Critical exponent analysis applied to surface EMG signals for multifunction myoelectric control

• Autorzy: Phinyomark A. , Phothisonothai M. , Phukpattaranont P. , Limsakul C.
• Języki publikacji: EN

Abstrakty

EN

Based on recent advances in non-linear analysis, the surface electromyography (sEMG) signal has been studied from the viewpoints of self-affinity and complexity. In this study, we examine usage of critical exponent analysis (CE) method, a fractal dimension (FD) estimator, to study properties of the sEMG signal and to deploy these properties to characterize different movements for gesture recognition. SEMG signals were recorded from thirty subjects with seven hand movements and eight muscle channels. Mean values and coefficient of variations of the CE from all experiments show that there are larger variations between hand movement types but there is small variation within the same type. It also shows that the CE feature related to the self-affine property for the sEMG signal extracted from different activities is in the range of 1.855∼2.754. These results have also been evaluated by analysis-of-variance (p-value). Results show that the CE feature is more suitable to use as a learning parameter for a classifier compared with other representative features including root mean square, median frequency and Higuchi's method. Most p-values of the CE feature were less than 0.0001. Thus the FD that is computed by the CE method can be applied to be used as a feature for a wide variety of sEMG applications.

Słowa kluczowe

EN

biomedical signal processing; electromyography signal; feature extraction; fractal analysis; human machine interface; pattern classification

• Wydawca: Komitet Metrologii i Aparatury Naukowej PAN
• Czasopismo: Metrology and Measurement Systems
• Rocznik: 2011
• Tom: Vol. 18, nr 4
• Strony: 645--658
• Opis fizyczny: Bibliogr. 27 poz., rys., tab., wykr.

Twórcy

autor

Phinyomark A.

autor

Phothisonothai M.

autor

Phukpattaranont P.

autor

Limsakul C.

• Department of Electrical Engineering, Faculty of Engineering, Prince of Songkhla University, 15 Kanjanavanich Road, Kho Hong, Hat Yai, Songkhla, 90112, Thailand,

