server.

[0165] Step 4:

[0166] The server generates or updates a virtual representation model based on operational data. This process involves comparison with past data and evaluation of the current state. Operational data is the input, and a virtual representation model is generated as the output.

[0167] Step 5:

[0168] The server analyzes emotional data using an emotion engine to evaluate the user's psychological state. This analysis utilizes techniques such as pattern matching and voice emotion analysis. Emotional data is the input, and the user's emotional state is evaluated as the output.

[0169] Step 6:

[0170] The server's mathematical analysis unit generates instructional information for behavioral improvement and psychological support based on a virtual representation model and emotional state. This includes specific behavioral instructions and emotional support messages. The inputs are a virtual representation model and emotional assessment, and the output is the generated instructional information.

[0171] Step 7:

[0172] The terminal displays instruction information received from the server on the display device. The user checks the direction of rehabilitation and encouraging messages on the display and then carries them out. The input is instruction information, and the output is information presented to the user.

[0173] Through this series of steps, the system can support rehabilitation from both a behavioral and emotional perspective.

[0174] (Application Example 2)

[0175] 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".

[0176] In factory environments, the challenge lies in simultaneously analyzing workers' actions and emotional states, and providing comprehensive support to improve work efficiency and mental health based on that analysis. In particular, obtaining feedback not only on actions but also on emotions is necessary to reduce worker fatigue and stress, thereby providing a safe and efficient work environment.

[0177] 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.

[0178] In this invention, the server includes a detection device for collecting motion data and emotion data, a computing device for generating and updating a virtual representation construction model using the information acquired by the detection device, and an analysis device for evaluating the generated virtual representation construction model and creating instruction information for motion improvement and emotion improvement. This enables the provision of individualized feedback based on the physical and emotional state of the worker, thereby improving work efficiency and maintaining mental health.

[0179] "Motion data" refers to data that records information about the physical movements and postures performed by a worker.

[0180] "Emotional data" refers to data collected and analyzed from information related to workers' facial expressions, tone of voice, and other emotional information.

[0181] A "detection device" is a device that includes equipment such as sensors, cameras, and microphones for collecting motion data and emotion data.

[0182] A "virtual representation construction model" is a model that reproduces the state of an operator, generated and updated based on collected behavioral and emotional data.

[0183] A "processing unit" is a device that generates and updates a virtual representation construction model using data acquired by a detection unit.

[0184] An "analysis device" is a device used to evaluate a virtual representation construction model and to create instructional information regarding performance improvement and emotional improvement.

[0185] "Instructional information" refers to specific guidelines and messages generated by the analysis device for improving behavior and emotions.

[0186] "Visual devices" refer to display monitors and digital display panels used to present instructional information to workers.

[0187] This invention is a system for real-time monitoring and analysis of worker behavior and emotions in a factory environment. This system primarily consists of three components: a server, terminals, and users.

[0188] The server acquires information through detection devices installed to collect behavioral and emotional data. These detection devices include sensors, cameras, and microphones, which are used to monitor the worker's movements, facial expressions, and voice. The acquired data is generated and updated as a virtual representation construction model by the computing unit. Specifically, Python and other analysis libraries are used to process and analyze the collected data.

[0189] The virtual representation construction model generated by the computing unit is further evaluated by the analysis unit. Here, mathematical analysis techniques are used to construct instruction information regarding the improvement of the worker's actions and emotions. This process utilizes cloud services such as Amazon AWS® and Google® Cloud API.

[0190] Instructions generated by the analysis device are presented to the user (worker) through a visual device. The visual device provides feedback and improvement instructions in a format that the worker can intuitively understand, allowing the worker to grasp their areas for improvement in real time and take appropriate action.

[0191] For example, if a worker is working in a specific posture for an extended period, a message such as "Please take a short break" will be displayed on the visual device. This allows workers to take breaks when necessary, leading to improved long-term work efficiency and better health.

[0192] An example of a prompt message for the generating AI model would be a specific instruction such as, "Analyze worker A's emotions from their facial expressions and provide feedback to optimize their health status in conjunction with their movement data." Based on this prompt, the system performs the necessary analysis and provides useful information to the worker.

[0193] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0194] Step 1:

[0195] The device acquires worker motion and emotional data using wearable sensors, a camera, and a microphone. During this process, the device collects movement information from the sensors and facial expressions and voice from the camera and microphone in real time, converting them into a digital format as initial processing. The input is raw data, while the output is data formatted for analysis.

[0196] Step 2:

[0197] The collected motion and emotion data are transmitted from the terminal to the server. The server receives this data and uses its computing power to generate and update a virtual representation construction model. Specifically, it creates a model that reproduces physical movement based on the motion data and constructs a virtual model that incorporates psychological states using the emotion data. The input is formatted data, and the output is the virtual representation construction model.

[0198] Step 3:

[0199] The server evaluates the generated virtual representation construction model using an analysis device. The analysis device uses mathematical analysis to analyze the worker's state from both behavioral and emotional perspectives. This generates specific instructional information for improving behavior and emotions. The input is the virtual model, and the output is instructional information based on behavior and emotions.

[0200] Step 4:

[0201] The generated instruction information is sent back to the terminal and presented to the user (worker) through the terminal's visual device. The terminal visually displays the instruction information, providing messages and animations in a format that the worker can intuitively understand. The input is the instruction information, and the output is the feedback displayed for the user.

[0202] Step 5:

[0203] The user, acting as the worker, adjusts their actions and emotions based on the feedback provided. This allows the worker to improve efficiency and reduce stress. While there are no explicit inputs or outputs in this step, the user's behavioral changes are achieved as the overall goal of the system.

[0204] 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.

[0205] 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.

[0206] 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.

[0207] [Second Embodiment]

[0208] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0209] 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.

[0210] 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).

[0211] 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.

[0212] 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.

[0213] 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).

[0214] 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.

[0215] 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.

[0216] 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.

[0217] 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.

[0218] 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.

[0219] 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".

[0220] This invention is an advanced system for rehabilitation support, typically consisting of three elements: a server, a terminal, and a user.

[0221] First, users wear wearable sensors while performing rehabilitation exercises in their daily lives. This allows various data related to their movements to be collected in real time. The terminal device is responsible for receiving the movement data collected from these sensors and transmitting that data to a server.

[0222] The server processes the received motion data to generate a virtual representation model of the user, or avatar. The server continuously updates the avatar's movements to reflect the user's actual actions. Furthermore, the server evaluates the model's movements through a mathematical analysis system to determine whether the user is performing appropriate movements during rehabilitation. Based on this evaluation, the server generates instructional information for improving the movements.

[0223] The generated instruction information is sent from the server to the terminal and visually presented to the user on the terminal's display. This allows the user to check their actions in real time and easily understand any necessary improvements.

[0224] As a concrete example, consider a situation where a user is undergoing knee rehabilitation. Data collected from sensors measures the degree of knee flexion and is sent to a server. Based on this information, the server constructs a virtual representation model and generates instruction information such as, "Your knee flexion is insufficient. Please bend it to the specified range." This information is then transmitted to the user in real time via a terminal. By adjusting their movements according to this feedback, the user can perform efficient and accurate rehabilitation.

[0225] Based on the above, the present invention is a system that improves the quality and motivation of rehabilitation by providing users with immediate feedback.

[0226] The following describes the processing flow.

[0227] Step 1:

[0228] The device receives motion data from wearable sensors worn by the user. The sensors measure information such as the user's body position and joint angles in real time, and collect this data and transmit it to the device.

[0229] Step 2:

[0230] The terminal sends the received operational data to the server. The data is converted to the appropriate format and transferred to the server in real time via a secure communication path.

[0231] Step 3:

[0232] The server receives operational data sent from the terminal. After receiving the data, it checks its integrity and filters out any abnormal data as needed.

[0233] Step 4:

[0234] The server generates and updates a virtual representation model based on the received data. An avatar is created to reproduce the user's current actions in real time.

[0235] Step 5:

[0236] The server's mathematical analysis unit analyzes the behavior of the virtual representation model. The analysis evaluates whether it matches the correct behavioral pattern and, if necessary, generates instructions for behavioral improvement.

[0237] Step 6:

[0238] The server sends the generated instruction information to the terminal. This information specifically indicates how the user's actions should be improved.

[0239] Step 7:

[0240] The terminal displays instruction information sent from the server on its display device. Through the terminal screen, users can visually see areas for improvement and guidance for correcting their actions.

[0241] Step 8:

[0242] Based on feedback received through the device, users can modify their rehabilitation movements. This allows users to learn the correct movements and continue the rehabilitation program.

[0243] (Example 1)

[0244] 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."

[0245] Conventional rehabilitation support systems have challenges in providing efficient rehabilitation due to delayed feedback on user movements and abstract improvement instructions. Furthermore, it has been difficult to provide exercise regeneration plans tailored to individual users.

[0246] 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.

[0247] In this invention, the server includes sensor means for collecting motion information, processing means for generating and updating a virtual representation model, and analysis means for creating instruction information for motion improvement. This makes it possible to evaluate the user's movements in real time, provide specific improvement instructions, and visually present individual motion recovery plans.

[0248] "Motion information" refers to data about the user's body movements, which is collected in real time by sensors.

[0249] A "sensor" is a device used to detect user actions and acquire information.

[0250] A "virtual representation model" is a 3D representation model generated based on user actions, and it is intended to mimic the user's actual actions.

[0251] "Processing means" refers to a computing device or software for generating and updating a virtual representation model using acquired operational information.

[0252] "Analysis means" refers to a device or system for evaluating the generated virtual representation model and creating instructional information for improving its operation.

[0253] "Instructional information" refers to information that includes specific instructions and guidelines to improve user behavior.

[0254] A "display means" is a device that visually presents instructional information to the user and provides it in a format that is easy for the user to understand.

[0255] A "prompt message generation means" is a means that has the function of generating specific instructions and information to provide feedback to the user.

[0256] This invention is a system for supporting rehabilitation and includes three elements: a user, a terminal, and a server. First, the user wears wearable sensors on their body to collect motion information through daily activities. This includes, for example, walking and joint movements.

