Dynamic grasp recognition using time clustering, Gaussian mixture models and hidden Markov models

The human hand has the capability of fulfilling various everyday-life tasks using the combination of biological mechanisms, sensors and controls. Autonomously controlling multifingered robots is a challenge, which holds the key to related multidisciplinary research and a wide spectrum of applications in intelligent robotics. We demonstrate the state of the art in recognizing continuous grasping gestures of human hands in this paper. We propose a novel time clustering method (TC) and modified Gaussian mixture models (GMMs) and compare them with hidden Markov models (HMMs). The TC outperforms the GMM and HMM methods in terms of recognition rate and potentially in computational cost. Future work is focused on real-time recognition and grasp qualitative description.