Enhancing Precision in Gesture Detection for Hand Recovery After Injury Using Leap Motion and Machine Learning.

This paper presents an improved solution for detecting gestures with a better precision using the Leap Motion sensor and Machine Learning support. A neural network is trained to recognize a hand rotation gesture expressing the grade of recovery, with a supination and pronation exercise. The supination-pronation movement is divided into 4 levels because the users are not usually able to perform a complete rotation gesture in hand recovery after injury. The neural network is trained with data representing the hand rotation angle measurements on the x, y and z axes. The Neural Network training is based on the Tensorflow library. 3 tests were carried out to test the network and eventually a 96% gesture-detection accuracy was achieved.