CN119580933A - AI-assisted limb rehabilitation system - Google Patents

AI-assisted limb rehabilitation system Download PDF

Info

Publication number
CN119580933A
CN119580933A CN202411571548.1A CN202411571548A CN119580933A CN 119580933 A CN119580933 A CN 119580933A CN 202411571548 A CN202411571548 A CN 202411571548A CN 119580933 A CN119580933 A CN 119580933A
Authority
CN
China
Prior art keywords
rehabilitation
user
training
module
unit
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Withdrawn
Application number
CN202411571548.1A
Other languages
Chinese (zh)
Inventor
马波
徐燕
李伯文
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Peking University Third Hospital Qinhuangdao Hospital
Original Assignee
Peking University Third Hospital Qinhuangdao Hospital
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Peking University Third Hospital Qinhuangdao Hospital filed Critical Peking University Third Hospital Qinhuangdao Hospital
Priority to CN202411571548.1A priority Critical patent/CN119580933A/en
Publication of CN119580933A publication Critical patent/CN119580933A/en
Withdrawn legal-status Critical Current

Links

Classifications

    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H20/00—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
    • G16H20/30—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to physical therapies or activities, e.g. physiotherapy, acupressure or exercising
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F3/00—Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
    • G06F3/01—Input arrangements or combined input and output arrangements for interaction between user and computer
    • G06F3/011—Arrangements for interaction with the human body, e.g. for user immersion in virtual reality
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/044—Recurrent networks, e.g. Hopfield networks
    • G06N3/0442—Recurrent networks, e.g. Hopfield networks characterised by memory or gating, e.g. long short-term memory [LSTM] or gated recurrent units [GRU]
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/045—Combinations of networks
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/0464—Convolutional networks [CNN, ConvNet]
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
    • G06V40/70—Multimodal biometrics, e.g. combining information from different biometric modalities
    • G—PHYSICS
    • G08—SIGNALLING
    • G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
    • G08B21/00—Alarms responsive to a single specified undesired or abnormal condition and not otherwise provided for
    • G08B21/02—Alarms for ensuring the safety of persons
    • G08B21/04—Alarms for ensuring the safety of persons responsive to non-activity, e.g. of elderly persons
    • G08B21/0407—Alarms for ensuring the safety of persons responsive to non-activity, e.g. of elderly persons based on behaviour analysis
    • G08B21/043—Alarms for ensuring the safety of persons responsive to non-activity, e.g. of elderly persons based on behaviour analysis detecting an emergency event, e.g. a fall
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H80/00—ICT specially adapted for facilitating communication between medical practitioners or patients, e.g. for collaborative diagnosis, therapy or health monitoring

Landscapes

  • Engineering & Computer Science (AREA)
  • Health & Medical Sciences (AREA)
  • Theoretical Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Health & Medical Sciences (AREA)
  • General Physics & Mathematics (AREA)
  • General Engineering & Computer Science (AREA)
  • Biophysics (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Biomedical Technology (AREA)
  • Data Mining & Analysis (AREA)
  • Molecular Biology (AREA)
  • Software Systems (AREA)
  • Mathematical Physics (AREA)
  • Computing Systems (AREA)
  • Medical Informatics (AREA)
  • Evolutionary Computation (AREA)
  • Computational Linguistics (AREA)
  • Artificial Intelligence (AREA)
  • Epidemiology (AREA)
  • Human Computer Interaction (AREA)
  • Primary Health Care (AREA)
  • Public Health (AREA)
  • Pathology (AREA)
  • Psychology (AREA)
  • Social Psychology (AREA)
  • Gerontology & Geriatric Medicine (AREA)
  • Business, Economics & Management (AREA)
  • Psychiatry (AREA)
  • Emergency Management (AREA)
  • Multimedia (AREA)
  • Physical Education & Sports Medicine (AREA)
  • Rehabilitation Tools (AREA)

Abstract

The invention discloses an AI auxiliary limb rehabilitation system, which belongs to the field of communication control systems, and combines a sensing module, an AI analysis module, a VR interaction module, a feedback module and the like to comprehensively acquire limb movement and physiological data, introduce game elements, improve the participation of patients through target setting, a reward mechanism and interesting tasks, generate an individualized rehabilitation plan by using a deep learning algorithm, adjust the plan according to psychological states by an emotion recognition unit, adapt the environment and adjust the training difficulty in real time to ensure the training safety and challenge, store data cloud to facilitate remote monitoring, and provide a brand-new, efficient and personalized solution system scheme for limb rehabilitation.

