System

Abstract

An object of the system according to the embodiment is to analyze a motion of a disabled person or an elderly person in real time, recognize an inappropriate motion or a risk of falling, and provide an appropriate instruction or advice.SOLUTION: In one embodiment, a system comprises a bionic sensor, a generation AI, a motion analyzer, and a notifier. The bionic sensor senses movement of the user. The generation AI analyzes the movements detected by the bionic sensor. The motion analysis unit recognizes an inappropriate motion or a risk of falling from the motion analyzed by the generation AI. The notification unit provides an instruction or advice to the user on the basis of the inappropriate motion or the risk of falling recognized by the motion analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology has the problem of not being able to adequately analyze the movements of physically disabled or elderly people in real time, recognize inappropriate movements or the risk of falling, and provide appropriate instructions and advice.

[0005] The system according to the embodiment aims to analyze the movements of physically disabled people and elderly people in real time, recognize inappropriate movements and the risk of falling, and provide appropriate instructions and advice. [Means for solving the problem]

[0006] A system according to an embodiment includes a bionic sensor, a generation AI, a motion analysis unit, and a notification unit. The bionic sensor detects a user's motion. The generation AI analyzes the motion detected by the bionic sensor. The motion analysis unit recognizes inappropriate motions and risk of falling from the motion analyzed by the generation AI. The notification unit provides instructions and advice to the user based on the inappropriate motions and risk of falling recognized by the motion analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can analyze the movements of physically disabled people and elderly people in real time, recognize inappropriate movements and the risk of falling, and provide appropriate instructions and advice. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10]1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

[0014] In the following embodiments, a communication I / F (Interface) with a symbol 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 including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

[0017] 1, a 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also 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 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may 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 a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The life assistance system according to an embodiment of the present invention combines bionic sensors and generative AI to provide life support for the physically disabled and elderly. This system detects the user's movements, which are analyzed by the generative AI, and recognizes inappropriate movements and the risk of falling, providing instructions and advice to the user. This allows the life assistance system to increase the user's safety and improve their quality of life.

[0029] A life assistance system according to an embodiment includes a bionic sensor, a generation AI, a motion analysis unit, and a notification unit. The bionic sensor detects a user's movements. For example, a sensor attached to a prosthetic arm or leg monitors the movements of the user's limbs in detail. The bionic sensor also acquires acceleration, angular velocity, position information, and the like to accurately grasp the user's movements. The generation AI analyzes the movements detected by the bionic sensor. For example, the generation AI analyzes movement patterns using deep learning to recognize inappropriate movements and the risk of falling. The generation AI can also learn changes in movement using reinforcement learning and take appropriate measures. The motion analysis unit recognizes inappropriate movements and the risk of falling from the movements analyzed by the generation AI. For example, the motion analysis unit recognizes movement patterns and detects abnormal movements. The motion analysis unit can also recognize the risk of falling using an anomaly detection algorithm. The notification unit provides instructions and advice to the user based on the inappropriate movements and the risk of falling recognized by the motion analysis unit. For example, the notification unit may issue a warning to the user using a voice notification. The notification unit may also provide advice to the user by sending a text message. Furthermore, the notification unit may also alert the user using a vibration alert. In this way, the life assistance system according to the embodiment can analyze the user's movements in real time and provide appropriate instructions and advice, thereby increasing the safety of the user and improving the quality of life.

[0030] Bionic sensors can detect additional biological signals such as myoelectric potential and skin temperature. For example, by detecting myoelectric potential, bionic sensors can analyze the user's muscle movements in detail. For example, when using a prosthetic hand, changes in myoelectric potential can be monitored in real time to accurately control hand movements. Bionic sensors can also detect skin temperature to analyze changes in the user's body temperature. For example, they can detect increases in body temperature during exercise and provide advice on appropriate rest. Bionic sensors can also detect heart rate to analyze the user's heart rate variability. For example, they can detect signs of stress or fatigue early and suggest appropriate countermeasures. This allows for more detailed analysis of movements.

[0031] Bionic sensors can incorporate algorithms that optimize sensor placement and dynamically adjust sensitivity according to the user's movements. For example, bionic sensors develop algorithms that optimize sensor placement according to the user's movements. For example, increasing sensor sensitivity around the ankles when walking reduces the risk of falls. Bionic sensors also build systems that dynamically adjust sensor sensitivity according to the user's movements. For example, increasing sensitivity when a sudden movement is detected to obtain more detailed data. Bionic sensors also incorporate algorithms that adjust sensor placement and sensitivity in real time to optimally monitor the user's movements. For example, automatically adjusting sensor sensitivity according to exercise intensity. This allows for optimal monitoring of the user's movements.

[0032] Bionic sensors can be incorporated into clothing and accessories, making them more natural to use in everyday life. For example, sensors can be incorporated into shirts or pants to monitor movement. Bionic sensors can also be incorporated into accessories, making them easier for users to use. For example, sensors can be incorporated into watches or bracelets to detect movement. Bionic sensors can also be incorporated into clothing and accessories to create systems that can monitor movement without the user being aware of it. For example, sensors can be incorporated into shoes to collect walking data. This allows movement to be monitored without the user being aware of it.

[0033] Bionic sensors can monitor the progress of rehabilitation and provide appropriate feedback. For example, bionic sensors will use sensors to build a system that monitors the progress of rehabilitation. For example, they will analyze joint movements in detail and evaluate the effectiveness of rehabilitation. Bionic sensors will also develop a system that detects movements during rehabilitation with sensors and provides appropriate feedback. For example, they will provide advice to encourage correct movements. Bionic sensors will also use sensors to monitor the progress of rehabilitation in real time and provide appropriate feedback to the user. For example, they will adjust the intensity and frequency of exercise. This will maximize the effectiveness of rehabilitation.

