system

Abstract

The system according to this embodiment aims to track the movements of rehabilitation patients and provide appropriate feedback and mental support. [Solution] The system according to the embodiment comprises a tracking unit, a monitoring unit, a feedback unit, a support unit, and a mental support unit. The tracking unit tracks the user's movements. The monitoring unit monitors the movements tracked by the tracking unit. The feedback unit provides feedback based on the movements monitored by the monitoring unit. The support unit supports the user's movements based on the feedback provided by the feedback unit. The mental support unit provides mental support based on the movements supported by the support unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that it is difficult to accurately track the movements of rehabilitation patients and provide appropriate feedback and mental support.

[0005] The system according to the embodiment aims to track the movements of rehabilitation patients and provide appropriate feedback and mental support.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a tracking unit, a monitoring unit, a feedback unit, a support unit, and a mental support unit. The tracking unit tracks the user's movements. The monitoring unit monitors the movements tracked by the tracking unit. The feedback unit provides feedback based on the movements monitored by the monitoring unit. The support unit supports the user's movements based on the feedback provided by the feedback unit. The mental support unit provides mental support based on the movements supported by the support unit. [Effects of the Invention]

[0007] The system according to this embodiment can track the movements of rehabilitation patients and provide appropriate feedback and mental support. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

[0011] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), or TPU (Tensor Processing Unit).

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0013] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] 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 only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

[0016] [First Embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] As shown in FIG. 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.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 comprises a computer 36, a receiving 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 receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice 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 unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (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.

[0022] 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.

[0023] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 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.

[0025] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The AI ​​rehabilitation support wear according to an embodiment of the present invention is a wearable device for rehabilitation patients. This device monitors the rehabilitation process and supports accurate movements by moving or giving verbal advice. It also provides mental support through dialogue. For example, the AI ​​rehabilitation support wear tracks the user's movements. For example, built-in sensors track and monitor the user's movements in real time. When the user is performing walking training, the sensors detect the movement of the feet and record the accurate walking pattern. Next, if the user's movements are not in the ideal form, the AI ​​rehabilitation support wear provides verbal feedback. If it is still difficult, the wear itself moves to support the user's correct movements. For example, when performing arm rehabilitation, the wear assists the arm movements and encourages correct movements. Furthermore, the AI ​​rehabilitation support wear provides mental support through dialogue. By talking to the wear, it is possible to have a conversation and confide any concerns about rehabilitation at any time. For example, if the user is feeling anxious about the progress of their rehabilitation, the wear will offer words of encouragement to maintain motivation. This wearable device automatically personalizes to suit individual situations and supports efficient and effective rehabilitation and motivation maintenance. For example, it suggests and implements an appropriate training plan based on the user's rehabilitation progress. Challenges faced by rehabilitation patients include difficulty understanding precise movements and maintaining motivation. This device is designed to address these challenges. For instance, sensors detect and provide feedback on factors such as the balance of left-right force and the appropriate grip strength. It also provides appropriate advice and maintains motivation based on rehabilitation progress. In this way, AI rehabilitation support wear provides comprehensive support for rehabilitation patients, maximizing the effectiveness of their rehabilitation.

[0029] The AI ​​rehabilitation support wear according to this embodiment comprises a tracking unit, a monitoring unit, a feedback unit, a support unit, and a mental support unit. The tracking unit tracks the user's movements. The tracking unit tracks the user's movements in real time, for example, using an integrated sensor. For example, if the user is performing walking training, the sensor detects the movement of the user's feet and records an accurate walking pattern. For example, if the user is performing arm rehabilitation, the sensor can detect the movement of the user's arms and record an accurate movement pattern. For example, if the user is performing daily living activities, the sensor can detect the user's movements and record an accurate movement pattern. The monitoring unit monitors the movements tracked by the tracking unit. For example, the monitoring unit monitors the user's movements in real time and evaluates the accuracy of the movements. For example, the monitoring unit can periodically monitor the user's movements and record changes in the movements. For example, the monitoring unit can monitor the user's movements over a long period of time and analyze movement trends. The feedback unit provides feedback based on the movements monitored by the monitoring unit. The feedback unit provides verbal feedback, for example, when the user's movements are not ideal. The feedback unit can also provide visual feedback, for example, when the user's movements are not ideal. The feedback unit can also provide haptic feedback, for example, when the user's movements are not ideal. The support unit supports the user's movements based on the feedback provided by the feedback unit. The support unit can, for example, have the wearer itself move to support the user's correct movements. The support unit can also, for example, use external devices to assist the user's movements. The support unit can also, for example, use rehabilitation equipment to assist the user's movements. The mental support unit provides mental support based on the movements supported by the support unit. The mental support unit can, for example, provide mental support through dialogue.The mental support unit can, for example, estimate the user's emotions and provide appropriate mental support. The mental support unit can also, for example, monitor the user's psychological state and provide appropriate mental support. This enables the AI ​​rehabilitation support software according to the embodiment to efficiently track, monitor, provide feedback, support, and mental support for the user's actions.

[0030] The tracking unit tracks the user's movements. For example, the tracking unit tracks the user's movements in real time using built-in sensors. Specifically, the tracking unit uses a combination of various sensors, such as accelerometers, gyroscopes, and pressure sensors. This allows for high-precision detection of the user's movements and the collection of detailed data. For example, when a user is undergoing walking training, sensors detect foot movements and record accurate walking patterns. This includes data such as foot position, speed, acceleration, and ground contact time. This data is collected in real time and transmitted to a central database. When a user is undergoing arm rehabilitation, sensors can also detect arm movements and record accurate movement patterns. Regarding arm movements, details such as joint angles, movement speed, and force application are recorded. Furthermore, when a user is performing daily living activities, sensors can detect these movements and record accurate movement patterns. For example, frequently performed daily living activities such as lifting objects or sitting down are tracked in detail. This allows the tracking unit to track a variety of user movements with high precision and to understand the progress of rehabilitation in detail. Furthermore, the tracking unit centrally manages the collected data and can collaborate with other systems and departments as needed. For example, the collected data is stored on a cloud server and made accessible to the monitoring and feedback units. Additionally, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. This allows the tracking unit to collect data efficiently and effectively, improving the overall system performance.

[0031] The monitoring unit monitors the movements tracked by the tracking unit. For example, the monitoring unit monitors the user's movements in real time and evaluates the accuracy of those movements. Specifically, the monitoring unit uses AI to analyze the collected data and evaluate whether the user's movements are being performed in an ideal manner. For example, in the case of gait training, the AI ​​analyzes the patterns of foot movements and detects any abnormalities by comparing them to normal gait patterns. The monitoring unit can also periodically monitor the user's movements and record changes in those movements. This allows for continuous monitoring of the progress of rehabilitation and adjustment of the rehabilitation plan as needed. Furthermore, the monitoring unit can monitor the user's movements over a long period and analyze movement trends. For example, based on long-term data, it can identify changes and improvement trends in the user's movement patterns and evaluate the effectiveness of rehabilitation. The monitoring unit can use anomaly detection algorithms to detect unusual patterns and abnormal data and issue warnings early. This allows the monitoring unit to not only grasp the situation in real time but also to handle long-term risk management and anomaly detection, improving the reliability and safety of the entire system. In addition, the monitoring unit can create customized rehabilitation plans for each user based on the collected data. This allows for the provision of optimal rehabilitation support tailored to the individual needs of each user.

[0032] The feedback unit provides feedback based on movements monitored by the monitoring unit. For example, the feedback unit provides verbal feedback if the user's movements are not ideal. Specifically, it uses speech synthesis technology to provide real-time voice feedback to the user. For example, it can give specific instructions such as, "Lift your leg a little higher" or "Move your arms a little slower." The feedback unit can also provide visual feedback if the user's movements are not ideal. For example, it can display videos or animations of correct movements on the user's display or smartphone screen, allowing the user to correct their movements. Furthermore, the feedback unit can also provide haptic feedback if the user's movements are not ideal. For example, it can use a vibration motor built into the wearer to provide vibration feedback to the user. This allows the user to intuitively understand that their movements are incorrect and correct them. The feedback unit can provide individualized feedback based on the user's movement data. For example, based on past data, it can specifically point out areas for improvement and points to pay attention to in the user's movements and provide advice to maximize the effectiveness of rehabilitation. In this way, the feedback unit can provide effective feedback to the user and support the progress of rehabilitation. Furthermore, the feedback unit can record the user's response to feedback and continuously improve the content and method of the feedback. This allows the feedback unit to provide the user with optimal feedback, maximizing the effectiveness of rehabilitation.

[0033] The support unit assists the user's movements based on feedback provided by the feedback unit. For example, the support unit can support the user's correct movements through the movement of the garment itself. Specifically, it uses actuators and motors built into the garment to assist the user's movements. For instance, in gait training, the garment assists the user's leg movements to maintain a correct walking pattern. In arm rehabilitation, the garment assists the user's arm movements to support correct movement. The support unit can also use external devices to assist the user's movements. For example, it can use an exoskeleton or rehabilitation robot to assist the user's movements and enhance the effectiveness of rehabilitation. Furthermore, the support unit can use rehabilitation equipment to assist the user's movements. For example, it can use a balance ball or resistance band to assist the user's movements and maximize the effectiveness of rehabilitation. Based on the user's movement data, the support unit can create individualized support plans. This allows for the provision of optimal support tailored to the user's individual needs. Furthermore, the support unit can collect user feedback and continuously improve the accuracy and effectiveness of the support. This allows the support unit to provide optimal support to the user and maximize the effectiveness of rehabilitation.

