Cerebral stroke patient upper limb rehabilitation evaluation system

Abstract

The invention relates to the technical field of cerebral apoplexy rehabilitation, in particular to a cerebral apoplexy patient upper limb rehabilitation evaluation system which comprises a motion capture module, an analysis module, a processing module, a training module, a feedback module, an optimization module and a sharing module. The analysis module performs analysis according to the data acquired by the motion capture module; the processing module formulates a training plan according to the analyzed illness state; the training module is used for training the patient according to the evaluation result; the feedback module is used for feeding back the training effect; according to the system, the training result of the patient can be comprehensively evaluated in real time, the training plan is adjusted in time according to the rehabilitation progress of the patient, the rehabilitation plan and the training mode are continuously optimized, the optimization module can gradually adapt to the individual difference and rehabilitation requirements of the patient, more accurate rehabilitation suggestions are provided, and the rehabilitation efficiency is improved. Therefore, the rehabilitation effect is obviously improved.

Description

Technical Field

[0001] The present invention relates to the technical field of stroke rehabilitation, and in particular to an upper limb rehabilitation assessment system for stroke patients. Background Art

[0002] Among stroke patients, there are hemiplegic patients with partial paralysis of left and right limbs, and systems to help these patients with rehabilitation training are becoming increasingly popular. Existing rehabilitation training for upper limb paralysis includes at least assisted movement rehabilitation training using upper limb rehabilitation robots, exoskeletons, etc. Rehabilitation robots can provide high-intensity, repetitive, task-specific, and interactive treatments for paralyzed upper limbs, while providing objective evaluation methods for motor function recovery and measuring kinematic and kinematic changes. However, upper limb rehabilitation robots cannot improve upper limb muscle strength. Motor function assessment is the basis for doctors to develop rehabilitation training plans for hemiplegic stroke patients. It can also be used as a means of evaluating the efficacy of various treatment options, and is crucial in the fields of sports rehabilitation and neuroscience.

[0003] Existing upper limb rehabilitation assessment system for stroke patients:

[0004] 1. Evaluation and optimization are inconvenient. Existing systems often have single functions and lack comprehensive and real-time evaluation of patients' training results. Training plans cannot be adjusted in time according to the patient's rehabilitation progress, thus affecting the rehabilitation effect.

[0005] Second, data sharing is difficult. These systems usually do not have data sharing functions, which makes it impossible for doctors, patients and other relevant personnel to obtain the latest rehabilitation assessment data in a timely manner and form effective rehabilitation team communication and collaboration, which in turn affects the overall efficiency and quality of rehabilitation treatment. Therefore, an upper limb rehabilitation assessment system for stroke patients is proposed. Summary of the invention

[0006] In view of this, the present invention provides an upper limb rehabilitation assessment system for stroke patients to solve or alleviate the technical problems existing in the prior art and at least provide a beneficial option.

[0007] The technical solution of the present invention is implemented as follows: a stroke patient upper limb rehabilitation assessment system, comprising a motion capture module, an analysis module, a processing module, a training module, a feedback module, an optimization module and a sharing module, wherein the motion capture module captures the patient's upper limb motion through a motion capture device;

[0008] The analysis module performs analysis based on the data collected by the motion capture module;

[0009] The processing module formulates a training plan according to the analyzed condition;

[0010] The training module trains the patient according to the evaluation results;

[0011] The feedback module provides feedback on the training effect;

[0012] The optimization module optimizes the evaluation results and the training plan according to the training effect;

[0013] The sharing module shares the rehabilitation assessment and training results.

[0014] Further preferably, the motion capture module captures the patient's upper limb motions through a motion capture device and collects the patient's upper limb motion data.

[0015] Further preferably, the analysis module performs analysis based on the data collected by the motion capture module, analyzes the condition based on the evaluation results, and generates an evaluation report.

[0016] Further preferably, the processing module is responsible for processing the collected upper limb movement data, processing the position, speed, acceleration and other data of the limb movement through algorithm analysis, and then formulating a training plan based on the analyzed condition.

[0017] Further preferably, in the training module, the system provides a variety of training modes for selection based on the evaluation results to train the patient. The system monitors the patient's exercise performance in real time and provides necessary feedback and adjustment suggestions.