[0257] Next, the terminal receives operational information from this sensor via Bluetooth or Wi-Fi and transmits it to the server. The terminal also displays the feedback received from the server, making it a crucial device for real-time visualization for the user. Mobile devices or tablets are commonly used as display devices.

[0258] The server constructs a virtual representation model of the user using a generated AI model based on the received motion information. This model is represented as a 3D model that digitally mimics the user's actual movements. The server then performs mathematical analysis to evaluate the movements of this virtual model and generate instructions for improving the movements. In particular, it verifies whether the joint angles and movement speeds conform to standard ranges.

[0259] The generated instruction information is sent to the terminal as a prompt and presented visually to the user. This allows the user to quickly receive specific guidance to improve their actions.

[0260] For example, if a user is undergoing knee rehabilitation, the sensor measures the knee's movement in detail and sends the data to a server. The server updates a virtual representation model based on the received data and generates an instruction such as, "Your knee is not bending enough. The target is 90 degrees." This instruction is communicated to the user in real time via the terminal.

[0261] An example of a prompt message might be, "Evaluate the user's rehabilitation progress based on the latest activity data and generate necessary instructions."

[0262] This invention enables users to receive efficient and effective rehabilitation through real-time feedback while following an individualized exercise regeneration plan.

[0263] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0264] Step 1: The user puts on a wearable sensor and begins rehabilitation. The sensor collects the user's movement information in real time and transmits it to the terminal. The input is the user's physical movements, and the output is movement information data. This data includes joint angles and acceleration, among other things.

[0265] Step 2: The terminal receives motion information from the sensor. The terminal formats this data and adapts it to the communication protocol in order to send it to the server. The input is raw data from the sensor, and the output is formatted data ready to be sent to the server.

[0266] Step 3: The server analyzes the behavioral information received from the terminal. Using a generative AI model, it generates a virtual representation model of the user and simulates their behavior. The input is the formatted data received from the terminal, and the output is the virtual representation model. The server removes noise from the data and captures the behavioral trends.

[0267] Step 4: The server performs mathematical analysis based on the virtual representation model to evaluate whether its operation meets the criteria. The input is the virtual representation model, and the output is the operation evaluation result. Specifically, it detects items that fall outside the standard range and identifies areas for improvement.

[0268] Step 5: The server generates instruction information for improving the operation based on the performance evaluation results. This instruction includes adjustments to the degree of knee flexion and movement speed. The input is the performance evaluation results, and the output is specific instruction information.

[0269] Step 6: The terminal presents the user with the instruction information received from the server. A display device is used to visualize the feedback in a way that is easy for the user to understand. The input is the instruction information from the server, and the output is the visualized feedback.

[0270] Step 7: The user adjusts their movements based on the feedback provided by the device. The user can improve their movements by performing rehabilitation according to the real-time information. This involves adjusting specific body movements based on the feedback.

[0271] (Application Example 1)

[0272] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0273] While the operation of factory machinery demands improved efficiency and precision, malfunctions and errors can lead to decreased productivity and the accumulation of errors. Conventional methods simply follow pre-set operation plans, lacking real-time feedback and making it difficult to correct operational accuracy on the spot. Therefore, there is a need for a feedback system that enables immediate correction of operations.

[0274] 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.

[0275] In this invention, the server includes means for using a measuring device to collect operational data, information processing means for generating a virtual representation structure using the operational data acquired by the measuring device, and analysis means for evaluating the operation of the generated virtual representation structure and creating instructions for operational correction. This makes it possible to evaluate the operation of a work machine in real time and immediately issue correction instructions when necessary.

[0276] "Motion data" refers to information related to the movement of an object collected from a measuring device, and includes physical parameters such as velocity, position, angle, and acceleration.

[0277] A "measuring device" is a device used to acquire motion data of an object, and includes components such as sensors.

[0278] A "virtual representation structure" is a model generated based on collected motion data, which visually or mathematically represents the motion of an object.

[0279] An "information processing device" is a computing device that generates and updates virtual representation structures and executes programs for processing operational data.

[0280] An "analysis device" is a device that evaluates the operation of a virtual representation structure and generates instructions for improving its operation.

[0281] The "display medium device" is a device for visually presenting the instruction content generated by the analysis device, and includes a screen and a display.

[0282] The "feedback means" is a function for analyzing operation data in real time and outputting an instruction for immediate operation correction.

[0283] The "connection part of the structure" is a connection site between a plurality of elements constituting an object, and is a part for controlling rotation and angle in operation.

[0284] To implement this invention, first, a dedicated program is installed on the terminal, and a measurement device is used to collect operation data. For the measurement device, an IMU (Inertial Measurement Unit) sensor or the like is used to collect the speed, position, angle, acceleration, etc. of the operation in real time. The collected data is transmitted to the server via the terminal.

[0285] On the server, a virtual representation structure is generated from the received operation data, and the virtual model is updated based on this structure. For the information processing for this, the programming language Python is used, and data analysis libraries such as NumPy and Pandas are used. Also, TensorFlow and PyTorch may be utilized to construct a machine learning model.

[0286] The virtual representation structure is operationally evaluated by the analysis device on the server, and the instruction content for operation improvement is generated. This instruction content is presented to the user in real time through the display medium device. For the display medium device, for example, a display or a head-mounted display is used.

[0287] As a concrete example, when a factory machine assembles parts, a terminal reads the operation data and sends it to a server. The server analyzes this data, generates a virtual representation structure, evaluates the operation, and immediately generates instructions such as, "The part is out of position; please make corrections," if necessary. This allows the machine to correct its operation and improve accuracy and efficiency.

[0288] An example of a prompt message for the generating AI model is: "Based on the operation data of the work machine, perform a real-time operation evaluation and generate any necessary correction instructions."

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

[0290] Step 1:

[0291] The terminal collects operational data using a measuring device. This measuring device, for example, uses an IMU sensor to measure data such as speed, position, angle, and acceleration in real time. This collected data is then transmitted directly to the server. The input is raw data from the measuring device, and the output is the data transmission to the server.

[0292] Step 2:

[0293] The server receives behavioral data from the terminal and generates a virtual representation structure. Here, Python is used, and libraries such as NumPy and Pandas are employed to analyze the data and build a model that visually represents the object's behavior. The input is behavioral data sent from the terminal, and the output is a virtual representation structure. This model performs appropriate calculations based on the data and is then abstracted.

[0294] Step 3:

[0295] The server performs performance evaluation on the generated virtual representation structure and uses an analysis device to identify areas for improvement. This process involves, for example, using machine learning algorithms to compare the results against predefined behavioral patterns. The input is the virtual representation structure, and the output is instruction information for performance improvement.

[0296] Step 4:

[0297] The server generates instructions for improving the operation using a generated AI model based on the analysis results. In this process, the prompt "Generate and present appropriate correction instructions based on the operation data" is used to prompt the AI ​​to generate part of the feedback. The input is the analysis results, and the output is the instruction information.

[0298] Step 5:

[0299] The user receives instructions for improving the operation from the server through the terminal's display media device. They also receive visual feedback using a display or head-mounted display. The input is instruction information, and the output is visual feedback. This allows the user to appropriately correct the operation.

[0300] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0301] This invention is a system that integrates motion data and emotional data to provide more effective rehabilitation support. This system is built around a server, terminals, and users, and by incorporating an emotion engine, it evaluates the user's emotional state and improves the quality of rehabilitation.

[0302] When a user performs rehabilitation, the terminal first obtains motion data through a wearable sensor. In addition, a camera and a microphone are installed in the terminal, and emotional data based on the user's expression and voice are also collected using these. This emotional data is analyzed in real time by an emotion engine and output as the user's emotional state.

[0303] The collected motion data and emotional data are transmitted to the server via the terminal. The server generates and updates a virtual expression model based on the motion data, and further analyzes the emotional data to understand the user's psychological state. Based on the information thus obtained, a mathematical analysis device evaluates both the user's motion and emotion, and creates instruction information for motion improvement and psychological support.

[0304] The instruction information is presented to the user through the display device of the terminal. Not only feedback regarding motion improvement but also messages considering the user's emotion are displayed. For example, when the user is tired, by providing a message such as "Take a little rest and refresh yourself", the user's motivation is maintained.

[0305] As a specific example, when the emotion engine determines that the user's expression is gloomy during knee rehabilitation, the server displays a support message for emotion improvement together with instructions regarding the motion. The user adjusts the motion according to it and further arranges the mental aspect, so that it becomes possible to maximize the rehabilitation effect.

[0306] In this way, the present invention is a system that supports effective and continuous rehabilitation by approaching from both the aspects of motion and emotion.

[0307] The processing flow will be described below.

[0308] Step 1:

[0309] The device receives motion data from wearable sensors worn by the user. The sensors collect information such as the user's body position and joint angles, and transmit this data in real time.

[0310] Step 2:

[0311] The device uses a camera and microphone to capture the user's facial expressions and voice, collecting emotional data. This includes changes in facial expressions and tone of voice.

[0312] Step 3:

[0313] The device sends collected behavioral and emotional data to the server. Data transmission is performed via a secure protocol.

[0314] Step 4:

[0315] The server generates a virtual representation model based on the received motion data and reproduces the user's current movements in real time.

[0316] Step 5:

[0317] The server uses an emotion engine to analyze emotional data and assess the user's psychological state. For example, it can identify feelings of fatigue or stress.

[0318] Step 6:

[0319] The server comprehensively analyzes behavioral and emotional data and uses mathematical analysis tools to generate instructions for improving the user's behavior and psychological support messages.

[0320] Step 7:

[0321] Instructions sent from the server are received by the terminal and displayed to the user on the display device. In addition to instructions to correct actions, the user can also receive advice tailored to their emotions.

[0322] Step 8:

[0323] Users adjust their rehabilitation exercises based on feedback from their devices and also receive emotional support through advice on feelings. This improves the effectiveness of the rehabilitation.

[0324] (Example 2)

[0325] 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".

[0326] Traditional rehabilitation support systems often focus on physical improvements based on movement data, and do not adequately consider the user's emotional state or psychological aspects. Therefore, there is a need for a new system that can maintain user motivation and enable more effective and continuous rehabilitation.

