Surface Electromyography Signal Acquisition and Classification Using Artificial Neural Networks (ANN)

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R.M.P.K.Rasnayake 1,* M.W.P Maduranga 2 J.P.D.M Sithara 3

1. Vento Innovation Lab, Sri Lanka

2. Department of Computer Engineering, General Sir John Kotelawala Defence University, Ratmalana, Sri Lanka

3. Department of Electrical and Computer Engineering, The Open University of Sri Lanka, Nugegoda, Sri Lanka

* Corresponding author.


Received: 28 Dec. 2021 / Revised: 25 Jan. 2022 / Accepted: 26 Mar. 2022 / Published: 8 Jun. 2022

Index Terms

Surface electromyography, wavelet transforms, Artificial Neural Network, Fourier Transform, Machine Learning for Signal processing.


An electromyography (EMG) is an analytical tool used to record muscles' electrical activity, which produces an electrical signal proportional to the level of muscle activity. EMG signal plays a vital role in bio-mechatronic engineering for designing intelligent prostheses and other rehabilitation devices. Analysis of EMG signals with powerful and advanced methodologies is an essential requirement in EMG signal processing, as the EMG signal is a complex nonlinear, non-stationary signal in nature. It is required to use advanced signal processing techniques rather than conventional methods to exact EMG signals' features. Fourier transforms (FT) are not the most appropriate tool for analyzing non-stationary signals such as EMG. In this work, we have developed a system that can be useful for disabled persons to get a regular lifestyle using a functioning part of the body. Here, we studied the electrocution gram behavior of human body parts to feature extraction and trained the neural network to simulate the movements of mechanical actuators such as robotic arms. The wavelet transformation has been used to get high-quality feature extraction from electro cardio grapy and develops proper faltering methods for cardio systems' electrical signals. Finally, an artificial neural network (ANN) is used to classify the EMG signals through exacted features. Classification results are presented in this paper.

Cite This Paper

R.M.P.K.Rasnayake, M.W.P Maduranga, J.P.D.M. Sithara, "Surface Electromyography Signal Acquisition and Classification Using Artificial Neural Networks (ANN)", International Journal of Modern Education and Computer Science(IJMECS), Vol.14, No.3, pp. 64-75, 2022. DOI:10.5815/ijmecs.2022.03.04


