Wavelet Transform to Recognize Muscular: Force Relationship Using sEMG Signals

AbstractBeing man–machine interaction, the use of surface electromyogram (sEMG) is increasing day by day. Generally, sEMG is a complex signal and is influenced by several external factors/artifacts. As removing these artifacts is not easy, feature extraction to obtain useful information hidden inside the signal becomes a different process. This paper presents methods of analyzing sEMG signals using discrete wavelet transform for extracting accurate patterns of the sEMG signals. The results obtained suggest having a good compromise between the percentage root mean square differences, root mean square difference value for the denoising and quality of reconstruction of the sEMG signal. Further a one way separated factorial analysis was performed to find out the effectiveness of analyzed sEMG signal for discrimination among different classes of groups Various possible types of wavelets with high level parameters were tested for denoising and results show that the best mother wavelets for tolerance of noise are fifth order of symmlets and bior6.8 whereas for reconstruction, wavelet functions bior5.5 and sym3 were the best.

[1]  Francesco Amigoni,et al.  Combining rate-adaptive cardiac pacing algorithms via multiagent negotiation , 2006, IEEE Transactions on Information Technology in Biomedicine.

[2]  Huosheng Hu,et al.  The Usefulness of Mean and Median Frequencies in Electromyography Analysis , 2012 .

[3]  S Micera,et al.  Control of Hand Prostheses Using Peripheral Information , 2010, IEEE Reviews in Biomedical Engineering.

[4]  Karan Veer,et al.  A technique for classification and decomposition of muscle signal for control of myoelectric prostheses based on wavelet statistical classifier , 2015 .

[5]  J.C. Pereira,et al.  Evaluation of adaptive/nonadaptive filtering and wavelet transform techniques for noise reduction in EMG mobile acquisition equipment , 2003, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[6]  Kevin B. Englehart,et al.  A wavelet-based continuous classification scheme for multifunction myoelectric control , 2001, IEEE Transactions on Biomedical Engineering.

[7]  Ele Pierre,et al.  Compression Approach of EMG Signal Using 2D Discrete Wavelet and Cosine Transforms , 2013 .

[8]  Pornchai Phukpattaranont,et al.  WAVELET-BASED DENOISING ALGORITHM FOR ROBUST EMG PATTERN RECOGNITION , 2011 .

[9]  David L. Donoho,et al.  De-noising by soft-thresholding , 1995, IEEE Trans. Inf. Theory.

[10]  Sridhar P. Arjunan,et al.  A sEMG model with experimentally based simulation parameters , 2010, 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology.

[11]  Pak-Chung Ching,et al.  On wavelet denoising and its applications to time delay estimation , 1999, IEEE Trans. Signal Process..

[12]  K. Veer,et al.  Wavelet and short-time Fourier transform comparison-based analysis of myoelectric signals , 2015 .

[13]  J. Kilby,et al.  Extracting Effective Features of SEMG Using Continuous Wavelet Transform , 2006, 2006 International Conference of the IEEE Engineering in Medicine and Biology Society.