Empirical Copula driven hand motion recognition via surface electromyography based templates

Authors:
Zhaojie Ju;Honghai Liu
Affiliations:
Intelligent Systems & Biomedical Robotics Group, School of Creative Technologies, University of Portsmouth, Portsmouth, UK;Intelligent Systems & Biomedical Robotics Group, School of Creative Technologies, University of Portsmouth, Portsmouth, UK
Venue:
ICIRA'10 Proceedings of the Third international conference on Intelligent robotics and applications - Volume Part I
Year:
2010

Citing 4
Cited 0

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Abstract

Current tendency of electromyography (EMG) based prosthetic hand is to enable the user to perform complex grasps or manipulations with natural muscle movements. In this paper, a new classifier is introduced to identify the naturally contracted surface EMG patterns for hand motion recognition. The recognition method utilizes a dependence structure as a motion template, which includes one-to-one correlations of surface EMG feature channels. Using an effective EMG feature, the proposed algorithm can successfully classify different complex motions from different subjects with a satisfactory recognition rate. To save the computational cost, re-sampling processing has been employed.