[0034] Generative AI can learn a user's movement patterns and perform individually optimized movement analysis. For example, generative AI can build a system that learns a user's movement patterns and performs individually optimized movement analysis. For example, it can learn a user's walking pattern and reduce the risk of falls. Generative AI can also collect data on a user's movement and perform individually optimized movement analysis based on that data. For example, it can optimize movements during rehabilitation. Generative AI can also learn a user's movement patterns and develop a system that provides individually optimized feedback. For example, it can provide advice to maximize the effectiveness of exercise. This makes it possible to perform individually optimized analysis of a user's movement.

[0035] When generative AI detects abnormal movement, it can identify the cause and propose specific improvement measures. For example, generative AI will build a system that, when it detects abnormal movement, identifies the cause and proposes specific improvement measures. For example, it will analyze the cause of loss of balance and propose appropriate measures. Furthermore, when generative AI detects abnormal movement, it will identify the cause and provide the user with specific improvement measures. For example, it will provide advice on improving posture when walking. Furthermore, generative AI will develop an algorithm that detects abnormal movement and identifies its cause. For example, if the cause is decreased muscle strength, it will suggest strength training. This will enable the cause of abnormal movement to be identified and appropriate improvement measures to be provided.

[0036] Generative AI can integrate a user's movement data with other health data to assess their overall health. For example, generative AI can integrate a user's movement data with other health data such as heart rate and blood pressure to build a system that assesses their overall health. For example, it can analyze heart rate fluctuations during exercise. Generative AI can also integrate a user's movement data with other health data to assess their overall health. For example, it can analyze the relationship between blood pressure fluctuations and movement. Generative AI can also integrate movement data with other health data to develop a system that comprehensively assesses a user's health. For example, it can analyze the relationship between exercise intensity and heart rate. This allows it to assess a user's overall health.

[0037] Generative AI can create personalized exercise programs based on user movement data. Generative AI, for example, builds a system that creates individually optimized exercise programs based on user movement data. For example, it suggests exercise menus based on the user's physical strength and goals. Generative AI also analyzes user movement data to create personalized exercise programs. For example, it suggests exercises suitable for users undergoing rehabilitation. Generative AI also develops a system that provides individually optimized exercise programs based on user movement data. For example, it creates a program to maximize the effects of exercise. This makes it possible to provide users with an optimized exercise program.

[0038] Generative AI can provide optimal instructions and advice in real time based on data on the user's movements. For example, generative AI could build a system that provides optimal instructions and advice in real time based on data on the user's movements. For example, if the user loses balance while walking, the system could immediately instruct the user to "correct their posture." Generative AI could also analyze the user's movement data and provide appropriate advice in real time. For example, the system could advise the user to "use the handrail" when climbing stairs. Generative AI could also develop a system that provides optimal instructions in real time based on data on the user's movements. For example, the system could instruct the user to "slow down" while exercising. This would allow the user to receive appropriate instructions and advice in real time.

[0039] Generative AI can analyze a user's past movement data and suggest long-term improvement measures. For example, generative AI can analyze a user's past movement data and build a system that suggests long-term improvement measures. For example, it can suggest improvements to exercise habits based on past exercise data. Generative AI can also analyze a user's past movement data and provide long-term improvement measures. For example, it can evaluate rehabilitation progress and suggest next steps. Generative AI can also develop a system that suggests long-term improvement measures based on a user's past movement data. For example, it can create a training plan to maximize the effects of exercise. This can provide long-term improvement measures for the user's movement.

[0040] The generative AI can provide advice to improve the efficiency of daily life actions based on data on the user's movements. For example, the generative AI builds a system that provides advice to improve the efficiency of daily life actions based on data on the user's movements. For example, it suggests actions to perform housework efficiently. The generative AI also analyzes data on the user's movements and provides specific advice to improve the efficiency of daily life actions. For example, it optimizes the flow of movement when shopping. The generative AI also develops a system that provides advice to improve the efficiency of daily life actions based on data on the user's movements. For example, it makes suggestions to improve the efficiency of actions while cooking. This makes it possible to provide advice to improve the efficiency of daily life actions.

[0041] Generative AI can evaluate rehabilitation progress and provide appropriate feedback based on user movement data. Generative AI, for example, builds a system that evaluates rehabilitation progress and provides appropriate feedback based on user movement data. For example, it evaluates the range of motion of joints and measures the effectiveness of rehabilitation. Generative AI also analyzes user movement data and evaluates rehabilitation progress. For example, it evaluates the state of muscle recovery and suggests the next step. Generative AI also develops a system that evaluates rehabilitation progress and provides appropriate feedback based on user movement data. For example, it provides advice to maximize the effectiveness of rehabilitation. This makes it possible to evaluate rehabilitation progress and provide appropriate feedback.

[0042] The generation AI can automatically generate and send a detailed situation report to emergency contacts when it detects abnormal movement. For example, the generation AI will build a system that automatically generates and sends a detailed situation report to emergency contacts when it detects abnormal movement. For example, it will report detailed information about a fall. The generation AI will also send a detailed situation report to emergency contacts when it detects abnormal movement. For example, it will send a report that includes the user's location and movement data. The generation AI will also develop a system that detects abnormal movement and automatically generates and sends a detailed situation report to emergency contacts. For example, it will report detailed information about the cause and circumstances of a fall. This allows detailed situation reports to be sent quickly when abnormal movement is detected.

[0043] The generating AI can send a notification including the user's location information when it detects abnormal movement. For example, the generating AI builds a system that sends a notification including the user's location information to an emergency contact when it detects abnormal movement. For example, it sends detailed location information of the location where the user fell. The generating AI also sends a notification including the user's location information when it detects abnormal movement. For example, it reports the user's current location based on GPS data. The generating AI also develops a system that detects abnormal movement and sends a notification including the user's location information to an emergency contact. For example, it sends location information if the user becomes unable to move. This makes it possible to send a notification including the user's location information when abnormal movement is detected.