[0327] 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.

[0328] In this invention, the server includes means for a measuring device for collecting motion data, means for analyzing the emotional state using the motion data, voice, and image data acquired by the measuring device, and means for a computing device that evaluates the operation of the generated virtual representation model and creates instruction information regarding motion improvement and psychological support. This makes it possible to manage the user's motion and emotions in an integrated manner and support rehabilitation from various aspects.

[0329] "Motion data" refers to information about the user's body movements and forms the basis of physical assessment in rehabilitation.

[0330] A "measuring device" is a device used to acquire user motion data, and generally refers to a device that includes sensors and cameras.

[0331] "Audio and image data" refers to information obtained from audio and images used to analyze the user's emotional state.

[0332] "Analysis means for analyzing emotional states" refers to technologies and devices that use audio and image data to analyze a user's emotions and psychological state.

[0333] A "virtual representation model" refers to a model that virtually reproduces a user's movements based on motion data.

[0334] A "computational device" refers to a device or system that processes data on actions and emotions and generates instructional information.

[0335] "Instructional information" refers to specific guidance and messages provided to users for the purpose of improving their performance or providing psychological support.

[0336] "Presentation means" refers to devices or methods for showing instructional information to a user visually or audibly.

[0337] This invention provides a system that integrates user actions and emotions to offer more effective rehabilitation support. The system consists primarily of a server, a terminal, and the user. When the user performs rehabilitation, the terminal acquires action data using wearable sensors. The terminal is also equipped with a camera and microphone, which collect emotional data from the user's facial expressions and voice.

[0338] The terminal transmits collected behavioral and emotional data to the server. The server uses specific analysis software to generate a virtual representation model based on the behavioral data and analyzes the emotional data using an emotion engine. Based on the analysis results, the mathematical analysis device evaluates the user's behavior and emotional state and creates instructional information for behavioral improvement and psychological support.

[0339] The terminal uses the instruction information received from the server to provide feedback to the user via the display device. Specifically, this includes instructions indicating actions the user should take to improve their condition, as well as messages to support their emotions. For example, if the user is tired, a message such as "Take a short rest and refresh yourself" might be displayed.

[0340] For example, if a user undergoing knee rehabilitation is determined to be in a negative emotional state based on their facial expressions, the server may generate a supportive message encouraging positive emotions along with instructions for action.

[0341] An example of a prompt message for the generating AI model would be: "Based on the facial expression and voice data of a user undergoing rehabilitation, determine their emotional state and generate instructional information to provide movement improvement and psychological support."

[0342] Thus, the present invention makes it possible to provide comprehensive rehabilitation support that takes into account not only the user's actions but also their emotional state.

[0343] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0344] Step 1:

[0345] When a user begins rehabilitation, the device collects data on physical movements in real time via wearable sensors. This data includes the user's limb movements, joint angles, and posture. The input is the user's physical movements, and the output is the generated data of these movements.

[0346] Step 2:

[0347] The device collects facial expression and voice data using its built-in camera and microphone. It uses facial expression analysis and voice analysis to understand the user's emotional state in real time. The input consists of the user's facial expressions and voice, and the output is generated as emotional data.

[0348] Step 3:

[0349] The device sends collected behavioral and emotional data to the server. This data transfer enables smooth data analysis on the server. The input consists of behavioral and emotional data, and the output is the data sent to the server.

[0350] Step 4:

[0351] The server generates or updates a virtual representation model based on operational data. This process involves comparison with past data and evaluation of the current state. Operational data is the input, and a virtual representation model is generated as the output.

[0352] Step 5:

[0353] The server analyzes emotional data using an emotion engine to evaluate the user's psychological state. This analysis utilizes techniques such as pattern matching and voice emotion analysis. Emotional data is the input, and the user's emotional state is evaluated as the output.

[0354] Step 6:

[0355] The server's mathematical analysis unit generates instructional information for behavioral improvement and psychological support based on a virtual representation model and emotional state. This includes specific behavioral instructions and emotional support messages. The inputs are a virtual representation model and emotional assessment, and the output is the generated instructional information.

[0356] Step 7:

[0357] The terminal displays instruction information received from the server on the display device. The user checks the direction of rehabilitation and encouraging messages on the display and then carries them out. The input is instruction information, and the output is information presented to the user.

[0358] Through this series of steps, the system can support rehabilitation from both a behavioral and emotional perspective.

[0359] (Application Example 2)

[0360] 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 as the "terminal".

[0361] In factory environments, the challenge lies in simultaneously analyzing workers' actions and emotional states, and providing comprehensive support to improve work efficiency and mental health based on that analysis. In particular, obtaining feedback not only on actions but also on emotions is necessary to reduce worker fatigue and stress, thereby providing a safe and efficient work environment.

[0362] 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.

[0363] In this invention, the server includes a detection device for collecting motion data and emotion data, a computing device for generating and updating a virtual representation construction model using the information acquired by the detection device, and an analysis device for evaluating the generated virtual representation construction model and creating instruction information for motion improvement and emotion improvement. This enables the provision of individualized feedback based on the physical and emotional state of the worker, thereby improving work efficiency and maintaining mental health.

[0364] "Motion data" refers to data that records information about the physical movements and postures performed by a worker.

[0365] "Emotional data" refers to data collected and analyzed from information related to workers' facial expressions, tone of voice, and other emotional information.

[0366] A "detection device" is a device that includes equipment such as sensors, cameras, and microphones for collecting motion data and emotion data.

[0367] A "virtual representation construction model" is a model that reproduces the state of an operator, generated and updated based on collected behavioral and emotional data.

[0368] A "processing unit" is a device that generates and updates a virtual representation construction model using data acquired by a detection unit.

[0369] An "analysis device" is a device used to evaluate a virtual representation construction model and to create instructional information regarding performance improvement and emotional improvement.

[0370] "Instructional information" refers to specific guidelines and messages generated by the analysis device for improving behavior and emotions.

[0371] "Visual devices" refer to display monitors and digital display panels used to present instructional information to workers.

[0372] This invention is a system for real-time monitoring and analysis of worker behavior and emotions in a factory environment. This system primarily consists of three components: a server, terminals, and users.

[0373] The server acquires information through detection devices installed to collect behavioral and emotional data. These detection devices include sensors, cameras, and microphones, which are used to monitor the worker's movements, facial expressions, and voice. The acquired data is generated and updated as a virtual representation construction model by the computing unit. Specifically, Python and other analysis libraries are used to process and analyze the collected data.

[0374] The virtual representation construction model generated by the computing unit is further evaluated by the analysis unit. Here, mathematical analysis techniques are used to construct instruction information regarding the improvement of the worker's actions and emotions. This process utilizes cloud services such as Amazon AWS and Google Cloud APIs.

[0375] Instructions generated by the analysis device are presented to the user (worker) through a visual device. The visual device provides feedback and improvement instructions in a format that the worker can intuitively understand, allowing the worker to grasp their areas for improvement in real time and take appropriate action.

[0376] For example, if a worker is working in a specific posture for an extended period, a message such as "Please take a short break" will be displayed on the visual device. This allows workers to take breaks when necessary, leading to improved long-term work efficiency and better health.

[0377] An example of a prompt message for the generating AI model would be a specific instruction such as, "Analyze worker A's emotions from their facial expressions and provide feedback to optimize their health status in conjunction with their movement data." Based on this prompt, the system performs the necessary analysis and provides useful information to the worker.

[0378] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0379] Step 1:

[0380] The device acquires worker motion and emotional data using wearable sensors, a camera, and a microphone. During this process, the device collects movement information from the sensors and facial expressions and voice from the camera and microphone in real time, converting them into a digital format as initial processing. The input is raw data, while the output is data formatted for analysis.

[0381] Step 2:

[0382] The collected motion and emotion data are transmitted from the terminal to the server. The server receives this data and uses its computing power to generate and update a virtual representation construction model. Specifically, it creates a model that reproduces physical movement based on the motion data and constructs a virtual model that incorporates psychological states using the emotion data. The input is formatted data, and the output is the virtual representation construction model.

[0383] Step 3:

[0384] The server evaluates the generated virtual representation construction model using an analysis device. The analysis device uses mathematical analysis to analyze the worker's state from both behavioral and emotional perspectives. This generates specific instructional information for improving behavior and emotions. The input is the virtual model, and the output is instructional information based on behavior and emotions.

[0385] Step 4:

[0386] The generated instruction information is sent back to the terminal and presented to the user (worker) through the terminal's visual device. The terminal visually displays the instruction information, providing messages and animations in a format that the worker can intuitively understand. The input is the instruction information, and the output is the feedback displayed for the user.

[0387] Step 5:

[0388] The user, acting as the worker, adjusts their actions and emotions based on the feedback provided. This allows the worker to improve efficiency and reduce stress. While there are no explicit inputs or outputs in this step, the user's behavioral changes are achieved as the overall goal of the system.

[0389] 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.

[0390] 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.

[0391] 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.

[0392] [Third Embodiment]

[0393] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0394] 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.

[0395] 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).

[0396] 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.

[0397] 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.

[0398] 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).

[0399] 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.

[0400] 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.

[0401] 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.

[0402] 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.

[0403] 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.

[0404] 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".

[0405] This invention is an advanced system for rehabilitation support, typically consisting of three elements: a server, a terminal, and a user.

[0406] First, users wear wearable sensors while performing rehabilitation exercises in their daily lives. This allows various data related to their movements to be collected in real time. The terminal device is responsible for receiving the movement data collected from these sensors and transmitting that data to a server.

[0407] The server processes the received motion data to generate a virtual representation model of the user, or avatar. The server continuously updates the avatar's movements to reflect the user's actual actions. Furthermore, the server evaluates the model's movements through a mathematical analysis system to determine whether the user is performing appropriate movements during rehabilitation. Based on this evaluation, the server generates instructional information for improving the movements.

[0408] The generated instruction information is sent from the server to the terminal and visually presented to the user on the terminal's display. This allows the user to check their actions in real time and easily understand any necessary improvements.