Description

AI auxiliary limb rehabilitation system
Technical Field
The invention relates to the field of communication control systems, in particular to an AI auxiliary limb rehabilitation system.
Background
Limb rehabilitation takes a vital role in the modern medical field. Whether the limb functions are impaired due to accidental injury or disease, or patients who need to recover the limb mobility after surgery, there is an urgent need for effective rehabilitation means to help them regain normal life and working capacity.
Traditional limb rehabilitation methods rely primarily on manual instruction from doctors and simple rehabilitation equipment, in which mode the rehabilitation process often lacks personalized customization. The illness state, the physical condition and the rehabilitation demands of different patients are greatly different, the traditional method is difficult to make the most suitable rehabilitation plan according to the specific condition of each patient, meanwhile, the traditional rehabilitation mode is boring and tedious, the interestingness and the interactivity are lacking, the active participation degree of the patients is difficult to be stimulated, the patients feel tired and tired easily in the long rehabilitation process, and the compliance of rehabilitation is reduced;
in addition, the real-time feedback mechanism of the traditional rehabilitation method is relatively weak. When a patient performs rehabilitation training, whether the movement of the patient is correct or not is difficult to accurately know, whether the rehabilitation requirement is met or not, a doctor cannot monitor the rehabilitation progress and the movement performance of the patient in real time, and the rehabilitation plan can be adjusted only through regular examination and evaluation, so that the efficiency of the rehabilitation process is low, and the optimal rehabilitation opportunity can be missed.
Along with the continuous progress of technology, the rapid development of artificial intelligence and virtual reality technology brings new opportunities for limb rehabilitation, combines the artificial intelligence with the virtual reality technology, creates a brand-new AI auxiliary limb rehabilitation system, and becomes an urgent need in the current medical rehabilitation field.
Disclosure of Invention
1. Technical problem to be solved
Aiming at the problems existing in the prior art, the invention aims to provide an AI auxiliary limb rehabilitation system which combines a sensing module, an AI analysis module, a VR interaction module, a feedback module and the like, comprehensively collects limb movement and physiological data, introduces game elements, improves the participation degree of patients through target setting, rewarding mechanism and interesting tasks, generates an individualized rehabilitation plan by using a deep learning algorithm, adjusts the plan according to psychological states, ensures the training safety and has challenge by self-adapting environment and adjusting the training difficulty in real time, is convenient for remote monitoring by data cloud storage, and provides a brand new, efficient and personalized solution system scheme for limb rehabilitation by means of compatibility and expansion monitoring capability of third-party equipment.
2. Technical proposal
In order to solve the problems, the invention adopts the following technical scheme.
AI assisted limb rehabilitation system comprising:
the sensing module is used for acquiring limb movement data of a user;
the AI analysis module is in communication connection with the sensing module and is used for processing and analyzing the limb movement data, generating a rehabilitation evaluation report of the user based on a deep learning algorithm and providing personalized advice for the rehabilitation progress of the user;
The VR interaction module is used for providing a virtual reality rehabilitation scene for the user, enhancing rehabilitation training experience of the user through tasks and guidance in the virtual environment, and enabling the user to complete specific rehabilitation actions in the immersive environment;
the feedback module is communicated with the VR interaction module and the AI analysis module and is used for feeding back the limb actions of the user in real time according to the AI analysis result, prompting correction actions and enhancing training effects;
The data cloud storage module is used for storing limb movement data and rehabilitation process data of a user in a cloud so as to enable doctors and rehabilitation specialists to remotely monitor and guide the rehabilitation process of the user, the data cloud storage module adopts a safe and reliable cloud storage technology to ensure the privacy and safety of the user data, and the doctors and the rehabilitation specialists can know the rehabilitation progress and state of the user in real time by remotely accessing the cloud data so as to provide personalized guidance and advice for the user;
the user excitation module is used for exciting the user to actively participate in rehabilitation training through a game rewarding mechanism and social elements so as to improve rehabilitation compliance and long-term training effect;
The system can adjust training difficulty in real time, ensures that rehabilitation training has challenging performance and can adapt to the capacity level of a patient, the system collects the movement data of the patient through the sensing module, the AI analysis module analyzes the data, the training difficulty is adjusted in real time according to the rehabilitation progress and capacity of the patient, when the capacity of the patient is improved, the system can automatically increase the training difficulty so as to keep the training challenging performance, and when the patient is tired or difficult, the system can reduce the training difficulty and ensure that the patient can finish training smoothly.
Further, the sensing module is in communication connection with a wearable sensor, a camera and a physiological signal acquisition device, so as to capture the body posture, the movement track and the physiological parameters of a user, wherein the wearable sensor adopts a high-precision inertial measurement unit, can accurately detect the joint angle and the movement acceleration of the user, the camera has high resolution and high frame rate, can capture the body actions of the user in real time, and the physiological signal acquisition device can be compatible with various third-party wearable equipment, can expand the monitoring capability of physiological signals of the user, including but not limited to heart rate, blood oxygen concentration, myoelectric signals and electroencephalogram signals, so that more comprehensive data support is provided for rehabilitation evaluation.
Further, the AI analysis module includes:
The motion trail prediction unit is used for predicting the limb motion trail of a user through an advanced neural network model and comparing the difference between actual actions and target actions, and can accurately predict the complex limb motion trail and provide real-time feedback by utilizing the combination of a long short-term memory network (LSTM) and a Convolutional Neural Network (CNN), wherein a specific calculation formula of a difference value is a difference value delta sigma (|actual action parameter-target action parameter|);
The rehabilitation evaluation unit is used for evaluating the rehabilitation progress of a user according to multi-dimensional limb data and physiological parameters and giving a dynamically adjusted rehabilitation plan by combining historical data, adopts a multi-index comprehensive evaluation method, comprises indexes such as joint activity, muscle strength, balance capacity, endurance and the like, and makes a personalized rehabilitation plan for the user through data analysis and a machine learning algorithm, wherein the calculation formula of the multi-index comprehensive evaluation method is that the rehabilitation progress score = alpha x joint activity score + beta x muscle strength score + gamma x balance capacity score + delta x endurance score, wherein alpha, beta, gamma and delta are weight coefficients, and the adjustment is carried out according to different rehabilitation stages and patient conditions;
The emotion recognition unit is used for evaluating the psychological state of the user by analyzing the facial expression and the voice emotion of the user in the rehabilitation process and dynamically adjusting the rehabilitation plan to increase the comfort level and the confidence of the user, and the emotion recognition unit is used for monitoring the emotion change of the user in real time by utilizing a computer vision and voice processing technology, so that when the user has negative emotion such as anxiety, depression and the like, the system can automatically adjust the rehabilitation plan, the interestingness and the interactivity are increased, and the participation level of the user is improved.
Further, the VR interaction module includes:
The task generating unit is used for generating adaptive virtual training tasks according to the rehabilitation requirements and the capacities of the users so as to improve the interestingness and participation degree of rehabilitation, and the task generating unit automatically generates virtual training tasks with different difficulties, such as balance training, strength training, coordination training and the like, according to the rehabilitation stages and targets of the users, the difficulty of the tasks can be automatically adjusted according to the performances of the users, and the users can be ensured to continuously progress in challenges, wherein the difficulty calculation formula of the tasks is task difficulty coefficient = f (user performance parameter), wherein f is a function relation determined according to experience;