[0034] The Mental Support Department provides mental support based on the actions supported by the Support Department. For example, the Mental Support Department provides mental support through dialogue. Specifically, it uses natural language processing technology to engage in dialogue with users and understand their psychological state. For instance, if a user is feeling anxious or stressed about rehabilitation, the Mental Support Department provides appropriate advice and words of encouragement. Furthermore, the Mental Support Department can also estimate the user's emotions and provide appropriate mental support. For example, it analyzes the user's facial expressions and tone of voice to estimate what emotions the user is experiencing. This helps users develop a positive attitude towards rehabilitation. The Mental Support Department can also monitor the user's psychological state and provide appropriate mental support. For example, if a user has lost motivation for rehabilitation, the Mental Support Department provides advice and sets goals to increase the user's motivation. This allows the user to actively engage in rehabilitation. Furthermore, the Mental Support Department can collect user feedback and continuously improve the content and methods of mental support. This allows the Mental Support Department to provide optimal mental support to users and maximize the effectiveness of rehabilitation.

[0035] The tracking unit can track the user's movements in real time using built-in sensors. For example, if the user is performing walking exercises, the sensors can detect the movement of the user's feet and record the accurate walking pattern. For example, if the user is performing arm rehabilitation, the sensors can detect the movement of the user's arms and record the accurate movement pattern. For example, if the user is performing daily living activities, the sensors can detect the user's movements and record the accurate movement pattern. This allows for the acquisition of accurate data by tracking the user's movements in real time. Some or all of the above-described processes in the tracking unit may be performed using AI, for example, or without AI. For example, the tracking unit can input data acquired from sensors into a generating AI and have the generating AI perform analysis of the movement pattern.

[0036] The feedback unit can provide verbal feedback if the user's movements are not ideal. The feedback unit can, for example, provide verbal feedback if the user's movements are not ideal. The feedback unit can also, for example, provide visual feedback if the user's movements are not ideal. The feedback unit can also, for example, provide haptic feedback if the user's movements are not ideal. This encourages correct actions by providing verbal feedback when the user's movements are not ideal. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input user movement data into a generating AI and have the generating AI generate the feedback content.

[0037] The support unit can support the user's correct movements by having the garment itself move. The support unit can, for example, have the garment itself move to support the user's correct movements. The support unit can, for example, use external devices to assist the user's movements. The support unit can, for example, use rehabilitation equipment to assist the user's movements. This enhances the effectiveness of rehabilitation by having the garment itself move to support the user's correct movements. Some or all of the above-described processes in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input user movement data into a generating AI and have the generating AI execute control of movement assistance.

[0038] The mental support unit can provide mental support through dialogue. The mental support unit can, for example, provide mental support through dialogue. The mental support unit can also, for example, estimate the user's emotions and provide appropriate mental support. The mental support unit can also, for example, monitor the user's psychological state and provide appropriate mental support. In this way, by providing mental support through dialogue, the user's motivation is maintained. Some or all of the above processes in the mental support unit may be performed using AI, for example, or without AI. For example, the mental support unit can input user dialogue data into a generating AI and have the generating AI generate the dialogue content.

[0039] The support unit can propose an appropriate training plan according to the user's rehabilitation progress. The support unit can, for example, propose an appropriate training plan according to the user's rehabilitation progress. The support unit can also, for example, adjust the training plan based on the user's rehabilitation progress. The support unit can also, for example, adjust the intensity of the training according to the user's rehabilitation progress. This maximizes the effectiveness of rehabilitation by proposing an appropriate training plan according to the user's rehabilitation progress. Some or all of the above processing in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input the user's rehabilitation progress data into a generating AI and have the generating AI generate a training plan.

[0040] The tracking unit can analyze the user's past behavior data and select the optimal tracking method. For example, the tracking unit can extract specific behavior patterns from the user's past behavior data and select a tracking method based on those patterns. The tracking unit can also analyze the user's past behavior data and adjust the tracking accuracy for each behavior. For example, the tracking unit can optimize the tracking frequency based on the user's past behavior data. This allows the optimal tracking method to be selected by analyzing the user's past behavior data. Some or all of the above processing in the tracking unit may be performed using AI, for example, or without AI. For example, the tracking unit can input past behavior data into a generating AI and have the generating AI select the optimal tracking method.

[0041] The tracking unit can acquire detailed data by focusing on specific parts of the user's body during tracking. For example, the tracking unit can track the user's leg movements and record walking patterns in detail. The tracking unit can also track the user's arm movements and record rehabilitation progress in detail. The tracking unit can also track the user's back movements to support posture improvement. This allows for the acquisition of detailed data by focusing on specific parts of the user's body. Some or all of the above processing in the tracking unit may be performed using AI, for example, or without AI. For example, the tracking unit can input motion data of specific body parts into a generating AI and have the generating AI acquire detailed data.

[0042] The tracking unit can prioritize acquiring highly relevant data based on the user's living environment information during tracking. For example, if the user is undergoing rehabilitation at home, the tracking unit will consider indoor environmental data when tracking. For example, if the user is undergoing rehabilitation outdoors, the tracking unit can also consider weather and terrain data when tracking. For example, if the user is undergoing rehabilitation at work, the tracking unit can also consider workplace environmental data when tracking. This allows for the acquisition of more appropriate data by prioritizing the acquisition of highly relevant data based on the user's living environment information. Some or all of the above processing in the tracking unit may be performed using AI, for example, or without AI. For example, the tracking unit can input living environment data into a generating AI and have the generating AI prioritize the acquisition of highly relevant data.

[0043] The tracking unit can analyze the user's social media activity and acquire relevant motion data during tracking. The tracking unit can adjust the accuracy of tracking based, for example, on rehabilitation progress information shared by the user on social media. The tracking unit can also select a tracking method based, for example, on advice from rehabilitation professionals followed by the user on social media. The tracking unit can also adjust the frequency of tracking based, for example, on information about rehabilitation communities the user participates in on social media. This allows the tracking unit to acquire relevant motion data by analyzing the user's social media activity. Some or all of the above processing in the tracking unit may be performed using, for example, AI, or not using AI. For example, the tracking unit can input social media data into a generating AI and have the generating AI acquire relevant motion data.

[0044] The monitoring unit can evaluate the current operation by referring to past monitoring data during monitoring. For example, the monitoring unit can evaluate the accuracy of the current operation based on the user's past monitoring data. The monitoring unit can also identify areas for improvement in the current operation by referring to the user's past monitoring data. The monitoring unit can also analyze the user's past monitoring data and evaluate the progress of the current operation. This allows for an accurate evaluation of the current operation by referring to past monitoring data. Some or all of the above-described processes in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input past monitoring data into a generating AI and have the generating AI perform an evaluation of the current operation.

[0045] The monitoring unit can apply different monitoring methods to each category of user movement during monitoring. For example, in the case of gait training, the monitoring unit can apply a method that monitors foot movements in detail. For example, in the case of arm rehabilitation, the monitoring unit can also apply a method that monitors arm movements in detail. For example, in the case of posture improvement, the monitoring unit can also apply a method that monitors back movements in detail. By applying different monitoring methods to each category of movement, more detailed data can be obtained. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input movement category data into a generating AI and have the generating AI execute the application of monitoring methods.

[0046] The monitoring unit can adjust the monitoring order based on the timing of user actions during monitoring. For example, the monitoring unit may prioritize monitoring actions recently performed by the user. The monitoring unit may also postpone monitoring actions performed by the user in the past. For example, the monitoring unit may prioritize monitoring actions performed by the user during a specific time period. This allows for efficient monitoring by adjusting the monitoring order based on the timing of user action submissions. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input submission timing data into a generating AI and have the generating AI perform the adjustment of the monitoring order.

[0047] The monitoring unit can improve the accuracy of monitoring by referring to the user's relevant literature during monitoring. For example, the monitoring unit can improve the accuracy of monitoring based on rehabilitation literature that the user is referring to. The monitoring unit can also improve the accuracy of monitoring by referring to advice from rehabilitation professionals that the user is following. The monitoring unit can also improve the accuracy of monitoring based on information from rehabilitation communities that the user is participating in. In this way, the accuracy of monitoring is improved by referring to the user's relevant literature. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without using AI. For example, the monitoring unit can input relevant literature data into a generating AI and have the generating AI perform the improvement of monitoring accuracy.

[0048] The feedback unit can adjust the level of detail of the feedback based on the importance of the action during the feedback process. For example, the feedback unit provides detailed feedback for important actions. For example, the feedback unit can also provide simpler feedback for less important actions. The feedback unit can also adjust the frequency of feedback according to the importance of the action. This allows for detailed feedback to be provided for important actions by adjusting the level of detail of the feedback based on the importance of the action. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input action importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the feedback.