[1]G Dr. S.I. Keethaponchalan, "Rehabilitation and reintegration of former rebels for sustainable peace in Sri Lanka: Postwar challenges," Depatmant of political science and public policy, University of Colombo, Economic review, Oct/Nov2004.
[2]Christ Lake, "Upper limb prosthetics: Using evidence-based practice to enhance patient care experiences," The academy Today, Supplement of the O & P EDGE, September 2011.
[3]D.G. Smith, W. Michael & J.H. Bowker, "Atlas of amputation and limb deficiencies-surgical, prosthetic and rehabilitation principles," American academy of orthopedic surgeons, 2004.
[4]Parker P., Englehart K., Hudgins B., "Myoelectric signal processing for control of powered limb prostheses," Journal of electromyography and kinesiology: official journal of the International Society of Electrophysiological Kinesiology, 2006, Volume 16, Issue 6, pp. 541–548.
[5]Erik Scheme, Kevin Englehart, "Electromyogram pattern recognition for control of powered Upper-limb prosthesis: State of the art and challanges for clinical use", Journal of Rehabilitation research &Develpoment,Volume 48, November 2011, pp. 643-660.
[6]SilvesterMicera, Jacopo Carpaneto&StanisaRaspopovic,, "Control of hand prosthesis using peripheral information",IEEE reviews in biomedical Engineering, Volume 03, 2010, pp.48-68.
[7]Buchenrieder K., "Processing of Myoelectric Signals by Feature Selection and Dimensionality Reduction for the Control of Powered Upper-Limb Prostheses", Computer Aided Systems Theory – EUROCAST 2007, Lecture Notes in Computer Science, 2007, Volume 4739/2007, pp. 1057–1065
[8]M. Oskoei and H. Hu, "Myoelectric control systems—A survey," Biomed. Signal Proc. Contr. J., volume 2, pp. 275–294, 2007.
[9]S. Karlsson, J. Yu, M. Akay, Time–frequency analysis of myoelectric signals during dynamic contractions: a comparative study, IEEE Trans. Biomed. Eng. 47 (2) (2000) 228–238.
[10]Gulshan, RuchikaThukral, Manmohan Singh, "Analysis of EMG signals based on Wavelet transform- A review", Journal of Emerging technologies and innovative research, July 2015, Volume 2, pp. 3132- 3135.
[11]J. Kilby, K. Prasad, "Continuous Wavelet Analysis," International Journal of Computer and Electrical Engineering, Volume 5, February 2013, pp 30 – 35.
[12]David Ozog, "Signal Analysis," May 2007,
[13]NebrasHussainGheab, SadeelSaleem, "Comparison study of Electromyography Using Wavelet and neural network," Al- Khwarizmi Engineering Journal, 2008, Volume 04, No. 03, pp 108-119.
[14]M.W.P Maduranga, Ruvan Abeysekera, "TreeLoc: An Ensemble Learning-based Approach for Range BasedIndoor Localization," Int. Journal of Wireless and Microwave Technologies, Vol.11, No.5, pp. 18-25, 2021.
[15]M.Sifuzzaman, M.R. Islam and M.Z. Ali, "Application of Wavelet Transform and its Advantages Compared to Fourier Transform," Journal of Physical Sciences, 2009, Volume 13, pp121-134.
[16]RobiPolikar, The Wavelet Tutorial,
[17]Elin Johansson, "Wavelet theory and some of its applications," Licentiate thesis, Department of Mathematics, Lulia University of Technology, ISSN 1402- 1757
[18]O. Riolu and M. Vetterli, ``Wavelets and Signal Processing,'' IEEE Signal Processing Magazine, October 1991, Volume 8, pp 14-38.
[19]Michael weeks, 'Digital Signal Processing using Matlab and Wavelets', 2nd edition
[20]Nimrod Peleg, "The history and Families of wavelets," December 2000,
[21]Cedric Vonesch, Thierry Blu, Micheal Unser, "Generalized Daubechies Wavelet Families," IEEE transactions on signal processing, Volume 55, September 2007, pp 4415- 4429.
[22]Eason, B. Noble, and I. N. Sneddon, “On certain integrals of Lipschitz-Hankel type involving products of Bessel functions,” Phil. Trans. Roy. Soc. London, vol. A247, pp. 529–551, April 1955.
[23]J. Clerk Maxwell, A Treatise on Electricity and Magnetism, 3rd ed., vol. 2. Oxford: Clarendon, 1892, pp.68–73.
[24]I. S. Jacobs and C. P. Bean, “Fine particles, thin films, and exchange anisotropy,” in Magnetism, vol. III, G. T. Rado and H. Suhl, Eds. New York: Academic, 1963, pp. 271–350.
[25]A.Phinyomark, A. Nuidod, P. Phukpattaranont, C. Limsakul, "Feature exaction and reduction of wavelet transform coefficients for EMG pattern classification," Electrical and electronic engineering, Signal Technology, 2012, No. 6, pp 27- 32.
[26]Johan Borglin, "Classification of hand movements using multi-channel EMG," Master's thesis in Biomedical Engineering,
[27]Wu Ping, BaoJia-li, Xia Qiang, I.C. Bruce, "The use of artificial neural networks in the Motor Program," 26thAnual International Conference of the IEEE EMBS, San Francisco, 2004, pp 4611- 4613.
[28]Bassant M., Elbagoury, Thomas Schrader, Meteb M., "Design of Neural network for Rehabilitation Robotics," International Conference on Circuits, Systems and control, ISBN 978-1-61804, pp 89-95.
[29]Dr. Scott Day, "Important Factors in Surface EMG Measurement," Bortech biomedical, Calgary,
[30]C.J.D.Luca, "The use of electromyography in Biomechanics," Journal of Applied biomechanics, 1997, volume 13, pp. 135-163.
[31]Thomas Kugelstadt, "Active filter design techniques," Op amps for everyone, Texas Instruments.
[32]Gianluca De Luca, "Fundamental Concepts in EMG signal Acquisition", DelsisInc, March 2003, Rev. 2.1
[33]Ogawa electrical design, "Filter design and analysis,"
[34]Rubana H. Chowdhury, Mamun B. I. Reaz, MohdAlauddin Bin Mohd Ali, "Surface Electromyography Signal Processing and Classification Techniques", Sensors 2013, pp 12431-12466.
[35]AngkoonPhiniomark, SirineeThongpanja, Huosheng Hu, "The usefulness of Mean and Median frequencies in Electromyography Analysis", Open access chapter, Instech.
[36]Y. S. P. Weerasinghe, M. W. P. Maduranga and M. B. Dissanayake, "RSSI and Feed Forward Neural Network (FFNN) Based Indoor Localization in WSN," 2019 National Information Technology Conference (NITC), 2019, pp. 35-40, doi: 10.1109/NITC48475.2019.9114515.
[37]Matlab wavelet toolbox!/wavelet%20toolbox%204%20user's%20guide%20(larger%20selection).pdf
[38]Matlab neural network tool box.