NDLI: Hybrid EMG classifier based on HMM and SVM for hand gesture recognition in prosthetics

Content Provider	IEEE Xplore Digital Library
Author	Rossi, M. Benatti, S. Farella, E. Benini, L.
Copyright Year	2015
Description	Author affiliation: Micrel Lab., Univ. of Bologna, Bologna, Italy (Rossi, M.; Benatti, S.; Benini, L.) \|\| E3DA - ICT Center, Fondazione Bruno Kessler, Trento, Italy (Farella, E.)
Abstract	Pattern recognition and classification algorithms are widely studied in natural gesture interfaces for upper limb prostheses. Robustness and accuracy of control systems are key challenge in such applications. To improve the classification performance, the conventional approach builds on classifier parameters tuning and/or feature extraction techniques. In this paper, we propose a complementary approach based on the combination of two heterogeneous classifiers: the Support Vector Machines and the Hidden Markov Models. This technique takes advantage of the robust time-independent classification of the SVM taking into account the information about history of the signal with the HMM. We demonstrate that, independently from the performance of the SVM, which can be further optimized with typical methods, the combined approach gains 12% recognition accuracy. We further comment on the applicability of this approach in resource constrained embedded implementations considering real-time requirements in the field of prosthesis control.
Starting Page	1700
Ending Page	1705
File Size	574401
Page Count	6
File Format	PDF
ISBN	9781479978007
DOI	10.1109/ICIT.2015.7125342
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-03-17
Publisher Place	Spain
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Hidden Markov models Accuracy Electromyography Transient analysis Prosthetics Muscles
Content Type	Text
Resource Type	Article

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