system

Abstract

We provide the system. [Solution] A system for generating rehabilitation plans for the elderly, A means of inputting the patient's age, sex, physique, athletic ability, symptoms, personality, and goals, A server that generates a rehabilitation plan based on the input information, A means for displaying the generated rehabilitation plan and allowing the user to review and adjust it, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] The present invention relates to a system that provides an optimal rehabilitation plan based on individual patient information in the rehabilitation of the elderly. In particular, it is difficult for the elderly to grasp their own condition, and standard rehabilitation plans often do not achieve sufficient effects. Therefore, it is an issue to provide an individualized rehabilitation plan according to the patient's age, gender, build, exercise ability, symptoms, personality, and goals, and maximize the rehabilitation effect. Also, it is an important issue to easily generate and adjust the rehabilitation plan and reduce the burden on medical staff.

Means for Solving the Problems

[0005] To solve the above problems, the present invention provides the following means: a system for generating a rehabilitation plan for the elderly, comprising means for inputting the patient's age, gender, physique, motor ability, symptoms, personality, and goals; a server for generating a rehabilitation plan based on the input information; and means for displaying the generated rehabilitation plan and allowing the user to confirm and adjust it. Furthermore, the server for generating the rehabilitation plan includes means for analyzing patient information using an algorithm and constructing an individualized rehabilitation menu. It also includes means for referencing past patient data and the latest rehabilitation methods when generating and optimizing the rehabilitation plan. This makes it possible to quickly provide an appropriate rehabilitation plan based on individual patient information, thereby reducing the burden on healthcare professionals while enhancing the effectiveness of rehabilitation.

[0006] The term "elderly" generally refers to people aged 65 or older, and especially includes those who require rehabilitation.

[0007] A "rehabilitation plan" refers to a treatment and exercise program designed according to the patient's symptoms and condition, with the aim of restoring the patient's function and improving their quality of life.

[0008] A "system" refers to a collection of software and hardware that processes various types of information and performs specific functions.

[0009] "Patient information" refers to data about the person undergoing rehabilitation, specifically including age, sex, physique, motor skills, symptoms, personality, and goals.

[0010] "Means of input" refers to the interface or device that allows users to provide patient information to the system.

[0011] A "server" refers to a central computer that receives, processes, and retransmits data over a network.

[0012] An "algorithm" refers to a set of steps or calculation methods designed to solve a specific problem.

[0013] "Analysis" refers to the process of thoroughly examining collected data and discovering its meaning and patterns.

[0014] An "individualized rehabilitation program" refers to a treatment and exercise program customized based on the specific needs and condition of each individual patient.

[0015] "Optimization" refers to the process of adjusting things to obtain the best possible result under given conditions.

[0016] "Past patient data" refers to archived data on the results of rehabilitation and treatment progress to date.

[0017] "Latest rehabilitation techniques" refer to the most effective treatments and exercises based on current medical research and practice. [Brief explanation of the drawing]

[0018] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of the data processing device and smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7]It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Modes for Carrying Out the Invention

[0019] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0020] First, the language used in the following description will be explained.

[0021] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), and APU (Accelerated Processing Unit).

[0022] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0023] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0024] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0025] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0026] [First Embodiment]

[0027] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0028] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0029] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0030] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0031] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0032] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0033] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0034] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0035] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0036] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0037] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0038] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0039] This invention relates to a system for generating rehabilitation plans for the elderly. Specific embodiments of this invention are described below.

[0040] A natural language explanation of the program's processing.

[0041] Entering patient information

[0042] As an initial step in generating a rehabilitation plan, the user enters patient information into a dedicated terminal. This includes detailed information such as age, gender, physical characteristics (height, weight, etc.), motor skills, symptoms, personality, and goals. For example, a physical therapist might enter information such as: 80-year-old female, weight 65kg, difficulty walking, lower back pain, cautious personality, and goal to be able to walk independently within six months.

[0043] Information transmission

[0044] The terminal securely transmits the entered patient information to the server in real time. Check functions and encrypted communication are used to maintain the accuracy and integrity of the information.

[0045] Rehabilitation plan generation

[0046] The server analyzes the received patient information based on an algorithm. Specifically, in addition to basic information such as age and physique, it considers details of symptoms, personality, and rehabilitation goals. For example, in the case of lower back pain, low-impact stretching and strength training are required. Also, if the patient has a cautious personality, a plan is created that minimizes the risks of rehabilitation and starts with small steps.

[0047] The server references past patient data and the latest rehabilitation techniques to generate an optimal rehabilitation plan. This plan includes specific exercises, duration, and frequency. For example, a detailed schedule might be set such as, "On the first day, do 15 minutes of light stretching, followed by two 20-minute walks."

[0048] Submitting and confirming your plan

[0049] The generated rehabilitation plan is sent from the server to the terminal. The user can review the plan on the terminal and make adjustments as needed. For example, they can change the frequency of walking from three times a week to four times a week. The adjusted plan is sent back to the server for further optimization as needed.

[0050] Optimizing and implementing the plan

[0051] The server optimizes the adjusted plan and finalizes it as the final rehabilitation plan. The finalized plan is saved on the server and made accessible to users and patients. This allows patients to undergo effective rehabilitation based on their individually customized rehabilitation plan.

[0052] Specific example

[0053] As a specific example, consider a 75-year-old male weighing 80 kg with mild dementia, undergoing rehabilitation after surgery on his left knee.

[0054] 1. The user enters the information.

[0055] The physical therapist enters the following information into the terminal: "75-year-old male, weight 80kg, mild dementia, post-left knee surgery, positive personality, goal is to be able to walk independently within 3 months."

[0056] 2. The device transmits information.

[0057] The device securely transmits this information to the server.

[0058] 3. The server generates a rehabilitation plan.

[0059] The server generates a plan that includes low-impact exercises and strength training suitable for left knee rehabilitation. It also incorporates additional exercises to accelerate the rehabilitation process, tailored to the patient's proactive personality.

[0060] 4. The server sends the plan.

[0061] The server sends the generated plan to the terminal.

[0062] 5. User confirmation and adjustment

[0063] The physical therapist reviews the plan and makes adjustments, such as "increasing the frequency of walking exercises from twice a week to three times a week."

[0064] 6. The server maintains an optimized plan.

[0065] An optimized plan is stored on the server, allowing patients to access the plan in real time and progress through their daily rehabilitation.

[0066] This allows for the provision of individualized rehabilitation plans to patients, maximizing the effectiveness of rehabilitation and promoting rapid recovery.

[0067] The following describes the processing flow.

[0068] Step 1:

[0069] The user enters patient information using a dedicated terminal. Specifically, they enter detailed information such as the patient's age, gender, physique (height and weight), physical ability, symptoms, personality, and rehabilitation goals. For example, they might set it to "78-year-old female, weight 60kg, lower back pain, cautious personality, goal: independent walking."

[0070] Step 2:

[0071] The terminal transmits patient information entered into it to the server in real time. During transmission, the data format is checked to maintain the accuracy and integrity of the patient data, and encrypted communication is used to send the data securely.

[0072] Step 3:

[0073] The server analyzes the patient information it receives. First, it analyzes basic information (age, gender, build, etc.), and then it considers details of symptoms (location and severity of pain, etc.) and personality (cautious, proactive, etc.). For example, a 78-year-old woman with lower back pain and a cautious personality would require low-impact, low-risk exercise.

[0074] Step 4:

[0075] The server references past patient data and the latest rehabilitation techniques, applying algorithms necessary for generating a rehabilitation plan. These algorithms construct an optimal rehabilitation program based on each patient's individual requirements, including stretching, strength training, and gait practice.

[0076] Step 5:

[0077] The server generates a rehabilitation plan and sends it to the terminal. Each plan includes specific exercises, duration, and frequency. For example, "On the first day, 15 minutes of light stretching, followed by 20 minutes of walking."

[0078] Step 6:

[0079] The user reviews the rehabilitation plan generated on their device and makes adjustments as needed. For example, they can adjust the frequency of walking exercises from twice a week to three times a week. The adjusted plan is then sent back to the server and reflected in the changes.

[0080] Step 7:

[0081] The server optimizes the adjusted rehabilitation plan. During the optimization process, further detailed adjustments are made to determine the optimal rehabilitation menu for each individual patient.

[0082] Step 8:

[0083] The final rehabilitation plan is saved on a server and made accessible to both the user and the patient in real time. Based on this plan, the patient can proceed with their daily rehabilitation in sequence.

[0084] In this way, the rehabilitation system of the present invention provides a personalized rehabilitation plan suitable for the elderly quickly and effectively.

[0085] (Example 1)

[0086] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0087] In rehabilitation for the elderly, a challenge lies in the lack of systems that can efficiently generate individualized rehabilitation plans and flexibly respond to patients' specific needs and progress. Furthermore, security concerns regarding data transmission and storage also need to be addressed.

[0088] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0089] In this invention, the server includes means for generating a rehabilitation plan using an algorithm based on input information, means for the algorithm to analyze patient information using a machine learning library, and means for sending the adjusted plan back to the server for optimization. This makes it possible to provide a system that generates personalized rehabilitation plans and can be used effectively and safely by users and patients.

[0090] "Input means" refers to devices or software that allow users to input detailed information about a patient, such as their age, gender, physique, athletic ability, symptoms, personality, and goals.

[0091] A "server" refers to a high-performance computer system that receives, processes, and stores data over a network.

[0092] An "algorithm" refers to a set of steps or formulas that run on a server, use machine learning libraries to analyze input patient information, and generate an individualized rehabilitation plan.

[0093] A "machine learning library" refers to a group of software tools used for analyzing patient information and generating rehabilitation plans, and includes tools such as TensorFlow and PyTorch.

[0094] "Generation means" refers to the functions and processes used to generate an optimal rehabilitation plan based on input information using an algorithm.

[0095] "Display means" refers to devices or software used to visually present the generated rehabilitation plan to the user.

[0096] "Confirmation means" refers to a function that allows users to view the generated rehabilitation plan and understand or confirm its contents.

[0097] "Adjustment mechanisms" refer to functions and processes that allow users to change or modify the content of the generated rehabilitation plan as needed.

[0098] "Transmission method" refers to the function or process for sending the adjusted rehabilitation plan back to the server to maintain data integrity and accuracy.

[0099] "Optimization methods" refer to the functions and processes used to re-analyze the adjusted rehabilitation plan and finalize it as the final rehabilitation plan.

[0100] "Encryption technology" refers to technologies used to prevent third parties from reading data during transmission or storage, and includes SSL / TLS, among others.

[0101] This invention relates to a system for generating rehabilitation plans for the elderly. Specific embodiments of this invention are described in detail below.

[0102] Entering patient information

[0103] To generate a rehabilitation plan, the user inputs patient information using a dedicated terminal (e.g., a tablet or PC). This patient information includes details such as age, gender, physical characteristics (height, weight, etc.), motor skills, symptoms, personality, and goals. The input information is checked for required fields by an input validation function displayed on the user interface. For example, a physical therapist might input information such as "80-year-old female, weight 65kg, difficulty walking, lower back pain, cautious personality, goal: to be able to walk independently within six months."

[0104] Sending patient information

[0105] The terminal sends the entered patient information to the server. Encryption technologies such as SSL / TLS are used to securely transfer the data. A function checks for data errors before transmission. For example, a warning message is displayed if there are any required fields that have not been entered.

[0106] Rehabilitation plan generation

[0107] The server generates a rehabilitation plan using an algorithm based on the received patient information. The algorithm is implemented in Python and utilizes machine learning libraries such as TensorFlow and PyTorch. It analyzes the submitted patient information and also refers to databases of past patient data and the latest rehabilitation methods. For example, in the case of a patient with lower back pain, it recommends low-impact stretching and strength training, and if the patient has a cautious personality, it generates a plan that starts with small steps.

[0108] Submission and confirmation of rehabilitation plan

[0109] The server sends the generated rehabilitation plan to the user's device. The created plan includes specific exercises, durations, and frequencies. For example, it might include a detailed schedule such as, "On the first day, do 15 minutes of light stretching, followed by two 20-minute walks." The user can review the plan on their device and make adjustments as needed. The adjusted plan is then sent back to the server for further optimization.

[0110] Optimizing and implementing the plan

[0111] The server optimizes the adjusted rehabilitation plan and finalizes it. The finalized plan is saved on the server and becomes accessible to both the user and the patient. This allows patients to undergo effective rehabilitation based on their individually customized rehabilitation plan.

[0112] Specific example

[0113] Here are some specific examples:

[0114] Specific example: A 75-year-old male, weighing 80 kg, with mild dementia, undergoing rehabilitation after surgery on his left knee.

[0115] 1. The user enters the information.

[0116] The physical therapist enters the following information into the terminal: "75-year-old male, weight 80kg, mild dementia, post-left knee surgery, positive personality, goal is to be able to walk independently within 3 months."

[0117] The terminal checks for any missing required fields, and if there are no errors, it proceeds to the next step.

[0118] 2. The device transmits information.

[0119] The terminal sends the entered information to the server.

[0120] Encrypt and securely transmit data using SSL / TLS.

[0121] 3. The server generates a rehabilitation plan.

[0122] Based on the received patient information, the server generates a rehabilitation plan using a generated AI model.

[0123] For example, it can generate a plan that includes low-impact exercises and strength training suitable for left knee rehabilitation, and incorporate additional activities to speed up progress according to the patient's proactive personality.

[0124] 4. The server sends the plan.

[0125] The server sends the generated rehabilitation plan to the terminal.

[0126] The plan includes a specific schedule such as, "On the first day, do 15 minutes of light stretching, followed by two 20-minute walks."

[0127] 5. The user reviews and adjusts the settings.

[0128] The physical therapist reviews the plan and makes adjustments, such as "increasing the frequency of walking exercises from twice a week to three times a week."

[0129] The revised plan will be sent back to the server.

[0130] 6. The server maintains an optimized plan.

[0131] The server stores optimized plans and makes them accessible to users and patients.

[0132] Patients can use a device to access their treatment plan in real time and carry out their daily rehabilitation.

[0133] Example of a prompt

[0134] The following prompts can be used to input patient information into the generating AI model and propose an appropriate rehabilitation plan:

[0135] A 75-year-old male, weighing 80 kg, with mild dementia, has undergone left knee surgery, and has an active personality. The goal is for him to be able to walk independently within 3 months. Please generate a rehabilitation plan for this patient.

[0136] In this way, it becomes possible to efficiently generate rehabilitation plans for the elderly and provide individualized care.

[0137] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0138] Step 1:

[0139] The user enters patient information.

[0140] The user uses a dedicated terminal to input detailed information about the patient, such as age, gender, physical characteristics (height, weight, etc.), athletic ability, symptoms, personality, and goals.

[0141] After input, the terminal screen will activate an input validation function to check all required fields. If there are any missing or incorrect entries, a warning message will be displayed.

[0142] An example of input data is: "An 80-year-old woman, weighing 65 kg, with difficulty walking, back pain, cautious personality, and a goal of being able to walk independently within six months."

[0143] In this step, the entered patient information is saved as digital data on the terminal.

[0144] Step 2:

[0145] The device sends information

[0146] The terminal sends the entered patient information to the server. Since encryption technology (e.g., SSL / TLS) is used for transmission, the data is transferred securely.

[0147] The integrity and accuracy of the data are checked during transmission.

[0148] The data sent will be in a standard format such as JSON, and each field will contain detailed information such as age, gender, physique, and athletic ability.

[0149] The output of this step is patient information that has been securely transmitted.

[0150] Step 3:

[0151] The server generates a rehabilitation plan.

[0152] The server uses an algorithm to generate a rehabilitation plan based on the patient information it receives.

[0153] The algorithm uses machine learning libraries (TensorFlow and PyTorch) and also references past patient data and databases of the latest rehabilitation methods.

[0154] Based on the input data, the system selects an appropriate rehabilitation menu tailored to the symptoms and personality, and proposes specific exercises and training plans.

[0155] For example, patients with lower back pain might be recommended low-impact stretches and strength training, while those with a cautious personality might be given a plan that starts with small steps.

[0156] In this step, the output is the generated rehabilitation plan.

[0157] Step 4:

[0158] The server sends the generated plan to the terminal.

[0159] The server sends the generated rehabilitation plan to the terminal. Encryption technology is used for transmission, ensuring the data is transferred securely.

[0160] A rehabilitation plan is generated that includes specific information such as the type of exercise, duration, and frequency.

[0161] For example, a specific schedule might be set, such as, "On the first day, do 15 minutes of light stretching, followed by two 20-minute walks."

[0162] The output of this step is the rehabilitation plan sent to the terminal.

[0163] Step 5:

[0164] Users can review and adjust their plans.

[0165] Users can review the rehabilitation plan generated on their device. They can adjust the content and frequency of exercises as needed.

[0166] For example, specific adjustments can be made, such as "increasing the frequency of walking from three times a week to four times a week."

[0167] The adjusted plan is sent back to the server, so the same checks and encrypted transmission are performed during the readjustment process.

[0168] The output of this step is the adjusted rehabilitation plan.

[0169] Step 6:

[0170] The server maintains an optimized plan.

[0171] The server optimizes the adjusted rehabilitation plan and finalizes it. The finalized plan is saved on the server and becomes accessible to users and patients.

[0172] A highly available database (such as an SQL database) is used for data storage in this step.

[0173] This allows patients to undergo effective rehabilitation based on individually customized rehabilitation plans.

[0174] The output of this step is the optimized rehabilitation plan saved on the server.

[0175] (Application Example 1)

[0176] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0177] In rehabilitation for the elderly, not only is individualized rehabilitation plans necessary, but real-time support for their implementation is also required. However, existing systems cannot provide immediate feedback on whether patients are performing rehabilitation correctly, which makes effective rehabilitation difficult. Furthermore, there is a need for guidance functions that are easy for the elderly to understand and intuitive to use.

[0178] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0179] In this invention, the server includes means for inputting the patient's age, gender, physique, motor skills, symptoms, personality, and goals; means for generating a rehabilitation plan based on the input information; means for displaying the generated rehabilitation plan and allowing the user to review and adjust it; and means for providing real-time audio guidance and visual feedback using a smart device. This makes it possible for the patient to receive real-time guidance and feedback when performing rehabilitation, thereby enhancing the effectiveness of the rehabilitation.

[0180] The term "elderly" generally refers to people aged 65 or older, and especially to those with physical limitations that require rehabilitation.

[0181] A "rehabilitation plan" refers to a detailed schedule that combines exercises and treatments tailored to the patient's physical condition and symptoms, with the aim of restoring the patient's health.

[0182] A "server" is a computer system that sends and receives data over a network, and processes and stores information.

[0183] A "smart device" is a device that is connected to the internet and capable of processing and displaying information, and includes, for example, smartphones, smart glasses, and head-mounted displays.

[0184] "Voice guide" refers to a function that converts text information into speech to provide guidance to the user.

[0185] "Visual feedback" refers to a function that allows users to visually confirm their actions by providing information through visual display devices such as screens.

[0186] An "algorithm" is a set of computational steps or processing rules, referring to a procedure designed to solve a specific problem.

[0187] A "personalized rehabilitation program" is a plan that includes exercises and treatments designed to suit the patient's specific needs and physical condition.

[0188] "Past patient data" refers to rehabilitation records and progress data of patients who have received treatment in the past.

[0189] "Latest rehabilitation methods" refer to rehabilitation methods designed based on the latest research and technologies in medicine and exercise science.

[0190] Specific embodiments for carrying out this invention will be described.

[0191] Overall system configuration

[0192] This invention is a system for generating rehabilitation plans for the elderly and providing real-time feedback and guidance. The system consists of the following components:

[0193] 1. Input method: A means of inputting the patient's age, sex, physique, motor skills, symptoms, personality, and goals. This may involve using an input device such as a tablet or computer.

[0194] 2. Server: The central hub of the system that generates rehabilitation plans using algorithms based on the entered patient information. The server references past patient data and the latest rehabilitation methods to construct an individualized plan.

[0195] 3. Smart devices: Devices worn by patients, such as smart glasses and head-mounted displays. These include features such as voice guidance and visual feedback.

[0196] 4. Display means: A means for displaying the generated rehabilitation plan, allowing the user (physical therapist) to review and adjust it. A display device such as a tablet or smartphone is used.

[0197] Hardware and software configuration

[0198] Server: A high-performance computer equipped with an Intel or AMD processor, consisting of a database management system (e.g., MySQL) and machine learning algorithms (e.g., TensorFlow).

[0199] Smart devices include smart glasses such as Google Glass and Microsoft HoloLens. These utilize speech recognition (e.g., Google Speech Recognition API), speech synthesis (e.g., Pyttsx3), and visual display (e.g., OpenCV).

[0200] Display method: Use an Apple iPad or Android tablet and install a dedicated application.

[0201] Explanation of the program's processing

[0202] The server receives patient information entered by the physical therapist and analyzes it using a specific algorithm. Specifically, the following steps are taken:

[0203] 1. Enter patient information:

[0204] The user enters the patient information necessary to generate a rehabilitation plan. For example, they might use a tablet to enter age, gender, physique, motor skills, symptoms, personality, and goals.

[0205] 2. Information Analysis:

[0206] The server analyzes the input information and generates an optimal rehabilitation plan by referencing past patient data and the latest rehabilitation techniques.

[0207] 3. Plan generation:

[0208] The server uses algorithms (e.g., machine learning models) to create a personalized rehabilitation plan. This plan includes specific exercises, their duration, and frequency.

[0209] 4. Feedback and guidance:

[0210] Smart devices are used to provide patients with real-time audio guidance and visual feedback. For example, Google Glass can be used to provide an audio guide to the patient saying, "Extend your arms and raise them to shoulder height," while the display shows the text, "Keep your back straight."

[0211] Specific example

[0212] Let's consider an example of a 75-year-old man using a rehabilitation support system for the elderly. A physical therapist inputs information into a tablet: "75-year-old male, weight 80kg, mild dementia, post-left knee surgery, goal is to be able to walk independently within 3 months." The server analyzes this information and generates an optimal rehabilitation plan. The patient wears Google Glass and performs exercises while receiving voice guidance and visual feedback.

[0213] Example of a prompt

[0214] "Please develop a rehabilitation support application for seniors. This application uses smart glasses to provide voice guidance for exercises, reminders, and visual feedback. Please use Python and the following libraries: GTTS, Pyttsx3, OpenCV, datetime."

[0215] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0216] Step 1:

[0217] The user uses a tablet device to input the patient's age, gender, physique, athletic ability, symptoms, personality, and goals. This data is sent to the server. The entered data is stored on the server and ready for analysis.

