Evaluation of adaptive/nonadaptive filtering and wavelet transform techniques for noise reduction in EMG mobile acquisition equipment

The myoelectric signal can be used to control many rehabilitation systems, for instance, prostheses and artificial neuromuscular electrical stimulation toward restoring movement to spinal cord injured subjects. These mobile systems are usually used in different environments and thus are being expose...

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Bibliographic Details
Published inIEEE transactions on neural systems and rehabilitation engineering Vol. 11; no. 1; pp. 60 - 69
Main Authors Ortolan, R.L., Mori, R.N., Pereira, R.R., Cabral, C.M.N., Pereira, J.C., Cliquet, A.
Format Journal Article
LanguageEnglish
Published United States IEEE 01.03.2003
Subjects
Adaptive filters
Algorithms
Control systems
Digital filters
Electromyography
Electromyography - methods
Feedback
Filtering
Finite impulse response filter
Humans
Monitoring, Ambulatory - methods
Movement
Muscle, Skeletal - physiology
Neuromuscular
Noise reduction
Prosthetics
Reproducibility of Results
Sensitivity and Specificity
Signal Processing, Computer-Assisted
Stochastic Processes
Wavelet transforms
Wrist Joint - physiology
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Summary:The myoelectric signal can be used to control many rehabilitation systems, for instance, prostheses and artificial neuromuscular electrical stimulation toward restoring movement to spinal cord injured subjects. These mobile systems are usually used in different environments and thus are being exposed to different noise levels with characteristics not completely known. In this article, three main techniques for noise reduction were evaluated: wavelet transform (WT), adaptive digital filters, and nonadaptive digital filters. The WT was used to reconstruct the signal with the components without noise information. Adaptive filters were designed using least mean square (LMS) and recursive least square (RLS) algorithms. Finite-impulse response (FIR) and infinite-impulse response (IIR) nonadaptive filters were used for comparison to both the adaptive filters and the signal reconstruction through the WT.
ISSN:1534-4320
DOI:10.1109/TNSRE.2003.810432