[0044] When generative AI detects abnormal movement, it can send a direct notification to a medical institution, encouraging a rapid response. For example, generative AI will build a system that sends a direct notification to a medical institution when it detects abnormal movement, encouraging a rapid response. For example, it will dispatch an ambulance when a user falls. Generative AI will also send a direct notification to a medical institution when it detects abnormal movement. For example, it will contact a medical institution if the user's health condition suddenly changes. Generative AI will also develop a system that detects abnormal movement and sends a direct notification to a medical institution. For example, it will contact a medical institution if the user loses consciousness. This will allow a notification to be sent to a medical institution quickly when abnormal movement is detected.

[0045] When abnormal movement is detected, the generating AI can reference the user's past health data and provide it to emergency contacts. For example, the generating AI will build a system that references the user's past health data and provides it to emergency contacts when it detects abnormal movement. For example, it will provide past medical history and allergy information. The generating AI will also reference the user's past health data when it detects abnormal movement and provide it to emergency contacts. For example, it will report on past fall history and rehabilitation progress. The generating AI will also develop a system that detects abnormal movement and references the user's past health data and provides it to emergency contacts. For example, it will provide the user's medication history. This will allow the user's past health data to be provided quickly when abnormal movement is detected.

[0046] The generative AI can provide specific advice to improve the efficiency of daily life movements based on data on the user's movements. For example, the generative AI builds a system that provides specific advice to improve the efficiency of daily life movements based on data on the user's movements. For example, it suggests movements to perform housework efficiently. The generative AI also analyzes data on the user's movements and provides specific advice to improve the efficiency of daily life movements. For example, it optimizes the flow of movement when shopping. The generative AI also develops a system that provides advice to improve the efficiency of daily life movements based on data on the user's movements. For example, it makes suggestions to improve the efficiency of movements while cooking. This makes it possible to provide specific advice to improve the efficiency of daily life movements.

[0047] Generative AI can evaluate rehabilitation progress and provide appropriate feedback based on user movement data. Generative AI, for example, builds a system that evaluates rehabilitation progress and provides appropriate feedback based on user movement data. For example, it evaluates the range of motion of joints and measures the effectiveness of rehabilitation. Generative AI also analyzes user movement data and evaluates rehabilitation progress. For example, it evaluates the state of muscle recovery and suggests the next step. Generative AI also develops a system that evaluates rehabilitation progress and provides appropriate feedback based on user movement data. For example, it provides advice to maximize the effectiveness of rehabilitation. This makes it possible to evaluate rehabilitation progress and provide appropriate feedback.

[0048] The generative AI can provide advice to improve the efficiency of daily life actions based on data on the user's movements. For example, the generative AI builds a system that provides advice to improve the efficiency of daily life actions based on data on the user's movements. For example, it suggests actions to perform housework efficiently. The generative AI also analyzes data on the user's movements and provides specific advice to improve the efficiency of daily life actions. For example, it optimizes the flow of movement when shopping. The generative AI also develops a system that provides advice to improve the efficiency of daily life actions based on data on the user's movements. For example, it makes suggestions to improve the efficiency of actions while cooking. This makes it possible to provide advice to improve the efficiency of daily life actions.

[0049] Generative AI can evaluate rehabilitation progress and provide appropriate feedback based on user movement data. Generative AI, for example, builds a system that evaluates rehabilitation progress and provides appropriate feedback based on user movement data. For example, it evaluates the range of motion of joints and measures the effectiveness of rehabilitation. Generative AI also analyzes user movement data and evaluates rehabilitation progress. For example, it evaluates the state of muscle recovery and suggests the next step. Generative AI also develops a system that evaluates rehabilitation progress and provides appropriate feedback based on user movement data. For example, it provides advice to maximize the effectiveness of rehabilitation. This makes it possible to evaluate rehabilitation progress and provide appropriate feedback.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The life assistance system can provide advice to improve the efficiency of daily activities based on data on the user's movements. For example, it can suggest movements to perform housework efficiently. It can also analyze the user's movement data and provide specific advice to improve the efficiency of daily activities. For example, it can optimize the flow of people when shopping. We will also develop a system that provides advice to improve the efficiency of daily activities based on data on the user's movements. For example, it can make suggestions to improve the efficiency of cooking. This makes it possible to provide advice to improve the efficiency of daily activities.

[0052] A life assistance system can evaluate rehabilitation progress and provide appropriate feedback based on data on the user's movements. For example, it can evaluate the range of motion of joints and measure the effectiveness of rehabilitation. It can also analyze the user's movement data to evaluate rehabilitation progress. For example, it can evaluate the state of muscle recovery and suggest the next step. It can also develop a system that evaluates rehabilitation progress and provides appropriate feedback based on the user's movement data. For example, it can provide advice to maximize the effectiveness of rehabilitation. This makes it possible to evaluate rehabilitation progress and provide appropriate feedback.

[0053] The life assistance system can automatically generate and send a detailed situation report to emergency contacts when it detects abnormal movement. For example, it can report the details of a fall. Also, when abnormal movement is detected, a detailed situation report can be sent to emergency contacts. For example, a report including the user's location information and movement data can be sent. We will also develop a system that detects abnormal movement and automatically generates and sends a detailed situation report to emergency contacts. For example, it can report the cause and circumstances of a fall in detail. This allows a detailed situation report to be sent quickly when abnormal movement is detected.

[0054] The life assistance system can provide specific advice to improve the efficiency of daily activities based on data on the user's movements. For example, it can suggest movements to perform housework efficiently. It can also analyze data on the user's movements and provide specific advice to improve the efficiency of daily activities. For example, it can optimize the flow of people when shopping. It can also develop a system that provides advice to improve the efficiency of daily activities based on data on the user's movements. For example, it can make suggestions to improve the efficiency of cooking. This makes it possible to provide specific advice to improve the efficiency of daily activities.