[0218] Step 2:

[0219] Based on the received patient information, the server references a database of past patients and the latest rehabilitation techniques. This database includes past rehabilitation case studies and exercise information based on the latest medical research. Using this data, the server executes an algorithm to generate the optimal rehabilitation plan for the patient.

[0220] Step 3:

[0221] The generated rehabilitation plan is sent from the server to the tablet device. The user (physiotherapist) reviews this plan on the tablet and makes adjustments as needed. For example, they can increase the frequency of walking from twice a week to three times a week. The adjusted plan is sent back to the server, and the final optimized rehabilitation plan is finalized.

[0222] Step 4:

[0223] The finalized rehabilitation plan is sent from the server to a smart device (e.g., smart glasses). Once the patient puts on the smart device and begins rehabilitation, voice guidance and visual feedback are provided in real time. Specifically, using Google Glass, voice instructions such as "Extend your arms and raise them to shoulder height" and visual instructions such as "Keep your back straight" are displayed on the screen.

[0224] Step 5:

[0225] When a patient performs rehabilitation, the smart device uses built-in sensors to collect movement data. This data is transmitted to a server in real time, which analyzes whether the patient's movements are following the plan. Based on the analysis, voice guidance and visual feedback are adjusted to help the patient perform rehabilitation correctly.

[0226] Step 6:

[0227] Rehabilitation progress is managed on a server and can be viewed by the user via a tablet or smart device. Based on progress, the server automatically updates the rehabilitation plan and suggests new exercises to the patient. This maximizes the effectiveness of the patient's rehabilitation.

[0228] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0229] This invention relates to a system for generating rehabilitation plans for the elderly that incorporates an emotion engine. Specific embodiments of this invention are described below.

[0230] A natural language explanation of the program's processing.

[0231] Entering patient information

[0232] As an initial step in generating a rehabilitation plan, the user enters patient information into a dedicated terminal. This includes detailed information such as age, gender, physical characteristics (height and weight), motor skills, symptoms, personality, and rehabilitation goals. For example, a physical therapist might enter: "80-year-old female, weight 65kg, difficulty walking, lower back pain, cautious personality, goal is to be able to walk independently within six months."

[0233] Information transmission

[0234] The terminal securely transmits entered patient information to the server in real time. Data format verification and encrypted communication are used to maintain the accuracy and integrity of the information.

[0235] Rehabilitation plan generation

[0236] The server analyzes the received patient information using an algorithm. A rehabilitation plan is generated that takes into account basic information, detailed symptoms, and personality. For example, for a 78-year-old woman with lower back pain and a cautious personality, a plan including low-impact, low-risk exercises will be created.

[0237] Analysis of emotional information

[0238] When a user undergoes rehabilitation, the emotion engine analyzes the user's emotional information in real time. This emotion engine determines the user's emotional state based on facial expressions, tone of voice, language patterns, and other factors. For example, if a user is feeling anxious or stressed about rehabilitation, this information will be detected by the emotion engine.

[0239] Adjusting the rehabilitation plan

[0240] The server dynamically adjusts the rehabilitation plan based on emotional information obtained from the emotion engine. For example, if a user is showing signs of fatigue, the intensity of the exercise can be temporarily reduced. Conversely, if a user is highly motivated, the pace of the plan can be accelerated.

[0241] View and check the plan

[0242] The generated and adjusted rehabilitation plan is sent from the server to the terminal. The user can review the plan on the terminal and make further adjustments. For example, specific changes such as "increase the frequency of walking exercises from twice a week to three times a week" can be made.

[0243] Specific example

[0244] As a specific example, consider a 75-year-old male weighing 80 kg with mild dementia, undergoing rehabilitation after surgery on his left knee.

[0245] 1. The user enters the information.

[0246] The physical therapist enters the following information into the terminal: "75-year-old male, weight 80kg, mild dementia, post-left knee surgery, positive personality, goal is to be able to walk independently within 3 months."

[0247] 2. The device transmits information.

[0248] The device sends this information to the server in real time.

[0249] 3. The server generates a rehabilitation plan.

[0250] Based on the input information, the server generates a plan that includes low-impact exercises and strength training suitable for post-operative rehabilitation of the left knee.

[0251] 4. Analysis using an emotion engine

[0252] When a user begins rehabilitation, the emotion engine analyzes the user's facial expressions and tone of voice to determine, for example, that "the user is highly motivated for rehabilitation."

[0253] 5. The server adjusts the plan.

[0254] Based on the user's high motivation, the server dynamically adjusts the rehabilitation plan, reflecting a plan that accelerates the pace of rehabilitation.

[0255] 6. Submit and confirm your plan

[0256] The final rehabilitation plan is sent from the server to the terminal, and after the physical therapist reviews the details, it is provided to the user.

[0257] As described above, the rehabilitation system of the present invention, which incorporates an emotion engine, can provide more effective rehabilitation by dynamically adjusting the rehabilitation plan according to the user's emotional state.

[0258] The following describes the processing flow.

[0259] Step 1:

[0260] The user enters patient information using a dedicated terminal. Specifically, they enter detailed information such as the patient's age, gender, physique (height and weight), physical ability, symptoms, personality, and rehabilitation goals. For example, they might set it to "78-year-old female, weight 60kg, lower back pain, cautious personality, goal: independent walking."

[0261] Step 2:

[0262] The terminal transmits patient information entered into it to the server in real time. During transmission, the data format is checked to maintain the accuracy and integrity of the patient data, and encrypted communication is used to send the data securely.

[0263] Step 3:

[0264] The server analyzes the patient information it receives. First, it analyzes basic information (age, gender, build, etc.), and then it considers details of the symptoms (location and severity of pain, etc.) and personality traits (cautious, proactive, etc.). For example, a 78-year-old woman with lower back pain and a cautious personality would require low-impact, low-risk exercise.

[0265] Step 4:

[0266] The server references past patient data and the latest rehabilitation techniques, applying algorithms necessary for generating a rehabilitation plan. These algorithms construct an optimal rehabilitation program based on each patient's individual requirements, including stretching, strength training, and gait practice.

[0267] Step 5:

[0268] The emotion engine analyzes the user's real-time emotional information. This includes analyzing the user's facial expressions, tone of voice, and language patterns to determine their emotional state. For example, if a user is feeling stressed, the emotion engine will detect it.

[0269] Step 6:

[0270] The server dynamically adjusts the rehabilitation plan based on emotional information analyzed by the emotion engine. For example, if a user is showing signs of fatigue, the intensity of the exercise can be temporarily reduced. Conversely, if a user is highly motivated, the pace of the plan can be accelerated.

[0271] Step 7:

[0272] The server generates a rehabilitation plan and sends it to the terminal. Each plan includes specific exercises, duration, and frequency. For example, "On the first day, 15 minutes of light stretching, followed by 20 minutes of walking."

[0273] Step 8:

[0274] The user reviews the rehabilitation plan generated on their device and makes adjustments as needed. For example, they can adjust the frequency of walking exercises from twice a week to three times a week. The adjusted plan is then sent back to the server and reflected in the changes.

[0275] Step 9:

[0276] The server optimizes the adjusted rehabilitation plan. During the optimization process, further detailed adjustments are made to determine the optimal rehabilitation menu for each individual patient.

[0277] Step 10:

[0278] The final rehabilitation plan is saved on the server and made accessible to the user and the patient in real time. Based on this plan, the patient can proceed with daily rehabilitation sequentially.

[0279] As described above, the rehabilitation system of the present invention combined with the emotion engine dynamically adjusts the rehabilitation plan according to the user's emotional state and provides more effective rehabilitation.

[0280] (Example 2)

[0281] Next, Example 2 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart device 14 is referred to as the "terminal".

[0282] In the rehabilitation of the elderly, it is required to provide a plan considering the individual situation and emotional state of the patient. However, the conventional rehabilitation plan generation system lacked the function of analyzing the patient's emotional information and dynamically adjusting the plan based on it. Therefore, it was difficult to provide an effective rehabilitation plan suitable for the patient.

[0283] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0284] In this invention, the server includes means for inputting the patient's age, gender, physique, exercise ability, symptoms, personality, and goals, means for generating a rehabilitation plan based on the input information, means for analyzing emotional information, means for dynamically adjusting the rehabilitation plan based on the emotional information, and means for displaying the generated rehabilitation plan for the user to confirm and adjust. Thereby, it becomes possible to provide an effective rehabilitation plan reflecting the individual situation and emotional state of the patient.

[0285] "Patient information" refers to detailed data including the patient's age, gender, physique, motor ability, symptoms, personality, and rehabilitation goals.

[0286] "Terminal" refers to a device that a user operates to input patient information and check and adjust the rehabilitation plan.

[0287] "Server" refers to a central processing unit that generates a rehabilitation plan based on the input patient information and performs analysis and dynamic adjustment.

[0288] "Algorithm" refers to a computational procedure or method used by the server to analyze patient information and generate a rehabilitation plan.

[0289] "Generated AI model" refers to an artificial intelligence model that uses an algorithm to analyze the input patient information and generate an individualized rehabilitation plan.

[0290] ​​​​​​​​​​​​​​​​​​​​​This invention relates to a system for generating rehabilitation plans for the elderly, providing an optimal rehabilitation plan tailored to the individual circumstances and emotional state of the patient. Specific embodiments of this invention are described below.

[0296] Entering patient information

[0297] As an initial step in generating a rehabilitation plan, the user enters patient information into a dedicated terminal. The terminal provides an interface for user input using a touchscreen and keyboard. Specific information includes age, gender, physical characteristics (height, weight), motor skills, symptoms, personality, and rehabilitation goals. For example, a physical therapist might enter "80-year-old female, weight 65kg, difficulty walking, lower back pain, cautious personality, goal: to be able to walk independently within six months."

[0298] Information transmission

[0299] The terminal securely transmits entered patient information to the server in real time. To maintain the accuracy and integrity of the information, data format verification and encrypted communication (SSL / TLS) are used. The terminal encrypts the information and sends it to the server. The server verifies the integrity of the received data and proceeds to the next processing step.

[0300] Rehabilitation plan generation

[0301] The server uses a generative AI model to analyze the patient information it receives. This model is built using machine learning frameworks such as TensorFlow or PyTorch. The server then generates an appropriate rehabilitation plan, taking into account basic information, detailed symptoms, and personality traits. For example, for a 78-year-old woman with back pain and a cautious personality, a plan including low-impact, low-risk exercises would be generated.

[0302] Analysis of emotional information

[0303] When the user undergoes rehabilitation, the emotion engine analyzes the user's expressions, voice tones, and language patterns in real time using the terminal's camera and microphone. Based on this, the user's emotional state is determined. For example, if the user feels anxiety or stress about the rehabilitation, that information is detected by the emotion engine.

[0304] Adjustment of the rehabilitation plan

[0305] The server dynamically adjusts the rehabilitation plan based on the emotion information obtained from the emotion engine. For example, if the user shows signs of fatigue, the intensity of the exercise is temporarily decreased. Conversely, if the user shows high motivation, an adjustment is made to increase the progress speed of the plan.

[0306] Display and confirmation of the plan

[0307] The generated and adjusted rehabilitation plan is sent from the server to the terminal. The user can check the plan on the terminal and make further detailed adjustments if necessary. For example, specific changes such as "increasing the frequency of walking exercises from twice a week to three times a week" are possible.

[0308] Specific example

[0309] As a specific example below, we take the rehabilitation of a 75-year-old male, weighing 80 kg, with mild dementia, after left knee surgery.

[0310] 1. The user inputs information

[0311] The physical therapist inputs into the terminal "a 75-year-old male, weighing 80 kg, with mild dementia, after left knee surgery, with a positive personality, and the goal is to be able to walk independently within three months".

[0312] 2. The terminal transmits the information

[0313] The terminal transmits this information to the server in real time.

[0314] 3. The server generates a rehabilitation plan.

[0315] Based on the input information, the server generates a plan that includes low-impact exercises and strength training suitable for post-operative rehabilitation of the left knee.

[0316] 4. Analysis using an emotion engine

[0317] When a user begins rehabilitation, the emotion engine analyzes the user's facial expressions and tone of voice to determine, for example, that "the user is highly motivated for rehabilitation."

[0318] 5. The server adjusts the plan.

[0319] Based on the user's high motivation, the server dynamically adjusts the rehabilitation plan, reflecting a plan that accelerates the pace of rehabilitation.

[0320] 6. Submit and confirm your plan

[0321] The final rehabilitation plan is sent from the server to the terminal, and after the physical therapist reviews the details, it is provided to the user.

[0322] Example of a prompt

[0323] 1. Prompts for generating a natural rehabilitation plan:

[0324] "Please generate a rehabilitation plan for an elderly patient. The patient is a 75-year-old male, weighing 80 kg, with mild dementia and a history of left knee surgery. He has an active personality, and the goal is for him to be able to walk independently within three months."

[0325] 2. Emotion Engine Analysis Prompt:

[0326] "Please analyze the emotional state of a 75-year-old patient undergoing rehabilitation. Based on their tone of voice and facial expressions, determine whether their motivation for rehabilitation is high or low."

[0327] 3. Prompt for adjustment of rehabilitation plan:

[0328] "Dynamically adjust the rehabilitation plan based on the user's emotional state. If the user shows high motivation, increase the pace of the exercises."

[0329] In this way, the rehabilitation plan generation system of the present invention can provide a more effective and optimal rehabilitation plan according to the individual circumstances and emotional state of the patient.

[0330] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0331] Step 1:

[0332] The user enters patient information.

[0333] The user (physical therapist) uses the terminal interface to input the patient's age, gender, physique (height, weight), motor skills, symptoms, personality, and rehabilitation goals. A specific example of input would be: "75-year-old male, weight 80kg, mild dementia, post-left knee surgery, positive personality, goal is to be able to walk independently within 3 months."

[0334] Input: Patient's basic information

[0335] Output: Patient data in a processable format

[0336] Step 2:

[0337] The device sends information to the server.

[0338] The terminal securely transmits entered patient information to the server in real time. The terminal verifies the accuracy of the information and transmits the data using encrypted communication (SSL / TLS). The server verifies that the received data is in the correct format.

[0339] Input: Patient data in a processable format

[0340] Output: Patient data sent to the server

[0341] Step 3:

[0342] The server generates a rehabilitation plan.

[0343] The server uses a generative AI model to analyze the received patient information. Specifically, it uses frameworks such as TensorFlow and PyTorch to generate an appropriate rehabilitation plan that takes into account basic information, detailed symptoms, and personality. Based on the input information, it generates a plan that includes low-impact exercises and strength training suitable for post-operative rehabilitation of the left knee, for example.

[0344] Input: Patient data sent to the server

[0345] Output: Initial Rehabilitation Plan

[0346] Step 4:

[0347] Analysis of emotional information

[0348] When a user undergoes rehabilitation, the emotion engine uses the device's camera and microphone to analyze the user's facial expressions and voice tone in real time. The emotion engine uses libraries such as OpenCV and DeepSpeech to determine the user's emotional state. For example, if the user is feeling stressed about the rehabilitation, that information will be detected.

[0349] Input: User data during rehabilitation (facial expressions, voice tone, language patterns)

[0350] Output: User sentiment information

[0351] Step 5:

[0352] The server dynamically adjusts the rehabilitation plan.

[0353] The server dynamically adjusts the rehabilitation plan based on emotional information obtained from the emotion engine. For example, if the user is showing signs of fatigue, the intensity of the exercises will be temporarily reduced. Conversely, if the user is showing high motivation, the pace of the plan will be increased.

[0354] Input: User sentiment information

[0355] Output: Adjusted rehabilitation plan

[0356] Step 6:

[0357] View and check the plan

[0358] The server sends the adjusted rehabilitation plan to the user's device, where the user reviews the plan. If further adjustments are needed, the user can modify the plan by operating the device. For example, they can adjust the frequency of walking exercises from twice a week to three times a week.

[0359] Input: Adjusted rehabilitation plan

[0360] Output: Final rehabilitation plan reviewed and adjusted by the user.

[0361] In this way, based on the data entered at each step, the server and terminal perform specific data processing and calculations, obtaining their respective outputs, thereby providing the user with an optimal rehabilitation plan.

[0362] (Application Example 2)

[0363] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0364] In rehabilitation for the elderly and people with disabilities, there is a need to provide effective plans tailored to each patient's individual circumstances and emotional state. However, conventional rehabilitation plans are usually limited to one-dimensional planning based on the patient's physical data, and there is no mechanism to dynamically adjust them to take into account the patient's emotional state and motivation. As a result, it has been difficult to maximize the effectiveness of rehabilitation and maintain patient motivation.

[0365] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0366] In this invention, the server includes means for inputting the patient's age, gender, physique, motor skills, symptoms, personality, and goals; means for generating a rehabilitation plan based on the input information; means for analyzing real-time emotional information and dynamically adjusting the rehabilitation plan based on the patient's emotions; and means for adjusting the pace of rehabilitation based on the collected emotional information. This makes it possible to create a rehabilitation plan that takes into account not only the physical data of each patient but also their emotional state, enabling effective rehabilitation while maintaining the patient's motivation.

[0367] "Elderly people" refers to those who are particularly old within the age group requiring rehabilitation, and generally refers to people aged 65 and over.

[0368] A "rehabilitation plan" refers to a detailed program designed to improve a patient's physical function and daily living activities.

[0369] A "system" refers to a collection of multiple devices or software programs combined to perform a specific function.

[0370] "Patient information" refers to individual data such as the patient's age, gender, physique, athletic ability, symptoms, personality, and goals.

[0371] A "server" refers to a central processing unit that provides services to other computers via a network.

[0372] "Emotional information" refers to data that indicates a patient's emotional state, and is acquired based on facial expressions, tone of voice, language patterns, and other factors.

[0373] "Dynamic adjustment" refers to a flexible response that adapts the plan in real time or near real time.

[0374] "Progression rate" refers to the pace at which exercises and training progress in a rehabilitation plan.

[0375] "Display means" refers to devices or software used to visually show the generated rehabilitation plan to the user.

[0376] "Input means" refers to devices or interfaces used to provide patient information to the system.

[0377] Modes for carrying out the invention

[0378] This invention relates to a system for generating rehabilitation plans for the elderly and dynamically adjusting them based on emotional information. The invention is particularly applicable to rehabilitation support applications using smart glasses. This system includes multiple means for collecting and analyzing patient information and generating effective rehabilitation plans.

[0379] 1. Collection of input data

[0380] The server utilizes the camera and microphone of smart glasses to collect basic patient information and emotional information through voice input and OCR functionality. These smart glasses are equipped with facial recognition and voice recognition capabilities, allowing for real-time acquisition of patient data. Specific hardware used includes smart glasses (e.g., Google Glass) and voice recognition APIs (e.g., Amazon Transcribe).

[0381] 2. Data transmission and encryption

[0382] The terminal sends the collected data to the server in real time. During transmission, the SSL / TLS protocol is used to ensure data security. Specifically, OpenSSL is used to implement encrypted communication.

[0383] 3. Generating a Rehabilitation Plan

[0384] The server generates rehabilitation plans based on collected patient information. To do this, it uses Python-based machine learning algorithms to construct plans tailored to each patient's individual needs. Specifically, it uses libraries such as Scikit-learn and Pandas to analyze data and generate plans.

[0385] 4. Analysis of emotional information

[0386] The server analyzes emotional information to detect the patient's emotional state. Specifically, it uses Microsoft Azure Cognitive Services (Face API, Speech API) to perform real-time emotional analysis. This allows the server to understand the patient's emotional state from their facial expressions and tone of voice.

[0387] 5. Dynamic adjustment of the rehabilitation plan

[0388] The server dynamically adjusts the rehabilitation plan based on emotional information. For example, if a patient shows signs of fatigue, the exercise intensity is temporarily reduced. Conversely, if motivation is high, the plan is advanced. AWS Lambda and dynamic databases (e.g., DynamoDB) are used for this adjustment.

[0389] 6. View and confirm the plan

[0390] The final rehabilitation plan is sent back to the smart glasses for the user to review. This allows physical therapists and rehabilitation center staff to fine-tune the plan according to the patient's condition.

[0391] Specific example

[0392] As a concrete example, suppose a 75-year-old male patient begins rehabilitation and wears smart glasses. These smart glasses analyze the patient's voice and facial expressions and may make the following adjustments to the initial plan.

[0393] If the patient is feeling anxious:

[0394] "The patient is anxious about rehabilitation. Please reduce the intensity of the next exercise and incorporate relaxation exercises."

[0395] Example of a prompt

[0396] "Generate a rehabilitation plan for a 75-year-old male, weighing 80kg, with mild dementia, post-left knee surgery, and an outgoing personality. His goal is to achieve independent walking within three months. Furthermore, use an emotion engine to acquire emotional information from the patient's facial expressions and tone of voice, and dynamically adjust the rehabilitation plan accordingly."

[0397] Thus, the system of the present invention can generate a rehabilitation plan based on individual patient information and dynamically adjust it according to the patient's emotional state, thereby providing more effective and personalized rehabilitation.

[0398] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0399] Step 1:

[0400] The server collects patient information using smart glasses. Patient age, gender, build, motor skills, symptoms, personality, and goals are entered using voice recognition and OCR functions. Example input data includes a 75-year-old male, weighing 80kg, post-knee surgery on the left knee, with an active personality and a goal of independent walking within three months. This data is collected using smart glasses such as Google Glass and Amazon Transcribe.

[0401] Step 2:

[0402] The terminal transmits collected patient information to the server in real time. To ensure data security, encrypted communication is performed using the SSL / TLS protocol. The input is patient information (e.g., 75-year-old male, 80kg weight, post-left knee surgery), and the output is encrypted data sent to the server.

[0403] Step 3:

[0404] The server generates a rehabilitation plan based on the received patient information. It uses a Python-based machine learning algorithm to analyze the data and construct the rehabilitation plan. The input is detailed patient information (e.g., age, weight, symptoms), and based on this, an individualized rehabilitation plan (e.g., low-intensity strength training and exercises) is generated. Specific libraries used include Scikit-learn and Pandas.

[0405] Step 4:

[0406] The server uses the smart glasses' camera and microphone to analyze the patient's emotional state in real time during rehabilitation. It analyzes the patient's facial expressions and voice tone using Microsoft Azure's Face API and Speech API. Real-time facial and voice data from the patient is collected as input data, and emotional information (e.g., anxiety, stress, motivation) is output as the analysis result.