Bibliografia

• [1] Koçer, S. (2010). Classification of EMG signals using neuro-fuzzy system and diagnosis of neuromuscular diseases. Journal of Medical Systems, 34(3), 321-329.
• [2] Oskoei, M. A., Hu, H. (2007). Myoelectric control systems-A survey. Biomedical Signal Processing and Control, 2(4), 275-294.
• [3] Boostani, R., Moradi, M. H. (2003). Evaluation of the forearm EMG signal features for the control of a prosthetic hand. Physiological Measurement, 24(2), 309-319.
• [4] Zecca, M., Micera, S., Carrozza, M. C., Dario, P. (2002). Control of multifunctional prosthetic hands by processing the electromyographic signal. Critical Reviews in Biomedical Engineering, 30(4-6), 459-485.
• [5] Oskoei, M. A., Hu, H. (2008). Support vector machine-based classification scheme for myoelectric control applied to upper limb. IEEE Transactions on Biomedical Engineering, 55(8), 1956-1965.
• [6] Lei, M., Wang, Z., Feng, Z. (2001). Detecting nonlinearity of action surface EMG signal. Physics Letters A, 290(5-6), 297-303.
• [7] Meng, Y., Liu, Y., Liu, B. (2005). Test nonlinear determinacy of electromyogram. In Proceedings of IEEE EMBS 2005. Shanghai, China, 4592-4595.
• [8] Padmanabhan, P., Puthusserypady, S. (2004). Nonlinear analysis of EMG signals-A chaotic approach. In Proceedings of IEEE EMBS 2004. San Francisco, CA, USA, 608-611.
• [9] Chen, W., Wang, Z., Xie, H., Yu, W. (2007). Characterization of surface EMG signal based on fuzzy entropy. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 15(2), 266-272.
• [10] Hu, X., Wang, Z., Ren, X. (2005). Classification of surface EMG signal with fractal dimension. Journal of Zhejiang University - Science B, 6(8), 844-848.
• [11] Gitter, J. A., Czerniecki, M. J. (1995). Fractal analysis of the electromyographic interference pattern. Journal of Neuroscience Methods, 58(1-2), 103-108.
• [12] Gupta, V., Suryanarayanan, S., Reddy, N. P. (1997). Fractal analysis of surface EMG signals from the biceps. International Journal of Medical Informatics, 45(3), 185-192.
• [13] Arjunan, S. P., Kumar, D. K. (2010). Decoding subtle forearm flexions using fractal features of surface electromyogram from single and multiple sensors. Journal of NeuroEngineering and Rehabilitation, 7(53).
• [14] Naik, G. R., Arjunan, S., Kumar, D. (2011). Applications of ICA and fractal dimension in sEMG signal processing for subtle movement analysis: a review. Australasian Physical & Engineering Science in Medicine, 34(2), 179-193.
• [15] Nakagawa, M. (1993). A critical exponent method to evaluate fractal dimensions of self-affine data. Journal of the Physical Society of Japan, 62(12), 4233-4239.
• [16] Petry, A., Barone, D. A. C. (2002). Speaker identification using nonlinear dynamical features. Chaos, Solitons & Fractals, 13(2), 221-231.
• [17] Sabanal, S., Nakagawa, M. (1996). The fractal properties of vocal sounds and their application in the speech recognition model. Chaos, Solitons & Fractals, 7(11), 1825-1843.
• [18] De Oliveira, L. P. L., Roque, W. L., Custódio, R. F. (1999). Lung sound analysis with time-dependent fractal dimensions. Chaos, Solitons & Fractals, 10(9), 1419-1423.
• [19] Nimkerdphol, K., Nakagawa, M. (2006). 3D locomotion and fractal analysis of Goldfish for acute toxicity bioassay. International Journal of Biological and Medical Sciences, 2(3), 180-185.
• [20] Nimkerdphol, K., Nakagawa, M. (2008). Effect of sodium hypochlorite on Zebrafish swimming behavior estimated by fractal dimension analysis. Journal of Bioscience and Bioengineering, 105(5), 486-492.
• [21] Phothisonothai, M., Nakagawa, M. (2005). EEG-based fractal analysis of different motor imagery tasks using critical exponent method. International Journal of Biological and Life Sciences, 1(3), 175-180.
• [22] Phothisonothai, M., Nakagawa, M. (2007). Fractal-based EEG data analysis of body parts movement imagery tasks. Journal of Physiological Sciences, 57(4), 217-226.
• [23] Phinyomark, A., Phothisonothai, M., Suklaead, P., Phukpattaranont, P., Limsakul, C. (2011). Fractal Analysis of Surface Electromyography (EMG) Signal for Identify Hand Movements Using Critical Exponent Analysis. In: Proceedings of ICSECS 2011. Universiti Malaysia Pahang, Kuantan, Malaysia, 703-713.
• [24] Goge, A. R., Chan, A. D. C. (2004). Investigating classification parameters for continuous myoelectrically controlled prostheses. In: Proceedings of CMBEC28. Quebec City, Canada, 141-144.
• [25] Esteller, R., Vachtsevanos, G., Echauz, J., Litt, B. (2001). A comparison of waveform fractal dimension algorithms. IEEE Transactions on Circuits and Systems I: Fundamental Theory and Applications, 48(2), 177-183.
• [26] Phinyomark, A., Hirunviriya, S., Limsakul, C., Phukpattaranont, P. (2010). Evaluation of EMG feature extraction for hand movement recognition based on Euclidean distance and standard deviation. In: Proceedings of ECTI-CON 2010. Chiang Mai, Thailand, 856-860.
• [27] Phinyomark, A., Hirunviriya, S., Nuidod, A., Phukpattaranont, P., Limsakul, C. (2011). Evaluation of EMG feature extraction for movement control of upper limb prostheses based on class separation index. In: Proceedings of BioMed 2011. Kuala Lumper, Malaysia, 750-754.