Abstract
The paper presents a concept of bioprosthesis control via recognition of user intent on the basis of myopotentials acquired from his body. The EMG signals characteristics and the problems of their measurement have been discussed. The contextual recognition has been considered and three description method for such approach (respecting 1st and 2nd -order context), using: Markov chains, fuzzy rules, neural networks, as well as the involved decision algorithms have been described. The algorithms have been experimentally tested as far as the decision quality is concerned.
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
Chan, A., Englehart, K., Hudgins, B., Lovely, D.F.: Hidden Markov model based classification of myoelectric signals in speech. In: 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Istanbul (2001)
Czogala, E., Leski, J.: Fuzzy and Neuro-Fuzzy Intelligent Systems. Physica Verlag, New York (2000)
De Luca, C.J.: The use of Surface Electromyography in Biomechanics. Journal of Applied Biomechanics 13(2) (1997)
Englehart, K., Hudgins, B., Parker, P.A., Stevenson, M.: Classification of the Myoelectric Signal using Time-Frequency Based Representations. Medical Eng. and Physics, Special Issue: Intelligent Data Analysis in Electromyography and Electroneurography (1999)
Krysztoforski, K., Wolczowski, A., Bedzinski, R., Helt, K.: Recognition of Palm Finger Movements on the Basis of EMG Signals with Application of Wavelets. TASK Quarterly 8(2) (2004)
Kurzynski, M.: Pattern Recognition - Statistical Approach. Wroclaw Univ. of Technol (1997) (in Polish)
Kurzynski, M.: Benchmark of Approaches to Sequential Diagnosis. In: Lisboa, P. (ed.) Artificial Neural Networks In Biomedicine, pp. 129–140. Springer, Heidelberg (1999)
Kurzynski, M., Zolnierek, A.: A Recursive Classifying Decision Rule for Second-Order Markov Chain. Control and Cybernetics 9, 141–147 (1980)
Morecki, A.: Control aspects of artificial hand. IFAC Control Aspect of Biomedical Engineering. Pergamon Press, Oxford (1987)
Wang, L.-X., Mendel, J.M.: Generating Fuzzy Rules by Learning from Examples. IEEE Trans. On Systems, Man and Cybernetics 22, 1414–1427 (1992)
Wolczowski, A.: Smart Hand: The Concept of Sensor based Control. In: Proc. of 7th IEEE Int. Symp. on Methods and Models in Automation and Robotics, Międzyzdroje (2001)
Wolczowski, A., Krysztoforski, K.: Control-measurement circuit of myoelectric prosthesis hand. In: Proc. of 13th Conf. of the European Society of Biomechanics, Acta of Bioengineering and Biomechanics, Wrocław. Supplement 1, vol. 4, pp. 576–577 (2002)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Wolczowski, A., Kurzynski, M. (2004). Control of Artificial Hand via Recognition of EMG Signals. In: Barreiro, J.M., MartÃn-Sánchez, F., Maojo, V., Sanz, F. (eds) Biological and Medical Data Analysis. ISBMDA 2004. Lecture Notes in Computer Science, vol 3337. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30547-7_36
Download citation
DOI: https://doi.org/10.1007/978-3-540-30547-7_36
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-23964-2
Online ISBN: 978-3-540-30547-7
eBook Packages: Springer Book Archive