[0407] Step 5:

[0408] The server dynamically adjusts the rehabilitation plan based on the collected emotional information. Emotional information (e.g., anxiety, fatigue) is taken as input, and adjustment data for the rehabilitation plan (e.g., reducing exercise intensity or inserting relaxation) is output. Specifically, AWS Lambda and DynamoDB are used to process and adjust this data.

[0409] Step 6:

[0410] The final rehabilitation plan is sent back to the smart glasses. The server sends the adjusted plan to the device so the user can review it. The input is the adjusted rehabilitation plan, and the output is a visual display of the plan's details. In this case, the device uses the Google Glass API to display the plan.

[0411] Through the steps described above, a system is realized that generates and dynamically adjusts rehabilitation plans based on the patient's individual information and emotional state.

[0412] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0413] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0414] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0415] [Second Embodiment]

[0416] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0417] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0418] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0419] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0420] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0421] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0422] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0423] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0424] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0425] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0426] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0427] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0428] This invention relates to a system for generating rehabilitation plans for the elderly. Specific embodiments of this invention are described below.

[0429] A natural language explanation of the program's processing.

[0430] Entering patient information

[0431] As an initial step in generating a rehabilitation plan, the user enters patient information into a dedicated terminal. This includes detailed information such as age, gender, physical characteristics (height, weight, etc.), motor skills, symptoms, personality, and goals. For example, a physical therapist might enter information such as: 80-year-old female, weight 65kg, difficulty walking, lower back pain, cautious personality, and goal to be able to walk independently within six months.

[0432] Information transmission

[0433] The terminal securely transmits the entered patient information to the server in real time. Check functions and encrypted communication are used to maintain the accuracy and integrity of the information.

[0434] Rehabilitation plan generation

[0435] The server analyzes the received patient information based on an algorithm. Specifically, in addition to basic information such as age and physique, it considers details of symptoms, personality, and rehabilitation goals. For example, in the case of lower back pain, low-impact stretching and strength training are required. Also, if the patient has a cautious personality, a plan is created that minimizes the risks of rehabilitation and starts with small steps.

[0436] The server references past patient data and the latest rehabilitation techniques to generate an optimal rehabilitation plan. This plan includes specific exercises, duration, and frequency. For example, a detailed schedule might be set such as, "On the first day, do 15 minutes of light stretching, followed by two 20-minute walks."

[0437] Submitting and confirming your plan

[0438] The generated rehabilitation plan is sent from the server to the terminal. The user can review the plan on the terminal and make adjustments as needed. For example, they can change the frequency of walking from three times a week to four times a week. The adjusted plan is sent back to the server for further optimization as needed.

[0439] Optimizing and implementing the plan

[0440] The server optimizes the adjusted plan and finalizes it as the final rehabilitation plan. The finalized plan is saved on the server and made accessible to users and patients. This allows patients to undergo effective rehabilitation based on their individually customized rehabilitation plan.

[0441] Specific example

[0442] As a specific example, consider a 75-year-old male weighing 80 kg with mild dementia, undergoing rehabilitation after surgery on his left knee.

[0443] 1. The user enters the information.

[0444] The physical therapist enters the following information into the terminal: "75-year-old male, weight 80kg, mild dementia, post-left knee surgery, positive personality, goal is to be able to walk independently within 3 months."

[0445] 2. The device transmits information.

[0446] The device securely transmits this information to the server.

[0447] 3. The server generates a rehabilitation plan.

[0448] The server generates a plan that includes low-impact exercises and strength training suitable for left knee rehabilitation. It also incorporates additional exercises to accelerate the rehabilitation process, tailored to the patient's proactive personality.

[0449] 4. The server sends the plan.

[0450] The server sends the generated plan to the terminal.

[0451] 5. User confirmation and adjustment

[0452] The physical therapist reviews the plan and makes adjustments, such as "increasing the frequency of walking exercises from twice a week to three times a week."

[0453] 6. The server maintains an optimized plan.

[0454] An optimized plan is stored on the server, allowing patients to access the plan in real time and progress through their daily rehabilitation.

[0455] This allows for the provision of individualized rehabilitation plans to patients, maximizing the effectiveness of rehabilitation and promoting rapid recovery.

[0456] The following describes the processing flow.

[0457] Step 1:

[0458] The user enters patient information using a dedicated terminal. Specifically, they enter detailed information such as the patient's age, gender, physique (height and weight), physical ability, symptoms, personality, and rehabilitation goals. For example, they might set it to "78-year-old female, weight 60kg, lower back pain, cautious personality, goal: independent walking."

[0459] Step 2:

[0460] The terminal transmits patient information entered into it to the server in real time. During transmission, the data format is checked to maintain the accuracy and integrity of the patient data, and encrypted communication is used to send the data securely.

[0461] Step 3:

[0462] The server analyzes the patient information it receives. First, it analyzes basic information (age, gender, build, etc.), and then it considers details of symptoms (location and severity of pain, etc.) and personality (cautious, proactive, etc.). For example, a 78-year-old woman with lower back pain and a cautious personality would require low-impact, low-risk exercise.

[0463] Step 4:

[0464] The server references past patient data and the latest rehabilitation techniques, applying algorithms necessary for generating a rehabilitation plan. These algorithms construct an optimal rehabilitation program based on each patient's individual requirements, including stretching, strength training, and gait practice.

[0465] Step 5:

[0466] The server generates a rehabilitation plan and sends it to the terminal. Each plan includes specific exercises, duration, and frequency. For example, "On the first day, 15 minutes of light stretching, followed by 20 minutes of walking."

[0467] Step 6:

[0468] The user reviews the rehabilitation plan generated on their device and makes adjustments as needed. For example, they can adjust the frequency of walking exercises from twice a week to three times a week. The adjusted plan is then sent back to the server and reflected in the changes.

[0469] Step 7:

[0470] The server optimizes the adjusted rehabilitation plan. During the optimization process, further detailed adjustments are made to determine the optimal rehabilitation menu for each individual patient.

[0471] Step 8:

[0472] The final rehabilitation plan is saved on a server and made accessible to both the user and the patient in real time. Based on this plan, the patient can proceed with their daily rehabilitation in sequence.

[0473] In this way, the rehabilitation system of the present invention provides a personalized rehabilitation plan suitable for the elderly quickly and effectively.

[0474] (Example 1)

[0475] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0476] In rehabilitation for the elderly, a challenge lies in the lack of systems that can efficiently generate individualized rehabilitation plans and flexibly respond to patients' specific needs and progress. Furthermore, security concerns regarding data transmission and storage also need to be addressed.

[0477] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0478] In this invention, the server includes means for generating a rehabilitation plan using an algorithm based on input information, means for the algorithm to analyze patient information using a machine learning library, and means for sending the adjusted plan back to the server for optimization. This makes it possible to provide a system that generates personalized rehabilitation plans and can be used effectively and safely by users and patients.

[0479] "Input means" refers to devices or software that allow users to input detailed information about a patient, such as their age, gender, physique, athletic ability, symptoms, personality, and goals.

[0480] A "server" refers to a high-performance computer system that receives, processes, and stores data over a network.

[0481] An "algorithm" refers to a set of steps or formulas that run on a server, use machine learning libraries to analyze input patient information, and generate an individualized rehabilitation plan.

[0482] A "machine learning library" refers to a group of software tools used for analyzing patient information and generating rehabilitation plans, and includes tools such as TensorFlow and PyTorch.

[0483] "Generation means" refers to the functions and processes used to generate an optimal rehabilitation plan based on input information using an algorithm.

[0484] "Display means" refers to devices or software used to visually present the generated rehabilitation plan to the user.

[0485] "Confirmation means" refers to a function that allows users to view the generated rehabilitation plan and understand or confirm its contents.

[0486] "Adjustment mechanisms" refer to functions and processes that allow users to change or modify the content of the generated rehabilitation plan as needed.

[0487] "Transmission method" refers to the function or process for sending the adjusted rehabilitation plan back to the server to maintain data integrity and accuracy.

[0488] "Optimization methods" refer to the functions and processes used to re-analyze the adjusted rehabilitation plan and finalize it as the final rehabilitation plan.

[0489] "Encryption technology" refers to technologies used to prevent third parties from reading data during transmission or storage, and includes SSL / TLS, among others.

[0490] This invention relates to a system for generating rehabilitation plans for the elderly. Specific embodiments of this invention are described in detail below.

[0491] Entering patient information

[0492] To generate a rehabilitation plan, the user inputs patient information using a dedicated terminal (e.g., a tablet or PC). This patient information includes details such as age, gender, physical characteristics (height, weight, etc.), motor skills, symptoms, personality, and goals. The input information is checked for required fields by an input validation function displayed on the user interface. For example, a physical therapist might input information such as "80-year-old female, weight 65kg, difficulty walking, lower back pain, cautious personality, goal: to be able to walk independently within six months."

[0493] Sending patient information

[0494] The terminal sends the entered patient information to the server. Encryption technologies such as SSL / TLS are used to securely transfer the data. A function checks for data errors before transmission. For example, a warning message is displayed if there are any required fields that have not been entered.

[0495] Rehabilitation plan generation

[0496] The server generates a rehabilitation plan using an algorithm based on the received patient information. The algorithm is implemented in Python and utilizes machine learning libraries such as TensorFlow and PyTorch. It analyzes the submitted patient information and also refers to databases of past patient data and the latest rehabilitation methods. For example, in the case of a patient with lower back pain, it recommends low-impact stretching and strength training, and if the patient has a cautious personality, it generates a plan that starts with small steps.

[0497] Submission and confirmation of rehabilitation plan

[0498] The server sends the generated rehabilitation plan to the user's device. The created plan includes specific exercises, durations, and frequencies. For example, it might include a detailed schedule such as, "On the first day, do 15 minutes of light stretching, followed by two 20-minute walks." The user can review the plan on their device and make adjustments as needed. The adjusted plan is then sent back to the server for further optimization.

[0499] Optimizing and implementing the plan

[0500] The server optimizes the adjusted rehabilitation plan and finalizes it. The finalized plan is saved on the server and becomes accessible to both the user and the patient. This allows patients to undergo effective rehabilitation based on their individually customized rehabilitation plan.

[0501] Specific example

[0502] Here are some specific examples:

[0503] Specific example: A 75-year-old male, weighing 80 kg, with mild dementia, undergoing rehabilitation after surgery on his left knee.

[0504] 1. The user enters the information.

[0505] The physical therapist enters the following information into the terminal: "75-year-old male, weight 80kg, mild dementia, post-left knee surgery, positive personality, goal is to be able to walk independently within 3 months."

[0506] The terminal checks for any missing required fields, and if there are no errors, it proceeds to the next step.

[0507] 2. The device transmits information.

[0508] The terminal sends the entered information to the server.

[0509] Encrypt and securely transmit data using SSL / TLS.

[0510] 3. The server generates a rehabilitation plan.

[0511] Based on the received patient information, the server generates a rehabilitation plan using a generated AI model.

[0512] For example, it can generate a plan that includes low-impact exercises and strength training suitable for left knee rehabilitation, and incorporate additional activities to speed up progress according to the patient's proactive personality.

[0513] 4. The server sends the plan.

[0514] The server sends the generated rehabilitation plan to the terminal.

[0515] The plan includes a specific schedule such as, "On the first day, do 15 minutes of light stretching, followed by two 20-minute walks."

[0516] 5. The user reviews and adjusts the settings.

[0517] The physical therapist reviews the plan and makes adjustments, such as "increasing the frequency of walking exercises from twice a week to three times a week."

[0518] The revised plan will be sent back to the server.

[0519] 6. The server maintains an optimized plan.

[0520] The server stores optimized plans and makes them accessible to users and patients.

[0521] Patients can use a device to access their treatment plan in real time and carry out their daily rehabilitation.

[0522] Example of a prompt

[0523] The following prompts can be used to input patient information into the generating AI model and propose an appropriate rehabilitation plan:

[0524] A 75-year-old male, weighing 80 kg, with mild dementia, has undergone left knee surgery, and has an active personality. The goal is for him to be able to walk independently within 3 months. Please generate a rehabilitation plan for this patient.

[0525] In this way, it becomes possible to efficiently generate rehabilitation plans for the elderly and provide individualized care.

[0526] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0527] Step 1:

[0528] The user enters patient information.

[0529] The user uses a dedicated terminal to input detailed information about the patient, such as age, gender, physical characteristics (height, weight, etc.), athletic ability, symptoms, personality, and goals.

[0530] After input, the terminal screen will activate an input validation function to check all required fields. If there are any missing or incorrect entries, a warning message will be displayed.

[0531] An example of input data is: "An 80-year-old woman, weighing 65 kg, with difficulty walking, back pain, cautious personality, and a goal of being able to walk independently within six months."

[0532] In this step, the entered patient information is saved as digital data on the terminal.

[0533] Step 2:

[0534] The device sends information

[0535] The terminal sends the entered patient information to the server. Since encryption technology (e.g., SSL / TLS) is used for transmission, the data is transferred securely.

[0536] The integrity and accuracy of the data are checked during transmission.

[0537] The data sent will be in a standard format such as JSON, and each field will contain detailed information such as age, gender, physique, and athletic ability.

[0538] The output of this step is patient information that has been securely transmitted.

[0539] Step 3:

[0540] The server generates a rehabilitation plan.

[0541] The server uses an algorithm to generate a rehabilitation plan based on the patient information it receives.

[0542] The algorithm uses machine learning libraries (TensorFlow and PyTorch) and also references past patient data and databases of the latest rehabilitation methods.

[0543] Based on the input data, the system selects an appropriate rehabilitation menu tailored to the symptoms and personality, and proposes specific exercises and training plans.

[0544] For example, patients with lower back pain might be recommended low-impact stretches and strength training, while those with a cautious personality might be given a plan that starts with small steps.

[0545] In this step, the output is the generated rehabilitation plan.

[0546] Step 4:

[0547] The server sends the generated plan to the terminal.

[0548] The server sends the generated rehabilitation plan to the terminal. Encryption technology is used for transmission, ensuring the data is transferred securely.

[0549] A rehabilitation plan is generated that includes specific information such as the type of exercise, duration, and frequency.

[0550] For example, a specific schedule might be set, such as, "On the first day, do 15 minutes of light stretching, followed by two 20-minute walks."

[0551] The output of this step is the rehabilitation plan sent to the terminal.

[0552] Step 5:

[0553] Users can review and adjust their plans.

[0554] Users can review the rehabilitation plan generated on their device. They can adjust the content and frequency of exercises as needed.

[0555] For example, specific adjustments can be made, such as "increasing the frequency of walking from three times a week to four times a week."

[0556] The adjusted plan is sent back to the server, so the same checks and encrypted transmission are performed during the readjustment process.

[0557] The output of this step is the adjusted rehabilitation plan.

[0558] Step 6:

[0559] The server maintains an optimized plan.

[0560] The server optimizes the adjusted rehabilitation plan and finalizes it. The finalized plan is saved on the server and becomes accessible to users and patients.

[0561] A highly available database (such as an SQL database) is used for data storage in this step.

[0562] This allows patients to undergo effective rehabilitation based on individually customized rehabilitation plans.

[0563] The output of this step is the optimized rehabilitation plan saved on the server.

[0564] (Application Example 1)

[0565] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0566] In rehabilitation for the elderly, not only is individualized rehabilitation plans necessary, but real-time support for their implementation is also required. However, existing systems cannot provide immediate feedback on whether patients are performing rehabilitation correctly, which makes effective rehabilitation difficult. Furthermore, there is a need for guidance functions that are easy for the elderly to understand and intuitive to use.

[0567] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0568] In this invention, the server includes means for inputting the patient's age, gender, physique, motor skills, symptoms, personality, and goals; means for generating a rehabilitation plan based on the input information; means for displaying the generated rehabilitation plan and allowing the user to review and adjust it; and means for providing real-time audio guidance and visual feedback using a smart device. This makes it possible for the patient to receive real-time guidance and feedback when performing rehabilitation, thereby enhancing the effectiveness of the rehabilitation.

[0569] The term "elderly" generally refers to people aged 65 or older, and especially to those with physical limitations that require rehabilitation.

[0570] A "rehabilitation plan" refers to a detailed schedule that combines exercises and treatments tailored to the patient's physical condition and symptoms, with the aim of restoring the patient's health.

[0571] A "server" is a computer system that sends and receives data over a network, and processes and stores information.

[0572] A "smart device" is a device that is connected to the internet and capable of processing and displaying information, and includes, for example, smartphones, smart glasses, and head-mounted displays.

[0573] "Voice guide" refers to a function that converts text information into speech to provide guidance to the user.

[0574] "Visual feedback" refers to a function that allows users to visually confirm their actions by providing information through visual display devices such as screens.

[0575] An "algorithm" is a set of computational steps or processing rules, referring to a procedure designed to solve a specific problem.

[0576] A "personalized rehabilitation program" is a plan that includes exercises and treatments designed to suit the patient's specific needs and physical condition.

[0577] "Past patient data" refers to rehabilitation records and progress data of patients who have received treatment in the past.

[0578] "Latest rehabilitation methods" refer to rehabilitation methods designed based on the latest research and technologies in medicine and exercise science.

[0579] Specific embodiments for carrying out this invention will be described.

[0580] Overall system configuration

[0581] This invention is a system for generating rehabilitation plans for the elderly and providing real-time feedback and guidance. The system consists of the following components:

[0582] 1. Input method: A means of inputting the patient's age, sex, physique, motor skills, symptoms, personality, and goals. This may involve using an input device such as a tablet or computer.

[0583] 2. Server: The central hub of the system that generates rehabilitation plans using algorithms based on the entered patient information. The server references past patient data and the latest rehabilitation methods to construct an individualized plan.

[0584] 3. Smart devices: Devices worn by patients, such as smart glasses and head-mounted displays. These include features such as voice guidance and visual feedback.

[0585] 4. Display means: A means for displaying the generated rehabilitation plan, allowing the user (physical therapist) to review and adjust it. A display device such as a tablet or smartphone is used.

[0586] Hardware and software configuration

[0587] Server: A high-performance computer equipped with an Intel or AMD processor, consisting of a database management system (e.g., MySQL) and machine learning algorithms (e.g., TensorFlow).

[0588] Smart devices include smart glasses such as Google Glass and Microsoft HoloLens. These utilize speech recognition (e.g., Google Speech Recognition API), speech synthesis (e.g., Pyttsx3), and visual display (e.g., OpenCV).

[0589] Display method: Use an Apple iPad or Android tablet and install a dedicated application.

[0590] Explanation of the program's processing

[0591] The server receives patient information entered by the physical therapist and analyzes it using a specific algorithm. Specifically, the following steps are taken:

[0592] 1. Enter patient information:

[0593] The user enters the patient information necessary to generate a rehabilitation plan. For example, they might use a tablet to enter age, gender, physique, motor skills, symptoms, personality, and goals.

[0594] 2. Information Analysis:

[0595] The server analyzes the input information and generates an optimal rehabilitation plan by referencing past patient data and the latest rehabilitation techniques.

[0596] 3. Plan generation:

[0597] The server uses algorithms (e.g., machine learning models) to create a personalized rehabilitation plan. This plan includes specific exercises, their duration, and frequency.

[0598] 4. Feedback and guidance:

[0599] Smart devices are used to provide patients with real-time audio guidance and visual feedback. For example, Google Glass can be used to provide an audio guide to the patient saying, "Extend your arms and raise them to shoulder height," while the display shows the text, "Keep your back straight."

[0600] Specific example

[0601] Let's consider an example of a 75-year-old man using a rehabilitation support system for the elderly. A physical therapist inputs information into a tablet: "75-year-old male, weight 80kg, mild dementia, post-left knee surgery, goal is to be able to walk independently within 3 months." The server analyzes this information and generates an optimal rehabilitation plan. The patient wears Google Glass and performs exercises while receiving voice guidance and visual feedback.

[0602] Example of a prompt

[0603] "Please develop a rehabilitation support application for seniors. This application uses smart glasses to provide voice guidance for exercises, reminders, and visual feedback. Please use Python and the following libraries: GTTS, Pyttsx3, OpenCV, datetime."

[0604] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0605] Step 1:

[0606] The user uses a tablet device to input the patient's age, gender, physique, athletic ability, symptoms, personality, and goals. This data is sent to the server. The entered data is stored on the server and ready for analysis.

[0607] Step 2:

[0608] Based on the received patient information, the server references a database of past patients and the latest rehabilitation techniques. This database includes past rehabilitation case studies and exercise information based on the latest medical research. Using this data, the server executes an algorithm to generate the optimal rehabilitation plan for the patient.

[0609] Step 3:

[0610] The generated rehabilitation plan is sent from the server to the tablet device. The user (physiotherapist) reviews this plan on the tablet and makes adjustments as needed. For example, they can increase the frequency of walking from twice a week to three times a week. The adjusted plan is sent back to the server, and the final optimized rehabilitation plan is finalized.

[0611] Step 4:

[0612] The finalized rehabilitation plan is sent from the server to a smart device (e.g., smart glasses). Once the patient puts on the smart device and begins rehabilitation, voice guidance and visual feedback are provided in real time. Specifically, using Google Glass, voice instructions such as "Extend your arms and raise them to shoulder height" and visual instructions such as "Keep your back straight" are displayed on the screen.

[0613] Step 5:

[0614] When a patient performs rehabilitation, the smart device uses built-in sensors to collect movement data. This data is transmitted to a server in real time, which analyzes whether the patient's movements are following the plan. Based on the analysis, voice guidance and visual feedback are adjusted to help the patient perform rehabilitation correctly.

[0615] Step 6:

[0616] Rehabilitation progress is managed on a server and can be viewed by the user via a tablet or smart device. Based on progress, the server automatically updates the rehabilitation plan and suggests new exercises to the patient. This maximizes the effectiveness of the patient's rehabilitation.

[0617] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0618] This invention relates to a system for generating rehabilitation plans for the elderly that incorporates an emotion engine. Specific embodiments of this invention are described below.

[0619] A natural language explanation of the program's processing.

[0620] Entering patient information

[0621] As an initial step in generating a rehabilitation plan, the user enters patient information into a dedicated terminal. This includes detailed information such as age, gender, physical characteristics (height and weight), motor skills, symptoms, personality, and rehabilitation goals. For example, a physical therapist might enter: "80-year-old female, weight 65kg, difficulty walking, lower back pain, cautious personality, goal is to be able to walk independently within six months."

[0622] Information transmission

[0623] The terminal securely transmits entered patient information to the server in real time. Data format verification and encrypted communication are used to maintain the accuracy and integrity of the information.

