Enhancing classification accuracy of wrist movement by denoising sEMG signals

Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS

Volumn , Issue , 2013, Pages 5762-5764

  Abbas, Baqar ^a   Farooq, Omar ^a   Uzzaman, Yusuf ^a   Khan, Abid Ali ^a   Vyas, A L ^b  

^a ALIGARH MUSLIM UNIVERSITY   (India)

^b INDIAN INSTITUTE OF TECHNOLOGY DELHI   (India)

Author keywords

[No Author keywords available]

Indexed keywords

CLASSIFICATION ACCURACY; GIVEN FEATURES; LINEAR CLASSIFIERS; MAXIMUM ACCURACIES; MOTHER WAVELETS; SPECTRAL FEATURE; SURFACE ELECTROMYOGRAM; WRIST MOVEMENTS;

WAVELET ANALYSIS;

DISCRETE WAVELET TRANSFORMS;

EID: 84886478600     PISSN: 1557170X     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/EMBC.2013.6610860     Document Type: Conference Paper

References (14)

1. S. Rasheed, D. W. Stashuk, & M. S. Kamel, "Diversity-based combination of non-parametric classifiers for EMG signal decomposition". Pattern Analysis and Applications, vol. 11, no. 3-4, pp. 385-408, 2008.
2. T. Vukova, M. Vydevska-Chichova, & N. Radicheva, "Fatigue induced changes in muscle fibre action potentials estimated by wavelet analysis". Journal of Electromyography and Kinesiology, vol. 18, pp. 397-409, 2008.
3. E. A. Clancy, E. L. Morin, and R. Merletti, "Sampling, Noise reduction and Amplitude Estimation Issues in Surface Electromyography". Journal of Electromyography and Kinesiology, vol. 12, no. I, pp. 1-16, Feb 2002.
4. L. A. C. Kallenberg, S. Preece, C. Nester, & H. J. Hermens, "Reproducibility of MUAP properties in array surface EMG recordings of the upper trapezius and stemocleidomastoid muscle". Journal of Electromyography and Kinesiology, Nov, 2008.
5. D. Farina, R. Merletti, & R. M. Enoka, "The extraction of neural strategies from the surface EMG". Journal of Applied Physiology, vol. 96, no. 4, pp. 1486-1495, 2009.
6. R. Merletti, & L. R. Lo Conte, "Surface EMG signal processing during isometric contractions". Journal of Electromyography and Kinesiology, vol. 7, no. 4, pp. 241-250, 1997.
7. G. Aschero, & P. Gizdulich, "Denoising of surface EMG with a modified Wiener filtering approach". Journal of Electromyography and Kinesiology. Feb, 2009.
8. S. H. Park and S. P. Lee, "EMG pattern recognition based on artificial intelligence techniques". IEEE Transactions on Rehabilitation Engineering, vol. 6, no. 4, 1998.
9. K. Englehart, & B. Hudgins, "A robust, real-time control scheme for multifunction myoelectric control". IEEE Transactions on Biomedical Engineering, vol. 50, no. 7, 2003.
10. K. Englehart, B. Hudgins, & P. A. Parker, "A wavelet-based continuous classification scheme for multifunction myoelectric control", IEEE Transactions on Biomedical Engineering, vol. 48, no.3, pp.302-311, 2001.
11. F. Sebelius, L. Eriksson, H. Holmberg, A. Levinson, G. Lundborg, N. Danielson et al. "Classification of motor commands using a modified self-organising feature map", Medical Engineering and Physics, vol. 27, pp. 403-413, 2005.
12. A. Phinyomark, C. Limsakul, and P. Phukpattaranont, "A Comparative Study of Wavelet Denoising for Multifunction Myoelectric Control, " Proc. Int. Conj Computer and Automation Engineering (ICCAE '09), pp. 21-25, Mar. 2009.
13. D. Donoho and I. M. Johnstone, "Ideal Spatial Adaptation by Wavelet Shrinkage, " Department of Statistics, Stanford University, USA, Apr. 1993.
14. S. Du and M. Vuskovic, "Temporal vs. spectral approach to feature extraction from prehensile EMG signals", Proc. of IEEE International Conference on Information Reuse and Integration pp.344-350, 2004.