Hand gesture recognition for post-stroke rehabilitation using leap motion

In order to enhance and/or improve recovery after stroke, rehabilitation needs to start early and be monitored by continuous and recurrent long-Term interventions in the clinic and home setting. The elderly is a high risk stroke group with advancing age, resulting in increasing demand of strengthened resource of hospitals and physiotherapist. The residential rehabilitation for stroke patients would effectively relieve shortages of medical resources. However, the residential rehabilitation for stroke patients faces with the lack of professional guidance, and physiotherapist cannot monitor the rehabilitation progress of stroke patients. These problems may lead to additional harm or deteriorate rehabilitation progress. In order to solve this problem, we develop a hand gesture recognition algorithm devoted to monitor the seven gestures for residential rehabilitation of the post-stroke patients. The gestures were performed by seventeen healthy young subjects. The results were assessed by k-fold cross validation method. The results show that the proposed hand gesture recognition algorithm using multi-class SVM and k-NN classifier achieve accuracy of 97.29% and 97.71%, respectively.