[0049] The feedback unit can apply different feedback algorithms depending on the category of movement during feedback. For example, in the case of gait training, the feedback unit applies a feedback algorithm specialized for foot movements. For example, in the case of arm rehabilitation, the feedback unit can also apply a feedback algorithm specialized for arm movements. For example, in the case of posture improvement, the feedback unit can also apply a feedback algorithm specialized for back movements. By applying different feedback algorithms depending on the category of movement, more appropriate feedback can be provided. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input movement category data into a generating AI and have the generating AI execute the application of the feedback algorithm.

[0050] The feedback unit can determine the priority of feedback based on the timing of action submission. For example, the feedback unit may prioritize feedback on actions recently performed by the user. The feedback unit may also postpone feedback on actions performed by the user in the past. For example, the feedback unit may prioritize feedback on actions performed by the user within a specific time period. This enables efficient feedback by prioritizing feedback based on the timing of action submission. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input submission timing data into a generating AI and have the generating AI determine the feedback priority.

[0051] The feedback unit can adjust the order of feedback based on the relevance of the actions during the feedback process. For example, the feedback unit can prioritize providing feedback to important actions. The feedback unit can also postpone providing feedback to less relevant actions. The feedback unit can also adjust the order of feedback according to the relevance of the actions. This allows for prioritizing feedback to important actions by adjusting the order of feedback based on the relevance of the actions. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input action relevance data into a generating AI and have the generating AI perform the adjustment of the feedback order.

[0052] The support unit can analyze the user's past behavioral data to select the optimal support method during support. For example, the support unit can extract specific behavioral patterns from the user's past behavioral data and select a support method based on those patterns. The support unit can also analyze the user's past behavioral data and adjust the accuracy of support for each behavior. For example, the support unit can optimize the frequency of support based on the user's past behavioral data. This allows the optimal support method to be selected by analyzing the user's past behavioral data. Some or all of the above processes in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input past behavioral data into a generating AI and have the generating AI select the optimal support method.

[0053] The support unit can customize the means of support based on the user's current physical condition during support. For example, the support unit can monitor the user's current physical condition in real time and provide the optimal means of support. For example, the support unit can also adjust the intensity of support according to the user's physical condition. For example, the support unit can also adjust the frequency of support considering the user's physical condition. This allows for the provision of more appropriate support by customizing the means of support based on the user's current physical condition. Some or all of the above processing in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input current physical condition data into a generating AI and have the generating AI perform the customization of the means of support.

[0054] The support unit can select the optimal support method based on the user's geographical location information during support. For example, if the user is undergoing rehabilitation at home, the support unit can provide support while considering indoor environmental data. For example, if the user is undergoing rehabilitation outdoors, the support unit can also provide support while considering weather and terrain data. For example, if the user is undergoing rehabilitation at work, the support unit can also provide support while considering workplace environmental data. This allows for the provision of more appropriate support by selecting the optimal support method based on the user's geographical location information. Some or all of the above processing in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input geographical location data into a generating AI and have the generating AI select the optimal support method.

[0055] The support unit can analyze a user's social media activity and propose support measures during support sessions. For example, the support unit can propose support measures based on information about the user's rehabilitation progress shared on social media. The support unit can also propose support measures based on advice from rehabilitation professionals the user follows on social media. The support unit can also propose support measures based on information from rehabilitation communities the user participates in on social media. By analyzing the user's social media activity, the support unit can propose more appropriate support measures. Some or all of the above processing in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input social media data into a generating AI and have the generating AI propose support measures.

[0056] The mental support unit can provide optimal support by referring to the user's past conversation history during mental support. For example, the mental support unit can provide optimal mental support based on the user's past conversation history. The mental support unit can also refer to the user's past conversation history and provide support that is appropriate to the user's current emotions. For example, the mental support unit can analyze the user's past conversation history and provide the most effective mental support. In this way, optimal mental support can be provided by referring to the user's past conversation history. Some or all of the above processing in the mental support unit may be performed using AI, for example, or without AI. For example, the mental support unit can input past conversation history data into a generating AI and have the generating AI perform the task of providing optimal mental support.

[0057] The mental support unit can customize the means of support based on the user's current psychological state during mental support. For example, the mental support unit can monitor the user's current psychological state in real time and provide the optimal mental support means. For example, the mental support unit can also adjust the intensity of support according to the user's psychological state. For example, the mental support unit can also adjust the frequency of support considering the user's psychological state. This allows for the provision of more appropriate mental support by customizing the means of support based on the user's current psychological state. Some or all of the above processing in the mental support unit may be performed using AI, for example, or without AI. For example, the mental support unit can input current psychological state data into a generating AI and have the generating AI perform the customization of the support means.

[0058] The mental support unit can select the optimal support method based on the user's living environment information during mental support. For example, if the user is undergoing rehabilitation at home, the mental support unit will provide mental support considering indoor environmental data. For example, if the user is undergoing rehabilitation outdoors, the mental support unit can also provide mental support considering weather and terrain data. For example, if the user is undergoing rehabilitation at work, the mental support unit can also provide mental support considering workplace environmental data. By selecting the optimal support method based on the user's living environment information, more appropriate mental support can be provided. Some or all of the above processing in the mental support unit may be performed using AI, for example, or without AI. For example, the mental support unit can input living environment information data into a generating AI and have the generating AI select the optimal support method.

[0059] The Mental Support Department can analyze a user's social media activity and propose support measures during mental support sessions. For example, the Mental Support Department can propose mental support measures based on rehabilitation progress information shared by the user on social media. The Mental Support Department can also propose mental support measures based on advice from rehabilitation professionals followed by the user on social media. The Mental Support Department can also propose mental support measures based on information from rehabilitation communities the user participates in on social media. By analyzing the user's social media activity, it is possible to propose more appropriate mental support measures. Some or all of the above processing in the Mental Support Department may be performed using AI, for example, or without AI. For example, the Mental Support Department can input social media data into a generating AI and have the generating AI propose support measures.

[0060] The mental support unit can select the optimal support method based on the user's rehabilitation progress during mental support sessions. For example, the mental support unit can monitor the user's rehabilitation progress in real time and provide the most appropriate mental support. The mental support unit can also adjust the intensity of support according to the user's rehabilitation progress. For example, the mental support unit can adjust the frequency of support considering the user's rehabilitation progress. This allows for the provision of more appropriate mental support by selecting the optimal support method based on the user's rehabilitation progress. Some or all of the above-described processes in the mental support unit may be performed using AI, for example, or without AI. For example, the mental support unit can input rehabilitation progress data into a generating AI and have the generating AI select the optimal support method.

[0061] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0062] AI rehabilitation support software can provide a visual dashboard to visualize the user's rehabilitation progress. For example, based on data collected by the tracking unit, the user's rehabilitation progress can be displayed in graphs and charts. This allows the user to grasp their progress at a glance and makes it easier to maintain motivation. The monitoring unit can also evaluate current progress by comparing it with past data and visually indicate areas for improvement. Furthermore, the feedback unit can provide specific advice to the user through the visual dashboard. For example, if a particular movement improves, that part can be highlighted to notify the user. This allows the user to feel how their efforts are leading to results.

[0063] AI rehabilitation support software can provide a function to compare a user's rehabilitation progress with that of other users. For example, based on data collected by the tracking unit, it can compare the user's progress with that of other users undergoing the same rehabilitation program. This allows the user to understand whether their progress is average, above average, or behind. The monitoring unit can also analyze data from other users and extract common challenges and success stories. Furthermore, the feedback unit can provide advice based on the success stories of other users. For example, it can introduce methods used by users who have improved a particular movement and encourage the user to use them as a reference. This allows users to leverage the experiences of others to advance their own rehabilitation more effectively.

[0064] AI rehabilitation support software can provide a function to share the user's rehabilitation progress with family and friends. For example, based on data collected by the tracking unit, it can inform family and friends of the user's rehabilitation progress. This makes it easier for the user to receive support from those around them and maintain motivation. The monitoring unit can also collect feedback from family and friends and incorporate it into the user's rehabilitation. Furthermore, the feedback unit can deliver encouraging messages from family and friends to the user. For example, when a specific goal is achieved, congratulatory messages from family and friends can be displayed. This allows the user to progress through rehabilitation while feeling the support of those around them.

[0065] AI rehabilitation support software can provide a function to set individual goals based on the user's rehabilitation progress. For example, based on data collected by the tracking unit, it can set specific goals according to the user's current condition. This allows the user to work on rehabilitation with clear goals. The monitoring unit can also evaluate progress toward goal achievement in real time and adjust goals as needed. Furthermore, the feedback unit can provide specific advice toward goal achievement. For example, if a particular movement is approaching the goal, it can highlight that part to inform the user. This allows the user to feel their own progress and maintain motivation toward achieving their goals.

[0066] AI rehabilitation support software can provide a function to customize rehabilitation programs based on the user's rehabilitation progress. For example, based on data collected by the tracking unit, it can suggest a rehabilitation program tailored to the user's current condition. This allows the user to execute the rehabilitation program that is best suited to them. The monitoring unit can also evaluate the effectiveness of the rehabilitation program in real time and adjust the program as needed. Furthermore, the feedback unit can provide specific advice according to the progress of the rehabilitation program. For example, if a particular movement improves, it can highlight that part and notify the user. This allows the user to feel their own progress and execute the optimal rehabilitation program.

[0067] The following briefly describes the processing flow for example form 1.

