Assessment of Wavelets Transform based Processing of Features of Forearm Muscle Signals for Prosthesis
M. Karuna1, Sitaramanjaneya Reddy2
1M.Karuna, Department of Electronics and Communication Engineering, Vignan Foundation for Science, Technology and Research, Guntur (Andhra Pradesh), India.
2Sitaramanjaneya Reddy, Department of Electronics and Communication Engineering, Vignan Foundation for Science, Technology and Research, Guntur (Andhra Pradesh), India.
Manuscript received on 25 November 2019 | Revised Manuscript received on 19 December 2019 | Manuscript Published on 30 December 2019 | PP: 168-171 | Volume-9 Issue-1S5 December 2019 | Retrieval Number: A10391291S52019/19©BEIESP | DOI: 10.35940/ijeat.A1039.1291S519
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: People who have lost forearm are suffer from hand mobility limitations due to trauma, disease or defect, Prosthesis arm help those people to do their daily actions. Researchers have been focused on developing artificial hand. In this regard, better processing of features of electromyographic (EMG) signal has a significant role from residual forearm muscle. To achieve this, Wavelet Transform (WT) technique has been applied because it is acceptable with the characteristics of EMG as a nonstationary signal. Results have shown that db5 wavelet decomposition performs best denoising at fifth level in other wavelets comparison. Furthermore, the ratio of Signal to Noise (S/N) and the error of percentage (PE) are calculated to evaluate the eminence and the usefulness features of EMG.
Keywords: EMG, WT, Decomposition, Denoising, Feature Extraction, Feature Selection
Scope of the Article: Digital Signal Processing Theory