Gesture recognition using leap motion: a comparison between machine learning algorithms

Abstract

In this paper we compare the effectiveness of various methods of machine learning algorithms for real-time hand gesture recognition, in order to find the most optimal way to identify static hand gestures, as well as the most optimal sample size for use during the training step of the algorithms. In our framework, Leap Motion and Unity were used to extract the data. The data was then used to be trained using Python and scikit-learn. Utilizing normalized information regarding the hands and fingers, we managed to get a hit rate of 97% using the decision tree classifier.