[0068] Step 1: The tracking unit tracks the user's movements. The tracking unit tracks the user's movements in real time, for example, using built-in sensors. If the user is undergoing walking training, the sensors detect the movement of the feet and record the accurate walking pattern. If the user is undergoing arm rehabilitation, the sensors can also detect the movement of the arms and record the accurate movement pattern. If the user is performing daily living activities, the sensors can also detect the movements and record the accurate movement pattern. Step 2: The monitoring unit monitors the actions tracked by the tracking unit. For example, the monitoring unit can monitor the user's actions in real time and evaluate the accuracy of those actions. It can also periodically monitor the user's actions and record any changes in those actions. It can also monitor the user's actions over a long period of time and analyze any trends in those actions. Step 3: The feedback unit provides feedback based on the actions monitored by the monitoring unit. For example, the feedback unit provides verbal feedback if the user's movements are not ideal. Visual and haptic feedback can also be provided. Step 4: The support unit assists the user's movements based on the feedback provided by the feedback unit. For example, the support unit may move the garment itself to support the user's correct movements. External devices or rehabilitation equipment may also be used to assist the user's movements. Step 5: The mental support unit provides mental support based on the actions supported by the support unit. The mental support unit provides mental support, for example, through dialogue. It can also estimate the user's emotions and provide appropriate mental support. It can also monitor the user's psychological state and provide appropriate mental support.

[0069] (Example of form 2) The AI ​​rehabilitation support wear according to an embodiment of the present invention is a wearable device for rehabilitation patients. This device monitors the rehabilitation process and supports accurate movements by moving or giving verbal advice. It also provides mental support through dialogue. For example, the AI ​​rehabilitation support wear tracks the user's movements. For example, built-in sensors track and monitor the user's movements in real time. When the user is performing walking training, the sensors detect the movement of the feet and record the accurate walking pattern. Next, if the user's movements are not in the ideal form, the AI ​​rehabilitation support wear provides verbal feedback. If it is still difficult, the wear itself moves to support the user's correct movements. For example, when performing arm rehabilitation, the wear assists the arm movements and encourages correct movements. Furthermore, the AI ​​rehabilitation support wear provides mental support through dialogue. By talking to the wear, it is possible to have a conversation and confide any concerns about rehabilitation at any time. For example, if the user is feeling anxious about the progress of their rehabilitation, the wear will offer words of encouragement to maintain motivation. This wearable device automatically personalizes to suit individual situations and supports efficient and effective rehabilitation and motivation maintenance. For example, it suggests and implements an appropriate training plan based on the user's rehabilitation progress. Challenges faced by rehabilitation patients include difficulty understanding precise movements and maintaining motivation. This device is designed to address these challenges. For instance, sensors detect and provide feedback on factors such as the balance of left-right force and the appropriate grip strength. It also provides appropriate advice and maintains motivation based on rehabilitation progress. In this way, AI rehabilitation support wear provides comprehensive support for rehabilitation patients, maximizing the effectiveness of their rehabilitation.

[0070] The AI ​​rehabilitation support wear according to this embodiment comprises a tracking unit, a monitoring unit, a feedback unit, a support unit, and a mental support unit. The tracking unit tracks the user's movements. The tracking unit tracks the user's movements in real time, for example, using an integrated sensor. For example, if the user is performing walking training, the sensor detects the movement of the user's feet and records an accurate walking pattern. For example, if the user is performing arm rehabilitation, the sensor can detect the movement of the user's arms and record an accurate movement pattern. For example, if the user is performing daily living activities, the sensor can detect the user's movements and record an accurate movement pattern. The monitoring unit monitors the movements tracked by the tracking unit. For example, the monitoring unit monitors the user's movements in real time and evaluates the accuracy of the movements. For example, the monitoring unit can periodically monitor the user's movements and record changes in the movements. For example, the monitoring unit can monitor the user's movements over a long period of time and analyze movement trends. The feedback unit provides feedback based on the movements monitored by the monitoring unit. The feedback unit provides verbal feedback, for example, when the user's movements are not ideal. The feedback unit can also provide visual feedback, for example, when the user's movements are not ideal. The feedback unit can also provide haptic feedback, for example, when the user's movements are not ideal. The support unit supports the user's movements based on the feedback provided by the feedback unit. The support unit can, for example, have the wearer itself move to support the user's correct movements. The support unit can also, for example, use external devices to assist the user's movements. The support unit can also, for example, use rehabilitation equipment to assist the user's movements. The mental support unit provides mental support based on the movements supported by the support unit. The mental support unit can, for example, provide mental support through dialogue.The mental support unit can, for example, estimate the user's emotions and provide appropriate mental support. The mental support unit can also, for example, monitor the user's psychological state and provide appropriate mental support. This enables the AI ​​rehabilitation support software according to the embodiment to efficiently track, monitor, provide feedback, support, and mental support for the user's actions.

[0071] The tracking unit tracks the user's movements. For example, the tracking unit tracks the user's movements in real time using built-in sensors. Specifically, the tracking unit uses a combination of various sensors, such as accelerometers, gyroscopes, and pressure sensors. This allows for high-precision detection of the user's movements and the collection of detailed data. For example, when a user is undergoing walking training, sensors detect foot movements and record accurate walking patterns. This includes data such as foot position, speed, acceleration, and ground contact time. This data is collected in real time and transmitted to a central database. When a user is undergoing arm rehabilitation, sensors can also detect arm movements and record accurate movement patterns. Regarding arm movements, details such as joint angles, movement speed, and force application are recorded. Furthermore, when a user is performing daily living activities, sensors can detect these movements and record accurate movement patterns. For example, frequently performed daily living activities such as lifting objects or sitting down are tracked in detail. This allows the tracking unit to track a variety of user movements with high precision and to understand the progress of rehabilitation in detail. Furthermore, the tracking unit centrally manages the collected data and can collaborate with other systems and departments as needed. For example, the collected data is stored on a cloud server and made accessible to the monitoring and feedback units. Additionally, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. This allows the tracking unit to collect data efficiently and effectively, improving the overall system performance.

[0072] The monitoring unit monitors the movements tracked by the tracking unit. For example, the monitoring unit monitors the user's movements in real time and evaluates the accuracy of those movements. Specifically, the monitoring unit uses AI to analyze the collected data and evaluate whether the user's movements are being performed in an ideal manner. For example, in the case of gait training, the AI ​​analyzes the patterns of foot movements and detects any abnormalities by comparing them to normal gait patterns. The monitoring unit can also periodically monitor the user's movements and record changes in those movements. This allows for continuous monitoring of the progress of rehabilitation and adjustment of the rehabilitation plan as needed. Furthermore, the monitoring unit can monitor the user's movements over a long period and analyze movement trends. For example, based on long-term data, it can identify changes and improvement trends in the user's movement patterns and evaluate the effectiveness of rehabilitation. The monitoring unit can use anomaly detection algorithms to detect unusual patterns and abnormal data and issue warnings early. This allows the monitoring unit to not only grasp the situation in real time but also to handle long-term risk management and anomaly detection, improving the reliability and safety of the entire system. In addition, the monitoring unit can create customized rehabilitation plans for each user based on the collected data. This allows for the provision of optimal rehabilitation support tailored to the individual needs of each user.

[0073] The feedback unit provides feedback based on movements monitored by the monitoring unit. For example, the feedback unit provides verbal feedback if the user's movements are not ideal. Specifically, it uses speech synthesis technology to provide real-time voice feedback to the user. For example, it can give specific instructions such as, "Lift your leg a little higher" or "Move your arms a little slower." The feedback unit can also provide visual feedback if the user's movements are not ideal. For example, it can display videos or animations of correct movements on the user's display or smartphone screen, allowing the user to correct their movements. Furthermore, the feedback unit can also provide haptic feedback if the user's movements are not ideal. For example, it can use a vibration motor built into the wearer to provide vibration feedback to the user. This allows the user to intuitively understand that their movements are incorrect and correct them. The feedback unit can provide individualized feedback based on the user's movement data. For example, based on past data, it can specifically point out areas for improvement and points to pay attention to in the user's movements and provide advice to maximize the effectiveness of rehabilitation. In this way, the feedback unit can provide effective feedback to the user and support the progress of rehabilitation. Furthermore, the feedback unit can record the user's response to feedback and continuously improve the content and method of the feedback. This allows the feedback unit to provide the user with optimal feedback, maximizing the effectiveness of rehabilitation.

[0074] The support unit assists the user's movements based on feedback provided by the feedback unit. For example, the support unit can support the user's correct movements through the movement of the garment itself. Specifically, it uses actuators and motors built into the garment to assist the user's movements. For instance, in gait training, the garment assists the user's leg movements to maintain a correct walking pattern. In arm rehabilitation, the garment assists the user's arm movements to support correct movement. The support unit can also use external devices to assist the user's movements. For example, it can use an exoskeleton or rehabilitation robot to assist the user's movements and enhance the effectiveness of rehabilitation. Furthermore, the support unit can use rehabilitation equipment to assist the user's movements. For example, it can use a balance ball or resistance band to assist the user's movements and maximize the effectiveness of rehabilitation. Based on the user's movement data, the support unit can create individualized support plans. This allows for the provision of optimal support tailored to the user's individual needs. Furthermore, the support unit can collect user feedback and continuously improve the accuracy and effectiveness of the support. This allows the support unit to provide optimal support to the user and maximize the effectiveness of rehabilitation.