[0055] A life assistance system can evaluate rehabilitation progress and provide appropriate feedback based on data on the user's movements. For example, it can evaluate the range of motion of joints and measure the effectiveness of rehabilitation. It can also analyze the user's movement data to evaluate rehabilitation progress. For example, it can evaluate the state of muscle recovery and suggest the next step. It can also develop a system that evaluates rehabilitation progress and provides appropriate feedback based on the user's movement data. For example, it can provide advice to maximize the effectiveness of rehabilitation. This makes it possible to evaluate rehabilitation progress and provide appropriate feedback.

[0056] The life assistance system can provide advice to improve the efficiency of daily activities based on data on the user's movements. For example, it can suggest movements to perform housework efficiently. It can also analyze the user's movement data and provide specific advice to improve the efficiency of daily activities. For example, it can optimize the flow of people when shopping. We will also develop a system that provides advice to improve the efficiency of daily activities based on data on the user's movements. For example, it can make suggestions to improve the efficiency of cooking. This makes it possible to provide advice to improve the efficiency of daily activities.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: Bionic sensors detect the user's movements. For example, sensors attached to a prosthetic arm or leg monitor the movements of the user's limbs in detail. The bionic sensors also acquire acceleration, angular velocity, position information, and other data to accurately grasp the user's movements. Step 2: The generative AI analyzes the movements detected by the bionic sensors. For example, the generative AI can use deep learning to analyze movement patterns and recognize inappropriate movements or risk of falling. The generative AI can also use reinforcement learning to learn changes in movement and respond appropriately. Step 3: The motion analysis unit recognizes inappropriate movements and the risk of falling from the movements analyzed by the generation AI. For example, the motion analysis unit performs pattern recognition of movements and detects abnormal movements. The motion analysis unit can also recognize the risk of falling using an anomaly detection algorithm. Step 4: The notification unit provides instructions or advice to the user based on the improper movements or risk of falling recognized by the movement analysis unit. For example, the notification unit may alert the user using a voice notification. The notification unit may also provide advice to the user by sending a text message. Furthermore, the notification unit may also alert the user using a vibration alert.

[0059] (Example 2) The life assistance system according to an embodiment of the present invention combines bionic sensors and generative AI to provide life support for the physically disabled and elderly. This system detects the user's movements, which are analyzed by the generative AI, and recognizes inappropriate movements and the risk of falling, providing instructions and advice to the user. This allows the life assistance system to increase the user's safety and improve their quality of life.

[0060] A life assistance system according to an embodiment includes a bionic sensor, a generation AI, a motion analysis unit, and a notification unit. The bionic sensor detects a user's movements. For example, a sensor attached to a prosthetic arm or leg monitors the movements of the user's limbs in detail. The bionic sensor also acquires acceleration, angular velocity, position information, and the like to accurately grasp the user's movements. The generation AI analyzes the movements detected by the bionic sensor. For example, the generation AI analyzes movement patterns using deep learning to recognize inappropriate movements and the risk of falling. The generation AI can also learn changes in movement using reinforcement learning and take appropriate measures. The motion analysis unit recognizes inappropriate movements and the risk of falling from the movements analyzed by the generation AI. For example, the motion analysis unit recognizes movement patterns and detects abnormal movements. The motion analysis unit can also recognize the risk of falling using an anomaly detection algorithm. The notification unit provides instructions and advice to the user based on the inappropriate movements and the risk of falling recognized by the motion analysis unit. For example, the notification unit may issue a warning to the user using a voice notification. The notification unit may also provide advice to the user by sending a text message. Furthermore, the notification unit may also alert the user using a vibration alert. In this way, the life assistance system according to the embodiment can analyze the user's movements in real time and provide appropriate instructions and advice, thereby increasing the safety of the user and improving the quality of life.

[0061] Bionic sensors can detect additional biological signals such as myoelectric potential and skin temperature. For example, by detecting myoelectric potential, bionic sensors can analyze the user's muscle movements in detail. For example, when using a prosthetic hand, changes in myoelectric potential can be monitored in real time to accurately control hand movements. Bionic sensors can also detect skin temperature to analyze changes in the user's body temperature. For example, they can detect increases in body temperature during exercise and provide advice on appropriate rest. Bionic sensors can also detect heart rate to analyze the user's heart rate variability. For example, they can detect signs of stress or fatigue early and suggest appropriate countermeasures. This allows for more detailed analysis of movements.

[0062] Bionic sensors can incorporate algorithms that optimize sensor placement and dynamically adjust sensitivity according to the user's movements. For example, bionic sensors develop algorithms that optimize sensor placement according to the user's movements. For example, increasing sensor sensitivity around the ankles when walking reduces the risk of falls. Bionic sensors also build systems that dynamically adjust sensor sensitivity according to the user's movements. For example, increasing sensitivity when a sudden movement is detected to obtain more detailed data. Bionic sensors also incorporate algorithms that adjust sensor placement and sensitivity in real time to optimally monitor the user's movements. For example, automatically adjusting sensor sensitivity according to exercise intensity. This allows for optimal monitoring of the user's movements.

[0063] The bionic sensor can use an emotion estimation function to detect a user's emotional state in real time and analyze their emotional response to changes in movement. For example, the bionic sensor uses the emotion estimation function to detect a user's emotional state in real time from their facial expressions and voice. For example, if the user is feeling anxious, the bionic sensor can analyze their movement changes in detail. The bionic sensor can also analyze the user's emotional state and build a system that understands their emotional response to changes in movement. For example, it can identify movement patterns that occur when stress levels rise. The bionic sensor can also analyze the user's emotional response to changes in movement based on the emotion estimation data and provide appropriate feedback. For example, it can provide advice on how to relax. This makes it possible to analyze movements taking the user's emotional state into account.

[0064] Bionic sensors can be incorporated into clothing and accessories, making them more natural to use in everyday life. For example, sensors can be incorporated into shirts or pants to monitor movement. Bionic sensors can also be incorporated into accessories, making them easier for users to use. For example, sensors can be incorporated into watches or bracelets to detect movement. Bionic sensors can also be incorporated into clothing and accessories to create systems that can monitor movement without the user being aware of it. For example, sensors can be incorporated into shoes to collect walking data. This allows movement to be monitored without the user being aware of it.

