Hand Rehabilitation via Gesture Recognition Using Leap Motion Controller
2018
This paper presents an approach for monitoring exercises of hand rehabilitation for past stroke patients. The developed solution uses a Leap Motion controller as hand-tracking 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.
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