[0409] As a concrete example, consider a situation where a user is undergoing knee rehabilitation. Data collected from sensors measures the degree of knee flexion and is sent to a server. Based on this information, the server constructs a virtual representation model and generates instruction information such as, "Your knee flexion is insufficient. Please bend it to the specified range." This information is then transmitted to the user in real time via a terminal. By adjusting their movements according to this feedback, the user can perform efficient and accurate rehabilitation.

[0410] Based on the above, the present invention is a system that improves the quality and motivation of rehabilitation by providing users with immediate feedback.

[0411] The following describes the processing flow.

[0412] Step 1:

[0413] The device receives motion data from wearable sensors worn by the user. The sensors measure information such as the user's body position and joint angles in real time, and collect this data and transmit it to the device.

[0414] Step 2:

[0415] The terminal sends the received operational data to the server. The data is converted to the appropriate format and transferred to the server in real time via a secure communication path.

[0416] Step 3:

[0417] The server receives operational data sent from the terminal. After receiving the data, it checks its integrity and filters out any abnormal data as needed.

[0418] Step 4:

[0419] The server generates and updates a virtual representation model based on the received data. An avatar is created to reproduce the user's current actions in real time.

[0420] Step 5:

[0421] The server's mathematical analysis unit analyzes the behavior of the virtual representation model. The analysis evaluates whether it matches the correct behavioral pattern and, if necessary, generates instructions for behavioral improvement.

[0422] Step 6:

[0423] The server sends the generated instruction information to the terminal. This information specifically indicates how the user's actions should be improved.

[0424] Step 7:

[0425] The terminal displays instruction information sent from the server on its display device. Through the terminal screen, users can visually see areas for improvement and guidance for correcting their actions.

[0426] Step 8:

[0427] Based on feedback received through the device, users can modify their rehabilitation movements. This allows users to learn the correct movements and continue the rehabilitation program.

[0428] (Example 1)

[0429] 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."

[0430] Conventional rehabilitation support systems have challenges in providing efficient rehabilitation due to delayed feedback on user movements and abstract improvement instructions. Furthermore, it has been difficult to provide exercise regeneration plans tailored to individual users.

[0431] 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.

[0432] In this invention, the server includes sensor means for collecting motion information, processing means for generating and updating a virtual representation model, and analysis means for creating instruction information for motion improvement. This makes it possible to evaluate the user's movements in real time, provide specific improvement instructions, and visually present individual motion recovery plans.

[0433] "Motion information" refers to data about the user's body movements, which is collected in real time by sensors.

[0434] A "sensor" is a device used to detect user actions and acquire information.

[0435] A "virtual representation model" is a 3D representation model generated based on user actions, and it is intended to mimic the user's actual actions.

[0436] "Processing means" refers to a computing device or software for generating and updating a virtual representation model using acquired operational information.

[0437] "Analysis means" refers to a device or system for evaluating the generated virtual representation model and creating instructional information for improving its operation.

[0438] "Instructional information" refers to information that includes specific instructions and guidelines to improve user behavior.

[0439] A "display means" is a device that visually presents instructional information to the user and provides it in a format that is easy for the user to understand.

[0440] A "prompt message generation means" is a means that has the function of generating specific instructions and information to provide feedback to the user.

[0441] This invention is a system for supporting rehabilitation and includes three elements: a user, a terminal, and a server. First, the user wears wearable sensors on their body to collect motion information through daily activities. This includes, for example, walking and joint movements.

[0442] Next, the terminal receives operational information from this sensor via Bluetooth or Wi-Fi and transmits it to the server. The terminal also displays the feedback received from the server, making it a crucial device for real-time visualization for the user. Mobile devices or tablets are commonly used as display devices.

[0443] The server constructs a virtual representation model of the user using a generated AI model based on the received motion information. This model is represented as a 3D model that digitally mimics the user's actual movements. The server then performs mathematical analysis to evaluate the movements of this virtual model and generate instructions for improving the movements. In particular, it verifies whether the joint angles and movement speeds conform to standard ranges.

[0444] The generated instruction information is sent to the terminal as a prompt and presented visually to the user. This allows the user to quickly receive specific guidance to improve their actions.

[0445] For example, if a user is undergoing knee rehabilitation, the sensor measures the knee's movement in detail and sends the data to a server. The server updates a virtual representation model based on the received data and generates an instruction such as, "Your knee is not bending enough. The target is 90 degrees." This instruction is communicated to the user in real time via the terminal.

[0446] An example of a prompt message might be, "Evaluate the user's rehabilitation progress based on the latest activity data and generate necessary instructions."

[0447] This invention enables users to receive efficient and effective rehabilitation through real-time feedback while following an individualized exercise regeneration plan.

[0448] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0449] Step 1: The user puts on a wearable sensor and begins rehabilitation. The sensor collects the user's movement information in real time and transmits it to the terminal. The input is the user's physical movements, and the output is movement information data. This data includes joint angles and acceleration, among other things.

[0450] Step 2: The terminal receives motion information from the sensor. The terminal formats this data and adapts it to the communication protocol in order to send it to the server. The input is raw data from the sensor, and the output is formatted data ready to be sent to the server.

[0451] Step 3: The server analyzes the behavioral information received from the terminal. Using a generative AI model, it generates a virtual representation model of the user and simulates their behavior. The input is the formatted data received from the terminal, and the output is the virtual representation model. The server removes noise from the data and captures the behavioral trends.

[0452] Step 4: The server performs mathematical analysis based on the virtual representation model to evaluate whether its operation meets the criteria. The input is the virtual representation model, and the output is the operation evaluation result. Specifically, it detects items that fall outside the standard range and identifies areas for improvement.

[0453] Step 5: The server generates instruction information for improving the operation based on the performance evaluation results. This instruction includes adjustments to the degree of knee flexion and movement speed. The input is the performance evaluation results, and the output is specific instruction information.

[0454] Step 6: The terminal presents the user with the instruction information received from the server. A display device is used to visualize the feedback in a way that is easy for the user to understand. The input is the instruction information from the server, and the output is the visualized feedback.

[0455] Step 7: The user adjusts their movements based on the feedback provided by the device. The user can improve their movements by performing rehabilitation according to the real-time information. This involves adjusting specific body movements based on the feedback.

[0456] (Application Example 1)

[0457] 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."

[0458] While the operation of factory machinery demands improved efficiency and precision, malfunctions and errors can lead to decreased productivity and the accumulation of errors. Conventional methods simply follow pre-set operation plans, lacking real-time feedback and making it difficult to correct operational accuracy on the spot. Therefore, there is a need for a feedback system that enables immediate correction of operations.

[0459] 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.

[0460] In this invention, the server includes means for using a measuring device to collect operational data, information processing means for generating a virtual representation structure using the operational data acquired by the measuring device, and analysis means for evaluating the operation of the generated virtual representation structure and creating instructions for operational correction. This makes it possible to evaluate the operation of a work machine in real time and immediately issue correction instructions when necessary.

[0461] "Motion data" refers to information related to the movement of an object collected from a measuring device, and includes physical parameters such as velocity, position, angle, and acceleration.

[0462] A "measuring device" is a device used to acquire motion data of an object, and includes components such as sensors.

[0463] A "virtual representation structure" is a model generated based on collected motion data, which visually or mathematically represents the motion of an object.

[0464] An "information processing device" is a computing device that generates and updates virtual representation structures and executes programs for processing operational data.

[0465] An "analysis device" is a device that evaluates the operation of a virtual representation structure and generates instructions for improving its operation.

[0466] A "display media device" is a device used to visually present the instructions generated by an analysis device, and includes screens and displays.

[0467] A "feedback mechanism" is a function that analyzes operational data in real time and immediately outputs instructions for correcting the operation.

[0468] A "structural connection point" is a link between multiple elements that make up an object, and it is the part that controls rotation and angle during movement.

[0469] To implement this invention, a dedicated program is first installed on the terminal, and a measuring device is used to collect motion data. The measuring device uses an IMU (Inertial Measurement Unit) sensor, etc., and can collect motion speed, position, angle, acceleration, etc. in real time. The collected data is transmitted to the server via the terminal.

[0470] The server generates a virtual representation structure from the received operational data and updates the virtual model based on this structure. The Python programming language is used for this information processing, and data analysis libraries such as NumPy and Pandas are employed. TensorFlow and PyTorch are sometimes used to build machine learning models.

[0471] The virtual representation structure is evaluated for operation by an analysis device on the server, and instructions for operation improvement are generated. These instructions are presented to the user in real time through a display media device. Examples of display media devices include displays and head-mounted displays.

[0472] As a concrete example, when a factory machine assembles parts, a terminal reads the operation data and sends it to a server. The server analyzes this data, generates a virtual representation structure, evaluates the operation, and immediately generates instructions such as, "The part is out of position; please make corrections," if necessary. This allows the machine to correct its operation and improve accuracy and efficiency.

[0473] An example of a prompt message for the generating AI model is: "Based on the operation data of the work machine, perform a real-time operation evaluation and generate any necessary correction instructions."

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

[0475] Step 1:

[0476] The terminal collects operational data using a measuring device. This measuring device, for example, uses an IMU sensor to measure data such as speed, position, angle, and acceleration in real time. This collected data is then transmitted directly to the server. The input is raw data from the measuring device, and the output is the data transmission to the server.

[0477] Step 2:

[0478] The server receives behavioral data from the terminal and generates a virtual representation structure. Here, Python is used, and libraries such as NumPy and Pandas are employed to analyze the data and build a model that visually represents the object's behavior. The input is behavioral data sent from the terminal, and the output is a virtual representation structure. This model performs appropriate calculations based on the data and is then abstracted.

[0479] Step 3:

[0480] The server performs performance evaluation on the generated virtual representation structure and uses an analysis device to identify areas for improvement. This process involves, for example, using machine learning algorithms to compare the results against predefined behavioral patterns. The input is the virtual representation structure, and the output is instruction information for performance improvement.

[0481] Step 4:

[0482] The server generates instructions for improving the operation using a generated AI model based on the analysis results. In this process, the prompt "Generate and present appropriate correction instructions based on the operation data" is used to prompt the AI ​​to generate part of the feedback. The input is the analysis results, and the output is the instruction information.