[0624] Rehabilitation plan generation

[0625] The server analyzes the received patient information using an algorithm. A rehabilitation plan is generated that takes into account basic information, detailed symptoms, and personality. For example, for a 78-year-old woman with lower back pain and a cautious personality, a plan including low-impact, low-risk exercises will be created.

[0626] Analysis of emotional information

[0627] When a user undergoes rehabilitation, the emotion engine analyzes the user's emotional information in real time. This emotion engine determines the user's emotional state based on facial expressions, tone of voice, language patterns, and other factors. For example, if a user is feeling anxious or stressed about rehabilitation, this information will be detected by the emotion engine.

[0628] Adjusting the rehabilitation plan

[0629] The server dynamically adjusts the rehabilitation plan based on emotional information obtained from the emotion engine. For example, if a user is showing signs of fatigue, the intensity of the exercise can be temporarily reduced. Conversely, if a user is highly motivated, the pace of the plan can be accelerated.

[0630] View and check the plan

[0631] The generated and adjusted rehabilitation plan is sent from the server to the terminal. The user can review the plan on the terminal and make further adjustments. For example, specific changes such as "increase the frequency of walking exercises from twice a week to three times a week" can be made.

[0632] Specific example

[0633] As a specific example, consider a 75-year-old male weighing 80 kg with mild dementia, undergoing rehabilitation after surgery on his left knee.

[0634] 1. The user enters the information.

[0635] The physical therapist enters the following information into the terminal: "75-year-old male, weight 80kg, mild dementia, post-left knee surgery, positive personality, goal is to be able to walk independently within 3 months."

[0636] 2. The device transmits information.

[0637] The device sends this information to the server in real time.

[0638] 3. The server generates a rehabilitation plan.

[0639] Based on the input information, the server generates a plan that includes low-impact exercises and strength training suitable for post-operative rehabilitation of the left knee.

[0640] 4. Analysis using an emotion engine

[0641] When a user begins rehabilitation, the emotion engine analyzes the user's facial expressions and tone of voice to determine, for example, that "the user is highly motivated for rehabilitation."

[0642] 5. The server adjusts the plan.

[0643] Based on the user's high motivation, the server dynamically adjusts the rehabilitation plan, reflecting a plan that accelerates the pace of rehabilitation.

[0644] 6. Submit and confirm your plan

[0645] The final rehabilitation plan is sent from the server to the terminal, and after the physical therapist reviews the details, it is provided to the user.

[0646] As described above, the rehabilitation system of the present invention, which incorporates an emotion engine, can provide more effective rehabilitation by dynamically adjusting the rehabilitation plan according to the user's emotional state.

[0647] The following describes the processing flow.

[0648] Step 1:

[0649] The user enters patient information using a dedicated terminal. Specifically, they enter detailed information such as the patient's age, gender, physique (height and weight), physical ability, symptoms, personality, and rehabilitation goals. For example, they might set it to "78-year-old female, weight 60kg, lower back pain, cautious personality, goal: independent walking."

[0650] Step 2:

[0651] The terminal transmits patient information entered into it to the server in real time. During transmission, the data format is checked to maintain the accuracy and integrity of the patient data, and encrypted communication is used to send the data securely.

[0652] Step 3:

[0653] The server analyzes the patient information it receives. First, it analyzes basic information (age, gender, build, etc.), and then it considers details of the symptoms (location and severity of pain, etc.) and personality traits (cautious, proactive, etc.). For example, a 78-year-old woman with lower back pain and a cautious personality would require low-impact, low-risk exercise.

[0654] Step 4:

[0655] The server references past patient data and the latest rehabilitation techniques, applying algorithms necessary for generating a rehabilitation plan. These algorithms construct an optimal rehabilitation program based on each patient's individual requirements, including stretching, strength training, and gait practice.

[0656] Step 5:

[0657] The emotion engine analyzes the user's real-time emotional information. This includes analyzing the user's facial expressions, tone of voice, and language patterns to determine their emotional state. For example, if a user is feeling stressed, the emotion engine will detect it.

[0658] Step 6:

[0659] The server dynamically adjusts the rehabilitation plan based on emotional information analyzed by the emotion engine. For example, if a user is showing signs of fatigue, the intensity of the exercise can be temporarily reduced. Conversely, if a user is highly motivated, the pace of the plan can be accelerated.

[0660] Step 7:

[0661] The server generates a rehabilitation plan and sends it to the terminal. Each plan includes specific exercises, duration, and frequency. For example, "On the first day, 15 minutes of light stretching, followed by 20 minutes of walking."

[0662] Step 8:

[0663] The user reviews the rehabilitation plan generated on their device and makes adjustments as needed. For example, they can adjust the frequency of walking exercises from twice a week to three times a week. The adjusted plan is then sent back to the server and reflected in the changes.

[0664] Step 9:

[0665] The server optimizes the adjusted rehabilitation plan. During the optimization process, further detailed adjustments are made to determine the optimal rehabilitation menu for each individual patient.

[0666] Step 10:

[0667] The final rehabilitation plan is saved on a server and made accessible to both the user and the patient in real time. Based on this plan, the patient can proceed with their daily rehabilitation in sequence.

[0668] As described above, the rehabilitation system of the present invention, which incorporates an emotion engine, dynamically adjusts the rehabilitation plan according to the user's emotional state, thereby providing more effective rehabilitation.

[0669] (Example 2)

[0670] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0671] In rehabilitation for the elderly, it is essential to provide plans that take into account the individual circumstances and emotional state of each patient. However, conventional rehabilitation plan generation systems lacked the ability to analyze the patient's emotional information and dynamically adjust the plan based on that information. As a result, it was difficult to provide effective rehabilitation plans that were appropriate for each patient.

[0672] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0673] In this invention, the server includes means for inputting the patient's age, gender, physique, motor skills, symptoms, personality, and goals; means for generating a rehabilitation plan based on the input information; means for analyzing emotional information; means for dynamically adjusting the rehabilitation plan based on the emotional information; and means for displaying the generated rehabilitation plan for the user to review and adjust. This makes it possible to provide an effective rehabilitation plan that reflects the individual circumstances and emotional state of the patient.

[0674] "Patient information" refers to detailed data including the patient's age, sex, physique, motor skills, symptoms, personality, and rehabilitation goals.

[0675] A "terminal" is a device that users operate to input patient information and to check and adjust rehabilitation plans.

[0676] A "server" is a central processing unit that generates rehabilitation plans based on the input patient information, and performs analysis and dynamic adjustments.

[0677] An "algorithm" is a set of computational procedures or methods that a server uses to analyze patient information and generate a rehabilitation plan.

[0678] A "generative AI model" is an artificial intelligence model that uses algorithms to analyze input patient information and generate personalized rehabilitation plans.

[0679] An "emotion engine" is a part of the software or hardware that analyzes a user's emotional information and determines their emotional state.

[0680] A "rehabilitation plan" is a specific menu and schedule of rehabilitation tailored to the patient's condition and goals.

[0681] "Dynamic adjustment" refers to the server modifying the content of the rehabilitation plan in real time based on emotional information.

[0682] "Encrypted communication" is a method of using cryptographic technology to ensure the security of data transmitted between a terminal and a server.

[0683] Modes for carrying out the invention

[0684] This invention relates to a system for generating rehabilitation plans for the elderly, providing an optimal rehabilitation plan tailored to the individual circumstances and emotional state of the patient. Specific embodiments of this invention are described below.

[0685] Entering patient information

[0686] As an initial step in generating a rehabilitation plan, the user enters patient information into a dedicated terminal. The terminal provides an interface for user input using a touchscreen and keyboard. Specific information includes age, gender, physical characteristics (height, weight), motor skills, symptoms, personality, and rehabilitation goals. For example, a physical therapist might enter "80-year-old female, weight 65kg, difficulty walking, lower back pain, cautious personality, goal: to be able to walk independently within six months."

[0687] Information transmission

[0688] The terminal securely transmits entered patient information to the server in real time. To maintain the accuracy and integrity of the information, data format verification and encrypted communication (SSL / TLS) are used. The terminal encrypts the information and sends it to the server. The server verifies the integrity of the received data and proceeds to the next processing step.

[0689] Rehabilitation plan generation

[0690] The server uses a generative AI model to analyze the patient information it receives. This model is built using machine learning frameworks such as TensorFlow or PyTorch. The server then generates an appropriate rehabilitation plan, taking into account basic information, detailed symptoms, and personality traits. For example, for a 78-year-old woman with back pain and a cautious personality, a plan including low-impact, low-risk exercises would be generated.

[0691] Analysis of emotional information

[0692] When a user undergoes rehabilitation, the emotion engine uses the device's camera and microphone to analyze the user's facial expressions, voice tone, and language patterns in real time. This allows the emotion engine to determine the user's emotional state. For example, if a user is feeling anxious or stressed about rehabilitation, this information will be detected by the emotion engine.

[0693] Adjusting the rehabilitation plan

[0694] The server dynamically adjusts the rehabilitation plan based on emotional information obtained from the emotion engine. For example, if the user shows signs of fatigue, the intensity of the exercises will be temporarily reduced. Conversely, if the user shows high motivation, the pace of the plan will be increased.

[0695] View and check the plan

[0696] The generated and adjusted rehabilitation plan is sent from the server to the terminal. The user can review the plan on the terminal and make further detailed adjustments as needed. For example, specific changes such as "increase the frequency of walking exercises from twice a week to three times a week" can be made.

[0697] Specific example

[0698] As a specific example, consider a 75-year-old male weighing 80 kg, with mild dementia, undergoing rehabilitation after surgery on his left knee.

[0699] 1. The user enters the information.

[0700] The physical therapist enters the following information into the terminal: "75-year-old male, weight 80kg, mild dementia, post-left knee surgery, positive personality, goal is to be able to walk independently within 3 months."

[0701] 2. The device transmits information.

[0702] The device sends this information to the server in real time.

[0703] 3. The server generates a rehabilitation plan.

[0704] Based on the input information, the server generates a plan that includes low-impact exercises and strength training suitable for post-operative rehabilitation of the left knee.

[0705] 4. Analysis using an emotion engine

[0706] When a user begins rehabilitation, the emotion engine analyzes the user's facial expressions and tone of voice to determine, for example, that "the user is highly motivated for rehabilitation."

[0707] 5. The server adjusts the plan.

[0708] Based on the user's high motivation, the server dynamically adjusts the rehabilitation plan, reflecting a plan that accelerates the pace of rehabilitation.

[0709] 6. Submit and confirm your plan

[0710] The final rehabilitation plan is sent from the server to the terminal, and after the physical therapist reviews the details, it is provided to the user.

[0711] Example of a prompt

[0712] 1. Prompts for generating a natural rehabilitation plan:

[0713] "Please generate a rehabilitation plan for an elderly patient. The patient is a 75-year-old male, weighing 80 kg, with mild dementia and a history of left knee surgery. He has an active personality, and the goal is for him to be able to walk independently within three months."

[0714] 2. Emotion Engine Analysis Prompt:

[0715] "Please analyze the emotional state of a 75-year-old patient undergoing rehabilitation. Based on their tone of voice and facial expressions, determine whether their motivation for rehabilitation is high or low."

[0716] 3. Prompt for adjustment of rehabilitation plan:

[0717] "Dynamically adjust the rehabilitation plan based on the user's emotional state. If the user shows high motivation, increase the pace of the exercises."

[0718] In this way, the rehabilitation plan generation system of the present invention can provide a more effective and optimal rehabilitation plan according to the individual circumstances and emotional state of the patient.

[0719] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0720] Step 1:

[0721] The user enters patient information.

[0722] The user (physical therapist) uses the terminal interface to input the patient's age, gender, physique (height, weight), motor skills, symptoms, personality, and rehabilitation goals. A specific example of input would be: "75-year-old male, weight 80kg, mild dementia, post-left knee surgery, positive personality, goal is to be able to walk independently within 3 months."

[0723] Input: Patient's basic information

[0724] Output: Patient data in a processable format

[0725] Step 2:

[0726] The device sends information to the server.

[0727] The terminal securely transmits entered patient information to the server in real time. The terminal verifies the accuracy of the information and transmits the data using encrypted communication (SSL / TLS). The server verifies that the received data is in the correct format.

[0728] Input: Patient data in a processable format

[0729] Output: Patient data sent to the server

[0730] Step 3:

[0731] The server generates a rehabilitation plan.

[0732] The server uses a generative AI model to analyze the received patient information. Specifically, it uses frameworks such as TensorFlow and PyTorch to generate an appropriate rehabilitation plan that takes into account basic information, detailed symptoms, and personality. Based on the input information, it generates a plan that includes low-impact exercises and strength training suitable for post-operative rehabilitation of the left knee, for example.

[0733] Input: Patient data sent to the server

[0734] Output: Initial Rehabilitation Plan

[0735] Step 4:

[0736] Analysis of emotional information

[0737] When a user undergoes rehabilitation, the emotion engine uses the device's camera and microphone to analyze the user's facial expressions and voice tone in real time. The emotion engine uses libraries such as OpenCV and DeepSpeech to determine the user's emotional state. For example, if the user is feeling stressed about the rehabilitation, that information will be detected.

[0738] Input: User data during rehabilitation (facial expressions, voice tone, language patterns)

[0739] Output: User sentiment information

[0740] Step 5:

[0741] The server dynamically adjusts the rehabilitation plan.

[0742] The server dynamically adjusts the rehabilitation plan based on emotional information obtained from the emotion engine. For example, if the user is showing signs of fatigue, the intensity of the exercises will be temporarily reduced. Conversely, if the user is showing high motivation, the pace of the plan will be increased.

[0743] Input: User sentiment information

[0744] Output: Adjusted rehabilitation plan

[0745] Step 6:

[0746] View and check the plan

[0747] The server sends the adjusted rehabilitation plan to the user's device, where the user reviews the plan. If further adjustments are needed, the user can modify the plan by operating the device. For example, they can adjust the frequency of walking exercises from twice a week to three times a week.

[0748] Input: Adjusted rehabilitation plan

[0749] Output: Final rehabilitation plan reviewed and adjusted by the user.

[0750] In this way, based on the data entered at each step, the server and terminal perform specific data processing and calculations, obtaining their respective outputs, thereby providing the user with an optimal rehabilitation plan.

[0751] (Application Example 2)

[0752] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0753] In rehabilitation for the elderly and people with disabilities, there is a need to provide effective plans tailored to each patient's individual circumstances and emotional state. However, conventional rehabilitation plans are usually limited to one-dimensional planning based on the patient's physical data, and there is no mechanism to dynamically adjust them to take into account the patient's emotional state and motivation. As a result, it has been difficult to maximize the effectiveness of rehabilitation and maintain patient motivation.

[0754] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0755] In this invention, the server includes means for inputting the patient's age, gender, physique, motor skills, symptoms, personality, and goals; means for generating a rehabilitation plan based on the input information; means for analyzing real-time emotional information and dynamically adjusting the rehabilitation plan based on the patient's emotions; and means for adjusting the pace of rehabilitation based on the collected emotional information. This makes it possible to create a rehabilitation plan that takes into account not only the physical data of each patient but also their emotional state, enabling effective rehabilitation while maintaining the patient's motivation.

[0756] "Elderly people" refers to those who are particularly old within the age group requiring rehabilitation, and generally refers to people aged 65 and over.

[0757] A "rehabilitation plan" refers to a detailed program designed to improve a patient's physical function and daily living activities.

[0758] A "system" refers to a collection of multiple devices or software programs combined to perform a specific function.

[0759] "Patient information" refers to individual data such as the patient's age, gender, physique, athletic ability, symptoms, personality, and goals.

[0760] A "server" refers to a central processing unit that provides services to other computers via a network.

[0761] "Emotional information" refers to data that indicates a patient's emotional state, and is acquired based on facial expressions, tone of voice, language patterns, and other factors.

[0762] "Dynamic adjustment" refers to a flexible response that adapts the plan in real time or near real time.

[0763] "Progression rate" refers to the pace at which exercises and training progress in a rehabilitation plan.

[0764] "Display means" refers to devices or software used to visually show the generated rehabilitation plan to the user.

[0765] "Input means" refers to devices or interfaces used to provide patient information to the system.

[0766] Modes for carrying out the invention

[0767] This invention relates to a system for generating rehabilitation plans for the elderly and dynamically adjusting them based on emotional information. The invention is particularly applicable to rehabilitation support applications using smart glasses. This system includes multiple means for collecting and analyzing patient information and generating effective rehabilitation plans.

[0768] 1. Collection of input data

[0769] The server utilizes the camera and microphone of smart glasses to collect basic patient information and emotional information through voice input and OCR functionality. These smart glasses are equipped with facial recognition and voice recognition capabilities, allowing for real-time acquisition of patient data. Specific hardware used includes smart glasses (e.g., Google Glass) and voice recognition APIs (e.g., Amazon Transcribe).

[0770] 2. Data transmission and encryption

[0771] The terminal sends the collected data to the server in real time. During transmission, the SSL / TLS protocol is used to ensure data security. Specifically, OpenSSL is used to implement encrypted communication.

[0772] 3. Generating a Rehabilitation Plan

[0773] The server generates rehabilitation plans based on collected patient information. To do this, it uses Python-based machine learning algorithms to construct plans tailored to each patient's individual needs. Specifically, it uses libraries such as Scikit-learn and Pandas to analyze data and generate plans.

[0774] 4. Analysis of emotional information

[0775] The server analyzes emotional information to detect the patient's emotional state. Specifically, it uses Microsoft Azure Cognitive Services (Face API, Speech API) to perform real-time emotional analysis. This allows the server to understand the patient's emotional state from their facial expressions and tone of voice.

[0776] 5. Dynamic adjustment of the rehabilitation plan

[0777] The server dynamically adjusts the rehabilitation plan based on emotional information. For example, if a patient shows signs of fatigue, the exercise intensity is temporarily reduced. Conversely, if motivation is high, the plan is advanced. AWS Lambda and dynamic databases (e.g., DynamoDB) are used for this adjustment.

[0778] 6. View and confirm the plan

[0779] The final rehabilitation plan is sent back to the smart glasses for the user to review. This allows physical therapists and rehabilitation center staff to fine-tune the plan according to the patient's condition.

[0780] Specific example

[0781] As a concrete example, suppose a 75-year-old male patient begins rehabilitation and wears smart glasses. These smart glasses analyze the patient's voice and facial expressions and may make the following adjustments to the initial plan.

[0782] If the patient is feeling anxious:

[0783] "The patient is anxious about rehabilitation. Please reduce the intensity of the next exercise and incorporate relaxation exercises."

[0784] Example of a prompt

[0785] "Generate a rehabilitation plan for a 75-year-old male, weighing 80kg, with mild dementia, post-left knee surgery, and an outgoing personality. His goal is to achieve independent walking within three months. Furthermore, use an emotion engine to acquire emotional information from the patient's facial expressions and tone of voice, and dynamically adjust the rehabilitation plan accordingly."

[0786] Thus, the system of the present invention can generate a rehabilitation plan based on individual patient information and dynamically adjust it according to the patient's emotional state, thereby providing more effective and personalized rehabilitation.

[0787] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0788] Step 1:

[0789] The server collects patient information using smart glasses. Patient age, gender, build, motor skills, symptoms, personality, and goals are entered using voice recognition and OCR functions. Example input data includes a 75-year-old male, weighing 80kg, post-knee surgery on the left knee, with an active personality and a goal of independent walking within three months. This data is collected using smart glasses such as Google Glass and Amazon Transcribe.

[0790] Step 2:

[0791] The terminal transmits collected patient information to the server in real time. To ensure data security, encrypted communication is performed using the SSL / TLS protocol. The input is patient information (e.g., 75-year-old male, 80kg weight, post-left knee surgery), and the output is encrypted data sent to the server.

[0792] Step 3:

[0793] The server generates a rehabilitation plan based on the received patient information. It uses a Python-based machine learning algorithm to analyze the data and construct the rehabilitation plan. The input is detailed patient information (e.g., age, weight, symptoms), and based on this, an individualized rehabilitation plan (e.g., low-intensity strength training and exercises) is generated. Specific libraries used include Scikit-learn and Pandas.

[0794] Step 4:

[0795] The server uses the smart glasses' camera and microphone to analyze the patient's emotional state in real time during rehabilitation. It analyzes the patient's facial expressions and voice tone using Microsoft Azure's Face API and Speech API. Real-time facial and voice data from the patient is collected as input data, and emotional information (e.g., anxiety, stress, motivation) is output as the analysis result.

[0796] Step 5:

[0797] The server dynamically adjusts the rehabilitation plan based on the collected emotional information. Emotional information (e.g., anxiety, fatigue) is taken as input, and adjustment data for the rehabilitation plan (e.g., reducing exercise intensity or inserting relaxation) is output. Specifically, AWS Lambda and DynamoDB are used to process and adjust this data.

[0798] Step 6:

[0799] The final rehabilitation plan is sent back to the smart glasses. The server sends the adjusted plan to the device so the user can review it. The input is the adjusted rehabilitation plan, and the output is a visual display of the plan's details. In this case, the device uses the Google Glass API to display the plan.

[0800] Through the steps described above, a system is realized that generates and dynamically adjusts rehabilitation plans based on the patient's individual information and emotional state.

[0801] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0802] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0803] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0804] [Third Embodiment]

[0805] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0806] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0807] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0808] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0809] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0810] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0811] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0812] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0813] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0814] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0815] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0816] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0817] This invention relates to a system for generating rehabilitation plans for the elderly. Specific embodiments of this invention are described below.

[0818] A natural language explanation of the program's processing.

[0819] Entering patient information

[0820] As an initial step in generating a rehabilitation plan, the user enters patient information into a dedicated terminal. This includes detailed information such as age, gender, physical characteristics (height, weight, etc.), motor skills, symptoms, personality, and goals. For example, a physical therapist might enter information such as: 80-year-old female, weight 65kg, difficulty walking, lower back pain, cautious personality, and goal to be able to walk independently within six months.

[0821] Information transmission

[0822] The terminal securely transmits the entered patient information to the server in real time. Check functions and encrypted communication are used to maintain the accuracy and integrity of the information.

[0823] Rehabilitation plan generation

[0824] The server analyzes the received patient information based on an algorithm. Specifically, in addition to basic information such as age and physique, it considers details of symptoms, personality, and rehabilitation goals. For example, in the case of lower back pain, low-impact stretching and strength training are required. Also, if the patient has a cautious personality, a plan is created that minimizes the risks of rehabilitation and starts with small steps.

