Multifunctional Prosthesis Control with Simulation of Myoelectric Signals


The skeletal muscle activation generates electric signals called myoelectric signals. In recent years a strong scientific activity has been developed in the recognition of limb movements from electromyography (EMG) signals recorded from non-invasive (surface) electrodes, in order to design systems for prosthetic control. Surface EMG acquire the activation of surrounding muscles and for that reason the obtained signal needs to be conditioned and processed, with pattern recognition techniques for extraction and classification. In this work EMG signals were acquired for two hand movements, “hand close” and “hand open”.  The EMG electrodes were placed on the forearm  and positioned over the extensor digitorum muscle, for the “hand open” and flexor digitorum muscle, for the “hand close”. Using MATLAB software the signal conditioning, feature extraction and classification were performed. The feature extraction process was carried with the Wavelet Packet Transform (WPT) technique and the classification process was done with two different techniques for comparison purposes, Neural Networks (NN) and Support Vector Machines (SVM). The results show that the SVM classifier used presented better classification performance compared to NN classifier used.

Keywords: EMG, Signal conditioning, Wavelet Packet Transform (WPT), Neural Networks (NN), Support Vector Machines (SVM)

[1] Lieber, R. L.: Skeletal Muscle Structure, Function, and Plasticity: The Physiological Basis of Rehabilitation. L. W. & Wilkins Press, Baltimore, USA, 2010.

[2] ADInstrments (Ed.). Physiology Experiments Manual – Windows, PowerLab/410. Bella Vista, Australia, 1999.

[3] Zecca, M.; Micera, S.; Carrozza, M. C.; Dario, P.: “Control of Multifunctional Prosthetic Hands by Processing the Electromyographic Signal”. Critical ReviewsTM In Biomedical Engineering, Vol. 30 (2002), pp. 459–485.

[4] Frant, E.; Milea, L.; But, V.: “Methods of Acquisition and Signal Processing for Myoelectric Control of Artificial Arms”. Romanian Journal of Information Science and Technology, Vol. 15, n°. 2 (2012), pp. 91–105.

[5] Purushothaman, G.; Ray, K. K.: “EMG based man-machine interaction – A pattern recognition research platform”. Robotics and Autonomous Systems Journal, Vol. 62, n°. 6 (2014), pp. 864–870.

[6] Khokhar, Z. O.; Xiao, Z. G.; Menon, C.: “Surface EMG pattern recognition for real-time control of a wrist exoskeleton”, BioMedical Engineering OnLine, Vol. 9 (2010).

[7] Gupta R.; Kulshreshtha, A.: Analysis of dual-channel surface electromyogram using second-order and higher-order spectral features. Proceedings of 2nd International Conference on Communication Control and Intelligent Systems, Mathura, India, 18-20 Nov 2016.

[8] Al-faiz, M. Z.; Miry, A. H. Artificial Human Arm Driven by EMG Signal, MATLAB - A Fundamental Tool for Scientific Computing and Engineering Applications, IntechOpen Ltd., London, UK, 2012.

[9] Bhoi, A.; Tamang, J.; Mishra, P.: ”Wavelet packet based Denoising of EMG Signal”. International Journal of Engineering Research and Development, Vol. 4, n°. 2 (2012).

[10] MathWorks. User’s Guides, 2015.

[11] Karlık, M.; Pourghassem, H.; Shahgholian, G.: A novel prosthetic hand control approach based on genetic algorithm and wavelet transform features. Proceedings of IEEE 7th International Colloquium on Signal Processing and Its Applications, Penang, Malaysia, 4-6 March 2011.

[12] Bach, P. F. Myoelectric signal features for upper limb prostheses, Norwegian University of Science and Technology, 2009.

[13] Singla, R.: “Comparison of SVM and ANN for classification of eye events in EEG”. Journal of Biomedical Science and Engineering, Vol. 04, n°. 01, (2011), pp. 62–69.

[14] Oskoei, M. O.; Huosheng, H.: Evaluation of Support Vector Machines in Upper Limb Motion Classification Using Myoelectric Signal”. Proceedings of 13th International Conference on Biomedical Engineering, Singapore, 3-6 December 2008.