Bio Potential Signal Conditioning using MATLAB on Electromyography Signals
Roopa J1, Rahul N2, Pragna G S3, Geetha K S4, B S Satyanarayana5, Govinda Raju M6
1Roopa J, Assistant Professor, Department of ECE, RVCE, Bangalore (Karnataka), India.
2Rahul N, U.G. Student, Department of ECE, RVCE, Bangalore (Karnataka), India.
3Pragna G S, U.G. Student, Department of ECE, RVCE, Bangalore (Karnataka), India.
4Geetha K S, Head, Department of ECE, RVCE, Bangalore (Karnataka), India.
5B S Satyanarayana, Emeritus Professor, Ex-vice Chancellor, BML University, (Haryana), India.
6Govinda Raju M, Assistant Professor, Department of ECE, RVCE, Bangalore (Karnataka), India.
Manuscript received on 19 August 2019 | Revised Manuscript received on 29 August 2019 | Manuscript Published on 16 September 2019 | PP: 448-452 | Volume-8 Issue-2S6 July 2019 | Retrieval Number: B10850782S619/2019©BEIESP | DOI: 10.35940/ijrte.B1085.0782S619
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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: In today’s world of medicine, technology plays a major role in creating solutions. Conventionally expensive equipment has been used for diagnosis involving bio signals. Such treatment is not available to most people. Most existing bio signal conditioning devices are very specific to their application, and others that are diverse require a large number of different sensors and are more expensive and cumbersome to use. There is a need for a signal conditioning circuit that is adaptable, employs digital processing and can be used to measure any of the different bio signals generated by our body with minimal modification. Use of MATLAB can greatly help researchers to analyse bio signals by giving them huge flexibility. MATLAB also has the added advantage of being able to be used on nearly all computing devices. Raw bio signal data has been captured and MATLAB has been used to mimic the various filters required to remove different types of noises that exist in a typical electromyography signal.
Keywords: Diagnosis, Signal Conditioning, Digital Processing, MATLAB, Electromyography.
Scope of the Article: Digital Signal Processing Theory