The environment self-adaptive unit is used for automatically adjusting the scene difficulty and task complexity in the virtual reality according to the real-time feedback and the physiological state of the user, ensuring the safety and challenge of the training process, and automatically adjusting the difficulty and complexity of the virtual environment by monitoring the physiological parameter and the athletic performance of the user in real time, for example, when the heart rate of the user is too high or fatigue signs appear, the system can automatically reduce the task difficulty, ensuring the safety of the training process, wherein the calculation formula of the scene difficulty and the task complexity is scene difficulty adjustment value=g (physiological parameter change value and athletic performance change value), and g is a function relation determined according to experience.
Further, the feedback module includes:
The real-time voice feedback unit is used for providing voice prompts to help the user correct the false actions in the process of completing the actions by the user, adopts a natural language processing technology, and can provide personalized voice prompts according to the actions and the performances of the user, such as 'one point raised by the arm', 'one point greatly stepped on the foot', and the like;
The visual feedback unit is used for displaying the correctness of the user action through animation or marks in the virtual reality environment so as to help the user understand how to improve the action quality, and the visual feedback unit utilizes the virtual reality technology to display the action track and correctness of the user in the virtual environment, for example, whether the joint angle of the user is correct or not through color marks, demonstrate the correct action mode through animation and the like.
Further, the user incentive module includes:
The synchronous game unit introduces game elements, improves participation and enthusiasm of patients by setting targets, rewarding mechanisms and interesting rehabilitation tasks, for example, sets different grades of rehabilitation tasks, enables the patients to obtain corresponding rewards such as virtual props, scores and the like after finishing one grade of tasks, and simultaneously enables the system to set rehabilitation scenes of different topics according to the preference of the patients to increase the interestingness of rehabilitation training;
the achievement system unit can excite the competition consciousness and achievement sense of the user and improve the participation degree of the user when the user completes a specific rehabilitation task or reaches a certain rehabilitation target and obtains corresponding achievement and rewards, such as medals, trophy and the like;
The social interaction platform unit can be used for enabling users to communicate and share with other rehabilitation patients and encourage and support each other, social contact of the users can be increased, and rehabilitation compliance of the users is improved.
Further, still communication connection has the safety protection module on the feedback module for monitor the user at the safe situation of rehabilitation training in-process, in time send out the alarm and stop training when the abnormal conditions appears, the safety protection module includes:
The fall detection unit is used for detecting whether a user falls through a sensor and an algorithm, immediately sending out an alarm and notifying related personnel when the user falls, and the fall detection unit adopts sensors such as an acceleration sensor, a gyroscope and the like, combines a machine learning algorithm, can accurately detect the falling condition of the user and timely sends out the alarm, wherein the fall detection calculation formula is that the falling probability=l (sensor acceleration change value and gyroscope change value), wherein l is a function relation determined according to experience;
The motion limit monitoring unit is used for monitoring the motion limit of a user, sending out an alarm in time and adjusting a training plan when the motion of the user exceeds a safety range, monitoring joint angles, motion speeds, strength and other parameters of the user through a sensor and an algorithm, automatically sending out an alarm and adjusting the training plan when the motion of the user exceeds the safety range, and ensuring the safety of the user, wherein the motion limit monitoring unit has a calculation formula of probability=m (joint angle change value, motion speed change value and strength change value) exceeding the safety range, wherein m is a function relation determined according to experience.
3. Advantageous effects
Compared with the prior art, the invention has the advantages that:
(1) According to the scheme, firstly, a wearable sensor, a camera and a physiological signal acquisition device are in communication connection with a sensing module, so that the body gesture, the movement track and various physiological parameters of a user can be captured, including heart rate, blood oxygen concentration, electromyographic signals, electroencephalogram signals and the like, comprehensive data support is provided for rehabilitation evaluation, then an AI analysis module can evaluate the rehabilitation progress of the user according to multi-dimensional body data and physiological parameters based on a deep learning algorithm, a dynamically adjusted rehabilitation plan is given by combining historical data, specific requirements of different users are met, and personalized rehabilitation plan effects are realized;
(2) According to the scheme, the synchronous game unit and the achievement system unit are arranged in the user incentive module, so that game elements are introduced in the training process, the participation degree and the enthusiasm of a patient are improved through setting targets, rewarding mechanisms and interesting rehabilitation tasks, the user can obtain corresponding achievement and rewards when finishing specific rehabilitation tasks or achieving certain rehabilitation targets, the competition consciousness and achievement sense of the user are stimulated, the participation degree of the user is improved, meanwhile, the social interaction platform unit is arranged, the user can communicate and share with other rehabilitation patients, mutual encouragement and support are achieved, the social interaction platform unit can increase social connection of the user, and rehabilitation compliance of the user is improved;
(3) According to the scheme, the emotion recognition unit in the AI analysis module evaluates the psychological state of the user by analyzing the facial expression and the voice emotion of the user in the rehabilitation process, and dynamically adjusts the rehabilitation plan to increase the comfort and the confidence of the user;
(4) According to the scheme, the environment self-adaptive unit of the VR interaction module automatically adjusts scene difficulty and task complexity in virtual reality according to real-time feedback and physiological states of a user, the training process is ensured to be safe and challenging, the safety protection module in communication connection on the feedback module can monitor the safety condition of the user in the rehabilitation training process in real time, and when abnormal conditions occur, an alarm is timely sent out and training is stopped, so that the safety in training is improved.
Drawings
FIG. 1 is a schematic diagram of the overall architecture of the system of the present invention;
FIG. 2 is a schematic diagram showing the composition of the subunits of the AI analysis module of the invention;
FIG. 3 is a schematic diagram illustrating the sub-unit composition of the VR interactive module of the present invention;
FIG. 4 is a schematic diagram of the sub-unit composition of the feedback module of the present invention;
FIG. 5 is a schematic diagram of the sub-unit composition of the user actuation module of the present invention;
Fig. 6 is a schematic diagram showing the composition of the subunits of the security module according to the present invention.
Detailed Description
The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings of the present invention, and it is apparent that the described embodiments are only some embodiments of the present invention, but not all embodiments, and all other embodiments obtained by those skilled in the art without making creative efforts based on the embodiments of the present invention are included in the protection scope of the present invention.
Example 1:
referring to fig. 1 to 6, when a patient suffers from hemiplegia due to a disease or other factors and has impaired life self-care ability, the AI-assisted limb rehabilitation system of the present invention can be used for rehabilitation training under the recommendation of a doctor, specifically as follows:
Firstly, the patient correctly wears the wearable sensor, the camera and the physiological signal acquisition device under the guidance of a professional, and the wearable sensor, the camera and the physiological signal acquisition device are tightly attached to the body of the patient, so that the limb posture, the movement track and the physiological parameters of the patient can be accurately captured. The high-precision inertial measurement unit in the wearable sensor senses fine changes of the joint angle and fluctuation of the movement acceleration of a patient at any time like an acute antenna, and the camera with high resolution and high frame rate is like a faithful observer, so that limb actions of the patient are comprehensively recorded, any detail is not put on, and the physiological signal acquisition device can monitor common physiological parameters such as heart rate, blood oxygen concentration and the like, is compatible with various third-party wearable devices, further expands the monitoring capability of the patient such as electromyographic signals and electroencephalogram signals and the like, and provides extremely comprehensive data support for rehabilitation evaluation;
