Title: Variance and IEMG: potential features to reduce false triggering in threshold based EMG prosthetic hand

Authors: Deepak Joshi, Kanika Kandpal, Sneh Anand

Addresses: Center for BioMedical Engineering, Indian Institute of Technology Delhi, Hauz Khas, New Delhi 110016, India. ' Center for BioMedical Engineering, Indian Institute of Technology Delhi, Hauz Khas, New Delhi 110016, India. ' Center for BioMedical Engineering, Indian Institute of Technology Delhi, Hauz Khas, New Delhi 110016, India

Abstract: This paper calculates and evaluates six features to reduce the chances of false triggering in threshold based EMG prosthetic hand. The results show that Variance and IEMG are the most effective features for classification of motions. ANOVA is used to statistically analyse the experimental results. The chances of false triggering for opening and closing are highly reduced as the highest ranking features have a significant difference, in value, for the three different grip motions. Both the features were significant at the 0.05 level of significance (P < 0.0001).

Keywords: Kalman filter; variance; false triggering; EMG prosthetic hands; integrated electromyography; gripping motions; hand grip; IEMG; fuzzy control; fuzzy logic; prosthetics; bioengineering; biomedical engineering.

DOI: 10.1504/IJBET.2010.034521

International Journal of Biomedical Engineering and Technology, 2010 Vol.4 No.2, pp.161 - 168

Published online: 07 Aug 2010 *

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