[0065] Bionic sensors can monitor the progress of rehabilitation and provide appropriate feedback. For example, bionic sensors will use sensors to build a system that monitors the progress of rehabilitation. For example, they will analyze joint movements in detail and evaluate the effectiveness of rehabilitation. Bionic sensors will also develop a system that detects movements during rehabilitation with sensors and provides appropriate feedback. For example, they will provide advice to encourage correct movements. Bionic sensors will also use sensors to monitor the progress of rehabilitation in real time and provide appropriate feedback to the user. For example, they will adjust the intensity and frequency of exercise. This will maximize the effectiveness of rehabilitation.

[0066] The bionic sensor can use its emotion estimation function to provide advice on how to relax when the user is feeling stressed. For example, the bionic sensor uses its emotion estimation function to build a system that provides advice on how to relax when the user is feeling stressed. For example, it suggests deep breathing or stretching. The bionic sensor also analyzes the user's emotional state in real time and provides advice on how to relax when stress increases. For example, it suggests playing relaxing music. The bionic sensor also provides specific advice on how to relax when the user is feeling stressed based on the emotion estimation data. For example, it suggests meditation or relaxation exercises. This helps reduce the user's stress.

[0067] Generative AI can learn a user's movement patterns and perform individually optimized movement analysis. For example, generative AI can build a system that learns a user's movement patterns and performs individually optimized movement analysis. For example, it can learn a user's walking pattern and reduce the risk of falls. Generative AI can also collect data on a user's movement and perform individually optimized movement analysis based on that data. For example, it can optimize movements during rehabilitation. Generative AI can also learn a user's movement patterns and develop a system that provides individually optimized feedback. For example, it can provide advice to maximize the effectiveness of exercise. This makes it possible to perform individually optimized analysis of a user's movement.

[0068] When generative AI detects abnormal movement, it can identify the cause and propose specific improvement measures. For example, generative AI will build a system that, when it detects abnormal movement, identifies the cause and proposes specific improvement measures. For example, it will analyze the cause of loss of balance and propose appropriate measures. Furthermore, when generative AI detects abnormal movement, it will identify the cause and provide the user with specific improvement measures. For example, it will provide advice on improving posture when walking. Furthermore, generative AI will develop an algorithm that detects abnormal movement and identifies its cause. For example, if the cause is decreased muscle strength, it will suggest strength training. This will enable the cause of abnormal movement to be identified and appropriate improvement measures to be provided.

[0069] The generative AI can use the emotion estimation function to analyze movements taking into account the user's emotional state and provide advice to reduce emotional stress. For example, the generative AI uses the emotion estimation function to build a system that analyzes movements taking into account the user's emotional state. For example, it can suggest exercises to help relax when stress levels rise. The generative AI can also analyze the user's emotional state in real time and provide specific advice to reduce emotional stress. For example, it can suggest exercises to relieve stress. The generative AI can also develop a system that analyzes movements taking into account the user's emotional state based on the emotion estimation data and provides appropriate feedback. For example, it can suggest breathing techniques to help relax. This makes it possible to analyze movements taking into account the user's emotional state, thereby reducing emotional stress.

[0070] Generative AI can integrate a user's movement data with other health data to assess their overall health. For example, generative AI can integrate a user's movement data with other health data such as heart rate and blood pressure to build a system that assesses their overall health. For example, it can analyze heart rate fluctuations during exercise. Generative AI can also integrate a user's movement data with other health data to assess their overall health. For example, it can analyze the relationship between blood pressure fluctuations and movement. Generative AI can also integrate movement data with other health data to develop a system that comprehensively assesses a user's health. For example, it can analyze the relationship between exercise intensity and heart rate. This allows it to assess a user's overall health.

[0071] Generative AI can create personalized exercise programs based on user movement data. Generative AI, for example, builds a system that creates individually optimized exercise programs based on user movement data. For example, it suggests exercise menus based on the user's physical strength and goals. Generative AI also analyzes user movement data to create personalized exercise programs. For example, it suggests exercises suitable for users undergoing rehabilitation. Generative AI also develops a system that provides individually optimized exercise programs based on user movement data. For example, it creates a program to maximize the effects of exercise. This makes it possible to provide users with an optimized exercise program.

[0072] The generative AI can use the emotion estimation function to reinforce movements that evoke positive emotions in the user and reduce movements that evoke negative emotions. For example, the generative AI uses the emotion estimation function to build a system that reinforces movements that evoke positive emotions in the user. For example, it can recommend exercises that the user enjoys. The generative AI can also analyze the user's emotional state and reinforce movements that evoke positive emotions and reduce movements that evoke negative emotions. For example, it can advise the user to avoid movements that cause stress. The generative AI can also use emotion estimation data to develop a system that reinforces movements that evoke positive emotions in the user and reduce movements that evoke negative emotions. For example, it can suggest exercises to help the user relax. This can bring out positive emotions in the user and reduce negative emotions.

[0073] Generative AI can provide optimal instructions and advice in real time based on data on the user's movements. For example, generative AI could build a system that provides optimal instructions and advice in real time based on data on the user's movements. For example, if the user loses balance while walking, the system could immediately instruct the user to "correct their posture." Generative AI could also analyze the user's movement data and provide appropriate advice in real time. For example, the system could advise the user to "use the handrail" when climbing stairs. Generative AI could also develop a system that provides optimal instructions in real time based on data on the user's movements. For example, the system could instruct the user to "slow down" while exercising. This would allow the user to receive appropriate instructions and advice in real time.