The AI analysis module then rapidly processes and analyzes the collected data. The motion trail prediction unit accurately predicts the limb motion trail of a patient through an advanced neural network model as if an intelligent predictor, finely compares the actual motion with the target motion, calculates a differential value with a value of delta sigma (i actual motion parameter-target motion parameter I), provides a definite direction for the subsequent rehabilitation guidance, the rehabilitation evaluation unit is like an exact referee, adopts a multi-index comprehensive evaluation method of joint activity, muscle strength, balance capacity, endurance and the like according to multi-dimensional limb data and physiological parameters, scoring the rehabilitation progress of the patient, wherein the rehabilitation progress score = alpha x joint activity score + beta x muscle strength score + gamma x balance capacity score + delta x endurance score, wherein alpha, beta, gamma and delta are weight coefficients, and are adjusted according to different rehabilitation stages and patient conditions, and the emotion recognition unit is also in the silently focusing on the psychological state of the patient, and by analyzing the facial expression and the voice emotion of the patient in the rehabilitation process, the rehabilitation plan is timely adjusted when the patient has negative emotion, so that interestingness and interactivity are increased, and warm support is given to the patient;
Then the VR interaction module provides virtual reality rehabilitation scenarios, so that the patient appears to be in a brand new world. The task generating unit generates adaptive virtual training tasks according to the rehabilitation requirements and the capacities of patients, the tasks are like interesting challenges, the fight of the patients is stimulated, for example, in the balance training tasks, the patients need to keep physical balance on a virtual shaking platform, as the performance of the patients is improved, the task difficulty coefficient=f (user performance parameter) can be correspondingly adjusted, the patients always keep in a challenged and growing state, the environment self-adaptive unit is like an intelligent daemon, the scene difficulty and the task complexity in virtual reality are automatically adjusted according to the real-time feedback and the physiological state of the patients, when the heart rate of the patients is increased or fatigue signs appear, the task difficulty is reduced, the safety of the training process is ensured, and the calculation formula of the scene difficulty adjustment value=g (physiological parameter change value and motion performance change value) is a function relation determined according to experience;
Meanwhile, the feedback module plays an important role at the moment. The real-time voice feedback unit provides a close voice prompt when the patient finishes the action, and the 'arm is lifted a little more, so that the' footstep is more stable. The personalized voice prompts help the patient to correct the false actions, the visual feedback unit displays the correctness of the actions of the user in the virtual reality environment through animation or marks, and when the joint angle of the patient is correct, green marks appear;
and the data cloud storage module safely stores the limb movement data and the rehabilitation process data of the patient in the cloud. Doctors and rehabilitation specialists can remotely monitor and guide the rehabilitation process of patients, the rehabilitation plans can be adjusted at any time according to the conditions of the patients, the achievement system units in the user incentive module set different grades of rehabilitation tasks for the patients, when the patients finish one grade of tasks, corresponding rewards such as virtual medals, trophy and the like can be obtained, the participation degree and enthusiasm of the patients are greatly improved, the participation degree score = h (target setting rationality, rewarding mechanism attraction and task interestingness), wherein h is a function relation determined according to experience, the social interaction platform unit also enables the patients to have opportunities to communicate and share experience with other rehabilitation patients, encourages and supports each other, and social connection and rehabilitation compliance of the patients are increased;
The uninterrupted rehabilitation training is carried out until the limb functions of the patient are obviously improved and recovered.
Example 2:
referring to fig. 1 to 6, when a certain athlete is injured by a knee joint due to injury, rehabilitation training is required by using the AI-assisted limb rehabilitation system of the present invention:
firstly, an athlete wears a wearable sensor, a camera and a physiological signal acquisition device. The wearable sensor accurately detects the joint angle and the movement acceleration of the athlete, provides an accurate data basis for subsequent analysis, the camera records the limb actions of the athlete at high resolution and high frame rate, and the physiological signal acquisition device monitors conventional physiological parameters and is compatible with third-party wearable equipment to acquire more comprehensive data such as heart rate, blood oxygen concentration, myoelectric signals and the like, so that powerful support is provided for rehabilitation evaluation;
The method comprises the steps that a motion trail prediction unit in an AI analysis module accurately predicts the limb motion trail of an athlete by utilizing the combination of a long-short-period memory network (LSTM) and a Convolutional Neural Network (CNN), compares actual actions with target actions, provides real-time feedback for the athlete, and evaluates the rehabilitation progress of the athlete according to multi-dimensional limb data and physiological parameters, wherein the rehabilitation progress score = alpha x joint activity score + beta x muscle strength score + gamma x balance ability score + delta x endurance score, wherein alpha, beta, gamma and delta are weight coefficients, and the emotion recognition unit dynamically adjusts according to the rehabilitation stage and physical condition of the athlete, so that the emotion recognition unit is also concerned about the psychological state of the athlete and ensures that the athlete keeps a positive heart state in the rehabilitation process;
In the VR interactive module, a task generation unit generates a series of challenging virtual training tasks according to the athlete's rehabilitation needs and capabilities. For example, in a strength training task, an athlete needs to lift a barbell which is gradually aggravated in a virtual environment, as the capacity of the athlete is improved, a task difficulty coefficient=f (user performance parameter) is also increased continuously, so that the athlete is always in a challenging self state, an environment self-adaptive unit automatically adjusts scene difficulty and task complexity in virtual reality according to real-time feedback and physiological states of the athlete, when the heart rate of the athlete is too high or fatigue signs appear, the task difficulty is automatically reduced, the safety of the training process is ensured, and a calculation formula is calculated, wherein the scene difficulty adjustment value=g (physiological parameter change value, athletic performance change value), and g is a function relation determined according to experience;
The feedback module provides timely and effective feedback for the athlete, and the real-time voice feedback unit provides professional voice prompts when the athlete finishes the action, so that the action of ' force points are accurate and ' I ' is smoother. The visual feedback unit displays the correctness of the action of the athlete in a virtual reality environment through animation or marks, and when the action of the athlete is standard, the golden marks appear;
The data cloud storage module stores limb movement data and rehabilitation progress data of the athlete in the cloud, a coach and a rehabilitation expert can remotely monitor and guide the rehabilitation process of the athlete, a achievement system unit in the user excitation module sets a higher rehabilitation target for the athlete, when the athlete completes one target, the athlete can obtain corresponding rewards and honors, the athlete is excited to challenge himself continuously, and the social interaction platform unit also enables the athlete to have the opportunity to exchange rehabilitation experience with other athletes and encourages and supports each other;
the uninterrupted rehabilitation training is carried out until the limb functions of the athlete are obviously improved and rehabilitated.
The above description is only a preferred embodiment of the present invention, but the scope of the present invention is not limited thereto. Any person skilled in the art, within the technical scope of the present disclosure, may apply to the present invention, and the technical solution and the improvement thereof are all covered by the protection scope of the present invention.