[0075] The Mental Support Department provides mental support based on the actions supported by the Support Department. For example, the Mental Support Department provides mental support through dialogue. Specifically, it uses natural language processing technology to engage in dialogue with users and understand their psychological state. For instance, if a user is feeling anxious or stressed about rehabilitation, the Mental Support Department provides appropriate advice and words of encouragement. Furthermore, the Mental Support Department can also estimate the user's emotions and provide appropriate mental support. For example, it analyzes the user's facial expressions and tone of voice to estimate what emotions the user is experiencing. This helps users develop a positive attitude towards rehabilitation. The Mental Support Department can also monitor the user's psychological state and provide appropriate mental support. For example, if a user has lost motivation for rehabilitation, the Mental Support Department provides advice and sets goals to increase the user's motivation. This allows the user to actively engage in rehabilitation. Furthermore, the Mental Support Department can collect user feedback and continuously improve the content and methods of mental support. This allows the Mental Support Department to provide optimal mental support to users and maximize the effectiveness of rehabilitation.

[0076] The tracking unit can track the user's movements in real time using built-in sensors. For example, if the user is performing walking exercises, the sensors can detect the movement of the user's feet and record the accurate walking pattern. For example, if the user is performing arm rehabilitation, the sensors can detect the movement of the user's arms and record the accurate movement pattern. For example, if the user is performing daily living activities, the sensors can detect the user's movements and record the accurate movement pattern. This allows for the acquisition of accurate data by tracking the user's movements in real time. Some or all of the above-described processes in the tracking unit may be performed using AI, for example, or without AI. For example, the tracking unit can input data acquired from sensors into a generating AI and have the generating AI perform analysis of the movement pattern.

[0077] The feedback unit can provide verbal feedback if the user's movements are not ideal. The feedback unit can, for example, provide verbal feedback if the user's movements are not ideal. The feedback unit can also, for example, provide visual feedback if the user's movements are not ideal. The feedback unit can also, for example, provide haptic feedback if the user's movements are not ideal. This encourages correct actions by providing verbal feedback when the user's movements are not ideal. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input user movement data into a generating AI and have the generating AI generate the feedback content.

[0078] The support unit can support the user's correct movements by having the garment itself move. The support unit can, for example, have the garment itself move to support the user's correct movements. The support unit can, for example, use external devices to assist the user's movements. The support unit can, for example, use rehabilitation equipment to assist the user's movements. This enhances the effectiveness of rehabilitation by having the garment itself move to support the user's correct movements. Some or all of the above-described processes in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input user movement data into a generating AI and have the generating AI execute control of movement assistance.

[0079] The mental support unit can provide mental support through dialogue. The mental support unit can, for example, provide mental support through dialogue. The mental support unit can also, for example, estimate the user's emotions and provide appropriate mental support. The mental support unit can also, for example, monitor the user's psychological state and provide appropriate mental support. In this way, by providing mental support through dialogue, the user's motivation is maintained. Some or all of the above processes in the mental support unit may be performed using AI, for example, or without AI. For example, the mental support unit can input user dialogue data into a generating AI and have the generating AI generate the dialogue content.

[0080] The support unit can propose an appropriate training plan according to the user's rehabilitation progress. The support unit can, for example, propose an appropriate training plan according to the user's rehabilitation progress. The support unit can also, for example, adjust the training plan based on the user's rehabilitation progress. The support unit can also, for example, adjust the intensity of the training according to the user's rehabilitation progress. This maximizes the effectiveness of rehabilitation by proposing an appropriate training plan according to the user's rehabilitation progress. Some or all of the above processing in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input the user's rehabilitation progress data into a generating AI and have the generating AI generate a training plan.

[0081] The tracking unit can estimate the user's emotions and adjust the tracking accuracy based on the estimated emotions. For example, if the user is stressed, the tracking unit can increase the tracking accuracy to obtain more detailed data. For example, if the user is relaxed, the tracking unit can also decrease the tracking accuracy to obtain simpler data. For example, if the user is tired, the tracking unit can reduce the frequency of tracking to alleviate the burden. This allows for the acquisition of more appropriate data by adjusting the tracking accuracy based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the tracking unit may be performed using AI, or not using AI. For example, the tracking unit can input user emotion data into the generative AI and have the generative AI adjust the tracking accuracy.

[0082] The tracking unit can analyze the user's past behavior data and select the optimal tracking method. For example, the tracking unit can extract specific behavior patterns from the user's past behavior data and select a tracking method based on those patterns. The tracking unit can also analyze the user's past behavior data and adjust the tracking accuracy for each behavior. For example, the tracking unit can optimize the tracking frequency based on the user's past behavior data. This allows the optimal tracking method to be selected by analyzing the user's past behavior data. Some or all of the above processing in the tracking unit may be performed using AI, for example, or without AI. For example, the tracking unit can input past behavior data into a generating AI and have the generating AI select the optimal tracking method.

[0083] The tracking unit can acquire detailed data by focusing on specific parts of the user's body during tracking. For example, the tracking unit can track the user's leg movements and record walking patterns in detail. The tracking unit can also track the user's arm movements and record rehabilitation progress in detail. The tracking unit can also track the user's back movements to support posture improvement. This allows for the acquisition of detailed data by focusing on specific parts of the user's body. Some or all of the above processing in the tracking unit may be performed using AI, for example, or without AI. For example, the tracking unit can input motion data of specific body parts into a generating AI and have the generating AI acquire detailed data.

[0084] The tracking unit can estimate the user's emotions and adjust the tracking frequency based on the estimated emotions. For example, if the user is stressed, the tracking unit can increase the tracking frequency to obtain more detailed data. For example, if the user is relaxed, the tracking unit can also decrease the tracking frequency to obtain simpler data. For example, if the user is tired, the tracking unit can reduce the burden by decreasing the tracking frequency. This allows for the acquisition of more appropriate data by adjusting the tracking frequency based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the tracking unit may be performed using AI, or not using AI. For example, the tracking unit can input user emotion data into the generative AI and have the generative AI adjust the tracking frequency.

[0085] The tracking unit can prioritize acquiring highly relevant data based on the user's living environment information during tracking. For example, if the user is undergoing rehabilitation at home, the tracking unit will consider indoor environmental data when tracking. For example, if the user is undergoing rehabilitation outdoors, the tracking unit can also consider weather and terrain data when tracking. For example, if the user is undergoing rehabilitation at work, the tracking unit can also consider workplace environmental data when tracking. This allows for the acquisition of more appropriate data by prioritizing the acquisition of highly relevant data based on the user's living environment information. Some or all of the above processing in the tracking unit may be performed using AI, for example, or without AI. For example, the tracking unit can input living environment data into a generating AI and have the generating AI prioritize the acquisition of highly relevant data.

[0086] The tracking unit can analyze the user's social media activity and acquire relevant motion data during tracking. The tracking unit can adjust the accuracy of tracking based, for example, on rehabilitation progress information shared by the user on social media. The tracking unit can also select a tracking method based, for example, on advice from rehabilitation professionals followed by the user on social media. The tracking unit can also adjust the frequency of tracking based, for example, on information about rehabilitation communities the user participates in on social media. This allows the tracking unit to acquire relevant motion data by analyzing the user's social media activity. Some or all of the above processing in the tracking unit may be performed using, for example, AI, or not using AI. For example, the tracking unit can input social media data into a generating AI and have the generating AI acquire relevant motion data.

[0087] The monitoring unit can estimate the user's emotions and adjust the display method of the monitoring based on the estimated user emotions. For example, if the user is tense, the monitoring unit can provide a simple and highly visible display method. For example, if the user is relaxed, the monitoring unit can also provide a display method that includes detailed information. For example, if the user is in a hurry, the monitoring unit can also provide a display method that gets straight to the point. By adjusting the display method of the monitoring based on the user's emotions, a more appropriate display becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the display method.

[0088] The monitoring unit can evaluate the current operation by referring to past monitoring data during monitoring. For example, the monitoring unit can evaluate the accuracy of the current operation based on the user's past monitoring data. The monitoring unit can also identify areas for improvement in the current operation by referring to the user's past monitoring data. The monitoring unit can also analyze the user's past monitoring data and evaluate the progress of the current operation. This allows for an accurate evaluation of the current operation by referring to past monitoring data. Some or all of the above-described processes in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input past monitoring data into a generating AI and have the generating AI perform an evaluation of the current operation.

[0089] The monitoring unit can apply different monitoring methods to each category of user movement during monitoring. For example, in the case of gait training, the monitoring unit can apply a method that monitors foot movements in detail. For example, in the case of arm rehabilitation, the monitoring unit can also apply a method that monitors arm movements in detail. For example, in the case of posture improvement, the monitoring unit can also apply a method that monitors back movements in detail. By applying different monitoring methods to each category of movement, more detailed data can be obtained. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input movement category data into a generating AI and have the generating AI execute the application of monitoring methods.

[0090] The monitoring unit can estimate the user's emotions and determine monitoring priorities based on the estimated emotions. For example, if the user is stressed, the monitoring unit may prioritize monitoring important actions. For example, if the user is relaxed, the monitoring unit may monitor all actions evenly. For example, if the user is tired, the monitoring unit may prioritize monitoring less strenuous actions. This allows for the priority acquisition of important data by determining monitoring priorities based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the monitoring unit may be performed using AI or not. For example, the monitoring unit can input user emotion data into a generative AI and have the generative AI determine the monitoring priorities.