[0483] Step 5:

[0484] The user receives instructions for improving the operation from the server through the terminal's display media device. They also receive visual feedback using a display or head-mounted display. The input is instruction information, and the output is visual feedback. This allows the user to appropriately correct the operation.

[0485] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0486] This invention is a system that integrates motion data and emotional data to provide more effective rehabilitation support. This system is built around a server, terminals, and users, and by incorporating an emotion engine, it evaluates the user's emotional state and improves the quality of rehabilitation.

[0487] When a user undergoes rehabilitation, the device first acquires motion data through wearable sensors. In addition, the device is equipped with a camera and microphone, which are used to collect emotional data from the user's facial expressions and voice. This emotional data is analyzed in real time by an emotion engine and output as the user's emotional state.

[0488] The collected motion and emotion data are transmitted to the server via the terminal. The server generates and updates a virtual representation model based on the motion data, and further analyzes the emotion data to understand the user's psychological state. Based on the information thus obtained, the mathematical analysis device evaluates both the user's motion and emotion, and creates instructional information for motion improvement and psychological support.

[0489] Instructions are presented to the user via the terminal's display. In addition to feedback on performance improvements, messages that consider the user's emotions are also displayed. For example, if the user is tired, a message such as "Take a short rest and refresh yourself" is provided to maintain the user's motivation.

[0490] For example, if the emotion engine detects that a user's expression is gloomy during knee rehabilitation, the server will display instructions regarding movement along with supportive messages aimed at improving their emotions. By following these instructions, the user can adjust their movements and improve their mental state, thereby maximizing the effectiveness of their rehabilitation.

[0491] Thus, the present invention is a system that supports effective and continuous rehabilitation by approaching it from both the functional and emotional aspects.

[0492] The following describes the processing flow.

[0493] Step 1:

[0494] The device receives motion data from wearable sensors worn by the user. The sensors collect information such as the user's body position and joint angles, and transmit this data in real time.

[0495] Step 2:

[0496] The device uses a camera and microphone to capture the user's facial expressions and voice, collecting emotional data. This includes changes in facial expressions and tone of voice.

[0497] Step 3:

[0498] The device sends collected behavioral and emotional data to the server. Data transmission is performed via a secure protocol.

[0499] Step 4:

[0500] The server generates a virtual representation model based on the received motion data and reproduces the user's current movements in real time.

[0501] Step 5:

[0502] The server uses an emotion engine to analyze emotional data and assess the user's psychological state. For example, it can identify feelings of fatigue or stress.

[0503] Step 6:

[0504] The server comprehensively analyzes behavioral and emotional data and uses mathematical analysis tools to generate instructions for improving the user's behavior and psychological support messages.

[0505] Step 7:

[0506] Instructions sent from the server are received by the terminal and displayed to the user on the display device. In addition to instructions to correct actions, the user can also receive advice tailored to their emotions.

[0507] Step 8:

[0508] Users adjust their rehabilitation exercises based on feedback from their devices and also receive emotional support through advice on feelings. This improves the effectiveness of the rehabilitation.

[0509] (Example 2)

[0510] 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."

[0511] Traditional rehabilitation support systems often focus on physical improvements based on movement data, and do not adequately consider the user's emotional state or psychological aspects. Therefore, there is a need for a new system that can maintain user motivation and enable more effective and continuous rehabilitation.

[0512] 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.

[0513] In this invention, the server includes means for a measuring device for collecting motion data, means for analyzing the emotional state using the motion data, voice, and image data acquired by the measuring device, and means for a computing device that evaluates the operation of the generated virtual representation model and creates instruction information regarding motion improvement and psychological support. This makes it possible to manage the user's motion and emotions in an integrated manner and support rehabilitation from various aspects.

[0514] "Motion data" refers to information about the user's body movements and forms the basis of physical assessment in rehabilitation.

[0515] A "measuring device" is a device used to acquire user motion data, and generally refers to a device that includes sensors and cameras.

[0516] "Audio and image data" refers to information obtained from audio and images used to analyze the user's emotional state.

[0517] "Analysis means for analyzing emotional states" refers to technologies and devices that use audio and image data to analyze a user's emotions and psychological state.

[0518] A "virtual representation model" refers to a model that virtually reproduces a user's movements based on motion data.

[0519] A "computational device" refers to a device or system that processes data on actions and emotions and generates instructional information.

[0520] "Instructional information" refers to specific guidance and messages provided to users for the purpose of improving their performance or providing psychological support.

[0521] "Presentation means" refers to devices or methods for showing instructional information to a user visually or audibly.

[0522] This invention provides a system that integrates user actions and emotions to offer more effective rehabilitation support. The system consists primarily of a server, a terminal, and the user. When the user performs rehabilitation, the terminal acquires action data using wearable sensors. The terminal is also equipped with a camera and microphone, which collect emotional data from the user's facial expressions and voice.

[0523] The terminal transmits collected behavioral and emotional data to the server. The server uses specific analysis software to generate a virtual representation model based on the behavioral data and analyzes the emotional data using an emotion engine. Based on the analysis results, the mathematical analysis device evaluates the user's behavior and emotional state and creates instructional information for behavioral improvement and psychological support.

[0524] The terminal uses the instruction information received from the server to provide feedback to the user via the display device. Specifically, this includes instructions indicating actions the user should take to improve their condition, as well as messages to support their emotions. For example, if the user is tired, a message such as "Take a short rest and refresh yourself" might be displayed.

[0525] For example, if a user undergoing knee rehabilitation is determined to be in a negative emotional state based on their facial expressions, the server may generate a supportive message encouraging positive emotions along with instructions for action.

[0526] An example of a prompt message for the generating AI model would be: "Based on the facial expression and voice data of a user undergoing rehabilitation, determine their emotional state and generate instructional information to provide movement improvement and psychological support."

[0527] Thus, the present invention makes it possible to provide comprehensive rehabilitation support that takes into account not only the user's actions but also their emotional state.

[0528] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0529] Step 1:

[0530] When a user begins rehabilitation, the device collects data on physical movements in real time via wearable sensors. This data includes the user's limb movements, joint angles, and posture. The input is the user's physical movements, and the output is the generated data of these movements.

[0531] Step 2:

[0532] The device collects facial expression and voice data using its built-in camera and microphone. It uses facial expression analysis and voice analysis to understand the user's emotional state in real time. The input consists of the user's facial expressions and voice, and the output is generated as emotional data.

[0533] Step 3:

[0534] The device sends collected behavioral and emotional data to the server. This data transfer enables smooth data analysis on the server. The input consists of behavioral and emotional data, and the output is the data sent to the server.

[0535] Step 4:

[0536] The server generates or updates a virtual representation model based on operational data. This process involves comparison with past data and evaluation of the current state. Operational data is the input, and a virtual representation model is generated as the output.

[0537] Step 5:

[0538] The server analyzes emotional data using an emotion engine to evaluate the user's psychological state. This analysis utilizes techniques such as pattern matching and voice emotion analysis. Emotional data is the input, and the user's emotional state is evaluated as the output.

[0539] Step 6:

[0540] The server's mathematical analysis unit generates instructional information for behavioral improvement and psychological support based on a virtual representation model and emotional state. This includes specific behavioral instructions and emotional support messages. The inputs are a virtual representation model and emotional assessment, and the output is the generated instructional information.

[0541] Step 7:

[0542] The terminal displays instruction information received from the server on the display device. The user checks the direction of rehabilitation and encouraging messages on the display and then carries them out. The input is instruction information, and the output is information presented to the user.

[0543] Through this series of steps, the system can support rehabilitation from both a behavioral and emotional perspective.

[0544] (Application Example 2)

[0545] 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."

[0546] In factory environments, the challenge lies in simultaneously analyzing workers' actions and emotional states, and providing comprehensive support to improve work efficiency and mental health based on that analysis. In particular, obtaining feedback not only on actions but also on emotions is necessary to reduce worker fatigue and stress, thereby providing a safe and efficient work environment.

[0547] 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.

[0548] In this invention, the server includes a detection device for collecting motion data and emotion data, a computing device for generating and updating a virtual representation construction model using the information acquired by the detection device, and an analysis device for evaluating the generated virtual representation construction model and creating instruction information for motion improvement and emotion improvement. This enables the provision of individualized feedback based on the physical and emotional state of the worker, thereby improving work efficiency and maintaining mental health.

[0549] "Motion data" refers to data that records information about the physical movements and postures performed by a worker.

[0550] "Emotional data" refers to data collected and analyzed from information related to workers' facial expressions, tone of voice, and other emotional information.

[0551] A "detection device" is a device that includes equipment such as sensors, cameras, and microphones for collecting motion data and emotion data.

[0552] A "virtual representation construction model" is a model that reproduces the state of an operator, generated and updated based on collected behavioral and emotional data.

[0553] A "processing unit" is a device that generates and updates a virtual representation construction model using data acquired by a detection unit.

[0554] An "analysis device" is a device used to evaluate a virtual representation construction model and to create instructional information regarding performance improvement and emotional improvement.

[0555] "Instructional information" refers to specific guidelines and messages generated by the analysis device for improving behavior and emotions.

[0556] "Visual devices" refer to display monitors and digital display panels used to present instructional information to workers.

[0557] This invention is a system for real-time monitoring and analysis of worker behavior and emotions in a factory environment. This system primarily consists of three components: a server, terminals, and users.

[0558] The server acquires information through detection devices installed to collect behavioral and emotional data. These detection devices include sensors, cameras, and microphones, which are used to monitor the worker's movements, facial expressions, and voice. The acquired data is generated and updated as a virtual representation construction model by the computing unit. Specifically, Python and other analysis libraries are used to process and analyze the collected data.

[0559] The virtual representation construction model generated by the computing unit is further evaluated by the analysis unit. Here, mathematical analysis techniques are used to construct instruction information regarding the improvement of the worker's actions and emotions. This process utilizes cloud services such as Amazon AWS and Google Cloud APIs.

[0560] Instructions generated by the analysis device are presented to the user (worker) through a visual device. The visual device provides feedback and improvement instructions in a format that the worker can intuitively understand, allowing the worker to grasp their areas for improvement in real time and take appropriate action.