[0018] Further preferably, the feedback module can also provide real-time visual and auditory feedback information, enhance the patient's enthusiasm and confidence in rehabilitation training, promote the rehabilitation process, and provide feedback on the training effect, the patient's upper limb motor ability, progress and existing problems, provide intuitive feedback to patients and doctors, improve the rehabilitation training effect and optimize the rehabilitation plan.

[0019] Further preferably, the optimization module optimizes the evaluation results and training plans according to the training effects, and accurately evaluates the patient's rehabilitation progress by real-time monitoring of the patient's movement data combined with algorithm analysis. Once it is found that the training effect is poor or there is a deviation, the system will immediately provide feedback and automatically or assist the rehabilitation therapist to adjust the training plan.

[0020] Further preferably, the sharing module shares the rehabilitation assessment and training results and shares the patient's upper limb movement data, assessment report and other key information with the rehabilitation physician and the patient in real time, ensuring that both parties can obtain the latest rehabilitation progress in a timely manner.

[0021] Further preferably, the processing module adjusts the rehabilitation plan in real time according to the patient's rehabilitation progress.

[0022] Further preferably, the feedback module transmits the patient's training data and feedback information to the doctor in real time.

[0023] The embodiment of the present invention has the following advantages due to the adoption of the above technical solution:

[0024] 1. The system of the present invention can comprehensively and real-time evaluate the patient's training results, timely adjust the training plan according to the patient's rehabilitation progress, continuously optimize the rehabilitation plan and training mode, and the optimization module can gradually adapt to the individual differences and rehabilitation needs of the patient, provide more accurate rehabilitation suggestions, thereby significantly improving the rehabilitation effect.

[0025] 2. The present invention has a powerful data sharing function, which enables doctors, patients and other relevant personnel to obtain the latest rehabilitation assessment data in a timely manner, form effective rehabilitation team communication and collaboration, and thus improve the overall efficiency and quality of rehabilitation treatment. Patients can also check their rehabilitation progress and training data at any time, participate more actively in rehabilitation training, and greatly promote communication and collaboration between rehabilitation teams.

[0026] The above summary is for illustrative purposes only and is not intended to be limiting in any way. In addition to the illustrative aspects, embodiments and features described above, further aspects, embodiments and features of the present invention will be readily apparent by reference to the accompanying drawings and the following detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0028] Figure 1 It is a system flow chart of the present invention. DETAILED DESCRIPTION

[0029] In the following, only some exemplary embodiments are briefly described. As those skilled in the art will appreciate, the described embodiments may be modified in various ways without departing from the spirit or scope of the present invention. Therefore, the drawings and descriptions are considered to be exemplary and non-restrictive in nature.

[0030] The embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0031] like Figure 1As shown, an embodiment of the present invention provides an upper limb rehabilitation assessment system for stroke patients, including a motion capture module, an analysis module, a processing module, a training module, a feedback module, an optimization module and a sharing module. The motion capture module captures the patient's upper limb motion through a motion capture device;

[0032] An analysis module performs analysis based on the data collected by the motion capture module;

[0033] The treatment module formulates a training plan based on the analyzed condition;

[0034] Training module, which trains patients based on the assessment results;

[0035] Feedback module, which provides feedback on training effects;

[0036] Optimization module, which optimizes evaluation results and training plans based on training effects;

[0037] Sharing module, for sharing rehabilitation assessment and training results.

[0038] In one embodiment, the motion capture module captures the patient's upper limb movements through a motion capture device and collects the patient's upper limb movement data; the main function of the motion capture module is to capture the patient's upper limb movement data in real time and accurately. The motion capture module is supported by advanced sensor technology and image processing algorithms. The motion capture module uses high-precision and high-sensitivity sensors, which can accurately record the movement trajectory, speed, strength and other parameters of the patient's arms, wrists, fingers and other parts. The sensor is tightly attached to the patient's upper limbs, which will not cause any discomfort to the patient or restrict his movement. At the same time, the sensor also has a wireless transmission function, which can transmit the captured data to the system in real time for analysis and processing. In terms of image processing algorithms, the motion capture module uses advanced computer vision technology, which can recognize the patient's movement pattern in real time and convert it into a digital signal, so that the doctor can understand the patient's upper limb movement more intuitively, and provide detailed data support for subsequent rehabilitation evaluation. The motion capture module also focuses on the patient's comfort and convenience to ensure that the patient can easily and happily complete the capture of the upper limb movement data. The module uses sensor technology and image processing algorithms to capture the patient's upper limb movement data. Its basic formula can be expressed as:

[0039] [D_{mot ion}=f(S_{sensor}, I_{image})];

[0040] Among them, (D_{mot i on}) represents the captured motion data, (S_{sensor}) represents the sensor signal, (I_{image}) represents the image processing result, and (f) represents the capture function, which is used to convert the sensor signal and image processing result into motion data.

[0041] In one embodiment, the analysis module analyzes the data collected by the motion capture module, analyzes the condition based on the evaluation results, and generates an evaluation report; the analysis module conducts in-depth analysis and processing of the data transmitted by the motion capture module. The analysis module uses algorithms and models to identify the patient's movement disorders, insufficient muscle strength and other problems, and evaluates the patient's rehabilitation progress. During the analysis process, the analysis module first pre-processes the incoming data, including data cleaning, denoising, normalization and other operations to ensure the accuracy and consistency of the data. The analysis module uses machine learning algorithms and statistical models to conduct in-depth analysis and mining of the data. By comparing the normal upper limb movement data, the analysis module accurately identifies the patient's movement disorders and insufficient muscle strength and other problems, and generates a detailed evaluation report. In addition, the analysis module can also update the evaluation report in real time according to the patient's rehabilitation progress, providing doctors with an intuitive display of rehabilitation effects. Doctors can understand the patient's rehabilitation status by viewing the evaluation report and formulate a more scientific rehabilitation plan. At the same time, the analysis module can also compare the evaluation results with the patient's historical data to evaluate the patient's rehabilitation speed and effect. The analysis module conducts in-depth analysis of the captured motion data, identifies movement disorders and evaluates rehabilitation progress. Its basic formula can be expressed as:

[0042] [R_{ana lys is}=g(D_{mot i on}, M_{norma l})];

[0043] Among them, (R_{ana l ys is}) represents the analysis results, including information such as movement disorders and rehabilitation progress, (M_{norma l}) represents normal upper limb movement data, and (g) represents the analysis function, which is used to compare movement data and normal data to obtain the analysis results.

[0044] In one embodiment, the processing module is responsible for processing the collected upper limb motion data, and through algorithm analysis, processes the position, speed, acceleration and other data of the limb motion, and then formulates a training plan according to the analyzed condition; the processing module is the decision-making part of the upper limb rehabilitation evaluation system, and its main function is to generate a personalized rehabilitation plan according to the results of the analysis module. The implementation of this module needs to fully consider the specific conditions of the patient and the doctor's advice to ensure the scientificity and effectiveness of the rehabilitation plan. During the processing, the processing module will first identify the patient's specific problems according to the results of the analysis module, such as movement disorders, insufficient muscle strength, etc. Then, the module will combine the patient's age, gender, physical condition and other factors, as well as the doctor's advice and treatment plan, to generate a personalized rehabilitation plan. The rehabilitation plan may include detailed information such as specific training movements, training intensity, and training time to ensure that the patient can perform scientific rehabilitation training according to the plan. The processing module generates a personalized rehabilitation plan based on the analysis results, and its basic formula can be expressed as follows:

[0045] [P_{rehab}=h(R_{ana l ys is}, A_{doctor})];

[0046] Among them, (P_{rehab}) represents the rehabilitation plan, (A_{doctor}) represents the doctor's advice, and (h) represents the processing function, which is used to generate a rehabilitation plan by combining the analysis results and the doctor's advice.