[0825] The server references past patient data and the latest rehabilitation techniques to generate an optimal rehabilitation plan. This plan includes specific exercises, duration, and frequency. For example, a detailed schedule might be set such as, "On the first day, do 15 minutes of light stretching, followed by two 20-minute walks."

[0826] Submitting and confirming your plan

[0827] The generated rehabilitation plan is sent from the server to the terminal. The user can review the plan on the terminal and make adjustments as needed. For example, they can change the frequency of walking from three times a week to four times a week. The adjusted plan is sent back to the server for further optimization as needed.

[0828] Optimizing and implementing the plan

[0829] The server optimizes the adjusted plan and finalizes it as the final rehabilitation plan. The finalized plan is saved on the server and made accessible to users and patients. This allows patients to undergo effective rehabilitation based on their individually customized rehabilitation plan.

[0830] Specific example

[0831] As a specific example, consider a 75-year-old male weighing 80 kg with mild dementia, undergoing rehabilitation after surgery on his left knee.

[0832] 1. The user enters the information.

[0833] The physical therapist enters the following information into the terminal: "75-year-old male, weight 80kg, mild dementia, post-left knee surgery, positive personality, goal is to be able to walk independently within 3 months."

[0834] 2. The device transmits information.

[0835] The device securely transmits this information to the server.

[0836] 3. The server generates a rehabilitation plan.

[0837] The server generates a plan that includes low-impact exercises and strength training suitable for left knee rehabilitation. It also incorporates additional exercises to accelerate the rehabilitation process, tailored to the patient's proactive personality.

[0838] 4. The server sends the plan.

[0839] The server sends the generated plan to the terminal.

[0840] 5. User confirmation and adjustment

[0841] The physical therapist reviews the plan and makes adjustments, such as "increasing the frequency of walking exercises from twice a week to three times a week."

[0842] 6. The server maintains an optimized plan.

[0843] An optimized plan is stored on the server, allowing patients to access the plan in real time and progress through their daily rehabilitation.

[0844] This allows for the provision of individualized rehabilitation plans to patients, maximizing the effectiveness of rehabilitation and promoting rapid recovery.

[0845] The following describes the processing flow.

[0846] Step 1:

[0847] The user enters patient information using a dedicated terminal. Specifically, they enter detailed information such as the patient's age, gender, physique (height and weight), physical ability, symptoms, personality, and rehabilitation goals. For example, they might set it to "78-year-old female, weight 60kg, lower back pain, cautious personality, goal: independent walking."

[0848] Step 2:

[0849] The terminal transmits patient information entered into it to the server in real time. During transmission, the data format is checked to maintain the accuracy and integrity of the patient data, and encrypted communication is used to send the data securely.

[0850] Step 3:

[0851] The server analyzes the patient information it receives. First, it analyzes basic information (age, gender, build, etc.), and then it considers details of symptoms (location and severity of pain, etc.) and personality (cautious, proactive, etc.). For example, a 78-year-old woman with lower back pain and a cautious personality would require low-impact, low-risk exercise.

[0852] Step 4:

[0853] The server references past patient data and the latest rehabilitation techniques, applying algorithms necessary for generating a rehabilitation plan. These algorithms construct an optimal rehabilitation program based on each patient's individual requirements, including stretching, strength training, and gait practice.

[0854] Step 5:

[0855] The server generates a rehabilitation plan and sends it to the terminal. Each plan includes specific exercises, duration, and frequency. For example, "On the first day, 15 minutes of light stretching, followed by 20 minutes of walking."

[0856] Step 6:

[0857] The user reviews the rehabilitation plan generated on their device and makes adjustments as needed. For example, they can adjust the frequency of walking exercises from twice a week to three times a week. The adjusted plan is then sent back to the server and reflected in the changes.

[0858] Step 7:

[0859] The server optimizes the adjusted rehabilitation plan. During the optimization process, further detailed adjustments are made to determine the optimal rehabilitation menu for each individual patient.

[0860] Step 8:

[0861] The final rehabilitation plan is saved on a server and made accessible to both the user and the patient in real time. Based on this plan, the patient can proceed with their daily rehabilitation in sequence.

[0862] In this way, the rehabilitation system of the present invention provides a personalized rehabilitation plan suitable for the elderly quickly and effectively.

[0863] (Example 1)

[0864] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0865] In rehabilitation for the elderly, a challenge lies in the lack of systems that can efficiently generate individualized rehabilitation plans and flexibly respond to patients' specific needs and progress. Furthermore, security concerns regarding data transmission and storage also need to be addressed.

[0866] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0867] In this invention, the server includes means for generating a rehabilitation plan using an algorithm based on input information, means for the algorithm to analyze patient information using a machine learning library, and means for sending the adjusted plan back to the server for optimization. This makes it possible to provide a system that generates personalized rehabilitation plans and can be used effectively and safely by users and patients.

[0868] "Input means" refers to devices or software that allow users to input detailed information about a patient, such as their age, gender, physique, athletic ability, symptoms, personality, and goals.

[0869] A "server" refers to a high-performance computer system that receives, processes, and stores data over a network.

[0870] An "algorithm" refers to a set of steps or formulas that run on a server, use machine learning libraries to analyze input patient information, and generate an individualized rehabilitation plan.

[0871] A "machine learning library" refers to a group of software tools used for analyzing patient information and generating rehabilitation plans, and includes tools such as TensorFlow and PyTorch.

[0872] "Generation means" refers to the functions and processes used to generate an optimal rehabilitation plan based on input information using an algorithm.

[0873] "Display means" refers to devices or software used to visually present the generated rehabilitation plan to the user.

[0874] "Confirmation means" refers to a function that allows users to view the generated rehabilitation plan and understand or confirm its contents.

[0875] "Adjustment mechanisms" refer to functions and processes that allow users to change or modify the content of the generated rehabilitation plan as needed.

[0876] "Transmission method" refers to the function or process for sending the adjusted rehabilitation plan back to the server to maintain data integrity and accuracy.

[0877] "Optimization methods" refer to the functions and processes used to re-analyze the adjusted rehabilitation plan and finalize it as the final rehabilitation plan.

[0878] "Encryption technology" refers to technologies used to prevent third parties from reading data during transmission or storage, and includes SSL / TLS, among others.

[0879] This invention relates to a system for generating rehabilitation plans for the elderly. Specific embodiments of this invention are described in detail below.

[0880] Entering patient information

[0881] To generate a rehabilitation plan, the user inputs patient information using a dedicated terminal (e.g., a tablet or PC). This patient information includes details such as age, gender, physical characteristics (height, weight, etc.), motor skills, symptoms, personality, and goals. The input information is checked for required fields by an input validation function displayed on the user interface. For example, a physical therapist might input information such as "80-year-old female, weight 65kg, difficulty walking, lower back pain, cautious personality, goal: to be able to walk independently within six months."

[0882] Sending patient information

[0883] The terminal sends the entered patient information to the server. Encryption technologies such as SSL / TLS are used to securely transfer the data. A function checks for data errors before transmission. For example, a warning message is displayed if there are any required fields that have not been entered.

[0884] Rehabilitation plan generation

[0885] The server generates a rehabilitation plan using an algorithm based on the received patient information. The algorithm is implemented in Python and utilizes machine learning libraries such as TensorFlow and PyTorch. It analyzes the submitted patient information and also refers to databases of past patient data and the latest rehabilitation methods. For example, in the case of a patient with lower back pain, it recommends low-impact stretching and strength training, and if the patient has a cautious personality, it generates a plan that starts with small steps.

[0886] Submission and confirmation of rehabilitation plan

[0887] The server sends the generated rehabilitation plan to the user's device. The created plan includes specific exercises, durations, and frequencies. For example, it might include a detailed schedule such as, "On the first day, do 15 minutes of light stretching, followed by two 20-minute walks." The user can review the plan on their device and make adjustments as needed. The adjusted plan is then sent back to the server for further optimization.

[0888] Optimizing and implementing the plan

[0889] The server optimizes the adjusted rehabilitation plan and finalizes it. The finalized plan is saved on the server and becomes accessible to both the user and the patient. This allows patients to undergo effective rehabilitation based on their individually customized rehabilitation plan.

[0890] Specific example

[0891] Here are some specific examples:

[0892] Specific example: A 75-year-old male, weighing 80 kg, with mild dementia, undergoing rehabilitation after surgery on his left knee.

[0893] 1. The user enters the information.

[0894] The physical therapist enters the following information into the terminal: "75-year-old male, weight 80kg, mild dementia, post-left knee surgery, positive personality, goal is to be able to walk independently within 3 months."

[0895] The terminal checks for any missing required fields, and if there are no errors, it proceeds to the next step.

[0896] 2. The device transmits information.

[0897] The terminal sends the entered information to the server.

[0898] Encrypt and securely transmit data using SSL / TLS.

[0899] 3. The server generates a rehabilitation plan.

[0900] Based on the received patient information, the server generates a rehabilitation plan using a generated AI model.

[0901] For example, it can generate a plan that includes low-impact exercises and strength training suitable for left knee rehabilitation, and incorporate additional activities to speed up progress according to the patient's proactive personality.

[0902] 4. The server sends the plan.

[0903] The server sends the generated rehabilitation plan to the terminal.

[0904] The plan includes a specific schedule such as, "On the first day, do 15 minutes of light stretching, followed by two 20-minute walks."

[0905] 5. The user reviews and adjusts the settings.

[0906] The physical therapist reviews the plan and makes adjustments, such as "increasing the frequency of walking exercises from twice a week to three times a week."

[0907] The revised plan will be sent back to the server.

[0908] 6. The server maintains an optimized plan.

[0909] The server stores optimized plans and makes them accessible to users and patients.

[0910] Patients can use a device to access their treatment plan in real time and carry out their daily rehabilitation.

[0911] Example of a prompt

[0912] The following prompts can be used to input patient information into the generating AI model and propose an appropriate rehabilitation plan:

[0913] A 75-year-old male, weighing 80 kg, with mild dementia, has undergone left knee surgery, and has an active personality. The goal is for him to be able to walk independently within 3 months. Please generate a rehabilitation plan for this patient.

[0914] In this way, it becomes possible to efficiently generate rehabilitation plans for the elderly and provide individualized care.

[0915] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0916] Step 1:

[0917] The user enters patient information.

[0918] The user uses a dedicated terminal to input detailed information about the patient, such as age, gender, physical characteristics (height, weight, etc.), athletic ability, symptoms, personality, and goals.

[0919] After input, the terminal screen will activate an input validation function to check all required fields. If there are any missing or incorrect entries, a warning message will be displayed.

[0920] An example of input data is: "An 80-year-old woman, weighing 65 kg, with difficulty walking, back pain, cautious personality, and a goal of being able to walk independently within six months."

[0921] In this step, the entered patient information is saved as digital data on the terminal.

[0922] Step 2:

[0923] The device sends information

[0924] The terminal sends the entered patient information to the server. Since encryption technology (e.g., SSL / TLS) is used for transmission, the data is transferred securely.

[0925] The integrity and accuracy of the data are checked during transmission.

[0926] The data sent will be in a standard format such as JSON, and each field will contain detailed information such as age, gender, physique, and athletic ability.

[0927] The output of this step is patient information that has been securely transmitted.

[0928] Step 3:

[0929] The server generates a rehabilitation plan.

[0930] The server uses an algorithm to generate a rehabilitation plan based on the patient information it receives.

[0931] The algorithm uses machine learning libraries (TensorFlow and PyTorch) and also references past patient data and databases of the latest rehabilitation methods.

[0932] Based on the input data, the system selects an appropriate rehabilitation menu tailored to the symptoms and personality, and proposes specific exercises and training plans.

[0933] For example, patients with lower back pain might be recommended low-impact stretches and strength training, while those with a cautious personality might be given a plan that starts with small steps.

[0934] In this step, the output is the generated rehabilitation plan.

[0935] Step 4:

[0936] The server sends the generated plan to the terminal.

[0937] The server sends the generated rehabilitation plan to the terminal. Encryption technology is used for transmission, ensuring the data is transferred securely.

[0938] A rehabilitation plan is generated that includes specific information such as the type of exercise, duration, and frequency.

[0939] For example, a specific schedule might be set, such as, "On the first day, do 15 minutes of light stretching, followed by two 20-minute walks."

[0940] The output of this step is the rehabilitation plan sent to the terminal.

[0941] Step 5:

[0942] Users can review and adjust their plans.

[0943] Users can review the rehabilitation plan generated on their device. They can adjust the content and frequency of exercises as needed.

[0944] For example, specific adjustments can be made, such as "increasing the frequency of walking from three times a week to four times a week."

[0945] The adjusted plan is sent back to the server, so the same checks and encrypted transmission are performed during the readjustment process.

[0946] The output of this step is the adjusted rehabilitation plan.

[0947] Step 6:

[0948] The server maintains an optimized plan.

[0949] The server optimizes the adjusted rehabilitation plan and finalizes it. The finalized plan is saved on the server and becomes accessible to users and patients.

[0950] A highly available database (such as an SQL database) is used for data storage in this step.

[0951] This allows patients to undergo effective rehabilitation based on individually customized rehabilitation plans.

[0952] The output of this step is the optimized rehabilitation plan saved on the server.

[0953] (Application Example 1)

[0954] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0955] In rehabilitation for the elderly, not only is individualized rehabilitation plans necessary, but real-time support for their implementation is also required. However, existing systems cannot provide immediate feedback on whether patients are performing rehabilitation correctly, which makes effective rehabilitation difficult. Furthermore, there is a need for guidance functions that are easy for the elderly to understand and intuitive to use.

[0956] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0957] In this invention, the server includes means for inputting the patient's age, gender, physique, motor skills, symptoms, personality, and goals; means for generating a rehabilitation plan based on the input information; means for displaying the generated rehabilitation plan and allowing the user to review and adjust it; and means for providing real-time audio guidance and visual feedback using a smart device. This makes it possible for the patient to receive real-time guidance and feedback when performing rehabilitation, thereby enhancing the effectiveness of the rehabilitation.

[0958] The term "elderly" generally refers to people aged 65 or older, and especially to those with physical limitations that require rehabilitation.

[0959] A "rehabilitation plan" refers to a detailed schedule that combines exercises and treatments tailored to the patient's physical condition and symptoms, with the aim of restoring the patient's health.

[0960] A "server" is a computer system that sends and receives data over a network, and processes and stores information.

[0961] A "smart device" is a device that is connected to the internet and capable of processing and displaying information, and includes, for example, smartphones, smart glasses, and head-mounted displays.

[0962] "Voice guide" refers to a function that converts text information into speech to provide guidance to the user.

[0963] "Visual feedback" refers to a function that allows users to visually confirm their actions by providing information through visual display devices such as screens.

[0964] An "algorithm" is a set of computational steps or processing rules, referring to a procedure designed to solve a specific problem.

[0965] A "personalized rehabilitation program" is a plan that includes exercises and treatments designed to suit the patient's specific needs and physical condition.

[0966] "Past patient data" refers to rehabilitation records and progress data of patients who have received treatment in the past.

[0967] "Latest rehabilitation methods" refer to rehabilitation methods designed based on the latest research and technologies in medicine and exercise science.

[0968] Specific embodiments for carrying out this invention will be described.

[0969] Overall system configuration

[0970] This invention is a system for generating rehabilitation plans for the elderly and providing real-time feedback and guidance. The system consists of the following components:

[0971] 1. Input method: A means of inputting the patient's age, sex, physique, motor skills, symptoms, personality, and goals. This may involve using an input device such as a tablet or computer.

[0972] 2. Server: The central hub of the system that generates rehabilitation plans using algorithms based on the entered patient information. The server references past patient data and the latest rehabilitation methods to construct an individualized plan.

[0973] 3. Smart devices: Devices worn by patients, such as smart glasses and head-mounted displays. These include features such as voice guidance and visual feedback.

[0974] 4. Display means: A means for displaying the generated rehabilitation plan, allowing the user (physical therapist) to review and adjust it. A display device such as a tablet or smartphone is used.

[0975] Hardware and software configuration

[0976] Server: A high-performance computer equipped with an Intel or AMD processor, consisting of a database management system (e.g., MySQL) and machine learning algorithms (e.g., TensorFlow).

[0977] Smart devices include smart glasses such as Google Glass and Microsoft HoloLens. These utilize speech recognition (e.g., Google Speech Recognition API), speech synthesis (e.g., Pyttsx3), and visual display (e.g., OpenCV).

[0978] Display method: Use an Apple iPad or Android tablet and install a dedicated application.

[0979] Explanation of the program's processing

[0980] The server receives patient information entered by the physical therapist and analyzes it using a specific algorithm. Specifically, the following steps are taken:

[0981] 1. Enter patient information:

[0982] The user enters the patient information necessary to generate a rehabilitation plan. For example, they might use a tablet to enter age, gender, physique, motor skills, symptoms, personality, and goals.

[0983] 2. Information Analysis:

[0984] The server analyzes the input information and generates an optimal rehabilitation plan by referencing past patient data and the latest rehabilitation techniques.

[0985] 3. Plan generation:

[0986] The server uses algorithms (e.g., machine learning models) to create a personalized rehabilitation plan. This plan includes specific exercises, their duration, and frequency.

[0987] 4. Feedback and guidance:

[0988] Smart devices are used to provide patients with real-time audio guidance and visual feedback. For example, Google Glass can be used to provide an audio guide to the patient saying, "Extend your arms and raise them to shoulder height," while the display shows the text, "Keep your back straight."

[0989] Specific example

[0990] Let's consider an example of a 75-year-old man using a rehabilitation support system for the elderly. A physical therapist inputs information into a tablet: "75-year-old male, weight 80kg, mild dementia, post-left knee surgery, goal is to be able to walk independently within 3 months." The server analyzes this information and generates an optimal rehabilitation plan. The patient wears Google Glass and performs exercises while receiving voice guidance and visual feedback.

[0991] Example of a prompt

[0992] "Please develop a rehabilitation support application for seniors. This application uses smart glasses to provide voice guidance for exercises, reminders, and visual feedback. Please use Python and the following libraries: GTTS, Pyttsx3, OpenCV, datetime."

[0993] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0994] Step 1:

[0995] The user uses a tablet device to input the patient's age, gender, physique, athletic ability, symptoms, personality, and goals. This data is sent to the server. The entered data is stored on the server and ready for analysis.

[0996] Step 2:

[0997] Based on the received patient information, the server references a database of past patients and the latest rehabilitation techniques. This database includes past rehabilitation case studies and exercise information based on the latest medical research. Using this data, the server executes an algorithm to generate the optimal rehabilitation plan for the patient.

[0998] Step 3:

[0999] The generated rehabilitation plan is sent from the server to the tablet device. The user (physiotherapist) reviews this plan on the tablet and makes adjustments as needed. For example, they can increase the frequency of walking from twice a week to three times a week. The adjusted plan is sent back to the server, and the final optimized rehabilitation plan is finalized.

[1000] Step 4:

[1001] The finalized rehabilitation plan is sent from the server to a smart device (e.g., smart glasses). Once the patient puts on the smart device and begins rehabilitation, voice guidance and visual feedback are provided in real time. Specifically, using Google Glass, voice instructions such as "Extend your arms and raise them to shoulder height" and visual instructions such as "Keep your back straight" are displayed on the screen.

[1002] Step 5:

[1003] When a patient performs rehabilitation, the smart device uses built-in sensors to collect movement data. This data is transmitted to a server in real time, which analyzes whether the patient's movements are following the plan. Based on the analysis, voice guidance and visual feedback are adjusted to help the patient perform rehabilitation correctly.

[1004] Step 6:

[1005] Rehabilitation progress is managed on a server and can be viewed by the user via a tablet or smart device. Based on progress, the server automatically updates the rehabilitation plan and suggests new exercises to the patient. This maximizes the effectiveness of the patient's rehabilitation.

[1006] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[1007] This invention relates to a system for generating rehabilitation plans for the elderly that incorporates an emotion engine. Specific embodiments of this invention are described below.

[1008] A natural language explanation of the program's processing.

[1009] Entering patient information

[1010] As an initial step in generating a rehabilitation plan, the user enters patient information into a dedicated terminal. This includes detailed information such as age, gender, physical characteristics (height and weight), motor skills, symptoms, personality, and rehabilitation goals. For example, a physical therapist might enter: "80-year-old female, weight 65kg, difficulty walking, lower back pain, cautious personality, goal is to be able to walk independently within six months."

[1011] Information transmission

[1012] The terminal securely transmits entered patient information to the server in real time. Data format verification and encrypted communication are used to maintain the accuracy and integrity of the information.

[1013] Rehabilitation plan generation

[1014] The server analyzes the received patient information using an algorithm. A rehabilitation plan is generated that takes into account basic information, detailed symptoms, and personality. For example, for a 78-year-old woman with lower back pain and a cautious personality, a plan including low-impact, low-risk exercises will be created.

[1015] Analysis of emotional information

[1016] When a user undergoes rehabilitation, the emotion engine analyzes the user's emotional information in real time. This emotion engine determines the user's emotional state based on facial expressions, tone of voice, language patterns, and other factors. For example, if a user is feeling anxious or stressed about rehabilitation, this information will be detected by the emotion engine.

[1017] Adjusting the rehabilitation plan

[1018] The server dynamically adjusts the rehabilitation plan based on emotional information obtained from the emotion engine. For example, if a user is showing signs of fatigue, the intensity of the exercise can be temporarily reduced. Conversely, if a user is highly motivated, the pace of the plan can be accelerated.

[1019] View and check the plan

[1020] The generated and adjusted rehabilitation plan is sent from the server to the terminal. The user can review the plan on the terminal and make further adjustments. For example, specific changes such as "increase the frequency of walking exercises from twice a week to three times a week" can be made.

[1021] Specific example

[1022] As a specific example, consider a 75-year-old male weighing 80 kg with mild dementia, undergoing rehabilitation after surgery on his left knee.

[1023] 1. The user enters the information.

[1024] The physical therapist enters the following information into the terminal: "75-year-old male, weight 80kg, mild dementia, post-left knee surgery, positive personality, goal is to be able to walk independently within 3 months."

[1025] 2. The device transmits information.

[1026] The device sends this information to the server in real time.

[1027] 3. The server generates a rehabilitation plan.

[1028] Based on the input information, the server generates a plan that includes low-impact exercises and strength training suitable for post-operative rehabilitation of the left knee.

[1029] 4. Analysis using an emotion engine

[1030] When a user begins rehabilitation, the emotion engine analyzes the user's facial expressions and tone of voice to determine, for example, that "the user is highly motivated for rehabilitation."

