Sensors, Vol. 22, Pages 3368: Quantitative Evaluation System of Wrist Motor Function for Stroke Patients Based on Force Feedback

Sensors, Vol. 22, Pages 3368: Quantitative Evaluation System of Wrist Motor Function for Stroke Patients Based on Force Feedback Sensors doi: 10.3390/s22093368 Authors: Kangjia Ding Bochao Zhang Zongquan Ling Jing Chen Liquan Guo Daxi Xiong Jiping Wang Motor function evaluation is a significant part of post-stroke rehabilitation protocols, and the evaluation of wrist motor function helps provide patients with individualized rehabilitation training programs. However, traditional assessment is coarsely graded, lacks quantitative analysis, and relies heavily on clinical experience. In order to objectively quantify wrist motor dysfunction in stroke patients, a novel quantitative evaluation system based on force feedback and machine learning algorithm was proposed. Sensors embedded in the force-feedback robot record the kinematic and movement data of the subject, and the rehabilitation doctor used an evaluation scale to score the wrist function of the subject. The quantitative evaluation models of wrist motion function based on random forest (RF), support vector machine regression (SVR), k-nearest neighbor (KNN), and back propagation neural network (BPNN) were established, respectively. To verify the effectiveness of the proposed quantitative evaluation system, 25 stroke patients and 10 healthy volunteers were recruited in this study. Experimental results show that the evaluation accuracy of the four models is all above 88%. The accuracy of BPNN model is 94.26%...
Source: Sensors - Category: Biotechnology Authors: Tags: Article Source Type: research