A myoelectric prosthetic limb can be directed by sEMG signals from amputee’s residual muscles. The capability of such prosthetic hand may be enhanced by classifying additional hand motion commands. As the amputee’s residual muscles are limited and it is essential to come up with the ways to identify as many hand motion directions as possible with sEMG signals recognized by few sensors. Recent algorithms for pattern recognition in sEMG signals are tested with limited recognition patterns and inconsistent classification accuracy. The proper choice of denoising algorithm has intense effect on classification rates. Therefore FIR-median hybrid (FMH) filter, and discrete wavelet transform (DWT) denoising methods are used in this work for filtering sEMG signals. Five time domain features are used for classification of motions and four different physical activities are classified using ANN. It is observed from the results that FMH filter removes noise more effectively as compared to DWT which improves the classification accuracy.
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