[1031] 5. The server adjusts the plan.

[1032] Based on the user's high motivation, the server dynamically adjusts the rehabilitation plan, reflecting a plan that accelerates the pace of rehabilitation.

[1033] 6. Submit and confirm your plan

[1034] The final rehabilitation plan is sent from the server to the terminal, and after the physical therapist reviews the details, it is provided to the user.

[1035] As described above, the rehabilitation system of the present invention, which incorporates an emotion engine, can provide more effective rehabilitation by dynamically adjusting the rehabilitation plan according to the user's emotional state.

[1036] The following describes the processing flow.

[1037] Step 1:

[1038] The user enters patient information using a dedicated terminal. Specifically, they enter detailed information such as the patient's age, gender, physique (height and weight), physical ability, symptoms, personality, and rehabilitation goals. For example, they might set it to "78-year-old female, weight 60kg, lower back pain, cautious personality, goal: independent walking."

[1039] Step 2:

[1040] The terminal transmits patient information entered into it to the server in real time. During transmission, the data format is checked to maintain the accuracy and integrity of the patient data, and encrypted communication is used to send the data securely.

[1041] Step 3:

[1042] The server analyzes the patient information it receives. First, it analyzes basic information (age, gender, build, etc.), and then it considers details of the symptoms (location and severity of pain, etc.) and personality traits (cautious, proactive, etc.). For example, a 78-year-old woman with lower back pain and a cautious personality would require low-impact, low-risk exercise.

[1043] Step 4:

[1044] The server references past patient data and the latest rehabilitation techniques, applying algorithms necessary for generating a rehabilitation plan. These algorithms construct an optimal rehabilitation program based on each patient's individual requirements, including stretching, strength training, and gait practice.

[1045] Step 5:

[1046] The emotion engine analyzes the user's real-time emotional information. This includes analyzing the user's facial expressions, tone of voice, and language patterns to determine their emotional state. For example, if a user is feeling stressed, the emotion engine will detect it.

[1047] Step 6:

[1048] The server dynamically adjusts the rehabilitation plan based on emotional information analyzed by the emotion engine. For example, if a user is showing signs of fatigue, the intensity of the exercise can be temporarily reduced. Conversely, if a user is highly motivated, the pace of the plan can be accelerated.

[1049] Step 7:

[1050] The server generates a rehabilitation plan and sends it to the terminal. Each plan includes specific exercises, duration, and frequency. For example, "On the first day, 15 minutes of light stretching, followed by 20 minutes of walking."

[1051] Step 8:

[1052] The user reviews the rehabilitation plan generated on their device and makes adjustments as needed. For example, they can adjust the frequency of walking exercises from twice a week to three times a week. The adjusted plan is then sent back to the server and reflected in the changes.

[1053] Step 9:

[1054] The server optimizes the adjusted rehabilitation plan. During the optimization process, further detailed adjustments are made to determine the optimal rehabilitation menu for each individual patient.

[1055] Step 10:

[1056] The final rehabilitation plan is saved on a server and made accessible to both the user and the patient in real time. Based on this plan, the patient can proceed with their daily rehabilitation in sequence.

[1057] As described above, the rehabilitation system of the present invention, which incorporates an emotion engine, dynamically adjusts the rehabilitation plan according to the user's emotional state, thereby providing more effective rehabilitation.

[1058] (Example 2)

[1059] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[1060] In rehabilitation for the elderly, it is essential to provide plans that take into account the individual circumstances and emotional state of each patient. However, conventional rehabilitation plan generation systems lacked the ability to analyze the patient's emotional information and dynamically adjust the plan based on that information. As a result, it was difficult to provide effective rehabilitation plans that were appropriate for each patient.

[1061] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[1062] In this invention, the server includes means for inputting the patient's age, gender, physique, motor skills, symptoms, personality, and goals; means for generating a rehabilitation plan based on the input information; means for analyzing emotional information; means for dynamically adjusting the rehabilitation plan based on the emotional information; and means for displaying the generated rehabilitation plan for the user to review and adjust. This makes it possible to provide an effective rehabilitation plan that reflects the individual circumstances and emotional state of the patient.

[1063] "Patient information" refers to detailed data including the patient's age, sex, physique, motor skills, symptoms, personality, and rehabilitation goals.

[1064] A "terminal" is a device that users operate to input patient information and to check and adjust rehabilitation plans.

[1065] A "server" is a central processing unit that generates rehabilitation plans based on the input patient information, and performs analysis and dynamic adjustments.

[1066] An "algorithm" is a set of computational procedures or methods that a server uses to analyze patient information and generate a rehabilitation plan.

[1067] A "generative AI model" is an artificial intelligence model that uses algorithms to analyze input patient information and generate personalized rehabilitation plans.

[1068] An "emotion engine" is a part of the software or hardware that analyzes a user's emotional information and determines their emotional state.

[1069] A "rehabilitation plan" is a specific menu and schedule of rehabilitation tailored to the patient's condition and goals.

[1070] "Dynamic adjustment" refers to the server modifying the content of the rehabilitation plan in real time based on emotional information.

[1071] "Encrypted communication" is a method of using cryptographic technology to ensure the security of data transmitted between a terminal and a server.

[1072] Modes for carrying out the invention

[1073] This invention relates to a system for generating rehabilitation plans for the elderly, providing an optimal rehabilitation plan tailored to the individual circumstances and emotional state of the patient. Specific embodiments of this invention are described below.

[1074] Entering patient information

[1075] As an initial step in generating a rehabilitation plan, the user enters patient information into a dedicated terminal. The terminal provides an interface for user input using a touchscreen and keyboard. Specific information includes age, gender, physical characteristics (height, weight), motor skills, symptoms, personality, and rehabilitation goals. For example, a physical therapist might enter "80-year-old female, weight 65kg, difficulty walking, lower back pain, cautious personality, goal: to be able to walk independently within six months."

[1076] Information transmission

[1077] The terminal securely transmits entered patient information to the server in real time. To maintain the accuracy and integrity of the information, data format verification and encrypted communication (SSL / TLS) are used. The terminal encrypts the information and sends it to the server. The server verifies the integrity of the received data and proceeds to the next processing step.

[1078] Rehabilitation plan generation

[1079] The server uses a generative AI model to analyze the patient information it receives. This model is built using machine learning frameworks such as TensorFlow or PyTorch. The server then generates an appropriate rehabilitation plan, taking into account basic information, detailed symptoms, and personality traits. For example, for a 78-year-old woman with back pain and a cautious personality, a plan including low-impact, low-risk exercises would be generated.

[1080] Analysis of emotional information

[1081] When a user undergoes rehabilitation, the emotion engine uses the device's camera and microphone to analyze the user's facial expressions, voice tone, and language patterns in real time. This allows the emotion engine to determine the user's emotional state. For example, if a user is feeling anxious or stressed about rehabilitation, this information will be detected by the emotion engine.

[1082] Adjusting the rehabilitation plan

[1083] The server dynamically adjusts the rehabilitation plan based on emotional information obtained from the emotion engine. For example, if the user shows signs of fatigue, the intensity of the exercises will be temporarily reduced. Conversely, if the user shows high motivation, the pace of the plan will be increased.

[1084] View and check the plan

[1085] The generated and adjusted rehabilitation plan is sent from the server to the terminal. The user can review the plan on the terminal and make further detailed adjustments as needed. For example, specific changes such as "increase the frequency of walking exercises from twice a week to three times a week" can be made.

[1086] Specific example

[1087] As a specific example, consider a 75-year-old male weighing 80 kg, with mild dementia, undergoing rehabilitation after surgery on his left knee.

[1088] 1. The user enters the information.

[1089] The physical therapist enters the following information into the terminal: "75-year-old male, weight 80kg, mild dementia, post-left knee surgery, positive personality, goal is to be able to walk independently within 3 months."

[1090] 2. The device transmits information.

[1091] The device sends this information to the server in real time.

[1092] 3. The server generates a rehabilitation plan.

[1093] Based on the input information, the server generates a plan that includes low-impact exercises and strength training suitable for post-operative rehabilitation of the left knee.

[1094] 4. Analysis using an emotion engine

[1095] When a user begins rehabilitation, the emotion engine analyzes the user's facial expressions and tone of voice to determine, for example, that "the user is highly motivated for rehabilitation."

[1096] 5. The server adjusts the plan.

[1097] Based on the user's high motivation, the server dynamically adjusts the rehabilitation plan, reflecting a plan that accelerates the pace of rehabilitation.

[1098] 6. Submit and confirm your plan

[1099] The final rehabilitation plan is sent from the server to the terminal, and after the physical therapist reviews the details, it is provided to the user.

[1100] Example of a prompt

[1101] 1. Prompts for generating a natural rehabilitation plan:

[1102] "Please generate a rehabilitation plan for an elderly patient. The patient is a 75-year-old male, weighing 80 kg, with mild dementia and a history of left knee surgery. He has an active personality, and the goal is for him to be able to walk independently within three months."

[1103] 2. Emotion Engine Analysis Prompt:

[1104] "Please analyze the emotional state of a 75-year-old patient undergoing rehabilitation. Based on their tone of voice and facial expressions, determine whether their motivation for rehabilitation is high or low."

[1105] 3. Prompt for adjustment of rehabilitation plan:

[1106] "Dynamically adjust the rehabilitation plan based on the user's emotional state. If the user shows high motivation, increase the pace of the exercises."

[1107] In this way, the rehabilitation plan generation system of the present invention can provide a more effective and optimal rehabilitation plan according to the individual circumstances and emotional state of the patient.

[1108] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1109] Step 1:

[1110] The user enters patient information.

[1111] The user (physical therapist) uses the terminal interface to input the patient's age, gender, physique (height, weight), motor skills, symptoms, personality, and rehabilitation goals. A specific example of input would be: "75-year-old male, weight 80kg, mild dementia, post-left knee surgery, positive personality, goal is to be able to walk independently within 3 months."

[1112] Input: Patient's basic information

[1113] Output: Patient data in a processable format

[1114] Step 2:

[1115] The device sends information to the server.

[1116] The terminal securely transmits entered patient information to the server in real time. The terminal verifies the accuracy of the information and transmits the data using encrypted communication (SSL / TLS). The server verifies that the received data is in the correct format.

[1117] Input: Patient data in a processable format

[1118] Output: Patient data sent to the server

[1119] Step 3:

[1120] The server generates a rehabilitation plan.

[1121] The server uses a generative AI model to analyze the received patient information. Specifically, it uses frameworks such as TensorFlow and PyTorch to generate an appropriate rehabilitation plan that takes into account basic information, detailed symptoms, and personality. Based on the input information, it generates a plan that includes low-impact exercises and strength training suitable for post-operative rehabilitation of the left knee, for example.

[1122] Input: Patient data sent to the server

[1123] Output: Initial Rehabilitation Plan

[1124] Step 4:

[1125] Analysis of emotional information

[1126] When a user undergoes rehabilitation, the emotion engine uses the device's camera and microphone to analyze the user's facial expressions and voice tone in real time. The emotion engine uses libraries such as OpenCV and DeepSpeech to determine the user's emotional state. For example, if the user is feeling stressed about the rehabilitation, that information will be detected.

[1127] Input: User data during rehabilitation (facial expressions, voice tone, language patterns)

[1128] Output: User sentiment information

[1129] Step 5:

[1130] The server dynamically adjusts the rehabilitation plan.

[1131] The server dynamically adjusts the rehabilitation plan based on emotional information obtained from the emotion engine. For example, if the user is showing signs of fatigue, the intensity of the exercises will be temporarily reduced. Conversely, if the user is showing high motivation, the pace of the plan will be increased.

[1132] Input: User sentiment information

[1133] Output: Adjusted rehabilitation plan

[1134] Step 6:

[1135] View and check the plan

[1136] The server sends the adjusted rehabilitation plan to the user's device, where the user reviews the plan. If further adjustments are needed, the user can modify the plan by operating the device. For example, they can adjust the frequency of walking exercises from twice a week to three times a week.

[1137] Input: Adjusted rehabilitation plan

[1138] Output: Final rehabilitation plan reviewed and adjusted by the user.

[1139] In this way, based on the data entered at each step, the server and terminal perform specific data processing and calculations, obtaining their respective outputs, thereby providing the user with an optimal rehabilitation plan.

[1140] (Application Example 2)

[1141] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[1142] In rehabilitation for the elderly and people with disabilities, there is a need to provide effective plans tailored to each patient's individual circumstances and emotional state. However, conventional rehabilitation plans are usually limited to one-dimensional planning based on the patient's physical data, and there is no mechanism to dynamically adjust them to take into account the patient's emotional state and motivation. As a result, it has been difficult to maximize the effectiveness of rehabilitation and maintain patient motivation.

[1143] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[1144] In this invention, the server includes means for inputting the patient's age, gender, physique, motor skills, symptoms, personality, and goals; means for generating a rehabilitation plan based on the input information; means for analyzing real-time emotional information and dynamically adjusting the rehabilitation plan based on the patient's emotions; and means for adjusting the pace of rehabilitation based on the collected emotional information. This makes it possible to create a rehabilitation plan that takes into account not only the physical data of each patient but also their emotional state, enabling effective rehabilitation while maintaining the patient's motivation.

[1145] "Elderly people" refers to those who are particularly old within the age group requiring rehabilitation, and generally refers to people aged 65 and over.

[1146] A "rehabilitation plan" refers to a detailed program designed to improve a patient's physical function and daily living activities.

[1147] A "system" refers to a collection of multiple devices or software programs combined to perform a specific function.

[1148] "Patient information" refers to individual data such as the patient's age, gender, physique, athletic ability, symptoms, personality, and goals.

[1149] A "server" refers to a central processing unit that provides services to other computers via a network.

[1150] "Emotional information" refers to data that indicates a patient's emotional state, and is acquired based on facial expressions, tone of voice, language patterns, and other factors.

[1151] "Dynamic adjustment" refers to a flexible response that adapts the plan in real time or near real time.

[1152] "Progression rate" refers to the pace at which exercises and training progress in a rehabilitation plan.

[1153] "Display means" refers to devices or software used to visually show the generated rehabilitation plan to the user.

[1154] "Input means" refers to devices or interfaces used to provide patient information to the system.

[1155] Modes for carrying out the invention

[1156] This invention relates to a system for generating rehabilitation plans for the elderly and dynamically adjusting them based on emotional information. The invention is particularly applicable to rehabilitation support applications using smart glasses. This system includes multiple means for collecting and analyzing patient information and generating effective rehabilitation plans.

[1157] 1. Collection of input data

[1158] The server utilizes the camera and microphone of smart glasses to collect basic patient information and emotional information through voice input and OCR functionality. These smart glasses are equipped with facial recognition and voice recognition capabilities, allowing for real-time acquisition of patient data. Specific hardware used includes smart glasses (e.g., Google Glass) and voice recognition APIs (e.g., Amazon Transcribe).

[1159] 2. Data transmission and encryption

[1160] The terminal sends the collected data to the server in real time. During transmission, the SSL / TLS protocol is used to ensure data security. Specifically, OpenSSL is used to implement encrypted communication.

[1161] 3. Generating a Rehabilitation Plan

[1162] The server generates rehabilitation plans based on collected patient information. To do this, it uses Python-based machine learning algorithms to construct plans tailored to each patient's individual needs. Specifically, it uses libraries such as Scikit-learn and Pandas to analyze data and generate plans.

[1163] 4. Analysis of emotional information

[1164] The server analyzes emotional information to detect the patient's emotional state. Specifically, it uses Microsoft Azure Cognitive Services (Face API, Speech API) to perform real-time emotional analysis. This allows the server to understand the patient's emotional state from their facial expressions and tone of voice.

[1165] 5. Dynamic adjustment of the rehabilitation plan

[1166] The server dynamically adjusts the rehabilitation plan based on emotional information. For example, if a patient shows signs of fatigue, the exercise intensity is temporarily reduced. Conversely, if motivation is high, the plan is advanced. AWS Lambda and dynamic databases (e.g., DynamoDB) are used for this adjustment.

[1167] 6. View and confirm the plan

[1168] The final rehabilitation plan is sent back to the smart glasses for the user to review. This allows physical therapists and rehabilitation center staff to fine-tune the plan according to the patient's condition.

[1169] Specific example

[1170] As a concrete example, suppose a 75-year-old male patient begins rehabilitation and wears smart glasses. These smart glasses analyze the patient's voice and facial expressions and may make the following adjustments to the initial plan.

[1171] If the patient is feeling anxious:

[1172] "The patient is anxious about rehabilitation. Please reduce the intensity of the next exercise and incorporate relaxation exercises."

[1173] Example of a prompt

[1174] "Generate a rehabilitation plan for a 75-year-old male, weighing 80kg, with mild dementia, post-left knee surgery, and an outgoing personality. His goal is to achieve independent walking within three months. Furthermore, use an emotion engine to acquire emotional information from the patient's facial expressions and tone of voice, and dynamically adjust the rehabilitation plan accordingly."

[1175] Thus, the system of the present invention can generate a rehabilitation plan based on individual patient information and dynamically adjust it according to the patient's emotional state, thereby providing more effective and personalized rehabilitation.

[1176] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1177] Step 1:

[1178] The server collects patient information using smart glasses. Patient age, gender, build, motor skills, symptoms, personality, and goals are entered using voice recognition and OCR functions. Example input data includes a 75-year-old male, weighing 80kg, post-knee surgery on the left knee, with an active personality and a goal of independent walking within three months. This data is collected using smart glasses such as Google Glass and Amazon Transcribe.

[1179] Step 2:

[1180] The terminal transmits collected patient information to the server in real time. To ensure data security, encrypted communication is performed using the SSL / TLS protocol. The input is patient information (e.g., 75-year-old male, 80kg weight, post-left knee surgery), and the output is encrypted data sent to the server.

[1181] Step 3:

[1182] The server generates a rehabilitation plan based on the received patient information. It uses a Python-based machine learning algorithm to analyze the data and construct the rehabilitation plan. The input is detailed patient information (e.g., age, weight, symptoms), and based on this, an individualized rehabilitation plan (e.g., low-intensity strength training and exercises) is generated. Specific libraries used include Scikit-learn and Pandas.

[1183] Step 4:

[1184] The server uses the smart glasses' camera and microphone to analyze the patient's emotional state in real time during rehabilitation. It analyzes the patient's facial expressions and voice tone using Microsoft Azure's Face API and Speech API. Real-time facial and voice data from the patient is collected as input data, and emotional information (e.g., anxiety, stress, motivation) is output as the analysis result.

[1185] Step 5:

[1186] The server dynamically adjusts the rehabilitation plan based on the collected emotional information. Emotional information (e.g., anxiety, fatigue) is taken as input, and adjustment data for the rehabilitation plan (e.g., reducing exercise intensity or inserting relaxation) is output. Specifically, AWS Lambda and DynamoDB are used to process and adjust this data.

[1187] Step 6:

[1188] The final rehabilitation plan is sent back to the smart glasses. The server sends the adjusted plan to the device so the user can review it. The input is the adjusted rehabilitation plan, and the output is a visual display of the plan's details. In this case, the device uses the Google Glass API to display the plan.

[1189] Through the steps described above, a system is realized that generates and dynamically adjusts rehabilitation plans based on the patient's individual information and emotional state.

[1190] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[1191] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1192] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[1193] [Fourth Embodiment]

[1194] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[1195] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1196] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1197] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[1198] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[1199] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[1200] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[1201] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[1202] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[1203] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1204] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1205] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[1206] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1207] This invention relates to a system for generating rehabilitation plans for the elderly. Specific embodiments of this invention are described below.

[1208] A natural language explanation of the program's processing.

[1209] Entering patient information

[1210] As an initial step in generating a rehabilitation plan, the user enters patient information into a dedicated terminal. This includes detailed information such as age, gender, physical characteristics (height, weight, etc.), motor skills, symptoms, personality, and goals. For example, a physical therapist might enter information such as: 80-year-old female, weight 65kg, difficulty walking, lower back pain, cautious personality, and goal to be able to walk independently within six months.

[1211] Information transmission

[1212] The terminal securely transmits the entered patient information to the server in real time. Check functions and encrypted communication are used to maintain the accuracy and integrity of the information.

[1213] Rehabilitation plan generation

[1214] The server analyzes the received patient information based on an algorithm. Specifically, in addition to basic information such as age and physique, it considers details of symptoms, personality, and rehabilitation goals. For example, in the case of lower back pain, low-impact stretching and strength training are required. Also, if the patient has a cautious personality, a plan is created that minimizes the risks of rehabilitation and starts with small steps.

[1215] The server references past patient data and the latest rehabilitation techniques to generate an optimal rehabilitation plan. This plan includes specific exercises, duration, and frequency. For example, a detailed schedule might be set such as, "On the first day, do 15 minutes of light stretching, followed by two 20-minute walks."

[1216] Submitting and confirming your plan

[1217] The generated rehabilitation plan is sent from the server to the terminal. The user can review the plan on the terminal and make adjustments as needed. For example, they can change the frequency of walking from three times a week to four times a week. The adjusted plan is sent back to the server for further optimization as needed.

[1218] Optimizing and implementing the plan

[1219] The server optimizes the adjusted plan and finalizes it as the final rehabilitation plan. The finalized plan is saved on the server and made accessible to users and patients. This allows patients to undergo effective rehabilitation based on their individually customized rehabilitation plan.

[1220] Specific example

[1221] As a specific example, consider a 75-year-old male weighing 80 kg with mild dementia, undergoing rehabilitation after surgery on his left knee.

[1222] 1. The user enters the information.

[1223] The physical therapist enters the following information into the terminal: "75-year-old male, weight 80kg, mild dementia, post-left knee surgery, positive personality, goal is to be able to walk independently within 3 months."

[1224] 2. The device transmits information.

[1225] The device securely transmits this information to the server.

[1226] 3. The server generates a rehabilitation plan.

[1227] The server generates a plan that includes low-impact exercises and strength training suitable for left knee rehabilitation. It also incorporates additional exercises to accelerate the rehabilitation process, tailored to the patient's proactive personality.

[1228] 4. The server sends the plan.

[1229] The server sends the generated plan to the terminal.

[1230] 5. User confirmation and adjustment

[1231] The physical therapist reviews the plan and makes adjustments, such as "increasing the frequency of walking exercises from twice a week to three times a week."

[1232] 6. The server maintains an optimized plan.

[1233] An optimized plan is stored on the server, allowing patients to access the plan in real time and progress through their daily rehabilitation.