[0047] In one embodiment, the training module provides a variety of training modes for selection according to the evaluation results, and trains the patient. The system monitors the patient's sports performance in real time and provides necessary feedback and adjustment suggestions; the main function of the training module is to guide the patient to perform specific rehabilitation training according to the rehabilitation plan generated by the processing module. During the training process, the training module will provide a variety of training modes, such as active training, passive training, and power training, etc., to meet the needs of different patients. At the same time, the training module will also formulate detailed training plans and schedules according to the patient's specific conditions and rehabilitation plans to ensure that the patient can perform orderly training according to the plan. During the training process, the training module will also monitor the patient's training data and feedback information in real time. Through sensors and image processing technology, the training module can accurately record the patient's training movements, strength, speed and other parameters, and provide real-time feedback to doctors and patients, which helps doctors and patients to understand the training effects and problems in a timely manner and adjust the training plans and programs accordingly. In addition, the training module also focuses on the comfort and safety of the patient to ensure that the patient can perform rehabilitation training in a safe and comfortable environment. The training module guides the patient to perform specific rehabilitation training, and its basic formula can be expressed as:

[0048] [T_{tra in}=i(P_{rehab}, S_{pat ient})];

[0049] Among them, (T_{train}) represents training guidance, (S_{pat i ent}) represents the specific condition of the patient, and (i) represents the training function, which is used to generate training guidance based on the rehabilitation plan and the patient's condition.

[0050] In one embodiment, the feedback module can also provide real-time visual and auditory feedback information to enhance the enthusiasm and confidence of patients in rehabilitation training, promote the rehabilitation process, and provide feedback on the training effect, the motor ability of the patient's upper limbs, progress and existing problems, provide intuitive feedback to patients and physicians, improve the rehabilitation training effect, and optimize the rehabilitation plan; the feedback module provides real-time training feedback and guidance to patients. The implementation of the feedback module needs to fully consider the needs and understanding ability of patients to ensure the accuracy and effectiveness of the feedback. During the feedback process, the feedback module will monitor the patient's training data and movement patterns in real time, and compare them with the standard training data. Through comparative analysis, the module can accurately evaluate the patient's training effect and existing problems, and provide feedback and guidance to the patient in real time. The feedback module can provide diversified feedback forms, such as voice prompts, image displays, text descriptions, etc., to meet the needs of different patients. The feedback module will also adjust the form and content of the feedback according to the patient's feedback and understanding ability to ensure that the patient can accurately understand and actively respond to the feedback. The feedback module provides real-time feedback to the patient during the training process, and its basic formula can be expressed as:

[0051] [F_{feedback}=j(T_{trai n}, D_{rea lt ime})];

[0052] Among them, (F_{feedback}) represents feedback information, (D_{real ime}) represents real-time training data, and (j) represents a feedback function, which is used to generate feedback information based on training guidance and real-time data.

[0053] In one embodiment, the optimization module optimizes the evaluation results and training plans according to the training effects, and accurately evaluates the patient's rehabilitation progress by real-time monitoring of the patient's motion data and combining algorithm analysis. Once the training effect is poor or there is a deviation, the system will immediately feedback and automatically or assist the rehabilitation therapist to adjust the training plan; the optimization module continuously optimizes the rehabilitation plan and training mode according to the patient's training effect and feedback data. The optimization module can gradually adapt to the individual differences and rehabilitation needs of the patient, provide more accurate rehabilitation suggestions, and make the rehabilitation process more intelligent and personalized. The optimization module continuously optimizes the rehabilitation plan and training mode according to the training effect and feedback data. Its basic formula can be expressed as: [

[0055] O_{opt imi ze}=k(F_{feedback}, E_{effect}) ];

[0057] Among them, (O_{opt imi ze}) represents the optimization result, (E_{effect}) represents the training effect, and (k) represents the optimization function, which is used to optimize the rehabilitation plan and training mode according to the feedback information and training effect.

[0058] In one embodiment, the sharing module shares the rehabilitation assessment and training results, and shares the patient's upper limb movement data, assessment report and other key information with the rehabilitation physician and the patient in real time, ensuring that both parties can obtain the latest rehabilitation progress in a timely manner; the sharing module realizes the sharing and transmission of the internal data of the upper limb rehabilitation assessment system, and shares the patient's rehabilitation assessment results, training plan, training data and other key information with doctors, patients and other relevant personnel in real time. Through the sharing module, doctors can have a more comprehensive understanding of the patient's rehabilitation status and formulate a more scientific rehabilitation plan; patients can also check their own rehabilitation progress and training data at any time, and participate in rehabilitation training more actively, which greatly promotes communication and collaboration between rehabilitation teams. The sharing module realizes the sharing and transmission of the internal data of the rehabilitation assessment system, and its basic formula can be expressed as: [

[0060] S_{share}=l(D_{all}, U_{user}) ];

[0062] Among them, (S_{share}) represents shared data, (D_{all}) represents all relevant data, (U_{user}) represents users (including doctors, patients, etc.), and (l) represents the sharing function, which is used to share data with corresponding users according to user needs and permissions.