[0091] The monitoring unit can adjust the monitoring order based on the timing of user actions during monitoring. For example, the monitoring unit may prioritize monitoring actions recently performed by the user. The monitoring unit may also postpone monitoring actions performed by the user in the past. For example, the monitoring unit may prioritize monitoring actions performed by the user during a specific time period. This allows for efficient monitoring by adjusting the monitoring order based on the timing of user action submissions. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input submission timing data into a generating AI and have the generating AI perform the adjustment of the monitoring order.

[0092] The monitoring unit can improve the accuracy of monitoring by referring to the user's relevant literature during monitoring. For example, the monitoring unit can improve the accuracy of monitoring based on rehabilitation literature that the user is referring to. The monitoring unit can also improve the accuracy of monitoring by referring to advice from rehabilitation professionals that the user is following. The monitoring unit can also improve the accuracy of monitoring based on information from rehabilitation communities that the user is participating in. In this way, the accuracy of monitoring is improved by referring to the user's relevant literature. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without using AI. For example, the monitoring unit can input relevant literature data into a generating AI and have the generating AI perform the improvement of monitoring accuracy.

[0093] The feedback unit can estimate the user's emotions and adjust the way feedback is presented based on the estimated emotions. For example, if the user is nervous, the feedback unit can provide a simple and easily visible display. For example, if the user is relaxed, the feedback unit can also provide a display that includes detailed information. For example, if the user is in a hurry, the feedback unit can also provide a display that gets straight to the point. This allows for more appropriate feedback by adjusting the way feedback is presented based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the feedback unit may be performed using AI, or not using AI. For example, the feedback unit can input user emotion data into the generative AI and have the generative AI adjust the way feedback is presented.

[0094] The feedback unit can adjust the level of detail of the feedback based on the importance of the action during the feedback process. For example, the feedback unit provides detailed feedback for important actions. For example, the feedback unit can also provide simpler feedback for less important actions. The feedback unit can also adjust the frequency of feedback according to the importance of the action. This allows for detailed feedback to be provided for important actions by adjusting the level of detail of the feedback based on the importance of the action. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input action importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the feedback.

[0095] The feedback unit can apply different feedback algorithms depending on the category of movement during feedback. For example, in the case of gait training, the feedback unit applies a feedback algorithm specialized for foot movements. For example, in the case of arm rehabilitation, the feedback unit can also apply a feedback algorithm specialized for arm movements. For example, in the case of posture improvement, the feedback unit can also apply a feedback algorithm specialized for back movements. By applying different feedback algorithms depending on the category of movement, more appropriate feedback can be provided. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input movement category data into a generating AI and have the generating AI execute the application of the feedback algorithm.

[0096] The feedback unit can estimate the user's emotions and adjust the length of the feedback based on the estimated emotions. For example, if the user is nervous, the feedback unit can provide short, concise feedback. For example, if the user is relaxed, the feedback unit can provide detailed feedback. For example, if the user is in a hurry, the feedback unit can provide quick and concise feedback. This allows for more appropriate feedback to be provided by adjusting the length of the feedback based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the feedback unit may be performed using AI or not. For example, the feedback unit can input user emotion data into the generative AI and have the generative AI adjust the length of the feedback.

[0097] The feedback unit can determine the priority of feedback based on the timing of action submission. For example, the feedback unit may prioritize feedback on actions recently performed by the user. The feedback unit may also postpone feedback on actions performed by the user in the past. For example, the feedback unit may prioritize feedback on actions performed by the user within a specific time period. This enables efficient feedback by prioritizing feedback based on the timing of action submission. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input submission timing data into a generating AI and have the generating AI determine the feedback priority.

[0098] The feedback unit can adjust the order of feedback based on the relevance of the actions during the feedback process. For example, the feedback unit can prioritize providing feedback to important actions. The feedback unit can also postpone providing feedback to less relevant actions. The feedback unit can also adjust the order of feedback according to the relevance of the actions. This allows for prioritizing feedback to important actions by adjusting the order of feedback based on the relevance of the actions. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input action relevance data into a generating AI and have the generating AI perform the adjustment of the feedback order.

[0099] The support unit can estimate the user's emotions and adjust its support methods based on the estimated emotions. For example, if the user is nervous, the support unit can provide support in a calm voice. If the user is relaxed, the support unit can also provide support in a cheerful voice. If the user is tired, the support unit can provide concise and effective support. By adjusting the support methods based on the user's emotions, more appropriate support can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the support unit may be performed using AI, for example, or not using AI. For example, the support unit can input user emotion data into a generative AI and have the generative AI adjust the support methods.

[0100] The support unit can analyze the user's past behavioral data to select the optimal support method during support. For example, the support unit can extract specific behavioral patterns from the user's past behavioral data and select a support method based on those patterns. The support unit can also analyze the user's past behavioral data and adjust the accuracy of support for each behavior. For example, the support unit can optimize the frequency of support based on the user's past behavioral data. This allows the optimal support method to be selected by analyzing the user's past behavioral data. Some or all of the above processes in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input past behavioral data into a generating AI and have the generating AI select the optimal support method.

[0101] The support unit can customize the means of support based on the user's current physical condition during support. For example, the support unit can monitor the user's current physical condition in real time and provide the optimal means of support. For example, the support unit can also adjust the intensity of support according to the user's physical condition. For example, the support unit can also adjust the frequency of support considering the user's physical condition. This allows for the provision of more appropriate support by customizing the means of support based on the user's current physical condition. Some or all of the above processing in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input current physical condition data into a generating AI and have the generating AI perform the customization of the means of support.

[0102] The support unit can estimate the user's emotions and determine the priority of support based on the estimated emotions. For example, if the user is stressed, the support unit will prioritize providing important support. For example, if the user is relaxed, the support unit may also provide all support evenly. For example, if the user is tired, the support unit may also prioritize providing less burdensome support. This allows for the priority of important support by determining the priority of support based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the support unit may be performed using AI or not using AI. For example, the support unit can input user emotion data into a generative AI and have the generative AI determine the support priority.

[0103] The support unit can select the optimal support method based on the user's geographical location information during support. For example, if the user is undergoing rehabilitation at home, the support unit can provide support while considering indoor environmental data. For example, if the user is undergoing rehabilitation outdoors, the support unit can also provide support while considering weather and terrain data. For example, if the user is undergoing rehabilitation at work, the support unit can also provide support while considering workplace environmental data. This allows for the provision of more appropriate support by selecting the optimal support method based on the user's geographical location information. Some or all of the above processing in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input geographical location data into a generating AI and have the generating AI select the optimal support method.

[0104] The support unit can analyze a user's social media activity and propose support measures during support sessions. For example, the support unit can propose support measures based on information about the user's rehabilitation progress shared on social media. The support unit can also propose support measures based on advice from rehabilitation professionals the user follows on social media. The support unit can also propose support measures based on information from rehabilitation communities the user participates in on social media. By analyzing the user's social media activity, the support unit can propose more appropriate support measures. Some or all of the above processing in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input social media data into a generating AI and have the generating AI propose support measures.

[0105] The mental support unit can estimate the user's emotions and adjust the method of mental support based on the estimated emotions. For example, if the user is tense, the mental support unit can provide mental support in a calm voice. For example, if the user is relaxed, the mental support unit can also provide mental support in a cheerful voice. For example, if the user is tired, the mental support unit can provide concise and effective mental support. In this way, by adjusting the method of mental support based on the user's emotions, more appropriate mental support can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the mental support unit may be performed using AI, for example, or without AI. For example, the mental support unit can input user emotion data into the generative AI and have the generative AI perform the adjustment of the mental support method.

[0106] The mental support unit can provide optimal support by referring to the user's past conversation history during mental support. For example, the mental support unit can provide optimal mental support based on the user's past conversation history. The mental support unit can also refer to the user's past conversation history and provide support that is appropriate to the user's current emotions. For example, the mental support unit can analyze the user's past conversation history and provide the most effective mental support. In this way, optimal mental support can be provided by referring to the user's past conversation history. Some or all of the above processing in the mental support unit may be performed using AI, for example, or without AI. For example, the mental support unit can input past conversation history data into a generating AI and have the generating AI perform the task of providing optimal mental support.

[0107] The mental support unit can customize the means of support based on the user's current psychological state during mental support. For example, the mental support unit can monitor the user's current psychological state in real time and provide the optimal mental support means. For example, the mental support unit can also adjust the intensity of support according to the user's psychological state. For example, the mental support unit can also adjust the frequency of support considering the user's psychological state. This allows for the provision of more appropriate mental support by customizing the means of support based on the user's current psychological state. Some or all of the above processing in the mental support unit may be performed using AI, for example, or without AI. For example, the mental support unit can input current psychological state data into a generating AI and have the generating AI perform the customization of the support means.