[1234] This allows for the provision of individualized rehabilitation plans to patients, maximizing the effectiveness of rehabilitation and promoting rapid recovery.

[1235] The following describes the processing flow.

[1236] Step 1:

[1237] The user enters patient information using a dedicated terminal. Specifically, they enter detailed information such as the patient's age, gender, physique (height and weight), physical ability, symptoms, personality, and rehabilitation goals. For example, they might set it to "78-year-old female, weight 60kg, lower back pain, cautious personality, goal: independent walking."

[1238] Step 2:

[1239] The terminal transmits patient information entered into it to the server in real time. During transmission, the data format is checked to maintain the accuracy and integrity of the patient data, and encrypted communication is used to send the data securely.

[1240] Step 3:

[1241] The server analyzes the patient information it receives. First, it analyzes basic information (age, gender, build, etc.), and then it considers details of symptoms (location and severity of pain, etc.) and personality (cautious, proactive, etc.). For example, a 78-year-old woman with lower back pain and a cautious personality would require low-impact, low-risk exercise.

[1242] Step 4:

[1243] The server references past patient data and the latest rehabilitation techniques, applying algorithms necessary for generating a rehabilitation plan. These algorithms construct an optimal rehabilitation program based on each patient's individual requirements, including stretching, strength training, and gait practice.

[1244] Step 5:

[1245] The server generates a rehabilitation plan and sends it to the terminal. Each plan includes specific exercises, duration, and frequency. For example, "On the first day, 15 minutes of light stretching, followed by 20 minutes of walking."

[1246] Step 6:

[1247] The user reviews the rehabilitation plan generated on their device and makes adjustments as needed. For example, they can adjust the frequency of walking exercises from twice a week to three times a week. The adjusted plan is then sent back to the server and reflected in the changes.

[1248] Step 7:

[1249] The server optimizes the adjusted rehabilitation plan. During the optimization process, further detailed adjustments are made to determine the optimal rehabilitation menu for each individual patient.

[1250] Step 8:

[1251] The final rehabilitation plan is saved on a server and made accessible to both the user and the patient in real time. Based on this plan, the patient can proceed with their daily rehabilitation in sequence.

[1252] In this way, the rehabilitation system of the present invention provides a personalized rehabilitation plan suitable for the elderly quickly and effectively.

[1253] (Example 1)

[1254] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1255] In rehabilitation for the elderly, a challenge lies in the lack of systems that can efficiently generate individualized rehabilitation plans and flexibly respond to patients' specific needs and progress. Furthermore, security concerns regarding data transmission and storage also need to be addressed.

[1256] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[1257] In this invention, the server includes means for generating a rehabilitation plan using an algorithm based on input information, means for the algorithm to analyze patient information using a machine learning library, and means for sending the adjusted plan back to the server for optimization. This makes it possible to provide a system that generates personalized rehabilitation plans and can be used effectively and safely by users and patients.

[1258] "Input means" refers to devices or software that allow users to input detailed information about a patient, such as their age, gender, physique, athletic ability, symptoms, personality, and goals.

[1259] A "server" refers to a high-performance computer system that receives, processes, and stores data over a network.

[1260] An "algorithm" refers to a set of steps or formulas that run on a server, use machine learning libraries to analyze input patient information, and generate an individualized rehabilitation plan.

[1261] A "machine learning library" refers to a group of software tools used for analyzing patient information and generating rehabilitation plans, and includes tools such as TensorFlow and PyTorch.

[1262] "Generation means" refers to the functions and processes used to generate an optimal rehabilitation plan based on input information using an algorithm.

[1263] "Display means" refers to devices or software used to visually present the generated rehabilitation plan to the user.

[1264] "Confirmation means" refers to a function that allows users to view the generated rehabilitation plan and understand or confirm its contents.

[1265] "Adjustment mechanisms" refer to functions and processes that allow users to change or modify the content of the generated rehabilitation plan as needed.

[1266] "Transmission method" refers to the function or process for sending the adjusted rehabilitation plan back to the server to maintain data integrity and accuracy.

[1267] "Optimization methods" refer to the functions and processes used to re-analyze the adjusted rehabilitation plan and finalize it as the final rehabilitation plan.

[1268] "Encryption technology" refers to technologies used to prevent third parties from reading data during transmission or storage, and includes SSL / TLS, among others.

[1269] This invention relates to a system for generating rehabilitation plans for the elderly. Specific embodiments of this invention are described in detail below.

[1270] Entering patient information

[1271] To generate a rehabilitation plan, the user inputs patient information using a dedicated terminal (e.g., a tablet or PC). This patient information includes details such as age, gender, physical characteristics (height, weight, etc.), motor skills, symptoms, personality, and goals. The input information is checked for required fields by an input validation function displayed on the user interface. For example, a physical therapist might input information such as "80-year-old female, weight 65kg, difficulty walking, lower back pain, cautious personality, goal: to be able to walk independently within six months."

[1272] Sending patient information

[1273] The terminal sends the entered patient information to the server. Encryption technologies such as SSL / TLS are used to securely transfer the data. A function checks for data errors before transmission. For example, a warning message is displayed if there are any required fields that have not been entered.

[1274] Rehabilitation plan generation

[1275] The server generates a rehabilitation plan using an algorithm based on the received patient information. The algorithm is implemented in Python and utilizes machine learning libraries such as TensorFlow and PyTorch. It analyzes the submitted patient information and also refers to databases of past patient data and the latest rehabilitation methods. For example, in the case of a patient with lower back pain, it recommends low-impact stretching and strength training, and if the patient has a cautious personality, it generates a plan that starts with small steps.

[1276] Submission and confirmation of rehabilitation plan

[1277] The server sends the generated rehabilitation plan to the user's device. The created plan includes specific exercises, durations, and frequencies. For example, it might include a detailed schedule such as, "On the first day, do 15 minutes of light stretching, followed by two 20-minute walks." The user can review the plan on their device and make adjustments as needed. The adjusted plan is then sent back to the server for further optimization.

[1278] Optimizing and implementing the plan

[1279] The server optimizes the adjusted rehabilitation plan and finalizes it. The finalized plan is saved on the server and becomes accessible to both the user and the patient. This allows patients to undergo effective rehabilitation based on their individually customized rehabilitation plan.

[1280] Specific example

[1281] Here are some specific examples:

[1282] Specific example: A 75-year-old male, weighing 80 kg, with mild dementia, undergoing rehabilitation after surgery on his left knee.

[1283] 1. The user enters the information.

[1284] The physical therapist enters the following information into the terminal: "75-year-old male, weight 80kg, mild dementia, post-left knee surgery, positive personality, goal is to be able to walk independently within 3 months."

[1285] The terminal checks for any missing required fields, and if there are no errors, it proceeds to the next step.

[1286] 2. The device transmits information.

[1287] The terminal sends the entered information to the server.

[1288] Encrypt and securely transmit data using SSL / TLS.

[1289] 3. The server generates a rehabilitation plan.

[1290] Based on the received patient information, the server generates a rehabilitation plan using a generated AI model.

[1291] For example, it can generate a plan that includes low-impact exercises and strength training suitable for left knee rehabilitation, and incorporate additional activities to speed up progress according to the patient's proactive personality.

[1292] 4. The server sends the plan.

[1293] The server sends the generated rehabilitation plan to the terminal.

[1294] The plan includes a specific schedule such as, "On the first day, do 15 minutes of light stretching, followed by two 20-minute walks."

[1295] 5. The user reviews and adjusts the settings.

[1296] The physical therapist reviews the plan and makes adjustments, such as "increasing the frequency of walking exercises from twice a week to three times a week."

[1297] The revised plan will be sent back to the server.

[1298] 6. The server maintains an optimized plan.

[1299] The server stores optimized plans and makes them accessible to users and patients.

[1300] Patients can use a device to access their treatment plan in real time and carry out their daily rehabilitation.

[1301] Example of a prompt

[1302] The following prompts can be used to input patient information into the generating AI model and propose an appropriate rehabilitation plan:

[1303] A 75-year-old male, weighing 80 kg, with mild dementia, has undergone left knee surgery, and has an active personality. The goal is for him to be able to walk independently within 3 months. Please generate a rehabilitation plan for this patient.

[1304] In this way, it becomes possible to efficiently generate rehabilitation plans for the elderly and provide individualized care.

[1305] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1306] Step 1:

[1307] The user enters patient information.

[1308] The user uses a dedicated terminal to input detailed information about the patient, such as age, gender, physical characteristics (height, weight, etc.), athletic ability, symptoms, personality, and goals.

[1309] After input, the terminal screen will activate an input validation function to check all required fields. If there are any missing or incorrect entries, a warning message will be displayed.

[1310] An example of input data is: "An 80-year-old woman, weighing 65 kg, with difficulty walking, back pain, cautious personality, and a goal of being able to walk independently within six months."

[1311] In this step, the entered patient information is saved as digital data on the terminal.

[1312] Step 2:

[1313] The device sends information

[1314] The terminal sends the entered patient information to the server. Since encryption technology (e.g., SSL / TLS) is used for transmission, the data is transferred securely.

[1315] The integrity and accuracy of the data are checked during transmission.

[1316] The data sent will be in a standard format such as JSON, and each field will contain detailed information such as age, gender, physique, and athletic ability.

[1317] The output of this step is patient information that has been securely transmitted.

[1318] Step 3:

[1319] The server generates a rehabilitation plan.

[1320] The server uses an algorithm to generate a rehabilitation plan based on the patient information it receives.

[1321] The algorithm uses machine learning libraries (TensorFlow and PyTorch) and also references past patient data and databases of the latest rehabilitation methods.

[1322] Based on the input data, the system selects an appropriate rehabilitation menu tailored to the symptoms and personality, and proposes specific exercises and training plans.

[1323] For example, patients with lower back pain might be recommended low-impact stretches and strength training, while those with a cautious personality might be given a plan that starts with small steps.

[1324] In this step, the output is the generated rehabilitation plan.

[1325] Step 4:

[1326] The server sends the generated plan to the terminal.

[1327] The server sends the generated rehabilitation plan to the terminal. Encryption technology is used for transmission, ensuring the data is transferred securely.

[1328] A rehabilitation plan is generated that includes specific information such as the type of exercise, duration, and frequency.

[1329] For example, a specific schedule might be set, such as, "On the first day, do 15 minutes of light stretching, followed by two 20-minute walks."

[1330] The output of this step is the rehabilitation plan sent to the terminal.

[1331] Step 5:

[1332] Users can review and adjust their plans.

[1333] Users can review the rehabilitation plan generated on their device. They can adjust the content and frequency of exercises as needed.

[1334] For example, specific adjustments can be made, such as "increasing the frequency of walking from three times a week to four times a week."

[1335] The adjusted plan is sent back to the server, so the same checks and encrypted transmission are performed during the readjustment process.

[1336] The output of this step is the adjusted rehabilitation plan.

[1337] Step 6:

[1338] The server maintains an optimized plan.

[1339] The server optimizes the adjusted rehabilitation plan and finalizes it. The finalized plan is saved on the server and becomes accessible to users and patients.

[1340] A highly available database (such as an SQL database) is used for data storage in this step.

[1341] This allows patients to undergo effective rehabilitation based on individually customized rehabilitation plans.

[1342] The output of this step is the optimized rehabilitation plan saved on the server.

[1343] (Application Example 1)

[1344] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1345] In rehabilitation for the elderly, not only is individualized rehabilitation plans necessary, but real-time support for their implementation is also required. However, existing systems cannot provide immediate feedback on whether patients are performing rehabilitation correctly, which makes effective rehabilitation difficult. Furthermore, there is a need for guidance functions that are easy for the elderly to understand and intuitive to use.

[1346] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[1347] In this invention, the server includes means for inputting the patient's age, gender, physique, motor skills, symptoms, personality, and goals; means for generating a rehabilitation plan based on the input information; means for displaying the generated rehabilitation plan and allowing the user to review and adjust it; and means for providing real-time audio guidance and visual feedback using a smart device. This makes it possible for the patient to receive real-time guidance and feedback when performing rehabilitation, thereby enhancing the effectiveness of the rehabilitation.

[1348] The term "elderly" generally refers to people aged 65 or older, and especially to those with physical limitations that require rehabilitation.

[1349] A "rehabilitation plan" refers to a detailed schedule that combines exercises and treatments tailored to the patient's physical condition and symptoms, with the aim of restoring the patient's health.

[1350] A "server" is a computer system that sends and receives data over a network, and processes and stores information.

[1351] A "smart device" is a device that is connected to the internet and capable of processing and displaying information, and includes, for example, smartphones, smart glasses, and head-mounted displays.

[1352] "Voice guide" refers to a function that converts text information into speech to provide guidance to the user.

[1353] "Visual feedback" refers to a function that allows users to visually confirm their actions by providing information through visual display devices such as screens.

[1354] An "algorithm" is a set of computational steps or processing rules, referring to a procedure designed to solve a specific problem.

[1355] A "personalized rehabilitation program" is a plan that includes exercises and treatments designed to suit the patient's specific needs and physical condition.

[1356] "Past patient data" refers to rehabilitation records and progress data of patients who have received treatment in the past.

[1357] "Latest rehabilitation methods" refer to rehabilitation methods designed based on the latest research and technologies in medicine and exercise science.

[1358] Specific embodiments for carrying out this invention will be described.

[1359] Overall system configuration

[1360] This invention is a system for generating rehabilitation plans for the elderly and providing real-time feedback and guidance. The system consists of the following components:

[1361] 1. Input method: A means of inputting the patient's age, sex, physique, motor skills, symptoms, personality, and goals. This may involve using an input device such as a tablet or computer.

[1362] 2. Server: The central hub of the system that generates rehabilitation plans using algorithms based on the entered patient information. The server references past patient data and the latest rehabilitation methods to construct an individualized plan.

[1363] 3. Smart devices: Devices worn by patients, such as smart glasses and head-mounted displays. These include features such as voice guidance and visual feedback.

[1364] 4. Display means: A means for displaying the generated rehabilitation plan, allowing the user (physical therapist) to review and adjust it. A display device such as a tablet or smartphone is used.

[1365] Hardware and software configuration

[1366] Server: A high-performance computer equipped with an Intel or AMD processor, consisting of a database management system (e.g., MySQL) and machine learning algorithms (e.g., TensorFlow).

[1367] Smart devices include smart glasses such as Google Glass and Microsoft HoloLens. These utilize speech recognition (e.g., Google Speech Recognition API), speech synthesis (e.g., Pyttsx3), and visual display (e.g., OpenCV).

[1368] Display method: Use an Apple iPad or Android tablet and install a dedicated application.

[1369] Explanation of the program's processing

[1370] The server receives patient information entered by the physical therapist and analyzes it using a specific algorithm. Specifically, the following steps are taken:

[1371] 1. Enter patient information:

[1372] The user enters the patient information necessary to generate a rehabilitation plan. For example, they might use a tablet to enter age, gender, physique, motor skills, symptoms, personality, and goals.

[1373] 2. Information Analysis:

[1374] The server analyzes the input information and generates an optimal rehabilitation plan by referencing past patient data and the latest rehabilitation techniques.

[1375] 3. Plan generation:

[1376] The server uses algorithms (e.g., machine learning models) to create a personalized rehabilitation plan. This plan includes specific exercises, their duration, and frequency.

[1377] 4. Feedback and guidance:

[1378] Smart devices are used to provide patients with real-time audio guidance and visual feedback. For example, Google Glass can be used to provide an audio guide to the patient saying, "Extend your arms and raise them to shoulder height," while the display shows the text, "Keep your back straight."

[1379] Specific example

[1380] Let's consider an example of a 75-year-old man using a rehabilitation support system for the elderly. A physical therapist inputs information into a tablet: "75-year-old male, weight 80kg, mild dementia, post-left knee surgery, goal is to be able to walk independently within 3 months." The server analyzes this information and generates an optimal rehabilitation plan. The patient wears Google Glass and performs exercises while receiving voice guidance and visual feedback.

[1381] Example of a prompt

[1382] "Please develop a rehabilitation support application for seniors. This application uses smart glasses to provide voice guidance for exercises, reminders, and visual feedback. Please use Python and the following libraries: GTTS, Pyttsx3, OpenCV, datetime."

[1383] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1384] Step 1:

[1385] The user uses a tablet device to input the patient's age, gender, physique, athletic ability, symptoms, personality, and goals. This data is sent to the server. The entered data is stored on the server and ready for analysis.

[1386] Step 2:

[1387] Based on the received patient information, the server references a database of past patients and the latest rehabilitation techniques. This database includes past rehabilitation case studies and exercise information based on the latest medical research. Using this data, the server executes an algorithm to generate the optimal rehabilitation plan for the patient.

[1388] Step 3:

[1389] The generated rehabilitation plan is sent from the server to the tablet device. The user (physiotherapist) reviews this plan on the tablet and makes adjustments as needed. For example, they can increase the frequency of walking from twice a week to three times a week. The adjusted plan is sent back to the server, and the final optimized rehabilitation plan is finalized.

[1390] Step 4:

[1391] The finalized rehabilitation plan is sent from the server to a smart device (e.g., smart glasses). Once the patient puts on the smart device and begins rehabilitation, voice guidance and visual feedback are provided in real time. Specifically, using Google Glass, voice instructions such as "Extend your arms and raise them to shoulder height" and visual instructions such as "Keep your back straight" are displayed on the screen.

[1392] Step 5:

[1393] When a patient performs rehabilitation, the smart device uses built-in sensors to collect movement data. This data is transmitted to a server in real time, which analyzes whether the patient's movements are following the plan. Based on the analysis, voice guidance and visual feedback are adjusted to help the patient perform rehabilitation correctly.

[1394] Step 6:

[1395] Rehabilitation progress is managed on a server and can be viewed by the user via a tablet or smart device. Based on progress, the server automatically updates the rehabilitation plan and suggests new exercises to the patient. This maximizes the effectiveness of the patient's rehabilitation.

[1396] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[1397] This invention relates to a system for generating rehabilitation plans for the elderly that incorporates an emotion engine. Specific embodiments of this invention are described below.

[1398] A natural language explanation of the program's processing.

[1399] Entering patient information

[1400] As an initial step in generating a rehabilitation plan, the user enters patient information into a dedicated terminal. This includes detailed information such as age, gender, physical characteristics (height and weight), motor skills, symptoms, personality, and rehabilitation goals. For example, a physical therapist might enter: "80-year-old female, weight 65kg, difficulty walking, lower back pain, cautious personality, goal is to be able to walk independently within six months."

[1401] Information transmission

[1402] The terminal securely transmits entered patient information to the server in real time. Data format verification and encrypted communication are used to maintain the accuracy and integrity of the information.

[1403] Rehabilitation plan generation

[1404] The server analyzes the received patient information using an algorithm. A rehabilitation plan is generated that takes into account basic information, detailed symptoms, and personality. For example, for a 78-year-old woman with lower back pain and a cautious personality, a plan including low-impact, low-risk exercises will be created.

[1405] Analysis of emotional information

[1406] When a user undergoes rehabilitation, the emotion engine analyzes the user's emotional information in real time. This emotion engine determines the user's emotional state based on facial expressions, tone of voice, language patterns, and other factors. For example, if a user is feeling anxious or stressed about rehabilitation, this information will be detected by the emotion engine.

[1407] Adjusting the rehabilitation plan

[1408] The server dynamically adjusts the rehabilitation plan based on emotional information obtained from the emotion engine. For example, if a user is showing signs of fatigue, the intensity of the exercise can be temporarily reduced. Conversely, if a user is highly motivated, the pace of the plan can be accelerated.

[1409] View and check the plan

[1410] The generated and adjusted rehabilitation plan is sent from the server to the terminal. The user can review the plan on the terminal and make further adjustments. For example, specific changes such as "increase the frequency of walking exercises from twice a week to three times a week" can be made.

[1411] Specific example

[1412] As a specific example, consider a 75-year-old male weighing 80 kg with mild dementia, undergoing rehabilitation after surgery on his left knee.

[1413] 1. The user enters the information.

[1414] The physical therapist enters the following information into the terminal: "75-year-old male, weight 80kg, mild dementia, post-left knee surgery, positive personality, goal is to be able to walk independently within 3 months."

[1415] 2. The device transmits information.

[1416] The device sends this information to the server in real time.

[1417] 3. The server generates a rehabilitation plan.

[1418] Based on the input information, the server generates a plan that includes low-impact exercises and strength training suitable for post-operative rehabilitation of the left knee.

[1419] 4. Analysis using an emotion engine

[1420] When a user begins rehabilitation, the emotion engine analyzes the user's facial expressions and tone of voice to determine, for example, that "the user is highly motivated for rehabilitation."

[1421] 5. The server adjusts the plan.

[1422] Based on the user's high motivation, the server dynamically adjusts the rehabilitation plan, reflecting a plan that accelerates the pace of rehabilitation.

[1423] 6. Submit and confirm your plan

[1424] The final rehabilitation plan is sent from the server to the terminal, and after the physical therapist reviews the details, it is provided to the user.

[1425] As described above, the rehabilitation system of the present invention, which incorporates an emotion engine, can provide more effective rehabilitation by dynamically adjusting the rehabilitation plan according to the user's emotional state.

[1426] The following describes the processing flow.

[1427] Step 1:

[1428] The user enters patient information using a dedicated terminal. Specifically, they enter detailed information such as the patient's age, gender, physique (height and weight), physical ability, symptoms, personality, and rehabilitation goals. For example, they might set it to "78-year-old female, weight 60kg, lower back pain, cautious personality, goal: independent walking."

[1429] Step 2:

[1430] The terminal transmits patient information entered into it to the server in real time. During transmission, the data format is checked to maintain the accuracy and integrity of the patient data, and encrypted communication is used to send the data securely.

[1431] Step 3:

[1432] The server analyzes the patient information it receives. First, it analyzes basic information (age, gender, build, etc.), and then it considers details of the symptoms (location and severity of pain, etc.) and personality traits (cautious, proactive, etc.). For example, a 78-year-old woman with lower back pain and a cautious personality would require low-impact, low-risk exercise.

[1433] Step 4:

[1434] The server references past patient data and the latest rehabilitation techniques, applying algorithms necessary for generating a rehabilitation plan. These algorithms construct an optimal rehabilitation program based on each patient's individual requirements, including stretching, strength training, and gait practice.

[1435] Step 5:

[1436] The emotion engine analyzes the user's real-time emotional information. This includes analyzing the user's facial expressions, tone of voice, and language patterns to determine their emotional state. For example, if a user is feeling stressed, the emotion engine will detect it.

