Abstract
Current tendency of electromyography (EMG) based prosthetic hand is to enable the user to perform complex grasps or manipulations with natural muscle movements. In this paper, a new classifier is introduced to identify the naturally contracted surface EMG patterns for hand motion recognition. The recognition method utilizes a dependence structure as a motion template, which includes one-to-one correlations of surface EMG feature channels. Using an effective EMG feature, the proposed algorithm can successfully classify different complex motions from different subjects with a satisfactory recognition rate. To save the computational cost, re-sampling processing has been employed.
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
Preview
Unable to display preview. Download preview PDF.
References
Castellini, C., van der Smagt, P.: Surface EMG in advanced hand prosthetics. Biological Cybernetics 100(1), 35–47 (2009)
Chu, J.U., Moon, I., Lee, Y.J., Kim, S.K., Mun, M.S.: A supervised feature-projection-based real-time EMG pattern recognition for multifunction myoelectric hand control. IEEE/ASME Transactions on Mechatronics 12(3), 282–290 (2007)
Huang, H.P., Liu, Y.H., Wong, C.S.: Automatic EMG feature evaluation for controlling a prosthetic hand Using a supervised Feature Mining Method: An Intelligent Approach. In: IEEE International Conference on Robotics and Automation, vol. 1, pp. 220–225. IEEE, Los Alamitos (1999/2003)
Phinyomark, A., Limsakul, C., Phukpattaranont, P.: Emg feature extraction for tolerance of 50 hz interference. In: Proc. of PSU-UNS Inter. Conf. on Engineering Technologies, ICET 2009, pp. 289–293 (2009)
Ahsan, M.R., Ibrahimy, M.I., Khalifa, O.O.: EMG Signal Classification for Human Computer Interaction: A Review. European Journal of Scientific Research 33(3), 480–501 (2009)
Tsenov, G., Zeghbib, A.H., Palis, F., Shoylev, N., Mladenov, V.: Neural networks for online classification of hand and finger movements using surface EMG signals. In: 8th Seminar on Neural Network Applications in Electrical Engineering, pp. 167–171 (2006)
Naik, G.R., Kumar, D.K., Palaniswami, M.: Multi run ICA and surface EMG based signal processing system for recognising hand gestures. In: 8th IEEE International Conference on Computer and Information Technology, CIT 2008, pp. 700–705 (2008)
Mobasser, F., Hashtrudi-Zaad, K.: A method for online estimation of human arm dynamics. In: Annual International Conference of the IEEE Engineering in Medicine and Biology Society, vol. 1, p. 2412 (2006)
Chan, A.D.C., Englehart, K.B.: Continuous myoelectric control for powered prostheses using hidden Markov models. IEEE Transactions on Biomedical Engineering 52(1), 121–124 (2005)
Huang, Y., Englehart, K.B., Hudgins, B., Chan, A.D.C.: Optimized Gaussian mixture models for upper limb motion classification. In: Annual International Conference of the IEEE Engineering in Medicine and Biology Society, vol. 1 (2004)
Kawano, S., Okumura, D., Tamura, H., Tanaka, H., Tanno, K.: Online learning method using support vector machine for surface-electromyogram recognition. Artificial Life and Robotics 13(2), 483–487 (2009)
Englehart, K., Hudgins, B.: A robust, real-time control scheme for multifunction myoelectric control. IEEE Transactions on Biomedical Engineering 50(7), 848–854 (2003)
Khezri, M., Jahed, M.: Real-time intelligent pattern recognition algorithm for surface EMG signals. BioMedical Engineering OnLine 6(1), 45 (2007)
Liu, Y.H., Huang, H.P., Weng, C.H.: Recognition of electromyographic signals using cascaded kernel learning machine. IEEE/ASME Transactions on Mechatronics 12(3), 253–264 (2007)
Wang, G., Ren, X., Li, L., Wang, Z.: Multifractal analysis of surface EMG signals for assessing muscle fatigue during static contractions. Journal of Zhejiang University-Science A 8(6), 910–915 (2007)
Mesin, L., Merletti, R., Rainoldi, A.: Surface EMG: The issue of electrode location. Journal of Electromyography and Kinesiology 19(5), 719–726 (2009)
Feinberg, J.: EMG: Myths and Facts. HSS Journal 2(1), 19–21 (2006)
Ju, Z., Liu, H.: Recognizing Hand Grasp and Manipulation through Empirical Copula. International Journal of Social Robotics (2010) (awaiting publishing)
Nelsen, R.B.: An introduction to copulas. Springer, Heidelberg (2006)
Kruskal, W.H.: Ordinal measures of association. Journal of the American Statistical Association 53(284), 814–861 (1958)
Lehmann, E.L.: Some concepts of dependence. The Annals of Mathematical Statistics 37(5), 1137–1153 (1966)
Liu, H., Brown, D.J., Coghill, G.M.: Fuzzy qualitative robot kinematics. IEEE Transactions on Fuzzy Systems 16(3), 802–822 (2008)
Liu, H.: A fuzzy qualitative framework for connecting robot qualitative and quantitative representations. IEEE Transactions on Fuzzy Systems 16(6), 1522–1530 (2008)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ju, Z., Liu, H. (2010). Empirical Copula Driven Hand Motion Recognition via Surface Electromyography Based Templates. In: Liu, H., Ding, H., Xiong, Z., Zhu, X. (eds) Intelligent Robotics and Applications. ICIRA 2010. Lecture Notes in Computer Science(), vol 6424. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16584-9_7
Download citation
DOI: https://doi.org/10.1007/978-3-642-16584-9_7
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-16583-2
Online ISBN: 978-3-642-16584-9
eBook Packages: Computer ScienceComputer Science (R0)