Post stroke rehabilitation exercises are often repetitive and monotonous. Interactive gaming technology plays an important role of encouraging patients to exercise more and makes rehabilitation exercises less monotonous. In this paper, a real-time method for interaction between human and computer is explored which utilizes Microsoft Kinect to measure hand movements and detect grasp gesture. This approach could be integrated into a computer game which, for instance, simulates the pick and place exercise during rehabilitation of individuals with stroke. The hand's binary image and its corresponding depth data were collected using Kinect. Then a SVM classifier was trained to detect grasp gesture. Trained model was tested online for classifying grasp and non-grasp hand gestures. The trained model was able to detect grasp gesture with 89.1% accuracy.
["Ra'na Sadeghi Chegani", 'Carlo Menon']
Tracking hand movements and detecting grasp