[0561] For example, if a worker is working in a specific posture for an extended period, a message such as "Please take a short break" will be displayed on the visual device. This allows workers to take breaks when necessary, leading to improved long-term work efficiency and better health.

[0562] An example of a prompt message for the generating AI model would be a specific instruction such as, "Analyze worker A's emotions from their facial expressions and provide feedback to optimize their health status in conjunction with their movement data." Based on this prompt, the system performs the necessary analysis and provides useful information to the worker.

[0563] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0564] Step 1:

[0565] The device acquires worker motion and emotional data using wearable sensors, a camera, and a microphone. During this process, the device collects movement information from the sensors and facial expressions and voice from the camera and microphone in real time, converting them into a digital format as initial processing. The input is raw data, while the output is data formatted for analysis.

[0566] Step 2:

[0567] The collected motion and emotion data are transmitted from the terminal to the server. The server receives this data and uses its computing power to generate and update a virtual representation construction model. Specifically, it creates a model that reproduces physical movement based on the motion data and constructs a virtual model that incorporates psychological states using the emotion data. The input is formatted data, and the output is the virtual representation construction model.

[0568] Step 3:

[0569] The server evaluates the generated virtual representation construction model using an analysis device. The analysis device uses mathematical analysis to analyze the worker's state from both behavioral and emotional perspectives. This generates specific instructional information for improving behavior and emotions. The input is the virtual model, and the output is instructional information based on behavior and emotions.

[0570] Step 4:

[0571] The generated instruction information is sent back to the terminal and presented to the user (worker) through the terminal's visual device. The terminal visually displays the instruction information, providing messages and animations in a format that the worker can intuitively understand. The input is the instruction information, and the output is the feedback displayed for the user.

[0572] Step 5:

[0573] The user, acting as the worker, adjusts their actions and emotions based on the feedback provided. This allows the worker to improve efficiency and reduce stress. While there are no explicit inputs or outputs in this step, the user's behavioral changes are achieved as the overall goal of the system.

[0574] 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.

[0575] 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.

[0576] 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.

[0577] [Fourth Embodiment]

[0578] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0579] 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.

[0580] 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).

[0581] 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.

[0582] 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.

[0583] 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).

[0584] 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.

[0585] 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.

[0586] 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.

[0587] 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.

[0588] 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.

[0589] 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.

[0590] 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".

[0591] This invention is an advanced system for rehabilitation support, typically consisting of three elements: a server, a terminal, and a user.

[0592] First, users wear wearable sensors while performing rehabilitation exercises in their daily lives. This allows various data related to their movements to be collected in real time. The terminal device is responsible for receiving the movement data collected from these sensors and transmitting that data to a server.

[0593] The server processes the received motion data to generate a virtual representation model of the user, or avatar. The server continuously updates the avatar's movements to reflect the user's actual actions. Furthermore, the server evaluates the model's movements through a mathematical analysis system to determine whether the user is performing appropriate movements during rehabilitation. Based on this evaluation, the server generates instructional information for improving the movements.

[0594] The generated instruction information is sent from the server to the terminal and visually presented to the user on the terminal's display. This allows the user to check their actions in real time and easily understand any necessary improvements.

[0595] As a concrete example, consider a situation where a user is undergoing knee rehabilitation. Data collected from sensors measures the degree of knee flexion and is sent to a server. Based on this information, the server constructs a virtual representation model and generates instruction information such as, "Your knee flexion is insufficient. Please bend it to the specified range." This information is then transmitted to the user in real time via a terminal. By adjusting their movements according to this feedback, the user can perform efficient and accurate rehabilitation.

[0596] Based on the above, the present invention is a system that improves the quality and motivation of rehabilitation by providing users with immediate feedback.

[0597] The following describes the processing flow.

[0598] Step 1:

[0599] The device receives motion data from wearable sensors worn by the user. The sensors measure information such as the user's body position and joint angles in real time, and collect this data and transmit it to the device.

[0600] Step 2:

[0601] The terminal sends the received operational data to the server. The data is converted to the appropriate format and transferred to the server in real time via a secure communication path.

[0602] Step 3:

[0603] The server receives operational data sent from the terminal. After receiving the data, it checks its integrity and filters out any abnormal data as needed.

[0604] Step 4:

[0605] The server generates and updates a virtual representation model based on the received data. An avatar is created to reproduce the user's current actions in real time.

[0606] Step 5:

[0607] The server's mathematical analysis unit analyzes the behavior of the virtual representation model. The analysis evaluates whether it matches the correct behavioral pattern and, if necessary, generates instructions for behavioral improvement.

[0608] Step 6:

[0609] The server sends the generated instruction information to the terminal. This information specifically indicates how the user's actions should be improved.

[0610] Step 7:

[0611] The terminal displays instruction information sent from the server on its display device. Through the terminal screen, users can visually see areas for improvement and guidance for correcting their actions.

[0612] Step 8:

[0613] Based on feedback received through the device, users can modify their rehabilitation movements. This allows users to learn the correct movements and continue the rehabilitation program.

[0614] (Example 1)

[0615] 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".

[0616] Conventional rehabilitation support systems have challenges in providing efficient rehabilitation due to delayed feedback on user movements and abstract improvement instructions. Furthermore, it has been difficult to provide exercise regeneration plans tailored to individual users.

[0617] 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.

[0618] In this invention, the server includes sensor means for collecting motion information, processing means for generating and updating a virtual representation model, and analysis means for creating instruction information for motion improvement. This makes it possible to evaluate the user's movements in real time, provide specific improvement instructions, and visually present individual motion recovery plans.

[0619] "Motion information" refers to data about the user's body movements, which is collected in real time by sensors.

[0620] A "sensor" is a device used to detect user actions and acquire information.

[0621] A "virtual representation model" is a 3D representation model generated based on user actions, and it is intended to mimic the user's actual actions.

[0622] "Processing means" refers to a computing device or software for generating and updating a virtual representation model using acquired operational information.

[0623] "Analysis means" refers to a device or system for evaluating the generated virtual representation model and creating instructional information for improving its operation.

[0624] "Instructional information" refers to information that includes specific instructions and guidelines to improve user behavior.

[0625] A "display means" is a device that visually presents instructional information to the user and provides it in a format that is easy for the user to understand.

[0626] A "prompt message generation means" is a means that has the function of generating specific instructions and information to provide feedback to the user.

[0627] This invention is a system for supporting rehabilitation and includes three elements: a user, a terminal, and a server. First, the user wears wearable sensors on their body to collect motion information through daily activities. This includes, for example, walking and joint movements.

[0628] Next, the terminal receives operational information from this sensor via Bluetooth or Wi-Fi and transmits it to the server. The terminal also displays the feedback received from the server, making it a crucial device for real-time visualization for the user. Mobile devices or tablets are commonly used as display devices.

[0629] The server constructs a virtual representation model of the user using a generated AI model based on the received motion information. This model is represented as a 3D model that digitally mimics the user's actual movements. The server then performs mathematical analysis to evaluate the movements of this virtual model and generate instructions for improving the movements. In particular, it verifies whether the joint angles and movement speeds conform to standard ranges.

[0630] The generated instruction information is sent to the terminal as a prompt and presented visually to the user. This allows the user to quickly receive specific guidance to improve their actions.

[0631] For example, if a user is undergoing knee rehabilitation, the sensor measures the knee's movement in detail and sends the data to a server. The server updates a virtual representation model based on the received data and generates an instruction such as, "Your knee is not bending enough. The target is 90 degrees." This instruction is communicated to the user in real time via the terminal.

[0632] An example of a prompt message might be, "Evaluate the user's rehabilitation progress based on the latest activity data and generate necessary instructions."

[0633] This invention enables users to receive efficient and effective rehabilitation through real-time feedback while following an individualized exercise regeneration plan.

[0634] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0635] Step 1: The user puts on a wearable sensor and begins rehabilitation. The sensor collects the user's movement information in real time and transmits it to the terminal. The input is the user's physical movements, and the output is movement information data. This data includes joint angles and acceleration, among other things.

[0636] Step 2: The terminal receives motion information from the sensor. The terminal formats this data and adapts it to the communication protocol in order to send it to the server. The input is raw data from the sensor, and the output is formatted data ready to be sent to the server.

[0637] Step 3: The server analyzes the behavioral information received from the terminal. Using a generative AI model, it generates a virtual representation model of the user and simulates their behavior. The input is the formatted data received from the terminal, and the output is the virtual representation model. The server removes noise from the data and captures the behavioral trends.

[0638] Step 4: The server performs mathematical analysis based on the virtual representation model to evaluate whether its operation meets the criteria. The input is the virtual representation model, and the output is the operation evaluation result. Specifically, it detects items that fall outside the standard range and identifies areas for improvement.

[0639] Step 5: The server generates instruction information for improving the operation based on the performance evaluation results. This instruction includes adjustments to the degree of knee flexion and movement speed. The input is the performance evaluation results, and the output is specific instruction information.

[0640] Step 6: The terminal presents the user with the instruction information received from the server. A display device is used to visualize the feedback in a way that is easy for the user to understand. The input is the instruction information from the server, and the output is the visualized feedback.

[0641] Step 7: The user adjusts their movements based on the feedback provided by the device. The user can improve their movements by performing rehabilitation according to the real-time information. This involves adjusting specific body movements based on the feedback.

[0642] (Application Example 1)

[0643] 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".

[0644] While the operation of factory machinery demands improved efficiency and precision, malfunctions and errors can lead to decreased productivity and the accumulation of errors. Conventional methods simply follow pre-set operation plans, lacking real-time feedback and making it difficult to correct operational accuracy on the spot. Therefore, there is a need for a feedback system that enables immediate correction of operations.

[0645] 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.

[0646] In this invention, the server includes means for using a measuring device to collect operational data, information processing means for generating a virtual representation structure using the operational data acquired by the measuring device, and analysis means for evaluating the operation of the generated virtual representation structure and creating instructions for operational correction. This makes it possible to evaluate the operation of a work machine in real time and immediately issue correction instructions when necessary.

[0647] "Motion data" refers to information related to the movement of an object collected from a measuring device, and includes physical parameters such as velocity, position, angle, and acceleration.

