NDLI: Identification of low level sEMG signals for individual finger prosthesis

Content Provider	IEEE Xplore Digital Library
Author	Villarejo, J.J. Costa, R.M. Bastos, T. Frizera, A.
Copyright Year	2014
Description	Author affiliation: Electr. Eng. Dept., Univ. Fed. do Espirito Santo, Vitoria, Brazil (Villarejo, J.J.; Bastos, T.; Frizera, A.) \|\| RENORBIO, Univ. Fed. do Espirito Santo, Vitoria, Brazil (Costa, R.M.)
Abstract	This research reports the identification of motor tasks in a human hand from weak myoelectric signals, aimed to control a prosthesis with individual finger flexion and wrist and grasps movements. The gestures were evaluated in two groups, independently. Four channel sEMG signals were captured on the forearm from able-body and amputees volunteers, taking into account low level contraction. Linear and non-linear parameters were extracted based on time and frequency domain and Detrended Fluctuation Analysis (DFA), to represent EMG patterns. The average classification accuracies were computed using Linear Discriminant Analysis (LDA) and Support Vector Machine (SVM) to evaluate the results. Confusion matrix from some experiments show the success rate identifying the gestures.
Starting Page	1
Ending Page	6
File Size	572499
Page Count	6
File Format	PDF
ISSN	23267844
e-ISBN	9781479956890
DOI	10.1109/BRC.2014.6880991
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-05-26
Publisher Place	Brazil
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Wrist Electrodes Support vector machines Myoelectric control Thumb Fractal analysis Low level contraction Muscles Electromyography SEMG Hand prostheses
Content Type	Text
Resource Type	Article

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