Publication Type:

Conference Paper

Source:

Proceedings of the 4th International Conference on Biosignals, Images and Instrumentation, ICBSII 2018, Institute of Electrical and Electronics Engineers Inc. (2018)

ISBN:

9781538644737

URL:

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85058115958&doi=10.1109%2fICBSII.2018.8524838&partnerID=40&md5=08d9c566e8a24162e981c2bfcfbe259d

Keywords:

Biomedical signal processing, Control systems, Cost effectiveness, Cost-effective implementations, Daubechies wavelet, Discrete wavelet transforms, Electromyography, Feature extraction, Myoelectric control, Optimal performance, Prosthetic control systems, Prosthetics, Rehabilitation devices, Surface electrode, Wavelet decomposition, Wavelet features, Wavelet transforms

Abstract:

Electromyography (EMG) finds enormous applications in clinical/biomedical, prosthesis and rehabilitation devices. The main objective of this paper is to develop a cost-effective implementation of a prosthetic control system based on EMG signals. Non-invasive surface electrodes are used to acquire the signal for various actions. Since the signal is highly contaminated with noise, they are not used in its raw form to handle any sort of device. Amplification and filtering are therefore inevitable and becomes the foremost task prior to further processing so as to obtain a high-quality signal. After the conditioning of the signal, multi-level decomposition based on wavelet transform is performed and features are extracted from all the levels. They are then reduced to find the optimal performance. Finally, the selected features are able to distinguish between various hand movements and therefore helps in the recognition of the intended motion of the amputee. © 2018 IEEE.

Notes:

cited By 0; Conference of 4th International Conference on Biosignals, Images and Instrumentation, ICBSII 2018 ; Conference Date: 22 March 2018 Through 24 March 2018; Conference Code:142165

Cite this Research Publication

S. M. Sunil and Dr. K. I. Ramachandran, “Myoelectric Control System Based on Wavelet Features”, in Proceedings of the 4th International Conference on Biosignals, Images and Instrumentation, ICBSII 2018, 2018.