[1437] Step 6:

[1438] The server dynamically adjusts the rehabilitation plan based on emotional information analyzed by the emotion engine. For example, if a user is showing signs of fatigue, the intensity of the exercise can be temporarily reduced. Conversely, if a user is highly motivated, the pace of the plan can be accelerated.

[1439] Step 7:

[1440] The server generates a rehabilitation plan and sends it to the terminal. Each plan includes specific exercises, duration, and frequency. For example, "On the first day, 15 minutes of light stretching, followed by 20 minutes of walking."

[1441] Step 8:

[1442] The user reviews the rehabilitation plan generated on their device and makes adjustments as needed. For example, they can adjust the frequency of walking exercises from twice a week to three times a week. The adjusted plan is then sent back to the server and reflected in the changes.

[1443] Step 9:

[1444] The server optimizes the adjusted rehabilitation plan. During the optimization process, further detailed adjustments are made to determine the optimal rehabilitation menu for each individual patient.

[1445] Step 10:

[1446] The final rehabilitation plan is saved on a server and made accessible to both the user and the patient in real time. Based on this plan, the patient can proceed with their daily rehabilitation in sequence.

[1447] As described above, the rehabilitation system of the present invention, which incorporates an emotion engine, dynamically adjusts the rehabilitation plan according to the user's emotional state, thereby providing more effective rehabilitation.

[1448] (Example 2)

[1449] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1450] In rehabilitation for the elderly, it is essential to provide plans that take into account the individual circumstances and emotional state of each patient. However, conventional rehabilitation plan generation systems lacked the ability to analyze the patient's emotional information and dynamically adjust the plan based on that information. As a result, it was difficult to provide effective rehabilitation plans that were appropriate for each patient.

[1451] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[1452] In this invention, the server includes means for inputting the patient's age, gender, physique, motor skills, symptoms, personality, and goals; means for generating a rehabilitation plan based on the input information; means for analyzing emotional information; means for dynamically adjusting the rehabilitation plan based on the emotional information; and means for displaying the generated rehabilitation plan for the user to review and adjust. This makes it possible to provide an effective rehabilitation plan that reflects the individual circumstances and emotional state of the patient.

[1453] "Patient information" refers to detailed data including the patient's age, sex, physique, motor skills, symptoms, personality, and rehabilitation goals.

[1454] A "terminal" is a device that users operate to input patient information and to check and adjust rehabilitation plans.

[1455] A "server" is a central processing unit that generates rehabilitation plans based on the input patient information, and performs analysis and dynamic adjustments.

[1456] An "algorithm" is a set of computational procedures or methods that a server uses to analyze patient information and generate a rehabilitation plan.

[1457] A "generative AI model" is an artificial intelligence model that uses algorithms to analyze input patient information and generate personalized rehabilitation plans.

[1458] An "emotion engine" is a part of the software or hardware that analyzes a user's emotional information and determines their emotional state.

[1459] A "rehabilitation plan" is a specific menu and schedule of rehabilitation tailored to the patient's condition and goals.

[1460] "Dynamic adjustment" refers to the server modifying the content of the rehabilitation plan in real time based on emotional information.

[1461] "Encrypted communication" is a method of using cryptographic technology to ensure the security of data transmitted between a terminal and a server.

[1462] Modes for carrying out the invention

[1463] This invention relates to a system for generating rehabilitation plans for the elderly, providing an optimal rehabilitation plan tailored to the individual circumstances and emotional state of the patient. Specific embodiments of this invention are described below.

[1464] Entering patient information

[1465] As an initial step in generating a rehabilitation plan, the user enters patient information into a dedicated terminal. The terminal provides an interface for user input using a touchscreen and keyboard. Specific information includes age, gender, physical characteristics (height, weight), motor skills, symptoms, personality, and rehabilitation goals. For example, a physical therapist might enter "80-year-old female, weight 65kg, difficulty walking, lower back pain, cautious personality, goal: to be able to walk independently within six months."

[1466] Information transmission

[1467] The terminal securely transmits entered patient information to the server in real time. To maintain the accuracy and integrity of the information, data format verification and encrypted communication (SSL / TLS) are used. The terminal encrypts the information and sends it to the server. The server verifies the integrity of the received data and proceeds to the next processing step.

[1468] Rehabilitation plan generation

[1469] The server uses a generative AI model to analyze the patient information it receives. This model is built using machine learning frameworks such as TensorFlow or PyTorch. The server then generates an appropriate rehabilitation plan, taking into account basic information, detailed symptoms, and personality traits. For example, for a 78-year-old woman with back pain and a cautious personality, a plan including low-impact, low-risk exercises would be generated.

[1470] Analysis of emotional information

[1471] When a user undergoes rehabilitation, the emotion engine uses the device's camera and microphone to analyze the user's facial expressions, voice tone, and language patterns in real time. This allows the emotion engine to determine the user's emotional state. For example, if a user is feeling anxious or stressed about rehabilitation, this information will be detected by the emotion engine.

[1472] Adjusting the rehabilitation plan

[1473] The server dynamically adjusts the rehabilitation plan based on emotional information obtained from the emotion engine. For example, if the user shows signs of fatigue, the intensity of the exercises will be temporarily reduced. Conversely, if the user shows high motivation, the pace of the plan will be increased.

[1474] View and check the plan

[1475] The generated and adjusted rehabilitation plan is sent from the server to the terminal. The user can review the plan on the terminal and make further detailed adjustments as needed. For example, specific changes such as "increase the frequency of walking exercises from twice a week to three times a week" can be made.

[1476] Specific example

[1477] As a specific example, consider a 75-year-old male weighing 80 kg, with mild dementia, undergoing rehabilitation after surgery on his left knee.

[1478] 1. The user enters the information.

[1479] The physical therapist enters the following information into the terminal: "75-year-old male, weight 80kg, mild dementia, post-left knee surgery, positive personality, goal is to be able to walk independently within 3 months."

[1480] 2. The device transmits information.

[1481] The device sends this information to the server in real time.

[1482] 3. The server generates a rehabilitation plan.

[1483] Based on the input information, the server generates a plan that includes low-impact exercises and strength training suitable for post-operative rehabilitation of the left knee.

[1484] 4. Analysis using an emotion engine

[1485] When a user begins rehabilitation, the emotion engine analyzes the user's facial expressions and tone of voice to determine, for example, that "the user is highly motivated for rehabilitation."

[1486] 5. The server adjusts the plan.

[1487] Based on the user's high motivation, the server dynamically adjusts the rehabilitation plan, reflecting a plan that accelerates the pace of rehabilitation.

[1488] 6. Submit and confirm your plan

[1489] The final rehabilitation plan is sent from the server to the terminal, and after the physical therapist reviews the details, it is provided to the user.

[1490] Example of a prompt

[1491] 1. Prompts for generating a natural rehabilitation plan:

[1492] "Please generate a rehabilitation plan for an elderly patient. The patient is a 75-year-old male, weighing 80 kg, with mild dementia and a history of left knee surgery. He has an active personality, and the goal is for him to be able to walk independently within three months."

[1493] 2. Emotion Engine Analysis Prompt:

[1494] "Please analyze the emotional state of a 75-year-old patient undergoing rehabilitation. Based on their tone of voice and facial expressions, determine whether their motivation for rehabilitation is high or low."

[1495] 3. Prompt for adjustment of rehabilitation plan:

[1496] "Dynamically adjust the rehabilitation plan based on the user's emotional state. If the user shows high motivation, increase the pace of the exercises."

[1497] In this way, the rehabilitation plan generation system of the present invention can provide a more effective and optimal rehabilitation plan according to the individual circumstances and emotional state of the patient.

[1498] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1499] Step 1:

[1500] The user enters patient information.

[1501] The user (physical therapist) uses the terminal interface to input the patient's age, gender, physique (height, weight), motor skills, symptoms, personality, and rehabilitation goals. A specific example of input would be: "75-year-old male, weight 80kg, mild dementia, post-left knee surgery, positive personality, goal is to be able to walk independently within 3 months."

[1502] Input: Patient's basic information

[1503] Output: Patient data in a processable format

[1504] Step 2:

[1505] The device sends information to the server.

[1506] The terminal securely transmits entered patient information to the server in real time. The terminal verifies the accuracy of the information and transmits the data using encrypted communication (SSL / TLS). The server verifies that the received data is in the correct format.

[1507] Input: Patient data in a processable format

[1508] Output: Patient data sent to the server

[1509] Step 3:

[1510] The server generates a rehabilitation plan.

[1511] The server uses a generative AI model to analyze the received patient information. Specifically, it uses frameworks such as TensorFlow and PyTorch to generate an appropriate rehabilitation plan that takes into account basic information, detailed symptoms, and personality. Based on the input information, it generates a plan that includes low-impact exercises and strength training suitable for post-operative rehabilitation of the left knee, for example.

[1512] Input: Patient data sent to the server

[1513] Output: Initial Rehabilitation Plan

[1514] Step 4:

[1515] Analysis of emotional information

[1516] When a user undergoes rehabilitation, the emotion engine uses the device's camera and microphone to analyze the user's facial expressions and voice tone in real time. The emotion engine uses libraries such as OpenCV and DeepSpeech to determine the user's emotional state. For example, if the user is feeling stressed about the rehabilitation, that information will be detected.

[1517] Input: User data during rehabilitation (facial expressions, voice tone, language patterns)

[1518] Output: User sentiment information

[1519] Step 5:

[1520] The server dynamically adjusts the rehabilitation plan.

[1521] The server dynamically adjusts the rehabilitation plan based on emotional information obtained from the emotion engine. For example, if the user is showing signs of fatigue, the intensity of the exercises will be temporarily reduced. Conversely, if the user is showing high motivation, the pace of the plan will be increased.

[1522] Input: User sentiment information

[1523] Output: Adjusted rehabilitation plan

[1524] Step 6:

[1525] View and check the plan

[1526] The server sends the adjusted rehabilitation plan to the user's device, where the user reviews the plan. If further adjustments are needed, the user can modify the plan by operating the device. For example, they can adjust the frequency of walking exercises from twice a week to three times a week.

[1527] Input: Adjusted rehabilitation plan

[1528] Output: Final rehabilitation plan reviewed and adjusted by the user.

[1529] In this way, based on the data entered at each step, the server and terminal perform specific data processing and calculations, obtaining their respective outputs, thereby providing the user with an optimal rehabilitation plan.

[1530] (Application Example 2)

[1531] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1532] In rehabilitation for the elderly and people with disabilities, there is a need to provide effective plans tailored to each patient's individual circumstances and emotional state. However, conventional rehabilitation plans are usually limited to one-dimensional planning based on the patient's physical data, and there is no mechanism to dynamically adjust them to take into account the patient's emotional state and motivation. As a result, it has been difficult to maximize the effectiveness of rehabilitation and maintain patient motivation.

[1533] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[1534] In this invention, the server includes means for inputting the patient's age, gender, physique, motor skills, symptoms, personality, and goals; means for generating a rehabilitation plan based on the input information; means for analyzing real-time emotional information and dynamically adjusting the rehabilitation plan based on the patient's emotions; and means for adjusting the pace of rehabilitation based on the collected emotional information. This makes it possible to create a rehabilitation plan that takes into account not only the physical data of each patient but also their emotional state, enabling effective rehabilitation while maintaining the patient's motivation.

[1535] "Elderly people" refers to those who are particularly old within the age group requiring rehabilitation, and generally refers to people aged 65 and over.

[1536] A "rehabilitation plan" refers to a detailed program designed to improve a patient's physical function and daily living activities.

[1537] A "system" refers to a collection of multiple devices or software programs combined to perform a specific function.

[1538] "Patient information" refers to individual data such as the patient's age, gender, physique, athletic ability, symptoms, personality, and goals.

[1539] A "server" refers to a central processing unit that provides services to other computers via a network.

[1540] "Emotional information" refers to data that indicates a patient's emotional state, and is acquired based on facial expressions, tone of voice, language patterns, and other factors.

[1541] "Dynamic adjustment" refers to a flexible response that adapts the plan in real time or near real time.

[1542] "Progression rate" refers to the pace at which exercises and training progress in a rehabilitation plan.

[1543] "Display means" refers to devices or software used to visually show the generated rehabilitation plan to the user.

[1544] "Input means" refers to devices or interfaces used to provide patient information to the system.

[1545] Modes for carrying out the invention

[1546] This invention relates to a system for generating rehabilitation plans for the elderly and dynamically adjusting them based on emotional information. The invention is particularly applicable to rehabilitation support applications using smart glasses. This system includes multiple means for collecting and analyzing patient information and generating effective rehabilitation plans.

[1547] 1. Collection of input data

[1548] The server utilizes the camera and microphone of smart glasses to collect basic patient information and emotional information through voice input and OCR functionality. These smart glasses are equipped with facial recognition and voice recognition capabilities, allowing for real-time acquisition of patient data. Specific hardware used includes smart glasses (e.g., Google Glass) and voice recognition APIs (e.g., Amazon Transcribe).

[1549] 2. Data transmission and encryption

[1550] The terminal sends the collected data to the server in real time. During transmission, the SSL / TLS protocol is used to ensure data security. Specifically, OpenSSL is used to implement encrypted communication.

[1551] 3. Generating a Rehabilitation Plan

[1552] The server generates rehabilitation plans based on collected patient information. To do this, it uses Python-based machine learning algorithms to construct plans tailored to each patient's individual needs. Specifically, it uses libraries such as Scikit-learn and Pandas to analyze data and generate plans.

[1553] 4. Analysis of emotional information

[1554] The server analyzes emotional information to detect the patient's emotional state. Specifically, it uses Microsoft Azure Cognitive Services (Face API, Speech API) to perform real-time emotional analysis. This allows the server to understand the patient's emotional state from their facial expressions and tone of voice.

[1555] 5. Dynamic adjustment of the rehabilitation plan

[1556] The server dynamically adjusts the rehabilitation plan based on emotional information. For example, if a patient shows signs of fatigue, the exercise intensity is temporarily reduced. Conversely, if motivation is high, the plan is advanced. AWS Lambda and dynamic databases (e.g., DynamoDB) are used for this adjustment.

[1557] 6. View and confirm the plan

[1558] The final rehabilitation plan is sent back to the smart glasses for the user to review. This allows physical therapists and rehabilitation center staff to fine-tune the plan according to the patient's condition.

[1559] Specific example

[1560] As a concrete example, suppose a 75-year-old male patient begins rehabilitation and wears smart glasses. These smart glasses analyze the patient's voice and facial expressions and may make the following adjustments to the initial plan.

[1561] If the patient is feeling anxious:

[1562] "The patient is anxious about rehabilitation. Please reduce the intensity of the next exercise and incorporate relaxation exercises."

[1563] Example of a prompt

[1564] "Generate a rehabilitation plan for a 75-year-old male, weighing 80kg, with mild dementia, post-left knee surgery, and an outgoing personality. His goal is to achieve independent walking within three months. Furthermore, use an emotion engine to acquire emotional information from the patient's facial expressions and tone of voice, and dynamically adjust the rehabilitation plan accordingly."

[1565] Thus, the system of the present invention can generate a rehabilitation plan based on individual patient information and dynamically adjust it according to the patient's emotional state, thereby providing more effective and personalized rehabilitation.

[1566] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1567] Step 1:

[1568] The server collects patient information using smart glasses. Patient age, gender, build, motor skills, symptoms, personality, and goals are entered using voice recognition and OCR functions. Example input data includes a 75-year-old male, weighing 80kg, post-knee surgery on the left knee, with an active personality and a goal of independent walking within three months. This data is collected using smart glasses such as Google Glass and Amazon Transcribe.

[1569] Step 2:

[1570] The terminal transmits collected patient information to the server in real time. To ensure data security, encrypted communication is performed using the SSL / TLS protocol. The input is patient information (e.g., 75-year-old male, 80kg weight, post-left knee surgery), and the output is encrypted data sent to the server.

[1571] Step 3:

[1572] The server generates a rehabilitation plan based on the received patient information. It uses a Python-based machine learning algorithm to analyze the data and construct the rehabilitation plan. The input is detailed patient information (e.g., age, weight, symptoms), and based on this, an individualized rehabilitation plan (e.g., low-intensity strength training and exercises) is generated. Specific libraries used include Scikit-learn and Pandas.

[1573] Step 4:

[1574] The server uses the smart glasses' camera and microphone to analyze the patient's emotional state in real time during rehabilitation. It analyzes the patient's facial expressions and voice tone using Microsoft Azure's Face API and Speech API. Real-time facial and voice data from the patient is collected as input data, and emotional information (e.g., anxiety, stress, motivation) is output as the analysis result.

[1575] Step 5:

[1576] The server dynamically adjusts the rehabilitation plan based on the collected emotional information. Emotional information (e.g., anxiety, fatigue) is taken as input, and adjustment data for the rehabilitation plan (e.g., reducing exercise intensity or inserting relaxation) is output. Specifically, AWS Lambda and DynamoDB are used to process and adjust this data.

[1577] Step 6:

[1578] The final rehabilitation plan is sent back to the smart glasses. The server sends the adjusted plan to the device so the user can review it. The input is the adjusted rehabilitation plan, and the output is a visual display of the plan's details. In this case, the device uses the Google Glass API to display the plan.

[1579] Through the steps described above, a system is realized that generates and dynamically adjusts rehabilitation plans based on the patient's individual information and emotional state.

[1580] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[1581] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1582] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[1583] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1584] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[1585] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[1586] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[1587] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[1588] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[1589] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[1590] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[1591] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[1592] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[1593] 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.

[1594] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[1595] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[1596] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[1597] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[1598] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[1599] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[1600] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[1601] The following is further disclosed regarding the embodiments described above.

[1602] (Claim 1)

[1603] A system for generating rehabilitation plans for the elderly,

[1604] A means of inputting the patient's age, sex, physique, athletic ability, symptoms, personality, and goals,

[1605] A server that generates a rehabilitation plan based on the input information,

[1606] A means for displaying the generated rehabilitation plan and allowing the user to review and adjust it,

[1607] A system that includes this.

[1608] (Claim 2)

[1609] The system according to claim 1, wherein the server that generates the rehabilitation plan includes means for analyzing patient information using an algorithm and constructing an individualized rehabilitation menu.

[1610] (Claim 3)

[1611] The system according to claim 1, which includes means for referencing past patient data and the latest rehabilitation methods when generating and optimizing the rehabilitation plan.

[1612] "Example 1"

[1613] (Claim 1)

[1614] A means of inputting the patient's age, sex, physique, athletic ability, symptoms, personality, and goals,

[1615] A server that generates a rehabilitation plan using an algorithm based on the input information,

[1616] The aforementioned algorithm includes means for analyzing patient information using a machine learning library,

[1617] A means for displaying the generated rehabilitation plan and allowing the user to review and adjust it,

[1618] A means of sending the adjusted plan back to the server and performing optimization,

[1619] The finalized rehabilitation plan is saved and made accessible to users and patients.

[1620] A system that includes this.

[1621] (Claim 2)

[1622] The system according to claim 1, which includes means for referencing past patient data and the latest rehabilitation methods when generating the aforementioned rehabilitation plan.

[1623] (Claim 3)

[1624] The system according to claim 1, further comprising means for protecting data security using encryption technology such as SSL / TLS when generating and optimizing the rehabilitation plan.

[1625] "Application Example 1"

[1626] (Claim 1)

[1627] A system for generating rehabilitation plans for the elderly,

[1628] A means of inputting the patient's age, sex, physique, athletic ability, symptoms, personality, and goals,

[1629] A server that generates a rehabilitation plan based on the input information,

[1630] A means for displaying the generated rehabilitation plan and allowing the user to review and adjust it,

[1631] A means of providing real-time audio guidance and visual feedback using smart devices,

[1632] A system that includes this.

[1633] (Claim 2)

[1634] The system according to claim 1, wherein the server that generates the rehabilitation plan includes means for analyzing patient information using an algorithm and constructing an individualized rehabilitation menu.

[1635] (Claim 3)

[1636] The system according to claim 1, which includes means for referencing past patient data and the latest rehabilitation methods when generating and optimizing the rehabilitation plan.

[1637] "Example 2 of combining an emotion engine"

[1638] (Claim 1)

[1639] A means of inputting the patient's age, sex, physique, athletic ability, symptoms, personality, and goals,

[1640] A server that generates a rehabilitation plan based on the input information,

[1641] A means for displaying the generated rehabilitation plan and allowing the user to review and adjust it,

[1642] A means of analyzing emotional information,

[1643] A means of dynamically adjusting the rehabilitation plan based on emotional information,

[1644] A system that includes this.

[1645] (Claim 2)

[1646] The system according to claim 1, comprising means for a server to analyze patient information using an algorithm and construct an individualized rehabilitation menu.

[1647] (Claim 3)

[1648] The system according to claim 1, comprising means for referencing past patient data and the latest rehabilitation techniques when generating and optimizing a rehabilitation plan.

[1649] "Application example 2 when combining with an emotional engine"

[1650] (Claim 1)

[1651] A system for generating rehabilitation plans for the elderly,

[1652] A means of inputting the patient's age, sex, physique, athletic ability, symptoms, personality, and goals,

[1653] A server that generates a rehabilitation plan based on the input information,

[1654] A means for displaying the generated rehabilitation plan and allowing the user to review and adjust it,

[1655] A means of analyzing real-time emotional information and dynamically adjusting the rehabilitation plan based on the patient's emotions,

[1656] A means of adjusting the pace of rehabilitation based on collected emotional information,

[1657] A system that includes this.

[1658] (Claim 2)

[1659] The system according to claim 1, wherein the server that generates the rehabilitation plan includes means for analyzing patient information using an algorithm and constructing an individualized rehabilitation menu.

[1660] (Claim 3)

[1661] The system according to claim 1, which includes means for referencing past patient data and the latest rehabilitation methods when generating and optimizing the rehabilitation plan. [Explanation of Symbols]

[1662] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A system for generating rehabilitation plans for the elderly, A means of inputting the patient's age, sex, physique, athletic ability, symptoms, personality, and goals, A server that generates a rehabilitation plan based on the input information, A means for displaying the generated rehabilitation plan and allowing the user to review and adjust it, A system that includes this.

2. The system according to claim 1, wherein the server that generates the rehabilitation plan includes means for analyzing patient information using an algorithm and constructing an individualized rehabilitation menu.

3. The system according to claim 1, which includes means for referencing past patient data and the latest rehabilitation methods when generating and optimizing the rehabilitation plan.

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