[0074] Generative AI can analyze a user's past movement data and suggest long-term improvement measures. For example, generative AI can analyze a user's past movement data and build a system that suggests long-term improvement measures. For example, it can suggest improvements to exercise habits based on past exercise data. Generative AI can also analyze a user's past movement data and provide long-term improvement measures. For example, it can evaluate rehabilitation progress and suggest next steps. Generative AI can also develop a system that suggests long-term improvement measures based on a user's past movement data. For example, it can create a training plan to maximize the effects of exercise. This can provide long-term improvement measures for the user's movement.

[0075] Using the emotion estimation function, the generative AI can provide emotional support by providing instructions and advice according to the user's emotional state. For example, using the emotion estimation function, the generative AI will build a system that provides instructions and advice according to the user's emotional state. For example, if the user is feeling anxious, the system may advise them to "relax." The generative AI will also analyze the user's emotional state in real time and provide emotional support. For example, when stress levels rise, the system may advise them to "take a deep breath." The generative AI will also develop a system that provides instructions and advice according to the user's emotional state based on the emotion estimation data. For example, it may provide advice to elicit positive emotions. This will enable support according to the user's emotional state.

[0076] The generative AI can provide advice to improve the efficiency of daily life actions based on data on the user's movements. For example, the generative AI builds a system that provides advice to improve the efficiency of daily life actions based on data on the user's movements. For example, it suggests actions to perform housework efficiently. The generative AI also analyzes data on the user's movements and provides specific advice to improve the efficiency of daily life actions. For example, it optimizes the flow of movement when shopping. The generative AI also develops a system that provides advice to improve the efficiency of daily life actions based on data on the user's movements. For example, it makes suggestions to improve the efficiency of actions while cooking. This makes it possible to provide advice to improve the efficiency of daily life actions.

[0077] Generative AI can evaluate rehabilitation progress and provide appropriate feedback based on user movement data. Generative AI, for example, builds a system that evaluates rehabilitation progress and provides appropriate feedback based on user movement data. For example, it evaluates the range of motion of joints and measures the effectiveness of rehabilitation. Generative AI also analyzes user movement data and evaluates rehabilitation progress. For example, it evaluates the state of muscle recovery and suggests the next step. Generative AI also develops a system that evaluates rehabilitation progress and provides appropriate feedback based on user movement data. For example, it provides advice to maximize the effectiveness of rehabilitation. This makes it possible to evaluate rehabilitation progress and provide appropriate feedback.

[0078] Using the emotion estimation function, the generative AI can provide instructions and advice that will make the user feel positive emotions. For example, using the emotion estimation function, the generative AI can build a system that provides instructions and advice that will make the user feel positive emotions. For example, it can recommend exercises that the user enjoys. The generative AI can also analyze the user's emotional state and provide specific advice to elicit positive emotions. For example, it can suggest exercises to help them relax. The generative AI can also develop a system that provides instructions and advice that will make the user feel positive emotions based on the emotion estimation data. For example, it can suggest music that will elicit positive emotions. This makes it possible to provide instructions and advice that will make the user feel positive emotions.

[0079] The generation AI can automatically generate and send a detailed situation report to emergency contacts when it detects abnormal movement. For example, the generation AI will build a system that automatically generates and sends a detailed situation report to emergency contacts when it detects abnormal movement. For example, it will report detailed information about a fall. The generation AI will also send a detailed situation report to emergency contacts when it detects abnormal movement. For example, it will send a report that includes the user's location and movement data. The generation AI will also develop a system that detects abnormal movement and automatically generates and sends a detailed situation report to emergency contacts. For example, it will report detailed information about the cause and circumstances of a fall. This allows detailed situation reports to be sent quickly when abnormal movement is detected.

[0080] The generating AI can send a notification including the user's location information when it detects abnormal movement. For example, the generating AI builds a system that sends a notification including the user's location information to an emergency contact when it detects abnormal movement. For example, it sends detailed location information of the location where the user fell. The generating AI also sends a notification including the user's location information when it detects abnormal movement. For example, it reports the user's current location based on GPS data. The generating AI also develops a system that detects abnormal movement and sends a notification including the user's location information to an emergency contact. For example, it sends location information if the user becomes unable to move. This makes it possible to send a notification including the user's location information when abnormal movement is detected.

[0081] When generative AI detects abnormal movement, it can send a direct notification to a medical institution, encouraging a rapid response. For example, generative AI will build a system that sends a direct notification to a medical institution when it detects abnormal movement, encouraging a rapid response. For example, it will dispatch an ambulance when a user falls. Generative AI will also send a direct notification to a medical institution when it detects abnormal movement. For example, it will contact a medical institution if the user's health condition suddenly changes. Generative AI will also develop a system that detects abnormal movement and sends a direct notification to a medical institution. For example, it will contact a medical institution if the user loses consciousness. This will allow a notification to be sent to a medical institution quickly when abnormal movement is detected.

[0082] When abnormal movement is detected, the generating AI can reference the user's past health data and provide it to emergency contacts. For example, the generating AI will build a system that references the user's past health data and provides it to emergency contacts when it detects abnormal movement. For example, it will provide past medical history and allergy information. The generating AI will also reference the user's past health data when it detects abnormal movement and provide it to emergency contacts. For example, it will report on past fall history and rehabilitation progress. The generating AI will also develop a system that detects abnormal movement and references the user's past health data and provides it to emergency contacts. For example, it will provide the user's medication history. This will allow the user's past health data to be provided quickly when abnormal movement is detected.

[0083] The generation AI can use the emotion estimation function to send notifications that take into account the emotional state of the user when faced with an emergency. For example, the generation AI uses the emotion estimation function to build a system that sends notifications that take into account the emotional state of the user when faced with an emergency. For example, if the user is in a state of panic, the system notifies that information. The generation AI also analyzes the user's emotional state in real time and sends notifications that take into account the emotional state in an emergency. For example, if the user is feeling anxious, the system notifies that information. The generation AI also develops a system that sends notifications that take into account the emotional state of the user when faced with an emergency, based on the emotion estimation data. For example, the system provides advice to help the user stay calm. This makes it possible to send notifications that take into account the emotional state of the user when faced with an emergency.