Claims (7)

1.AI辅助肢体康复系统，其特征在于，包括：1. AI-assisted limb rehabilitation system, characterized by comprising: 传感模块，用于获取用户的肢体运动数据；A sensor module, used to obtain the user's body movement data; AI分析模块，与所述传感模块通信连接，用于处理和分析所述肢体运动数据，基于深度学习算法生成用户的康复评估报告，并对用户的康复进程提供个性化的建议；An AI analysis module, which is in communication with the sensor module and is used to process and analyze the limb movement data, generate a user's rehabilitation assessment report based on a deep learning algorithm, and provide personalized suggestions for the user's rehabilitation progress; VR交互模块，用于为用户提供虚拟现实康复场景，通过虚拟环境中的任务和引导，增强用户的康复训练体验，使用户能够在沉浸式环境中完成特定康复动作；VR interaction module is used to provide users with virtual reality rehabilitation scenes, enhance users' rehabilitation training experience through tasks and guidance in the virtual environment, and enable users to complete specific rehabilitation movements in an immersive environment; 反馈模块，与所述VR交互模块和所述AI分析模块通信，用于根据 AI 分析结果对用户的肢体动作进行实时反馈，提示纠正动作，增强训练效果；A feedback module, communicating with the VR interaction module and the AI analysis module, is used to provide real-time feedback on the user's body movements according to the AI analysis results, prompt corrective movements, and enhance training effects; 数据云端存储模块，用于将用户的肢体运动数据和康复进程数据存储在云端，以便医生和康复专家远程监控和指导用户的康复过程，所述数据云端存储模块采用安全可靠的云存储技术，确保用户数据的隐私和安全，医生和康复专家可以通过远程访问云端数据，实时了解用户的康复进度和状态，为用户提供个性化的指导和建议；A data cloud storage module is used to store the user's limb movement data and rehabilitation process data in the cloud so that doctors and rehabilitation experts can remotely monitor and guide the user's rehabilitation process. The data cloud storage module adopts safe and reliable cloud storage technology to ensure the privacy and security of user data. Doctors and rehabilitation experts can remotely access cloud data to understand the user's rehabilitation progress and status in real time and provide personalized guidance and suggestions to users; 用户激励模块，与所述反馈模块通信连接，通过游戏化奖励机制和社交元素，激励用户积极参与康复训练，以提高康复依从性和长期训练效果；A user incentive module, which is in communication with the feedback module and motivates users to actively participate in rehabilitation training through a gamification reward mechanism and social elements, so as to improve rehabilitation compliance and long-term training effects; 收集和运动数据分析模块，系统能够实时调整训练难度，确保康复训练既具挑战性又能适应患者的能力水平，系统通过传感模块收集患者的运动数据，AI分析模块对这些数据进行分析，根据患者的康复进度和能力实时调整训练难度，当患者的能力提高时，系统会自动增加训练难度，以保持训练的挑战性，当患者出现疲劳或困难时，系统会降低训练难度，确保患者能够顺利完成训练。The system can adjust the training difficulty in real time through the collection and motion data analysis module to ensure that the rehabilitation training is both challenging and adaptable to the patient's ability level. The system collects the patient's motion data through the sensor module, and the AI analysis module analyzes this data to adjust the training difficulty in real time according to the patient's rehabilitation progress and ability. When the patient's ability improves, the system will automatically increase the training difficulty to maintain the challenging nature of the training. When the patient becomes tired or has difficulty, the system will reduce the training difficulty to ensure that the patient can complete the training smoothly. 2.根据权利要求1所述的AI辅助肢体康复系统，其特征在于：所述传感模块通信连接有穿戴式传感器、摄像头和生理信号采集装置，以捕捉用户的肢体姿势、运动轨迹以及生理参数，其中，所述穿戴式传感器采用高精度的惯性测量单元，能够准确检测用户的关节角度和运动加速度，所述摄像头具备高分辨率和高帧率，可实时捕捉用户的肢体动作，所述生理信号采集装置能够与多种第三方可穿戴设备兼容，扩展对用户生理信号的监测能力，包括但不限于心率、血氧浓度、肌电信号和脑电信号，从而为康复评估提供更全面的数据支持。2. The AI-assisted limb rehabilitation system according to claim 1 is characterized in that: the sensing module is communicatively connected with a wearable sensor, a camera and a physiological signal acquisition device to capture the user's limb posture, motion trajectory and physiological parameters, wherein the wearable sensor adopts a high-precision inertial measurement unit, which can accurately detect the user's joint angle and motion acceleration, the camera has high resolution and high frame rate, and can capture the user's limb movements in real time, and the physiological signal acquisition device can be compatible with a variety of third-party wearable devices to expand the monitoring capabilities of the user's physiological signals, including but not limited to heart rate, blood oxygen concentration, electromyographic signals and electroencephalographic signals, thereby providing more comprehensive data support for rehabilitation evaluation. 3.