Upper limb motion recognition for unsupervised stroke rehabilitation based on Support Vector Machine

Abstract

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 tilt sensors, to identify the complex upper limb movements such as flexion and extension of elbow, flexion of elbow and touch the head, from a stroke patient's rehabilitation program. 18 different movements from a stroke patient's rehabilitation training program were adopted to verify and validate this system with 12 of them in the training group and 6 of them in the testing group. After preprocessing and the feature extraction of the acquired motion data, the Support Vector Machine (SVM) recognition approach was employed to establish a small sample identification model. Finally, the data of testing group in the upper limb rehabilitation training program were used to identify the developed model. It has been found that the recognition accuracy from this developed model was 100%. This result provides a well reference for further development of an automated system for stroke patient rehabilitation motion recognition.