[0084] The generative AI can provide specific advice to improve the efficiency of daily life movements based on data on the user's movements. For example, the generative AI builds a system that provides specific advice to improve the efficiency of daily life movements based on data on the user's movements. For example, it suggests movements to perform housework efficiently. The generative AI also analyzes data on the user's movements and provides specific advice to improve the efficiency of daily life movements. For example, it optimizes the flow of movement when shopping. The generative AI also develops a system that provides advice to improve the efficiency of daily life movements based on data on the user's movements. For example, it makes suggestions to improve the efficiency of movements while cooking. This makes it possible to provide specific advice to improve the efficiency of daily life movements.

[0085] Generative AI can evaluate rehabilitation progress and provide appropriate feedback based on user movement data. Generative AI, for example, builds a system that evaluates rehabilitation progress and provides appropriate feedback based on user movement data. For example, it evaluates the range of motion of joints and measures the effectiveness of rehabilitation. Generative AI also analyzes user movement data and evaluates rehabilitation progress. For example, it evaluates the state of muscle recovery and suggests the next step. Generative AI also develops a system that evaluates rehabilitation progress and provides appropriate feedback based on user movement data. For example, it provides advice to maximize the effectiveness of rehabilitation. This makes it possible to evaluate rehabilitation progress and provide appropriate feedback.

[0086] Using the emotion estimation function, the generative AI can provide lifestyle support that helps users feel positive emotions. For example, the generative AI uses the emotion estimation function to build a system that provides lifestyle support that helps users feel positive emotions. For example, it can recommend exercises that the user enjoys. The generative AI can also analyze the user's emotional state and provide specific lifestyle support to elicit positive emotions. For example, it can suggest exercises for relaxation. The generative AI can also develop a system that provides lifestyle support that helps users feel positive emotions based on the emotion estimation data. For example, it can suggest music that elicits positive emotions. This makes it possible to provide lifestyle support that helps users feel positive emotions.

[0087] The generative AI can provide advice to improve the efficiency of daily life actions based on data on the user's movements. For example, the generative AI builds a system that provides advice to improve the efficiency of daily life actions based on data on the user's movements. For example, it suggests actions to perform housework efficiently. The generative AI also analyzes data on the user's movements and provides specific advice to improve the efficiency of daily life actions. For example, it optimizes the flow of movement when shopping. The generative AI also develops a system that provides advice to improve the efficiency of daily life actions based on data on the user's movements. For example, it makes suggestions to improve the efficiency of actions while cooking. This makes it possible to provide advice to improve the efficiency of daily life actions.

[0088] Generative AI can evaluate rehabilitation progress and provide appropriate feedback based on user movement data. Generative AI, for example, builds a system that evaluates rehabilitation progress and provides appropriate feedback based on user movement data. For example, it evaluates the range of motion of joints and measures the effectiveness of rehabilitation. Generative AI also analyzes user movement data and evaluates rehabilitation progress. For example, it evaluates the state of muscle recovery and suggests the next step. Generative AI also develops a system that evaluates rehabilitation progress and provides appropriate feedback based on user movement data. For example, it provides advice to maximize the effectiveness of rehabilitation. This makes it possible to evaluate rehabilitation progress and provide appropriate feedback.

[0089] Using the emotion estimation function, the generative AI can provide lifestyle support that helps users feel positive emotions. For example, the generative AI uses the emotion estimation function to build a system that provides lifestyle support that helps users feel positive emotions. For example, it can recommend exercises that the user enjoys. The generative AI can also analyze the user's emotional state and provide specific lifestyle support to elicit positive emotions. For example, it can suggest exercises for relaxation. The generative AI can also develop a system that provides lifestyle support that helps users feel positive emotions based on the emotion estimation data. For example, it can suggest music that elicits positive emotions. This makes it possible to provide lifestyle support that helps users feel positive emotions.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The life assistance system can provide advice to improve the efficiency of daily activities based on data on the user's movements. For example, it can suggest movements to perform housework efficiently. It can also analyze data on the user's movements and provide specific advice to improve the efficiency of daily activities. For example, it can optimize the flow of people when shopping. We will also develop a system that provides advice to improve the efficiency of daily activities based on data on the user's movements. For example, it can make suggestions to improve the efficiency of cooking. This makes it possible to provide advice to improve the efficiency of daily activities.

[0092] A life assistance system can evaluate rehabilitation progress and provide appropriate feedback based on data on the user's movements. For example, it can evaluate the range of motion of joints and measure the effectiveness of rehabilitation. It can also analyze the user's movement data to evaluate rehabilitation progress. For example, it can evaluate the state of muscle recovery and suggest the next step. It can also develop a system that evaluates rehabilitation progress and provides appropriate feedback based on the user's movement data. For example, it can provide advice to maximize the effectiveness of rehabilitation. This makes it possible to evaluate rehabilitation progress and provide appropriate feedback.

[0093] Using emotion estimation functions, the life assistance system can provide instructions and advice that will make the user feel positive emotions. For example, it can recommend exercises that the user enjoys. It can also analyze the user's emotional state and provide specific advice to elicit positive emotions. For example, it can suggest exercises to help them relax. We will also develop a system that provides instructions and advice that will make the user feel positive emotions based on emotion estimation data. For example, it can suggest music that will elicit positive emotions. This will enable it to provide instructions and advice that will make the user feel positive emotions.

[0094] The life assistance system can automatically generate and send a detailed situation report to emergency contacts when it detects abnormal movement. For example, it can report the details of a fall. Also, when abnormal movement is detected, a detailed situation report can be sent to emergency contacts. For example, a report including the user's location information and movement data can be sent. We will also develop a system that detects abnormal movement and automatically generates and sends a detailed situation report to emergency contacts. For example, it can report the cause and circumstances of a fall in detail. This allows a detailed situation report to be sent quickly when abnormal movement is detected.