[0063] In one embodiment, the processing module adjusts the rehabilitation plan in real time according to the patient's rehabilitation progress; by real-time monitoring of the patient's training data and feedback information, the processing module can promptly identify the patient's problems and progress, and adjust the rehabilitation plan accordingly to ensure the continuity and effectiveness of rehabilitation training.

[0064] In one embodiment, the feedback module transmits the patient's training data and feedback information to the doctor in real time; the doctor can understand the patient's training status and existing problems by viewing the feedback information, and adjust the rehabilitation plan and training program accordingly, which helps to establish a closer communication and collaboration relationship between the doctor and the patient, and jointly promote the patient's rehabilitation process.

[0065] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of various changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A stroke patient upper limb rehabilitation assessment system, characterized by: It includes a motion capture module, an analysis module, a processing module, a training module, a feedback module, an optimization module and a sharing module. The motion capture module captures the patient's upper limb movements through a motion capture device. The analysis module performs analysis based on the data collected by the motion capture module; The processing module formulates a training plan according to the analyzed condition; The training module trains the patient according to the evaluation results; The feedback module provides feedback on the training effect; The optimization module optimizes the evaluation results and training plans according to the training effect; The sharing module shares the rehabilitation assessment and training results.

2. The upper limb rehabilitation assessment system for stroke patients according to claim 1, characterized in that: The motion capture module captures the patient's upper limb motion through a motion capture device and collects the patient's upper limb motion data.

3. The upper limb rehabilitation assessment system for stroke patients according to claim 1, characterized in that: The analysis module analyzes the data collected by the motion capture module, analyzes the condition based on the evaluation results, and generates an evaluation report.

4. The upper limb rehabilitation assessment system for stroke patients according to claim 1, characterized in that: The processing module is responsible for processing the collected upper limb movement data, and through algorithm analysis, processes the position, speed, acceleration and other data of the limb movement, and then formulates a training plan based on the analyzed condition.

5. The upper limb rehabilitation assessment system for stroke patients according to claim 1, characterized in that: In the training module, based on the evaluation results, the system provides a variety of training modes to choose from to train the patient. The system monitors the patient's exercise performance in real time and provides necessary feedback and adjustment suggestions.

6. The upper limb rehabilitation assessment system for stroke patients according to claim 1, characterized in that: The feedback module can also provide real-time visual and auditory feedback information to enhance the patient's enthusiasm and confidence in rehabilitation training, promote the rehabilitation process, and provide feedback on the training effect, the patient's upper limb motor ability, progress and existing problems, providing intuitive feedback to patients and doctors, improving the rehabilitation training effect and optimizing the rehabilitation plan.

7. The upper limb rehabilitation assessment system for stroke patients according to claim 1, characterized in that: The optimization module optimizes the evaluation results and training plans according to the training effects, and accurately evaluates the patient's rehabilitation progress by real-time monitoring of the patient's movement data combined with algorithm analysis. Once the training effect is found to be poor or there is a deviation, the system will immediately provide feedback and automatically or assist the rehabilitation therapist to adjust the training plan.

8. The upper limb rehabilitation assessment system for stroke patients according to claim 1, characterized in that: The sharing module shares the rehabilitation assessment and training results, and shares the patient's upper limb movement data, assessment report and other key information with the rehabilitation physician and the patient in real time, ensuring that both parties can obtain the latest rehabilitation progress in a timely manner.

9. The upper limb rehabilitation assessment system for stroke patients according to claim 4, characterized in that: The processing module adjusts the rehabilitation plan in real time according to the patient's rehabilitation progress.

10. The upper limb rehabilitation assessment system for stroke patients according to claim 6, characterized in that: The feedback module transmits the patient's training data and feedback information to the doctor in real time.

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