Comparative Analysis of Various Types of Classifier for Surface EMG Signal in Order to Improve Classification Accuracy
Surface EMG is an important signal originating from human body while doing different movements. This can be utilized for various applications like movement classification, diagnosing neuromuscular disorders, prosthetic control and many more. Surface EMG signal analysis is complex in nature because of its random nature. Several researchers are trying to provide solutions for tackling this problem in the form of improving acquisition circuit for surface EMG signal, increasing the density of sensors during acquisition process, extracting novel features which could give more information and so on. One of the crucial stages while analyzing surface EMG signal is selection of feature sets and classification algorithm. In present work the authors tried different time domain feature sets and their combinations to improve classification accuracy. It was observed that a combination of feature sets improves classification accuracy (95.7%) but response time is increased. The present study explains the optimized solution for the aforesaid problem. It was also observed that Ensemble classifier in bagged tree variant gives maximum classification accuracy but takes too much time in training and classification.
KeywordsAcquisition of sEMG Time domain features Classification algorithms Pattern recognition and sEMG control
The authors wish to thank Department of Science and technology, Government of India for providing financial assistanceship.
- 1.Parker, P.A., Scott, R.N.: Myoelectric control of prostheses. Crit. Rev. Biomed. Eng. 13, 283–310 (1986)Google Scholar
- 2.Parker, P.A., Englehart, K., Hudgins, B.: The control of upper limb prostheses. In: Merletti, R., Parker, P.A. (eds.) Electromyography: Physiology, Engineering, and Non-invasive Applications. Wiley/IEEE Press (2004)Google Scholar
- 4.Tsai, A.C., Hsieh, T.H., Luh, J.J., Lin, T.T.: A comparison of upper-limb motion pattern recognition using EMG signals during dynamic and isometric muscle contractions. Biomed. Signal Process. Control, 17–26 (2014)Google Scholar
- 5.Hogan, N., Mann, R.W.: Myoelectric signal processing: optimal estimation applied to electromyography-part I: derivation of the optimal myoprocessor, BME-27. IEEE Trans. Biomed. Eng., 382–395 (1980)Google Scholar
- 7.Nurhazimah, N., Mohd, A.A.R., Shin-Ichiroh, Y., Siti, A.A., Hairi, Z., Saiful, A.M.: A review of classification techniques of EMG signals during Isotonic and Isometric contractions. Sensors 16, 1304 (2016)Google Scholar
- 8.Chowdhury, R.H., et al.: Surface electromyography signal processing and classification techniques. Sensors, 12431–12466 (2013)Google Scholar
- 9.Theodorodis, S., Koutroumbas, K.: Pattern Recognition, 4th edn. Elsevier, Amsterdam (2009)Google Scholar