[0095] Using the emotion estimation function, the life assistance system can send notifications that take into account the emotional state of the user when faced with an emergency. For example, if the user is in a state of panic, the system can notify that information. The system can also analyze the user's emotional state in real time and send notifications that take into account the emotional state in an emergency. For example, if the user is feeling anxious, the system can notify that information. We will also develop a system that uses emotion estimation data to send notifications that take into account the emotional state of the user when faced with an emergency. For example, the system can provide advice to the user on how to stay calm. This makes it possible to send notifications that take into account the emotional state of the user when faced with an emergency.

[0096] The life assistance system can provide specific advice to improve the efficiency of daily activities based on data on the user's movements. For example, it can suggest movements to perform housework efficiently. It can also analyze data on the user's movements and provide specific advice to improve the efficiency of daily activities. For example, it can optimize the flow of people when shopping. It can also develop a system that provides advice to improve the efficiency of daily activities based on data on the user's movements. For example, it can make suggestions to improve the efficiency of cooking. This makes it possible to provide specific advice to improve the efficiency of daily activities.

[0097] Using the emotion estimation function, the life assistance system can provide life assistance that helps users to feel positive emotions. For example, it can recommend exercises that the user enjoys. It can also analyze the user's emotional state and provide specific life assistance to elicit positive emotions. For example, it can suggest exercises for relaxation. It can also develop a system that provides life assistance that helps users to feel positive emotions based on emotion estimation data. For example, it can suggest music that elicits positive emotions. This makes it possible to provide life assistance that helps users to feel positive emotions.

[0098] A life assistance system can evaluate rehabilitation progress and provide appropriate feedback based on data on the user's movements. For example, it can evaluate the range of motion of joints and measure the effectiveness of rehabilitation. It can also analyze the user's movement data to evaluate rehabilitation progress. For example, it can evaluate the state of muscle recovery and suggest the next step. It can also develop a system that evaluates rehabilitation progress and provides appropriate feedback based on the user's movement data. For example, it can provide advice to maximize the effectiveness of rehabilitation. This makes it possible to evaluate rehabilitation progress and provide appropriate feedback.

[0099] Using emotion estimation functions, the life assistance system can provide emotional support by providing instructions and advice according to the user's emotional state. For example, if the user is feeling anxious, the system can advise them to "relax." The system can also analyze the user's emotional state in real time and provide emotional support. For example, if stress levels rise, the system can advise them to "take a deep breath." We will also develop a system that provides instructions and advice according to the user's emotional state based on emotion estimation data. For example, the system can provide advice to elicit positive emotions. This will enable support according to the user's emotional state.

[0100] The life assistance system can provide advice to improve the efficiency of daily activities based on data on the user's movements. For example, it can suggest movements to perform housework efficiently. It can also analyze data on the user's movements and provide specific advice to improve the efficiency of daily activities. For example, it can optimize the flow of people when shopping. We will also develop a system that provides advice to improve the efficiency of daily activities based on data on the user's movements. For example, it can make suggestions to improve the efficiency of cooking. This makes it possible to provide advice to improve the efficiency of daily activities.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: Bionic sensors detect the user's movements. For example, sensors attached to a prosthetic arm or leg monitor the movements of the user's limbs in detail. The bionic sensors also acquire acceleration, angular velocity, position information, and other data to accurately grasp the user's movements. Step 2: The generative AI analyzes the movements detected by the bionic sensors. For example, the generative AI can use deep learning to analyze movement patterns and recognize inappropriate movements or risk of falling. The generative AI can also use reinforcement learning to learn changes in movement and respond appropriately. Step 3: The motion analysis unit recognizes inappropriate movements and the risk of falling from the movements analyzed by the generation AI. For example, the motion analysis unit performs pattern recognition of movements and detects abnormal movements. The motion analysis unit can also recognize the risk of falling using an anomaly detection algorithm. Step 4: The notification unit provides instructions or advice to the user based on the improper movements or risk of falling recognized by the movement analysis unit. For example, the notification unit may alert the user using a voice notification. The notification unit may also provide advice to the user by sending a text message. Furthermore, the notification unit may also alert the user using a vibration alert.

[0103] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0104] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0105] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, 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.

[0106] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0107] 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.

[0108] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0109] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0110] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0111] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0112] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0113] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0114] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0115] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0116] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0117] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0118] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0119] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0120] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0121] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0122] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0123] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0124] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0125] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0126] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0127] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0128] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0129] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0130] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0131] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0132] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0133] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0134] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0135] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0136] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0137] 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.

[0138] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0139] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0140] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0141] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0142] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0143] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0144] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0145] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0146] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0147] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0148] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0149] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0150] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0151] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0152] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0153] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0154] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0155] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0156] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0157] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0158] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0159] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0160] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0161] 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.

[0162] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0163] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0164] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific process may be a single processor.

[0165] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0166] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0167] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0168] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0169] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0170] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a bionic sensor; Generative AI and a motion analysis unit; a notification unit, The bionic sensor comprises: Detects user movements, The generated AI is analyzing the movements sensed by the bionic sensor; The motion analysis unit The generated AI recognizes inappropriate movements and the risk of falling from the analyzed movements, The notification unit Providing instructions and advice to the user based on the inappropriate movements and risk of falling recognized by the movement analysis unit A system characterized by:

2. The bionic sensor comprises: Detect additional biosignals such as myoelectric potential and skin temperature 2. The system of claim 1.

3. The bionic sensor comprises: Implement algorithms that optimize placement and dynamically adjust sensitivity based on user movement 2. The system of claim 1.

4. The bionic sensor comprises: Detecting the user's emotional state in real time and analyzing emotional responses to changes in movement 2. The system of claim 1.

5. The bionic sensor comprises: Incorporating it into clothing and accessories makes it more natural for everyday use 2. The system of claim 1.

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