A hidden markov model-based approach to grasping hand gestures classification

Abstract

Gesture recognition is a hot topic in research, due to its appealing applications in real-life contexts, from remote control to assistive robotics. In this paper we focus on grasping gestures recognition. This kind of gestures is particularly interesting, because it requires not only analyzing hand trajectories, but also fingers position and fingertip forces, of utmost importance in manipulation tasks. We used a discrete HMM-based model for gesture recognition. Input codebooks for the model are gesture elementary phases, obtained through a LLS-regression segmentation algorithm, and feature vectors representing hand position over time.