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In order to monitor the rehabilitation training of stroke patients in unsupervised situation and provide rehabilitation advice for rehabilitation clinicians, a wireless upper limb motion recognition system has been developed using 9-axis sensors, to identify the complex upper limb movements from stroke patients’ rehabilitation program, such as Bobath handshake, paraplegia hand touch shoulder, elbow flexion and extension, shoulder joint horizontal outreach and elbow flexion touch head. 155 different exercises from 9 stroke patients’ rehabilitation training program were adopted to verify and validate the system with 100 of them in the training group and the other 55 in the testing group. After preprocessing and the feature extraction of the acquired motion data of the data of training group, BP Neural Network, Radial Basis Function (RBF) Neural Network and Support Vector Machine (SVM) recognition approach were employed to establish three small sample identification models. Then, the data of testing group in the upper limb rehabilitation training program were used to identify the developed models. Finally, the results of three kinds of motion recognition models were compared and analyzed. It had been found that the recognition accuracy of the developed models was above 90% respectively, and SVM model had less time consuming and higher accuracy. This result provides a well reference for further development of an automated system for stroke patient rehabilitation motion recognition. Keywords-motion recognition; BP neural network; RBF neural network; support vector machine

Motion Recognition for Stroke Rehabilitation Based on BP, RBF Neural Network and Support Vector Machine