[0648] A "measuring device" is a device used to acquire motion data of an object, and includes components such as sensors.

[0649] A "virtual representation structure" is a model generated based on collected motion data, which visually or mathematically represents the motion of an object.

[0650] An "information processing device" is a computing device that generates and updates virtual representation structures and executes programs for processing operational data.

[0651] An "analysis device" is a device that evaluates the operation of a virtual representation structure and generates instructions for improving its operation.

[0652] A "display media device" is a device used to visually present the instructions generated by an analysis device, and includes screens and displays.

[0653] A "feedback mechanism" is a function that analyzes operational data in real time and immediately outputs instructions for correcting the operation.

[0654] A "structural connection point" is a link between multiple elements that make up an object, and it is the part that controls rotation and angle during movement.

[0655] To implement this invention, a dedicated program is first installed on the terminal, and a measuring device is used to collect motion data. The measuring device uses an IMU (Inertial Measurement Unit) sensor, etc., and can collect motion speed, position, angle, acceleration, etc. in real time. The collected data is transmitted to the server via the terminal.

[0656] The server generates a virtual representation structure from the received operational data and updates the virtual model based on this structure. The Python programming language is used for this information processing, and data analysis libraries such as NumPy and Pandas are employed. TensorFlow and PyTorch are sometimes used to build machine learning models.

[0657] The virtual representation structure is evaluated for operation by an analysis device on the server, and instructions for operation improvement are generated. These instructions are presented to the user in real time through a display media device. Examples of display media devices include displays and head-mounted displays.

[0658] As a concrete example, when a factory machine assembles parts, a terminal reads the operation data and sends it to a server. The server analyzes this data, generates a virtual representation structure, evaluates the operation, and immediately generates instructions such as, "The part is out of position; please make corrections," if necessary. This allows the machine to correct its operation and improve accuracy and efficiency.

[0659] An example of a prompt message for the generating AI model is: "Based on the operation data of the work machine, perform a real-time operation evaluation and generate any necessary correction instructions."

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

[0661] Step 1:

[0662] The terminal collects operational data using a measuring device. This measuring device, for example, uses an IMU sensor to measure data such as speed, position, angle, and acceleration in real time. This collected data is then transmitted directly to the server. The input is raw data from the measuring device, and the output is the data transmission to the server.

[0663] Step 2:

[0664] The server receives behavioral data from the terminal and generates a virtual representation structure. Here, Python is used, and libraries such as NumPy and Pandas are employed to analyze the data and build a model that visually represents the object's behavior. The input is behavioral data sent from the terminal, and the output is a virtual representation structure. This model performs appropriate calculations based on the data and is then abstracted.

[0665] Step 3:

[0666] The server performs performance evaluation on the generated virtual representation structure and uses an analysis device to identify areas for improvement. This process involves, for example, using machine learning algorithms to compare the results against predefined behavioral patterns. The input is the virtual representation structure, and the output is instruction information for performance improvement.

[0667] Step 4:

[0668] The server generates instructions for improving the operation using a generated AI model based on the analysis results. In this process, the prompt "Generate and present appropriate correction instructions based on the operation data" is used to prompt the AI ​​to generate part of the feedback. The input is the analysis results, and the output is the instruction information.

[0669] Step 5:

[0670] The user receives instructions for improving the operation from the server through the terminal's display media device. They also receive visual feedback using a display or head-mounted display. The input is instruction information, and the output is visual feedback. This allows the user to appropriately correct the operation.

[0671] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0672] This invention is a system that integrates motion data and emotional data to provide more effective rehabilitation support. This system is built around a server, terminals, and users, and by incorporating an emotion engine, it evaluates the user's emotional state and improves the quality of rehabilitation.

[0673] When a user undergoes rehabilitation, the device first acquires motion data through wearable sensors. In addition, the device is equipped with a camera and microphone, which are used to collect emotional data from the user's facial expressions and voice. This emotional data is analyzed in real time by an emotion engine and output as the user's emotional state.

[0674] The collected motion and emotion data are transmitted to the server via the terminal. The server generates and updates a virtual representation model based on the motion data, and further analyzes the emotion data to understand the user's psychological state. Based on the information thus obtained, the mathematical analysis device evaluates both the user's motion and emotion, and creates instructional information for motion improvement and psychological support.

[0675] Instructions are presented to the user via the terminal's display. In addition to feedback on performance improvements, messages that consider the user's emotions are also displayed. For example, if the user is tired, a message such as "Take a short rest and refresh yourself" is provided to maintain the user's motivation.

[0676] For example, if the emotion engine detects that a user's expression is gloomy during knee rehabilitation, the server will display instructions regarding movement along with supportive messages aimed at improving their emotions. By following these instructions, the user can adjust their movements and improve their mental state, thereby maximizing the effectiveness of their rehabilitation.

[0677] Thus, the present invention is a system that supports effective and continuous rehabilitation by approaching it from both the functional and emotional aspects.

[0678] The following describes the processing flow.

[0679] Step 1:

[0680] The device receives motion data from wearable sensors worn by the user. The sensors collect information such as the user's body position and joint angles, and transmit this data in real time.

[0681] Step 2:

[0682] The device uses a camera and microphone to capture the user's facial expressions and voice, collecting emotional data. This includes changes in facial expressions and tone of voice.

[0683] Step 3:

[0684] The device sends collected behavioral and emotional data to the server. Data transmission is performed via a secure protocol.

[0685] Step 4:

[0686] The server generates a virtual representation model based on the received motion data and reproduces the user's current movements in real time.

[0687] Step 5:

[0688] The server uses an emotion engine to analyze emotional data and assess the user's psychological state. For example, it can identify feelings of fatigue or stress.

[0689] Step 6:

[0690] The server comprehensively analyzes behavioral and emotional data and uses mathematical analysis tools to generate instructions for improving the user's behavior and psychological support messages.

[0691] Step 7:

[0692] Instructions sent from the server are received by the terminal and displayed to the user on the display device. In addition to instructions to correct actions, the user can also receive advice tailored to their emotions.

[0693] Step 8:

[0694] Users adjust their rehabilitation exercises based on feedback from their devices and also receive emotional support through advice on feelings. This improves the effectiveness of the rehabilitation.

[0695] (Example 2)

[0696] 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".

[0697] Traditional rehabilitation support systems often focus on physical improvements based on movement data, and do not adequately consider the user's emotional state or psychological aspects. Therefore, there is a need for a new system that can maintain user motivation and enable more effective and continuous rehabilitation.

[0698] 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.

[0699] In this invention, the server includes means for a measuring device for collecting motion data, means for analyzing the emotional state using the motion data, voice, and image data acquired by the measuring device, and means for a computing device that evaluates the operation of the generated virtual representation model and creates instruction information regarding motion improvement and psychological support. This makes it possible to manage the user's motion and emotions in an integrated manner and support rehabilitation from various aspects.

[0700] "Motion data" refers to information about the user's body movements and forms the basis of physical assessment in rehabilitation.

[0701] A "measuring device" is a device used to acquire user motion data, and generally refers to a device that includes sensors and cameras.

[0702] "Audio and image data" refers to information obtained from audio and images used to analyze the user's emotional state.

[0703] "Analysis means for analyzing emotional states" refers to technologies and devices that use audio and image data to analyze a user's emotions and psychological state.

[0704] A "virtual representation model" refers to a model that virtually reproduces a user's movements based on motion data.

[0705] A "computational device" refers to a device or system that processes data on actions and emotions and generates instructional information.

[0706] "Instructional information" refers to specific guidance and messages provided to users for the purpose of improving their performance or providing psychological support.

[0707] "Presentation means" refers to devices or methods for showing instructional information to a user visually or audibly.

[0708] This invention provides a system that integrates user actions and emotions to offer more effective rehabilitation support. The system consists primarily of a server, a terminal, and the user. When the user performs rehabilitation, the terminal acquires action data using wearable sensors. The terminal is also equipped with a camera and microphone, which collect emotional data from the user's facial expressions and voice.

[0709] The terminal transmits collected behavioral and emotional data to the server. The server uses specific analysis software to generate a virtual representation model based on the behavioral data and analyzes the emotional data using an emotion engine. Based on the analysis results, the mathematical analysis device evaluates the user's behavior and emotional state and creates instructional information for behavioral improvement and psychological support.

[0710] The terminal uses the instruction information received from the server to provide feedback to the user via the display device. Specifically, this includes instructions indicating actions the user should take to improve their condition, as well as messages to support their emotions. For example, if the user is tired, a message such as "Take a short rest and refresh yourself" might be displayed.

[0711] For example, if a user undergoing knee rehabilitation is determined to be in a negative emotional state based on their facial expressions, the server may generate a supportive message encouraging positive emotions along with instructions for action.

[0712] An example of a prompt message for the generating AI model would be: "Based on the facial expression and voice data of a user undergoing rehabilitation, determine their emotional state and generate instructional information to provide movement improvement and psychological support."

[0713] Thus, the present invention makes it possible to provide comprehensive rehabilitation support that takes into account not only the user's actions but also their emotional state.

[0714] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0715] Step 1:

[0716] When a user begins rehabilitation, the device collects data on physical movements in real time via wearable sensors. This data includes the user's limb movements, joint angles, and posture. The input is the user's physical movements, and the output is the generated data of these movements.

[0717] Step 2:

[0718] The device collects facial expression and voice data using its built-in camera and microphone. It uses facial expression analysis and voice analysis to understand the user's emotional state in real time. The input consists of the user's facial expressions and voice, and the output is generated as emotional data.

[0719] Step 3:

[0720] The device sends collected behavioral and emotional data to the server. This data transfer enables smooth data analysis on the server. The input consists of behavioral and emotional data, and the output is the data sent to the server.

[0721] Step 4:

[0722] The server generates or updates a virtual representation model based on operational data. This process involves comparison with past data and evaluation of the current state. Operational data is the input, and a virtual representation model is generated as the output.

[0723] Step 5:

[0724] The server analyzes emotional data using an emotion engine to evaluate the user's psychological state. This analysis utilizes techniques such as pattern matching and voice emotion analysis. Emotional data is the input, and the user's emotional state is evaluated as the output.