[0108] The mental support unit can estimate the user's emotions and determine the priority of mental support based on the estimated emotions. For example, if the user is stressed, the mental support unit will prioritize providing important mental support. For example, if the user is relaxed, the mental support unit can also provide overall mental support evenly. For example, if the user is tired, the mental support unit can also prioritize providing less burdensome mental support. In this way, by determining the priority of mental support based on the user's emotions, important mental support can be provided preferentially. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the mental support unit may be performed using AI, for example, or not using AI. For example, the mental support unit can input user emotion data into a generative AI and have the generative AI perform the determination of mental support priorities.

[0109] The mental support unit can select the optimal support method based on the user's living environment information during mental support. For example, if the user is undergoing rehabilitation at home, the mental support unit will provide mental support considering indoor environmental data. For example, if the user is undergoing rehabilitation outdoors, the mental support unit can also provide mental support considering weather and terrain data. For example, if the user is undergoing rehabilitation at work, the mental support unit can also provide mental support considering workplace environmental data. By selecting the optimal support method based on the user's living environment information, more appropriate mental support can be provided. Some or all of the above processing in the mental support unit may be performed using AI, for example, or without AI. For example, the mental support unit can input living environment information data into a generating AI and have the generating AI select the optimal support method.

[0110] The Mental Support Department can analyze a user's social media activity and propose support measures during mental support sessions. For example, the Mental Support Department can propose mental support measures based on rehabilitation progress information shared by the user on social media. The Mental Support Department can also propose mental support measures based on advice from rehabilitation professionals followed by the user on social media. The Mental Support Department can also propose mental support measures based on information from rehabilitation communities the user participates in on social media. By analyzing the user's social media activity, it is possible to propose more appropriate mental support measures. Some or all of the above processing in the Mental Support Department may be performed using AI, for example, or without AI. For example, the Mental Support Department can input social media data into a generating AI and have the generating AI propose support measures.

[0111] The mental support unit can select the optimal support method based on the user's rehabilitation progress during mental support sessions. For example, the mental support unit can monitor the user's rehabilitation progress in real time and provide the most appropriate mental support. The mental support unit can also adjust the intensity of support according to the user's rehabilitation progress. For example, the mental support unit can adjust the frequency of support considering the user's rehabilitation progress. This allows for the provision of more appropriate mental support by selecting the optimal support method based on the user's rehabilitation progress. Some or all of the above-described processes in the mental support unit may be performed using AI, for example, or without AI. For example, the mental support unit can input rehabilitation progress data into a generating AI and have the generating AI select the optimal support method.

[0112] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0113] AI rehabilitation support software can provide a visual dashboard to visualize the user's rehabilitation progress. For example, based on data collected by the tracking unit, the user's rehabilitation progress can be displayed in graphs and charts. This allows the user to grasp their progress at a glance and makes it easier to maintain motivation. The monitoring unit can also evaluate current progress by comparing it with past data and visually indicate areas for improvement. Furthermore, the feedback unit can provide specific advice to the user through the visual dashboard. For example, if a particular movement improves, that part can be highlighted to notify the user. This allows the user to feel how their efforts are leading to results.

[0114] AI rehabilitation support software can provide a function to compare a user's rehabilitation progress with that of other users. For example, based on data collected by the tracking unit, it can compare the user's progress with that of other users undergoing the same rehabilitation program. This allows the user to understand whether their progress is average, above average, or behind. The monitoring unit can also analyze data from other users and extract common challenges and success stories. Furthermore, the feedback unit can provide advice based on the success stories of other users. For example, it can introduce methods used by users who have improved a particular movement and encourage the user to use them as a reference. This allows users to leverage the experiences of others to advance their own rehabilitation more effectively.

[0115] AI rehabilitation support software can provide a function to share the user's rehabilitation progress with family and friends. For example, based on data collected by the tracking unit, it can inform family and friends of the user's rehabilitation progress. This makes it easier for the user to receive support from those around them and maintain motivation. The monitoring unit can also collect feedback from family and friends and incorporate it into the user's rehabilitation. Furthermore, the feedback unit can deliver encouraging messages from family and friends to the user. For example, when a specific goal is achieved, congratulatory messages from family and friends can be displayed. This allows the user to progress through rehabilitation while feeling the support of those around them.

[0116] AI rehabilitation support software can provide a function to set individual goals based on the user's rehabilitation progress. For example, based on data collected by the tracking unit, it can set specific goals according to the user's current condition. This allows the user to work on rehabilitation with clear goals. The monitoring unit can also evaluate progress toward goal achievement in real time and adjust goals as needed. Furthermore, the feedback unit can provide specific advice toward goal achievement. For example, if a particular movement is approaching the goal, it can highlight that part to inform the user. This allows the user to feel their own progress and maintain motivation toward achieving their goals.

[0117] AI rehabilitation support software can provide a function to customize rehabilitation programs based on the user's rehabilitation progress. For example, based on data collected by the tracking unit, it can suggest a rehabilitation program tailored to the user's current condition. This allows the user to execute the rehabilitation program that is best suited to them. The monitoring unit can also evaluate the effectiveness of the rehabilitation program in real time and adjust the program as needed. Furthermore, the feedback unit can provide specific advice according to the progress of the rehabilitation program. For example, if a particular movement improves, it can highlight that part and notify the user. This allows the user to feel their own progress and execute the optimal rehabilitation program.

[0118] AI rehabilitation support software can estimate the user's emotions and adjust the rehabilitation program based on those emotions. For example, based on data collected by the tracking unit, if the user is feeling stressed, the intensity of the rehabilitation program is reduced. This allows the user to continue rehabilitation without undue strain. The monitoring unit can also evaluate changes in the user's emotions in real time and adjust the program as needed. Furthermore, the feedback unit can provide specific advice tailored to the user's emotions. For example, if the user is relaxed, it can suggest slightly increasing the intensity of the rehabilitation program. This allows the user to perform the optimal rehabilitation program according to their emotions.

[0119] AI rehabilitation support software can estimate the user's emotions and adjust the content of feedback based on those emotions. For example, based on data collected by the tracking unit, if the user is feeling tense, the feedback can be made simple and easy to understand. This allows the user to accept the feedback without feeling stressed. The monitoring unit can also evaluate changes in the user's emotions in real time and adjust the content of feedback as needed. Furthermore, the feedback unit can provide specific advice tailored to the user's emotions. For example, if the user is relaxed, detailed feedback can be provided to promote a deeper understanding. This allows the user to receive optimal feedback that matches their emotions.

[0120] AI rehabilitation support software can estimate the user's emotions and adjust the content of mental support based on those emotions. For example, based on data collected by the tracking unit, if the user is feeling anxious, the mental support unit will offer words of encouragement. This allows the user to engage in rehabilitation with peace of mind. The monitoring unit can also evaluate changes in the user's emotions in real time and adjust the content of mental support as needed. Furthermore, the feedback unit can provide specific advice tailored to the user's emotions. For example, if the user is relaxed, praising their rehabilitation progress can further motivate them. In this way, the user receives optimal mental support that matches their emotions.

[0121] AI rehabilitation support software can estimate the user's emotions and adjust how it reports rehabilitation progress based on those emotions. For example, based on data collected by the tracking unit, if the user is feeling stressed, the progress report can be made more concise. This allows the user to understand their progress without feeling burdened. The monitoring unit can also evaluate changes in the user's emotions in real time and adjust the progress reporting method as needed. Furthermore, the feedback unit can provide specific advice tailored to the user's emotions. For example, if the user is relaxed, a detailed progress report can be provided to help them feel the effects of their rehabilitation. This allows the user to receive the most appropriate progress report based on their emotions.

[0122] AI rehabilitation support software can estimate the user's emotions and adjust rehabilitation goals based on those estimates. For example, based on data collected by the tracking unit, if the user is feeling anxious, the goal can be set lower. This makes it easier for the user to achieve their goals without undue pressure. The monitoring unit can also evaluate changes in the user's emotions in real time and adjust goal settings as needed. Furthermore, the feedback unit can provide specific advice tailored to the user's emotions. For example, if the user is relaxed, a slightly higher goal can be set to encourage them to take on a challenge. This allows the user to set optimal goals that match their emotions and engage in rehabilitation accordingly.

[0123] The following briefly describes the processing flow for example form 2.

[0124] Step 1: The tracking unit tracks the user's movements. The tracking unit tracks the user's movements in real time, for example, using built-in sensors. If the user is undergoing walking training, the sensors detect the movement of the feet and record the accurate walking pattern. If the user is undergoing arm rehabilitation, the sensors can also detect the movement of the arms and record the accurate movement pattern. If the user is performing daily living activities, the sensors can also detect the movements and record the accurate movement pattern. Step 2: The monitoring unit monitors the actions tracked by the tracking unit. For example, the monitoring unit can monitor the user's actions in real time and evaluate the accuracy of those actions. It can also periodically monitor the user's actions and record any changes in those actions. It can also monitor the user's actions over a long period of time and analyze any trends in those actions. Step 3: The feedback unit provides feedback based on the actions monitored by the monitoring unit. For example, the feedback unit provides verbal feedback if the user's movements are not ideal. Visual and haptic feedback can also be provided. Step 4: The support unit assists the user's movements based on the feedback provided by the feedback unit. For example, the support unit may move the garment itself to support the user's correct movements. External devices or rehabilitation equipment may also be used to assist the user's movements. Step 5: The mental support unit provides mental support based on the actions supported by the support unit. The mental support unit provides mental support, for example, through dialogue. It can also estimate the user's emotions and provide appropriate mental support. It can also monitor the user's psychological state and provide appropriate mental support.

