Dynamic Grasp Recognition Using Time Clustering, Gaussian Mixture Models and Hidden Markov Models

Authors:
Zhaojie Ju;Honghai Liu;Xiangyang Zhu;Youlun Xiong
Affiliations:
Institute of Industrial Research, University of Portsmouth, UK;Institute of Industrial Research, University of Portsmouth, UK;Robotics Institute, Shanghai Jiao Tong University, China;School of Mechanical Science and Engineering, Huazhong University of Science and Technology, China
Venue:
ICIRA '08 Proceedings of the First International Conference on Intelligent Robotics and Applications: Part I
Year:
2008

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Abstract

The human hand has the capability of fulfilling various everyday-life tasks using the combination of biological mechanisms, sensors and controls. How autonomously learning and 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 methods based on Gaussian Mixture Models (GMMs) and Hidden Markov Models (HMMs) individually. 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.