根据权利要求1所述的AI辅助肢体康复系统，其特征在于：所述AI 分析模块包括：3. The AI-assisted limb rehabilitation system according to claim 1, wherein the AI analysis module comprises: 运动轨迹预测单元，用于通过先进的神经网络模型预测用户的肢体运动轨迹，比较实际动作与目标动作的差异，所述运动轨迹预测单元利用长短期记忆网络（LSTM）和卷积神经网络（CNN）的组合，能够准确预测复杂的肢体运动轨迹，并提供实时的反馈，所述差异值具体计算公式为：差异值=∑(|实际动作参数-目标动作参数|)；The motion trajectory prediction unit is used to predict the user's limb motion trajectory through an advanced neural network model and compare the difference between the actual action and the target action. The motion trajectory prediction unit uses a combination of a long short-term memory network (LSTM) and a convolutional neural network (CNN) to accurately predict complex limb motion trajectories and provide real-time feedback. The specific calculation formula of the difference value is: difference value = ∑(|actual action parameter - target action parameter|); 康复评估单元，用于根据多维度的肢体数据和生理参数评估用户的康复进度，并结合历史数据给出动态调整的康复计划，所述康复评估单元采用多指标综合评估方法，包括关节活动度、肌肉力量、平衡能力和耐力等指标，通过数据分析和机器学习算法，为用户制定个性化的康复计划，所述多指标综合评估方法的计算公式为：康复进度得分=α×关节活动度得分+β×肌肉力量得分+γ×平衡能力得分+δ×耐力得分，其中α、β、γ、δ为权重系数，根据不同康复阶段和患者情况进行调整；A rehabilitation assessment unit is used to assess the user's rehabilitation progress based on multi-dimensional limb data and physiological parameters, and to provide a dynamically adjusted rehabilitation plan in combination with historical data. The rehabilitation assessment unit adopts a multi-index comprehensive assessment method, including indicators such as joint range of motion, muscle strength, balance ability and endurance, and formulates a personalized rehabilitation plan for the user through data analysis and machine learning algorithms. The calculation formula of the multi-index comprehensive assessment method is: rehabilitation progress score = α×joint range of motion score + β×muscle strength score + γ×balance ability score + δ×endurance score, where α, β, γ, and δ are weight coefficients, which are adjusted according to different rehabilitation stages and patient conditions; 情感识别单元，通过分析用户在康复过程中的面部表情和语音情绪，评估用户的心理状态，并动态调整康复计划以增加用户的舒适度和信心，所述情感识别单元利用计算机视觉和语音处理技术，实时监测用户的情绪变化，当用户出现焦虑、沮丧等负面情绪时，系统会自动调整康复计划，增加趣味性和互动性，提高用户的参与度。The emotion recognition unit analyzes the user's facial expressions and voice emotions during the rehabilitation process, evaluates the user's psychological state, and dynamically adjusts the rehabilitation plan to increase the user's comfort and confidence. The emotion recognition unit uses computer vision and voice processing technology to monitor the user's emotional changes in real time. When the user has negative emotions such as anxiety and depression, the system will automatically adjust the rehabilitation plan to increase fun and interactivity and improve user participation. 4.根据权利要求1所述的AI辅助肢体康复系统，其特征在于：所述VR 交互模块包括：4. The AI-assisted limb rehabilitation system according to claim 1, wherein the VR interaction module comprises: 任务生成单元，用于根据用户的康复需求和能力生成适应性的虚拟训练任务，以提高康复的趣味性和参与度，所述任务生成单元根据用户的康复阶段和目标，自动生成不同难度的虚拟训练任务，例如平衡训练、力量训练和协调性训练等，任务的难度可以根据用户的表现自动调整，确保用户在挑战中不断进步，所述任务的难度计算公式为：任务难度系数 =f (用户表现参数)，其中 f 为根据经验确定的函数关系；A task generation unit is used to generate adaptive virtual training tasks according to the rehabilitation needs and abilities of the user to improve the interest and participation of rehabilitation. The task generation unit automatically generates virtual training tasks of different difficulty levels according to the rehabilitation stage and goals of the user, such as balance training, strength training and coordination training. The difficulty of the task can be automatically adjusted according to the performance of the user to ensure that the user continues to make progress in the challenge. The difficulty calculation formula of the task is: task difficulty coefficient = f (user performance parameter), where f is a functional relationship determined based on experience; 环境自适应单元，用于根据用户的实时反馈和生理状态自动调整虚拟现实中的场景难度和任务复杂度，确保训练过程安全并具有挑战性，所述环境自适应单元通过实时监测用户的生理参数和运动表现，自动调整虚拟环境的难度和复杂度，例如当用户的心率过高或出现疲劳迹象时，系统会自动降低任务难度，确保训练过程的安全，所述场景难度和任务复杂度的计算公式为：场景难度调整值 = g (生理参数变化值、运动表现变化值)，其中 g 为根据经验确定的函数关系。The environment adaptation unit is used to automatically adjust the scene difficulty and task complexity in virtual reality according to the user's real-time feedback and physiological state to ensure that the training process is safe and challenging. The environment adaptation unit automatically adjusts the difficulty and complexity of the virtual environment by real-time monitoring of the user's physiological parameters and athletic performance. For example, when the user's heart rate is too high or there are signs of fatigue, the system will automatically reduce the task difficulty to ensure the safety of the training process. The calculation formula for the scene difficulty and task complexity is: scene difficulty adjustment value = g (physiological parameter change value, athletic performance change value), where g is a functional relationship determined based on experience. 5.根据权利要求1所述的AI辅助肢体康复系统，其特征在于：所述反馈模块包括：5. The AI-assisted limb rehabilitation system according to claim 1, wherein the feedback module comprises: 实时语音反馈单元，用于在用户完成动作过程中提供语音提示，帮助用户纠正错误动作，所述实时语音反馈单元采用自然语言处理技术，能够根据用户的动作和表现提供个性化的语音提示，例如 “手臂抬高一点”、“脚步迈大一点” 等；A real-time voice feedback unit is used to provide voice prompts when the user completes an action to help the user correct wrong actions. The real-time voice feedback unit uses natural language processing technology to provide personalized voice prompts based on the user's actions and performance, such as "raise your arms a little higher" or "take bigger steps", etc. 