Emg Signal Classification Using Fuzzy Logic

Year 2017, Volume: 5 Issue: 2, 97 - 101, 01.09.2017

Osman Ulkır Gokhan Gokmen , Erkan Kaplanoglu

https://doi.org/10.17694/bajece.337941

Cited By: 12

Abstract

Electromyography
(EMG) signals are an important technique in the control applications of
prostatic hand. These signals, which are measured from the skin surface, are used
to perform movements such as wrist flexion / extension, forearm supination /
pronation and hand opening / closing of prosthetic devices. In this study, root
mean square, waveform length and kurtosis methods were applied to extracted EMG
signals from flexor carpi radialis and extensor carpi radialis muscles by using
two channel surface electrodes. A fuzzy logic based classification method has
been applied to classify the extracted signal features. With this method,
classification for different gripping movements has been successfully
accomplished.   

Keywords

Surface EMG, fuzzy logic, feature extraction, EMG classification

References

• [1] M. Diakides, J.D. Bronzino, D.R. Peterson, “Medical Infrared Imaging: Principles and Practices”, CRC press, 2012.
• [2] E. Criswell, “Cram's introduction to surface electromyography”, Jones & Bartlett Publishers, 2010.
• [3] J. He, D. Zhang, N. Jiang, X. Sheng, D. Farina, X. Zhu, “User adaptation in long-term, open-loop myoelectric training: implications for EMG pattern recognition in prosthesis control”, Journal of Neural Engineering, vol. 12, no. 4, 2015.
• [4] K. Englehart, B. Hudgins, P.A. Parker, M. Stevenson, “Classification of the myoelectric signal using time-frequency based representations”, Medical Engineering & Physics, vol. 21, no. 6, pp. 431-438, 1999.
• [5] X. Chen, D. Zhang, X. Zhu, "Application of a self-enhancing classification method to electromyography pattern recognition for multifunctional prosthesis control”, Journal of Neuroengineering and Rehabilitation, vol. 10, no. 1, 2013.Electromagnetic fields in the occupational and general environment, (2011); The 10 kHz - 300 GHz frequency bands normalized parameter values and measurement requirements, HN 80, No.V-199, 2011.
• [6] H.J. Fariman, S.A. Ahmad, M.H. Marhaba, M.A.J. Ghasab, P.H. Chappell, “Simple and computationally efficient movement classification approach for EMG-controlled prosthetic hand: ANFIS vs. Artificial Neural Network”, Intelligent Automation & Soft Computing, vol. 21, no. 4, pp. 559-573, 2015.
• [7] M. Ariyanto, W. Caesarendra, K.A. Mustaqim, M. Irfan, J.A. Pakpahan, J.D. Setiawan, A.R. Winoto, “Finger movement pattern recognition method using artificial neural network based on electromyography (EMG) sensor”, in Automation, Cognitive Science, Optics, Micro Electro-Mechanical System, and Information Technology (ICACOMIT), 2015, International Conference, pp. 12-17.
• [8] Q. Wu, J.F. Mao, C.F. Wei, S. Fu, R. Law, L. Ding, C.H. Yang, “Hybrid BF–PSO and fuzzy support vector machine for diagnosis of fatigue status using EMG signal features”, Neurocomputing, vol. 173, no. 3, pp. 483-500, 2016.
• [9] F. Rajablou, M. Ghanbari, “Identifying the condition of arm through classification of EMG signals by the use of hybrid trained adaptive neural fuzzy inference systems (ANFIS)”, Bulletin of the Georgian National Academy of Sciences, vol. 9, no. 2, pp. 404-410, 2015.
• [10] A. Subasi, “Classification of EMG signals using combined features and soft computing techniques”, Applied soft Computing, vol. 12, no. 8, pp. 2188-2198, 2012.
• [11] H.B. Xie, H. Huang, J. Wu, L. Liu, “A comparative study of surface EMG classification by fuzzy relevance vector machine and fuzzy support vector machine”, Physiological Measurement, vol. 36, no. 2, pp. 191-206, 2015.
• [12] P. Konrad, “The abc of EMG: A practical introduction to kinesiological electromyography”, Noraxon Inc., 2005.