[0125] 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.

[0126] Data generation model 58 is a form of 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> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. 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 (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0127] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0128] Each of the multiple elements described above, including the tracking unit, monitoring unit, feedback unit, support unit, and mental support unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the tracking unit tracks the user's movements in real time using the sensors of the smart device 14 and records the movement patterns using the specific processing unit 290 of the data processing unit 12. The monitoring unit monitors the user's movements using the control unit 46A of the smart device 14 and evaluates the accuracy of the movements using the specific processing unit 290 of the data processing unit 12. The feedback unit provides verbal, visual, and tactile feedback based on the user's movements using the specific processing unit 290 of the data processing unit 12. The support unit supports the user's correct movements by having the wear itself move using the control unit 46A of the smart device 14. The mental support unit provides conversational mental support using the control unit 46A of the smart device 14 and estimates the user's emotions using the specific processing unit 290 of the data processing unit 12 to provide appropriate mental support. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0129] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0130] 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.

[0131] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0132] 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.

[0133] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0134] 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, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0135] 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.

[0136] 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 by the processor 28. The storage 32 stores the specific processing program 56.

[0137] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0138] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0139] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0140] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0141] 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.

[0142] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. 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 inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0143] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0144] Each of the multiple elements described above, including the tracking unit, monitoring unit, feedback unit, support unit, and mental support unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the tracking unit tracks the user's movements in real time using the sensors of the smart glasses 214 and records the movement patterns using the specific processing unit 290 of the data processing unit 12. The monitoring unit monitors the user's movements using the control unit 46A of the smart glasses 214 and evaluates the accuracy of the movements using the specific processing unit 290 of the data processing unit 12. The feedback unit provides verbal, visual, and tactile feedback based on the user's movements using the specific processing unit 290 of the data processing unit 12. The support unit supports the user's correct movements by having the wearer itself move using the control unit 46A of the smart glasses 214. The mental support unit provides conversational mental support using the control unit 46A of the smart glasses 214 and estimates the user's emotions using the specific processing unit 290 of the data processing unit 12 to provide appropriate mental support. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0145] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0146] 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.

[0147] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0148] 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.

[0149] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0150] 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, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0151] 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.

[0152] 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.

[0153] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0154] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0155] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0156] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0157] 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.

[0158] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. 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 inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0159] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0160] Each of the multiple elements described above, including the tracking unit, monitoring unit, feedback unit, support unit, and mental support unit, is implemented, for example, by at least one of the headset terminal 314 and the data processing unit 12. For example, the tracking unit tracks the user's movements in real time using sensors in the headset terminal 314 and records the movement patterns using a specific processing unit 290 in the data processing unit 12. The monitoring unit monitors the user's movements using a control unit 46A in the headset terminal 314 and evaluates the accuracy of the movements using a specific processing unit 290 in the data processing unit 12. The feedback unit provides verbal, visual, and tactile feedback based on the user's movements using a specific processing unit 290 in the data processing unit 12. The support unit supports the user's correct movements by having the wear itself move using a control unit 46A in the headset terminal 314. The mental support unit provides conversational mental support using a control unit 46A in the headset terminal 314 and estimates the user's emotions using a specific processing unit 290 in the data processing unit 12 to provide appropriate mental support. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0161] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0162] 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.

[0163] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0164] 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.

[0165] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0166] 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 image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0167] 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.

[0168] 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. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0169] 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.

[0170] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0171] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0172] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0173] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0174] 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.

[0175] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. 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 inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0176] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0177] Each of the multiple elements described above, including the tracking unit, monitoring unit, feedback unit, support unit, and mental support unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the tracking unit tracks the user's movements in real time using the robot 414's sensors and records the movement patterns using the specific processing unit 290 of the data processing unit 12. The monitoring unit monitors the user's movements using, for example, the control unit 46A of the robot 414 and evaluates the accuracy of the movements using the specific processing unit 290 of the data processing unit 12. The feedback unit provides verbal, visual, and tactile feedback based on the user's movements using, for example, the specific processing unit 290 of the data processing unit 12. The support unit supports the user's correct movements by having the wear itself move using, for example, the control unit 46A of the robot 414. The mental support unit provides conversational mental support using, for example, the control unit 46A of the robot 414 and estimates the user's emotions using the specific processing unit 290 of the data processing unit 12 to provide appropriate mental support. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0178] 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.

[0179] Figure 9 shows the 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.

[0180] 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.

[0181] 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.

[0182] 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, and motorcycles, 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 based, for example, 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.

[0183] 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."

[0184] 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.

[0185] 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 method for the specific process may be used, which includes computer 22 and multiple other computers.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0194] 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 other things 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.

[0195] 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.

[0196] (Note 1) A tracking unit that tracks user actions, A monitoring unit that monitors the operation tracked by the aforementioned tracking unit, A feedback unit provides feedback based on the operation monitored by the monitoring unit, A support unit that supports the user's movements based on the feedback provided by the aforementioned feedback unit, The system includes a mental support unit that provides mental support based on the operations supported by the aforementioned support unit. A system characterized by the following features. (Note 2) The aforementioned tracking unit is Built-in sensors track user movements in real time. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned feedback unit is Provide verbal feedback when the user's actions are not ideal. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned support unit is The wearer itself moves to support the user's correct movements. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned mental support unit is Providing mental support through dialogue The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned support unit is We propose an appropriate training plan based on the user's rehabilitation progress. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned tracking unit is It estimates the user's emotions and adjusts the tracking accuracy based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned tracking unit is Analyze the user's past behavior data and select the optimal tracking method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned tracking unit is During tracking, the system focuses on specific parts of the user's body to obtain detailed data. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned tracking unit is It estimates the user's emotions and adjusts the tracking frequency based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned tracking unit is During tracking, the system prioritizes acquiring highly relevant data based on the user's living environment information. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned tracking unit is During tracking, the system analyzes the user's social media activity and obtains relevant behavioral data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The monitoring unit, It estimates the user's emotions and adjusts how monitoring is displayed based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The monitoring unit, During monitoring, past monitoring data is referenced to evaluate the current operation. The system described in Appendix 1, characterized by the features described herein. (Note 15) The monitoring unit, During monitoring, different monitoring methods are applied for each category of user behavior. The system described in Appendix 1, characterized by the features described herein. (Note 16) The monitoring unit, It estimates user sentiment and determines monitoring priorities based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 17) The monitoring unit, During monitoring, the monitoring order is adjusted based on when the user's actions were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 18) The monitoring unit, During monitoring, referencing relevant user literature improves the accuracy of the monitoring. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned feedback unit is It estimates the user's emotions and adjusts how feedback is expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned feedback unit is When providing feedback, adjust the level of detail in the feedback based on the importance of the action. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned feedback unit is During feedback, different feedback algorithms are applied depending on the category of the action. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned feedback unit is It estimates the user's emotions and adjusts the length of the feedback based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned feedback unit is When providing feedback, we prioritize feedback based on when the actions were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned feedback unit is During feedback, adjust the order of feedback based on the relevance of the actions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned support unit is It estimates the user's emotions and adjusts the support method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned support unit is During support, we analyze the user's past behavioral data to select the most suitable support method. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned support unit is During support, customize the support methods based on the user's current physical condition. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned support unit is The system estimates the user's emotions and determines support priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned support unit is During support, the optimal support method is selected based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned support unit is During support, we analyze the user's social media activity and suggest support methods. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned mental support unit is The system estimates the user's emotions and adjusts the method of mental support based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned mental support unit is When providing mental support, we refer to the user's past conversation history to provide the most appropriate support. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned mental support unit is During mental support, customize the support methods based on the user's current psychological state. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned mental support unit is It estimates the user's emotions and determines the priority of mental support based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned mental support unit is When providing mental support, the optimal support method is selected based on information about the user's living environment. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned mental support unit is During mental support, we analyze the user's social media activity and propose support methods. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned mental support unit is During mental support, the optimal support method is selected based on the user's rehabilitation progress. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0197] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A tracking unit that tracks user actions, A monitoring unit that monitors the operation tracked by the aforementioned tracking unit, A feedback unit provides feedback based on the operation monitored by the monitoring unit, A support unit that supports the user's movements based on the feedback provided by the aforementioned feedback unit, The system includes a mental support unit that provides mental support based on the operations supported by the aforementioned support unit. A system characterized by the following features.

2. The aforementioned tracking unit is Built-in sensors track user movements in real time. The system according to feature 1.

3. The aforementioned feedback unit is Provide verbal feedback when the user's actions are not ideal. The system according to feature 1.

4. The aforementioned support unit is The wearer itself moves to support the user's correct movements. The system according to feature 1.

5. The aforementioned mental support unit is Providing mental support through dialogue The system according to feature 1.

6. The aforementioned support unit is We propose an appropriate training plan based on the user's rehabilitation progress. The system according to feature 1.

7. The aforementioned tracking unit is It estimates the user's emotions and adjusts the tracking accuracy based on the estimated user emotions. The system according to feature 1.

8. The aforementioned tracking unit is Analyze the user's past behavior data and select the optimal tracking method. The system according to feature 1.

Images