视觉反馈单元，用于在虚拟现实环境中通过动画或标记显示用户动作的正确性，以帮助用户理解如何改善动作质量，所述视觉反馈单元利用虚拟现实技术，在虚拟环境中显示用户的动作轨迹和正确性，例如通过颜色标记显示用户的关节角度是否正确，通过动画演示正确的动作方式等。The visual feedback unit is used to display the correctness of the user's actions through animation or marking in a virtual reality environment to help the user understand how to improve the quality of the action. The visual feedback unit uses virtual reality technology to display the user's action trajectory and correctness in a virtual environment. For example, it uses color markings to show whether the user's joint angles are correct, and demonstrates the correct action method through animation. 6.根据权利要求1所述的AI辅助肢体康复系统，其特征在于：所述用户激励模块包括：6. The AI-assisted limb rehabilitation system according to claim 1, characterized in that: the user incentive module comprises: 同步游戏单元，引入了游戏化元素，通过设定目标、奖励机制和趣味性的康复任务，提高患者的参与度和积极性，例如，设置不同等级的康复任务，患者完成一个等级的任务后可以获得相应的奖励，如虚拟道具、积分等，同时，系统可以根据患者的喜好设置不同主题的康复场景，增加康复训练的趣味性；The synchronous game unit introduces gamification elements to improve the patient's participation and enthusiasm by setting goals, reward mechanisms and interesting rehabilitation tasks. For example, different levels of rehabilitation tasks are set, and patients can get corresponding rewards after completing a level of tasks, such as virtual props, points, etc. At the same time, the system can set rehabilitation scenes with different themes according to the patient's preferences to increase the fun of rehabilitation training; 成就系统单元，用户在完成特定的康复任务或达到一定的康复目标时，会获得相应的成就和奖励，例如勋章、奖杯等，成就系统单元可以激发用户的竞争意识和成就感，提高用户的参与度；Achievement system unit: when users complete specific rehabilitation tasks or reach certain rehabilitation goals, they will receive corresponding achievements and rewards, such as medals and trophies. The achievement system unit can stimulate users’ sense of competition and achievement, and improve their participation. 社交互动平台单元，用户可以与其他康复患者进行交流和分享，互相鼓励和支持，社交互动平台单元可以增加用户的社交联系，提高用户的康复依从性。The social interaction platform unit allows users to communicate and share with other rehabilitation patients, encourage and support each other. The social interaction platform unit can increase users' social connections and improve their rehabilitation compliance. 7.根据权利要求1所述的AI辅助肢体康复系统，其特征在于：所述反馈模块上还通信连接有安全保护模块，用于监测用户在康复训练过程中的安全状况，当出现异常情况时及时发出警报并停止训练，所述安全保护模块包括：7. The AI-assisted limb rehabilitation system according to claim 1 is characterized in that: the feedback module is also communicatively connected to a safety protection module for monitoring the safety status of the user during rehabilitation training, and promptly issuing an alarm and stopping training when an abnormal situation occurs, and the safety protection module includes: 跌倒检测单元，通过传感器和算法检测用户是否发生跌倒，当检测到跌倒时立即发出警报并通知相关人员，所述跌倒检测单元采用加速度传感器和陀螺仪等传感器，结合机器学习算法，能够准确检测用户的跌倒情况，并及时发出警报，所述跌倒检测计算公式为：跌倒概率 =l(传感器加速度变化值、陀螺仪变化值)，其中l为根据经验确定的函数关系；The fall detection unit detects whether the user falls through sensors and algorithms. When a fall is detected, an alarm is immediately issued and relevant personnel are notified. The fall detection unit uses sensors such as acceleration sensors and gyroscopes, combined with machine learning algorithms, to accurately detect the user's fall and issue an alarm in time. The fall detection calculation formula is: fall probability = l (sensor acceleration change value, gyroscope change value), where l is a functional relationship determined based on experience; 运动极限监测单元，用于监测用户的运动极限，当用户的运动超过安全范围时及时发出警报并调整训练计划，所述运动极限监测单元通过传感器和算法监测用户的关节角度、运动速度和力量等参数，当用户的运动超过安全范围时，系统会自动发出警报并调整训练计划，确保用户的安全，所述运动极限监测单元计算公式为：超出安全范围概率 = m (关节角度变化值、运动速度变化值、力量变化值)，其中 m 为根据经验确定的函数关系。The motion limit monitoring unit is used to monitor the user's motion limit. When the user's motion exceeds the safe range, an alarm is issued in time and the training plan is adjusted. The motion limit monitoring unit monitors the user's joint angle, motion speed, strength and other parameters through sensors and algorithms. When the user's motion exceeds the safe range, the system will automatically issue an alarm and adjust the training plan to ensure the user's safety. The calculation formula of the motion limit monitoring unit is: probability of exceeding the safety range = m (joint angle change value, motion speed change value, strength change value), where m is a functional relationship determined based on experience.
CN202411571548.1A 2024-11-06 2024-11-06 AI-assisted limb rehabilitation system Withdrawn CN119580933A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202411571548.1A CN119580933A (en) 2024-11-06 2024-11-06 AI-assisted limb rehabilitation system