[0725] Step 6:

[0726] The server's mathematical analysis unit generates instructional information for behavioral improvement and psychological support based on a virtual representation model and emotional state. This includes specific behavioral instructions and emotional support messages. The inputs are a virtual representation model and emotional assessment, and the output is the generated instructional information.

[0727] Step 7:

[0728] The terminal displays instruction information received from the server on the display device. The user checks the direction of rehabilitation and encouraging messages on the display and then carries them out. The input is instruction information, and the output is information presented to the user.

[0729] Through this series of steps, the system can support rehabilitation from both a behavioral and emotional perspective.

[0730] (Application Example 2)

[0731] 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".

[0732] In factory environments, the challenge lies in simultaneously analyzing workers' actions and emotional states, and providing comprehensive support to improve work efficiency and mental health based on that analysis. In particular, obtaining feedback not only on actions but also on emotions is necessary to reduce worker fatigue and stress, thereby providing a safe and efficient work environment.

[0733] 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.

[0734] In this invention, the server includes a detection device for collecting motion data and emotion data, a computing device for generating and updating a virtual representation construction model using the information acquired by the detection device, and an analysis device for evaluating the generated virtual representation construction model and creating instruction information for motion improvement and emotion improvement. This enables the provision of individualized feedback based on the physical and emotional state of the worker, thereby improving work efficiency and maintaining mental health.

[0735] "Motion data" refers to data that records information about the physical movements and postures performed by a worker.

[0736] "Emotional data" refers to data collected and analyzed from information related to workers' facial expressions, tone of voice, and other emotional information.

[0737] A "detection device" is a device that includes equipment such as sensors, cameras, and microphones for collecting motion data and emotion data.

[0738] A "virtual representation construction model" is a model that reproduces the state of an operator, generated and updated based on collected behavioral and emotional data.

[0739] A "processing unit" is a device that generates and updates a virtual representation construction model using data acquired by a detection unit.

[0740] An "analysis device" is a device used to evaluate a virtual representation construction model and to create instructional information regarding performance improvement and emotional improvement.

[0741] "Instructional information" refers to specific guidelines and messages generated by the analysis device for improving behavior and emotions.

[0742] "Visual devices" refer to display monitors and digital display panels used to present instructional information to workers.

[0743] This invention is a system for real-time monitoring and analysis of worker behavior and emotions in a factory environment. This system primarily consists of three components: a server, terminals, and users.

[0744] The server acquires information through detection devices installed to collect behavioral and emotional data. These detection devices include sensors, cameras, and microphones, which are used to monitor the worker's movements, facial expressions, and voice. The acquired data is generated and updated as a virtual representation construction model by the computing unit. Specifically, Python and other analysis libraries are used to process and analyze the collected data.

[0745] The virtual representation construction model generated by the computing unit is further evaluated by the analysis unit. Here, mathematical analysis techniques are used to construct instruction information regarding the improvement of the worker's actions and emotions. This process utilizes cloud services such as Amazon AWS and Google Cloud APIs.

[0746] Instructions generated by the analysis device are presented to the user (worker) through a visual device. The visual device provides feedback and improvement instructions in a format that the worker can intuitively understand, allowing the worker to grasp their areas for improvement in real time and take appropriate action.

[0747] For example, if a worker is working in a specific posture for an extended period, a message such as "Please take a short break" will be displayed on the visual device. This allows workers to take breaks when necessary, leading to improved long-term work efficiency and better health.

[0748] An example of a prompt message for the generating AI model would be a specific instruction such as, "Analyze worker A's emotions from their facial expressions and provide feedback to optimize their health status in conjunction with their movement data." Based on this prompt, the system performs the necessary analysis and provides useful information to the worker.

[0749] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0750] Step 1:

[0751] The device acquires worker motion and emotional data using wearable sensors, a camera, and a microphone. During this process, the device collects movement information from the sensors and facial expressions and voice from the camera and microphone in real time, converting them into a digital format as initial processing. The input is raw data, while the output is data formatted for analysis.

[0752] Step 2:

[0753] The collected motion and emotion data are transmitted from the terminal to the server. The server receives this data and uses its computing power to generate and update a virtual representation construction model. Specifically, it creates a model that reproduces physical movement based on the motion data and constructs a virtual model that incorporates psychological states using the emotion data. The input is formatted data, and the output is the virtual representation construction model.

[0754] Step 3:

[0755] The server evaluates the generated virtual representation construction model using an analysis device. The analysis device uses mathematical analysis to analyze the worker's state from both behavioral and emotional perspectives. This generates specific instructional information for improving behavior and emotions. The input is the virtual model, and the output is instructional information based on behavior and emotions.

[0756] Step 4:

[0757] The generated instruction information is sent back to the terminal and presented to the user (worker) through the terminal's visual device. The terminal visually displays the instruction information, providing messages and animations in a format that the worker can intuitively understand. The input is the instruction information, and the output is the feedback displayed for the user.

[0758] Step 5:

[0759] The user, acting as the worker, adjusts their actions and emotions based on the feedback provided. This allows the worker to improve efficiency and reduce stress. While there are no explicit inputs or outputs in this step, the user's behavioral changes are achieved as the overall goal of the system.

[0760] 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.

[0761] 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.

[0762] 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.

[0763] 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.

[0764] 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.

[0765] 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.

[0766] 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.

[0767] 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.

[0768] 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."

[0769] 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.

[0770] 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.

[0771] 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.

[0772] 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.

[0773] 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.

[0774] 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.

[0775] 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.

[0776] 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.

[0777] 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.

[0778] 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.

[0779] 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.

[0780] 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.

[0781] The following is further disclosed regarding the embodiments described above.

[0782] (Claim 1)

[0783] Sensors for collecting motion data,

[0784] A processing unit that generates and updates a virtual representation model using motion data acquired by the sensor,

[0785] A mathematical analysis device that evaluates the operation of the generated virtual representation model and creates instruction information for improving its operation,

[0786] A display device that displays the instruction information,

[0787] A system that includes this.

[0788] (Claim 2)

[0789] The system according to claim 1, wherein the mathematical analysis device creates an individual motion regeneration plan based on motion data and displays it on the display device.

[0790] (Claim 3)

[0791] The system according to claim 1, wherein the motion improvement instruction information generated based on the motion evaluation of the virtual representation model includes correction of joint angles.

[0792] "Example 1"

[0793] (Claim 1)

[0794] Sensor means for collecting motion information,

[0795] A processing means for generating and updating a virtual representation model using motion information acquired by the sensor means,

[0796] An analysis means for evaluating the behavior of the generated virtual representation model and creating instruction information for improving its behavior,

[0797] A display means that displays the instruction information and allows the user to check the operation in real time,

[0798] A prompt message generation means that provides feedback to the user by visualizing the behavior based on the generated virtual representation model,

[0799] A system that includes this.

[0800] (Claim 2)

[0801] The system according to claim 1, wherein the analysis means creates an individual exercise regeneration plan based on the motion information and presents it visually in the display means.

[0802] (Claim 3)

[0803] The system according to claim 1, wherein the motion improvement instruction information generated based on the motion evaluation of the virtual representation model includes adjustment of the range of motion of the joints.

[0804] "Application Example 1"

[0805] (Claim 1)

[0806] A measuring device for collecting operational data,

[0807] An information processing device that generates and updates a virtual representation structure using the operation data acquired by the measuring device,

[0808] An analysis device that evaluates the operation of the generated virtual representation structure and creates instructions for improving its operation,

[0809] A display media device that displays the contents of the instruction,

[0810] A feedback mechanism that collects operational data in real time and immediately outputs instructions for operational correction,

[0811] A system that includes this.

[0812] (Claim 2)

[0813] The system according to claim 1, wherein the analysis device creates individual motion regeneration procedures based on motion data and displays them on the display medium device.

[0814] (Claim 3)

[0815] The system according to claim 1, wherein the instructions for improving the operation generated based on the operational evaluation of the virtual representation structure include correcting the angles of the connection parts of multiple structures.

[0816] "Example 2 of combining an emotion engine"

[0817] (Claim 1)

[0818] A measuring device for collecting operational data,

[0819] An analysis means for analyzing emotional states using motion data, audio, and image data acquired by the measuring device,

[0820] A computing device that evaluates the operation of the generated virtual representation model and creates instructional information regarding operation improvement and psychological support,

[0821] A presentation means for displaying and audibly presenting the instruction information,

[0822] A system that includes this.

[0823] (Claim 2)

[0824] The system according to claim 1, wherein the computing device creates an individual motor regeneration plan and psychological support message based on the operation data and emotional state, and displays and plays them aloud on the presentation means.

[0825] (Claim 3)

[0826] The system according to claim 1, wherein the motion improvement instruction information generated based on the motion evaluation of the virtual representation model includes messages for joint correction and psychological support.

[0827] "Application example 2 when combining with an emotional engine"

[0828] (Claim 1)

[0829] A detection device for collecting motion data and emotion data,

[0830] A computing device that generates and updates a virtual representation construction model using information acquired by the detection device,

[0831] An analysis device that evaluates the generated virtual representation construction model and creates instruction information for improving behavior and emotion,

[0832] A visual device that displays the instruction information,

[0833] A system that includes this.

[0834] (Claim 2)

[0835] The system according to claim 1, wherein the analysis device creates individual exercise and psychological regeneration plans based on motion data and emotional data, and displays them using the visual device.

[0836] (Claim 3)

[0837] The system according to claim 1, wherein the instruction information generated based on the evaluation of the virtual representation construction model includes correction of joint angles and improvement of emotional state. [Explanation of symbols]

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

Claims

1. Sensors for collecting motion data, A processing unit that generates and updates a virtual representation model using motion data acquired by the sensor, A mathematical analysis device that evaluates the operation of the generated virtual representation model and creates instruction information for improving its operation, A display device that displays the instruction information, A system that includes this.

2. The system according to claim 1, wherein the mathematical analysis device creates an individual motion regeneration plan based on motion data and displays it on the display device.

3. The system according to claim 1, wherein the motion improvement instruction information generated based on the motion evaluation of the virtual representation model includes correction of joint angles.

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