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Journal of NeuroEngineering and Rehabilitation

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Open Access 01-12-2013 | Research

Application of a self-enhancing classification method to electromyography pattern recognition for multifunctional prosthesis control

Authors: Xinpu Chen, Dingguo Zhang, Xiangyang Zhu

Published in: Journal of NeuroEngineering and Rehabilitation | Issue 1/2013

Abstract

Background

The nonstationary property of electromyography (EMG) signals usually makes the pattern recognition (PR) based methods ineffective after some time in practical application for multinational prosthesis. The conventional EMG PR, which is accomplished in two separate steps: training and testing, ignores the mismatch between training and testing conditions and often discards the useful information in testing dataset.

Method

This paper presents a novel self-enhancing approach to improve the classification performance of the electromyography (EMG) pattern recognition (PR). The proposed self-enhancing method incorporates the knowledge beyond the training condition to the classifiers from the testing data. The widely-used linear discriminant analysis (LDA) and quadratic discriminant analysis (QDA) are extended to self-enhancing LDA (SELDA) and self-enhancing QDA (SEQDA) by continuously updating their model parameters such as the class mean vectors, the class covariances and the pooled covariance. Autoregressive (AR) and Fourier-derived cepstral (FC) features are adopted. Experimental data in two different protocols are used to evaluate performance of the proposed methods in short-term and long-term application respectively.

Results

In protocol of short-term EMG, based on AR and FC, the recognition accuracy of SEQDA and SELDA is 2.2% and 1.6% higher than conventional that of QDA and LDA respectively. The mean results of SEQDA(C) and SEQDA (M) are improved by 2.2% and 0.75% for AR, and 1.99% and 1.1% for FC respectively when compared to QDA. The mean results of SELDA(C) and SELDA (M) are improved by 0.48% and 1.55% for AR, and 0.67% and 1.22% for FC when compared to LDA. In protocol of long-term EMG, the mean result of SEQDA is 3.15% better than that of QDA.

Conclusion

The experimental results show that the self-enhancing classifiers significantly outperform the original versions using both AR and FC coefficient feature sets. The performance of SEQDA is superior to SELDA. In addition, preliminary study on long-term EMG data is conducted to verify the performance of SEQDA.

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Parker P, Englehart K, Hudgins B: Myoelectric signal processing for control of powered limb prostheses. J Electromyogr Kinesiol 2006,16(6):541-548. 10.1016/j.jelekin.2006.08.006CrossRefPubMed

Hudgins B, Parker P, Scott R: A new strategy for multifunction myoelectric control. Biomed Eng IEEE Trans 1993, 40: 82-94. 10.1109/10.204774CrossRef

Merletti R, Parker P: Electromyography: Physiology, Engineering, and Noninvasive Applications. Hoboken: John Wiley & Sons, Inc.; 2004.CrossRef

Liu H: Exploring human hand capabilities into embedded multifingered object manipulation. Ind Informatics, IEEE Trans 2011,7(3):389-398.CrossRef

Ajiboye A, Weir R: A heuristic fuzzy logic approach to EMG pattern recognition for multifunctional prosthesis control. Neural Syst Rehabil Eng IEEE Trans 2005,13(3):280-291. 10.1109/TNSRE.2005.847357CrossRef

Chan A, Englehart K: Continuous myoelectric control for powered prostheses using hidden Markov models. IEEE Trans Biomed Eng 2005, 52: 121-124. 10.1109/TBME.2004.836492CrossRefPubMed

Englehart K, Hudgins B: A robust, real-time control scheme for multifunction myoelectric control. Biomed Eng IEEE Trans 2003,50(7):848-854. 10.1109/TBME.2003.813539CrossRef

Hargrove L, Li G, Englehart K, Hudgins B: Principal components analysis preprocessing for improved classification accuracies in pattern-recognition-based myoelectric control. IEEE Trans Biomed Eng 2009,56(5):1407-1414.CrossRefPubMed

Huang Y, Englehart K, Hudgins B, Chan A: A Gaussian mixture model based classification scheme for myoelectric control of powered upper limb prostheses. IEEE Trans Biomed Eng 2005,52(11):1801-1811. 10.1109/TBME.2005.856295CrossRefPubMed

10.

Oskoei M, Hu H: Support vector machine-based classification scheme for myoelectric control applied to upper limb. IEEE Trans Biomed Eng 2008,55(8):1956-1965.CrossRefPubMed

11.

Graupe D, Cline W: Functional separation of EMG signals via ARMA identification methods for prosthesis control purposes. Syst Man Cybern IEEE Trans 1975,5(2):252-259.CrossRef

12.

Hu X, Nenov V: Multivariate AR modeling of electromyography for the classification of upper arm movements. Clin Neurophysiol 2004,115(6):1276-1287. 10.1016/j.clinph.2003.12.030CrossRefPubMed

13.

Stavdahl O, Fougner MCAEK A Scheme: Resolving the limb position effect in myoelectric pattern recognition. Neural Syst Rehabil Eng IEEE Trans 2011,19(6):644-651.CrossRef

14.

Shenoy P, Miller K, Crawford B, Rao R: Online electromyographic control of a robotic prosthesis. Biomed Eng IEEE Trans 2008,55(3):1128-1135.CrossRef

15.