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202411571548.1A CN119580933A (en) 2024-11-06 2024-11-06 AI-assisted limb rehabilitation system

Publications (1)

Publication Number Publication Date
CN119580933A true CN119580933A (en) 2025-03-07

Family

ID=94808025

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202411571548.1A Withdrawn CN119580933A (en) 2024-11-06 2024-11-06 AI-assisted limb rehabilitation system

Country Status (1)

Country Link
CN (1) CN119580933A (en)

Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN119889581A (en) * 2025-03-26 2025-04-25 陕西省第二人民医院(陕西省老年病医院) Health information management method for rehabilitation training
CN120260821A (en) * 2025-04-28 2025-07-04 致和健康科技(郑州)有限公司 A scenario-interactive active and passive limb rehabilitation training system based on virtual three-dimensional games
CN120304829A (en) * 2025-06-12 2025-07-15 合肥华祯智能科技有限公司 Psychological state assessment method, system, and readable storage medium based on environmental perception
CN120564988A (en) * 2025-04-15 2025-08-29 中日友好医院(中日友好临床医学研究所) A multi-dimensional management system and method for SHPT patients after surgery based on EMA
CN120746402A (en) * 2025-08-29 2025-10-03 中国人民解放军空军军医大学 VR-based virtual training method and system for CT room operation

Cited By (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN119889581A (en) * 2025-03-26 2025-04-25 陕西省第二人民医院(陕西省老年病医院) Health information management method for rehabilitation training
CN119889581B (en) * 2025-03-26 2025-06-20 陕西省第二人民医院(陕西省老年病医院) Health information management method for rehabilitation training
CN120564988A (en) * 2025-04-15 2025-08-29 中日友好医院(中日友好临床医学研究所) A multi-dimensional management system and method for SHPT patients after surgery based on EMA
CN120260821A (en) * 2025-04-28 2025-07-04 致和健康科技(郑州)有限公司 A scenario-interactive active and passive limb rehabilitation training system based on virtual three-dimensional games
CN120304829A (en) * 2025-06-12 2025-07-15 合肥华祯智能科技有限公司 Psychological state assessment method, system, and readable storage medium based on environmental perception
CN120746402A (en) * 2025-08-29 2025-10-03 中国人民解放军空军军医大学 VR-based virtual training method and system for CT room operation

Similar Documents

Publication Publication Date Title
JP7200851B2 (en) LEARNING DEVICE, REHABILITATION SUPPORT SYSTEM, METHOD, PROGRAM, AND LEARNED MODEL
CN119580933A (en) AI-assisted limb rehabilitation system
Schönauer et al. Chronic pain rehabilitation with a serious game using multimodal input
EP3758012B1 (en) Processing system, walking training system, processing method, and program
US11527330B2 (en) Learning system, rehabilitation support system, method, program, and trained model
JPH10151223A (en) Wellness system
CN112137846A (en) Learning system, walking training system, method, program, and learning completion model
US20170000388A1 (en) System and method for mapping moving body parts
WO2021067250A1 (en) Systems and methods for reaction measurement
CN112137838A (en) Learning device, walking training system, method, program, and learning completion model
US20240188848A1 (en) Learning system, walking training system, method, program, and trained model
KR102346680B1 (en) Training and exercise system based on virtual reality or augmented reality for dizziness and disequilibrium
US11185736B2 (en) Systems and methods for wearable devices that determine balance indices
CN118266040A (en) Program, information processing apparatus, and information processing method
JP7326927B2 (en) LEARNING DEVICE, REHABILITATION SUPPORT SYSTEM, METHOD, PROGRAM, AND LEARNED MODEL
JP2025516336A (en) Systems and methods for testing and training brain function - Patents.com
US11210966B2 (en) Rehabilitation support system, rehabilitation support method, and rehabilitation support program
JP2021003521A (en) Learning device, rehabilitation support system, method, program, and learned model
CN118807180B (en) Self-adaptive motion regulation and control system of body-building equipment
JP7140060B2 (en) LEARNING DEVICE, REHABILITATION SUPPORT SYSTEM, METHOD, PROGRAM, AND LEARNED MODEL
CN116098613A (en) A rehabilitation system for cervical spine measurement and remote diagnosis
CN120510995A (en) Rehabilitation skill action standardization method and system combining multimode physiological signals and human body posture analysis
CN118824464A (en) A system and method for vestibular function rehabilitation training and evaluation using VR equipment
CN116439951A (en) Rehabilitation training system and method based on active participation intention of user

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
WW01 Invention patent application withdrawn after publication
WW01 Invention patent application withdrawn after publication

Application publication date: 20250307