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Research Progress and Prospect of Biofeedback Technology Combined with Occupational Therapy in Hand Function Rehabilitation after Stroke
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作者 Guokai Gu Guodi Wen +4 位作者 Yuhao Li Ya Kong Kun Chen Debin Li Xiao Chen 《Journal of Clinical and Nursing Research》 2025年第12期146-151,共6页
Hand function impairment after stroke has become a key and difficult issue in clinical rehabilitation due to complex neural innervation and a long recovery cycle.Biofeedback technology combined with occupational thera... Hand function impairment after stroke has become a key and difficult issue in clinical rehabilitation due to complex neural innervation and a long recovery cycle.Biofeedback technology combined with occupational therapy can make up for the limitations of single therapy and provide a new solution for hand function rehabilitation after stroke.This paper systematically sorts out the theoretical basis and clinical research progress of biofeedback technology combined with occupational therapy in hand function rehabilitation after stroke,and looks forward to the future development direction,aiming to provide reference for clinical rehabilitation practice and scientific research. 展开更多
关键词 STROKE hand function rehabilitation Biofeedback technology Occupational therapy NEUROPLASTICITY
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Research on Key Technologies of Hand Function Rehabilitation Training Evaluation System Based on Leap Motion 被引量:1
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作者 Zhiguo Xiao Yifei Zhao +2 位作者 Nianfeng Li Shang Zhou Hu Xu 《Journal of Computer and Communications》 2021年第1期19-35,共17页
This paper proposes an immersive training system for patients with hand dysfunction who can perform rehabilitation training independently. The system uses Leap Motion binocular vision sensors to collect human hand inf... This paper proposes an immersive training system for patients with hand dysfunction who can perform rehabilitation training independently. The system uses Leap Motion binocular vision sensors to collect human hand information, and uses the improved PCA<sub><img src="Edit_d6662636-9073-4fbd-855f-9a36e871d5a4.png" width="10" height="15" alt="" /></sub> (Principal Component Analysis) to perform data fusion on the real-time data collected by the sensor to obtain more hands with fewer principal components, and improve the stability and accuracy of the data. Immediately, the use of improved SVM<sub><img src="Edit_10c78725-e09e-4dcf-ae05-e21205df4acc.png" width="10" height="15" alt="" /></sub> (Support Vector Machine) and KNN<sub><img src="Edit_0ee97f55-2773-4b48-93b3-93f61aa25577.png" width="10" height="15" alt="" /></sub> (K-Nearest Neighbor Algorithm) for gesture recognition and classification is proposed to enable patients to perform rehabilitation training more effectively. Finally, the effective evaluation results of the rehabilitation effect of patients by the idea of AHP<sub><img src="Edit_70dd1964-28be-4137-afa5-9a184704f08e.png" width="10" height="15" alt="" /></sub> (Analytic Hierarchy Process) are taken as necessary reference factors for doctors to follow up treatment. Various experimental results show that the system has achieved the expected results and has a good application prospect. 展开更多
关键词 Leap Motion IMMERSIVE AHP PCA SVM KNN hand function rehabilitation Evaluation System
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