Hand Rehabilitation via Gesture Recognition Using Leap Motion Controller

This paper presents a low cost approach for monitoring exercises of hand rehabilitation for past stroke patients. The developed solution uses a Leap Motion controller as handtracking device and embeds a supervised Machine Learning methodology. Support Vector Machine (SVM) is used in order to assess the correctness of a set of simple rehabilitation exercises performed with a single hand. The basic SVM model was extended with particular interest for defining feature vectors in a continues environment. The proposed method incorporated leap motion data, normalization of angles and gesture recognition. A software system was developed to provide patients with a set of exercise corrections and guidance for rehabilitation.