Boostani R, Moradi M: Evaluation of the forearm EMG signal features for the control of a prosthetic hand. Physiol Meas 2003,24(2):309-319. 10.1088/0967-3334/24/2/307CrossRefPubMed

16.

Kang W, Shiu J, Cheng C, Lai J, Tsao H, Kuo T: The application of cepstral coefficients and maximum likelihood method in EMG pattern recognition [movements classification]. Biomed Eng IEEE Trans 1995,42(8):777-785. 10.1109/10.398638CrossRef

17.

Englehart K, Hudgins B, Parker P, Stevenson M: Classification of the myoelectric signal using time-frequency based representations. Med Eng Phys 1999,21(6-7):431-438. 10.1016/S1350-4533(99)00066-1CrossRefPubMed

18.

Wang G, Yan Z, Hu X, Xie H, Wang Z: Classification of surface EMG signals using harmonic wavelet packet transform. Physiol Meas 2006,27(12):1255-1268. 10.1088/0967-3334/27/12/001CrossRefPubMed

19.

Huang H, Zhang F, Sun Y, He H: Design of a robust EMG sensing interface for pattern classification. J Neural Eng 2010, 7: 056005. 10.1088/1741-2560/7/5/056005PubMedCentralCrossRefPubMed

20.

Chan F, Yang Y, Lam F, Zhang Y, Parker P: Fuzzy EMG classification for prosthesis control. IEEE Trans Rehabil Eng 2000,8(3):305-311. 10.1109/86.867872CrossRefPubMed

21.

Peleg D, Braiman E, Yom-Tov E, Inbar G: Classification of finger activation for use in a robotic prosthesis arm. Neural Syst Rehabil Eng IEEE Trans 2002,10(4):290-293. 10.1109/TNSRE.2002.806831CrossRef

22.

Momen K, Krishnan S, Chau T: Real-Time classification of forearm electromyographic signals corresponding to user-selected intentional movements for multifunction prosthesis control. IEEE Trans Neural Syst Rehabil Eng 2007,15(4):535-542.CrossRefPubMed

23.

Zhou P, Lowery M, Englehart K, Huang H, Li G, Hargrove L, Dewald J, Kuiken T: Decoding a new neural machine interface for control of artificial limbs. J Neurophysiol 2974.,98(5):

24.

Chan A, Green G: Myoelectric control development toolbox. In Proc. 30th Conf. on Canadian Medical and Biological Engineering Society. Toronto, ON; 2007.

25.

Sensinger J, Lock B, Kuiken T: Adaptive pattern recognition of myoelectric signals: exploration of conceptual framework an practical algorithms. Neural Syst Rehabil Eng IEEE Trans 2009,17(3):270-278.CrossRef

26.

Hargrove L, Englehart K, Hudgins B: A training strategy to reduce classification degradation due to electrode displacements in pattern recognition based myoelectric control. Biomed Signal Process Control 2008,3(2):175-180. 10.1016/j.bspc.2007.11.005CrossRef

27.

Nishikawa D, Yu W, Maruishi M, Watanabe I, Yokoi H, Mano Y, Kakazu Y: On-line learning based electromyogram to forearm motion classifier with motor skill evaluation. JSME Int J Ser C, Mech Syst Mach Elements Manuf 2000,43(4):906-915. 10.1299/jsmec.43.906CrossRef

28.

Hargrove L, Englehart K, Hudgins B: A comparison of surface and intramuscular myoelectric signal classification. IEEE Trans Biomed Eng 2007,54(5):847-853.CrossRefPubMed

29.

Bilodeau M, Cincera M, Arsenault A, Gravel D: Normality and stationarity of EMG signals of elbow flexor muscles during ramp and step isometric contractions. J Electromyography Kinesiol 1997,7(2):87-96. 10.1016/S1050-6411(96)00024-7CrossRef

30.

Campbell J: Speaker recognition: A tutorial. Proc IEEE 1997,85(9):1437-1462. 10.1109/5.628714CrossRef

31.

Rao K, Yip P: Discrete Cosine Transform: Algorithms, Advantages, Applications. San Diego: Academic Press, Professional, Inc; 1990.CrossRef

32.

Chen X, Zhu X, Zhang D: Use of the discriminant Fourier-derived cepstrum with feature-level post-processing for surface electromyographic signal classification. Physiol Meas 2009, 30: 1399-1413. 10.1088/0967-3334/30/12/008CrossRefPubMed

33.

Fukunaga K: Introduction to, Statistical Pattern Recognition. San Diego: Academic press; 1990.

34.

Duda R, Hart P, Stork D: Pattern Classification. New York: Wiley-Interscience; 2000.

35.

Zar J: Biostatistical Analysis. Upper Saddle River: Prentice-Hall, Inc; 1999.

Title: Application of a self-enhancing classification method to electromyography pattern recognition for multifunctional prosthesis control
Authors: Xinpu Chen
Dingguo Zhang
Xiangyang Zhu
Publication date: 01-12-2013
Publisher: BioMed Central
Published in: Journal of NeuroEngineering and Rehabilitation / Issue 1/2013
Electronic ISSN: 1743-0003
DOI: https://doi.org/10.1186/1743-0003-10-44

ACC 2024 Congress

Springer Medicine

Application of a self-enhancing classification method to electromyography pattern recognition for multifunctional prosthesis control

Abstract

Background

Method

Results

Conclusion

ACC 2024 Congress

Springer Medicine

Abstract

Background

Method

Results

Conclusion

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