Abstract
A novel method for hand movement pattern recognition from electromyography (EMG) biological signals is proposed. These signals are recorded by a three-channel data acquisition system using surface electrodes placed over the forearm, and then processed to recognize five hand movements: opening, closing, supination, flexion, and extension. Such method combines the Hilbert–Huang analysis with a fuzzy clustering classifier. A set of metrics, calculated from the time contour of the Hilbert Spectrum, is used to compute a discriminating three-dimensional feature space. The classification task in this feature-space is accomplished by a two-stage procedure where training cases are initially clustered with a fuzzy algorithm, and test cases are then classified applying a nearest-prototype rule. Empirical analysis of the proposed method reveals an average accuracy rate of 96% in the recognition of surface EMG signals.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Andrade, A.O., Kyberd, P., Nasuto, S.J.: The application of the hilbert spectrum to the analysis of electromyographic signals. Information Sciences 178(9), 2176–2193 (2008)
Babuška, R., van der Veen, P., Kaymak, U.: Improved covariance estimation for Gustafson–Kessel clustering. In: Proceedings of the 2002 IEEE International Conference on Fuzzy Systems, vol. 2, pp. 1081–1085. Honolulu, Hawaii (2002)
Chu, J.U., Moon, I., Mun, M.S.: A real-time emg pattern recognition based on linear-nonlinear feature projection for multifunction myoelectric hand. In: ICORR 2005 9th International Conference on Rehabilitation Robotics, pp. 295–298 (2005)
Englehart, K., Hudgins, B., Chan, A.: Short time fourier analysis of the electromyogram: Fast movements and constant contraction. Technology and Disability 15, 95–103 (2003)
Gustafson, D.E., Kessel, W.C.: Fuzzy clustering with a fuzzy covariance matrix. IEEE Conference on Decision and Control including the 17th Symposium on Adaptive Processes (1978)
Huang, H.P., Chen, C.Y.: Development of a myoelectric discrimination system for a multi-degree prosthetic hand. In: Proceedings of the 1999 IEEE International Conference on Robotics and Automation, vol. 3, pp. 2392–2397 (1999)
Huang, N.E., Shen, Z., Long, S.R., Wu, M.C., Shih, H.H., Zheng, Q., Yen, N.C., Tung, C.C., Liu, H.H.: The empirical mode decomposition and the hilbert spectrum for nonlinear and non-stationary time series analysis. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 454, 903–995 (1998)
Karlik, B., Tokhi, O.M.: A fuzzy clustering neural network architecture for multifunction upper-limb prosthesis. IEEE Transactions on Biomedical Engineering 50, 1255–1261 (2003)
Marshall, P.W., Murphy, B.A.: Muscle activation changes after exercise rehabilitation for chronic low back pain. Archives of Physical Medicine and Rehabilitation 89(7), 1305–1313 (2008)
Momen, K., Krishnan, S.: Real-time classification of forearm electromyographic signals corresponding to user-selected intentional movements for multifunction prosthesis control. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2007)
Wang, G., Wang, Z., Chen, W., Zhuang., J.: Classification of surface emg signals using optimal wavelet packet method based on davies-bouldin criterion. Springer Medical and Biological Engineering 44(10), 865–872 (2006)
Zardoshti-Kermani, M., Wheeler, B., Badie, K., Hashemi, R.: EMG feature evaluation for movement control of upper extremity prostheses. IEEE Transactions on Rehabilitation Engineering 3(4), 324–333 (1995)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer Science+Business Media, LLC
About this chapter
Cite this chapter
Pinzon-Morales, RD., Baquero-Duarte, KA., Orozco-Gutierrez, AA., Grisales-Palacio, VH. (2011). Pattern Recognition of Surface EMG Biological Signals by Means of Hilbert Spectrum and Fuzzy Clustering. In: Arabnia, H., Tran, QN. (eds) Software Tools and Algorithms for Biological Systems. Advances in Experimental Medicine and Biology, vol 696. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-7046-6_20
Download citation
DOI: https://doi.org/10.1007/978-1-4419-7046-6_20
Published:
Publisher Name: Springer, New York, NY
Print ISBN: 978-1-4419-7045-9
Online ISBN: 978-1-4419-7046-6
eBook Packages: Biomedical and Life SciencesBiomedical and Life Sciences (R0)