Nonnegative matrix factorization for the identification of EMG finger movements: evaluation using matrix analysis.

Abstract:

:Surface electromyography (sEMG) is widely used in evaluating the functional status of the hand to assist in hand gesture recognition, prosthetics and rehabilitation applications. The sEMG is a noninvasive, easy to record signal of superficial muscles from the skin surface. Considering the nonstationary characteristics of sEMG, recent feature selection of hand gesture recognition using sEMG signals necessitate designers to use nonnegative matrix factorization (NMF)-based methods. This method exploits both the additive and sparse nature of signals by extracting accurate and reliable measurements of sEMG features using a minimum number of sensors. The testing has been conducted for simple and complex finger flexions using several experiments with artificial neural network classification scheme. It is shown, both by simulation and experimental studies, that the proposed algorithm is able to classify ten finger flexions (five simple and five complex finger flexions) recorded from two sEMG sensors up to 92% (95% for simple and 87% for complex flexions) accuracy. The recognition performances of simple and complex finger flexions are also validated with NMF permutation matrix analysis.

authors

Naik GR,Nguyen HT

doi

10.1109/JBHI.2014.2326660

subject

Has Abstract

pub_date

2015-03-01 00:00:00

pages

478-485

issue

2

eissn

2168-2194

issn

2168-2208

pii

6824741

journal_volume

19

pub_type

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