EMG feature evaluation for improving myoelectric pattern recognition robustness

Expert Systems with Applications

Volumn 40, Issue 12, 2013, Pages 4832-4840

  Phinyomark, Angkoon ^a,b   Quaine, Franck ^a   Charbonnier, Sylvie ^a   Serviere, Christine ^a   Tarpin Bernard, Franck ^b   Laurillau, Yann ^b  

^a GIPSA LAB   (France)

^b UNIV GRENOBLE ALPES   (France)

Author keywords

Electromyography (EMG); Feature extraction; Linear discriminant analysis; Myoelectric control; Sample entropy

Indexed keywords

BIOMEDICAL SIGNAL PROCESSING; DISCRIMINANT ANALYSIS; ELECTROMYOGRAPHY; ENTROPY; FEATURE EXTRACTION; FREQUENCY DOMAIN ANALYSIS; PATTERN RECOGNITION; TIME DOMAIN ANALYSIS;

CLASSIFICATION ACCURACY; FEATURE EVALUATION; LINEAR DISCRIMINANT ANALYSIS; MULTIPLE FEATURES; MYOELECTRIC CONTROL; OPTIMUM CONFIGURATIONS; REAL-TIME OPERATION; SAMPLE ENTROPY;

CLASSIFICATION (OF INFORMATION);

EID: 84885055916     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2013.02.023     Document Type: Article

References (45)

1. Anatomy of the human body [Image] (1918). Retrieved November 4, 2012, from Wikipedia.
2. Arjunan, S. P., & Kumar, D. K. (2010). Decoding subtle forearm flexionsusing fractal features ofsurface electromyogram from single and multiple sensors. Journal of NeuroEngineering and Rehabilitation, 7(53).
3. Benko, H., Saponas, T. S., Morris, D., & Tan, D. (2009). Enhancing input onand above the interactive surface with muscle sensing. In Proceedings of ACM international conference on interactive tabletops and surfaces (pp. 93-100).
4. Boschmann, A., Kaufmann, P., Platzner, M., & Winkler, M. (2009). Towards multi- movement hand prostheses:Combining adaptive classificationwith high precision sockets. In Proceedings of second European conference technically assisted rehabilitation.
5. Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5-32. (Pubitemid 32933532)
6. Chen, L., Geng, Y., & Li, G. (2011). Effect ofupper-limb positions onmotion pattern recognition using electromyography. In Proceedings of 4th international congress on image and signal processing (pp. 139-142).
7. Ehtiati, T., Kinsner, W., & Moussavi, Z. K. (1998). Multifractal characterization ofthe electromyogram signals inpresence offatigue. In Proceedings of IEEE conference on electrical and computer engineering (pp. 866-869).
8. Englehart, K., Hudgins, B., & Parker, P. A. (2001). Awavelet-based continuous classification scheme for multifunction myoelectric control. IEEE Transactions on Biomedical Engineering, 48(3), 302-311. (Pubitemid 32249578)
9. Fougner, A., Scheme, E., Chan, A. D. C., Englehart, K., & Stavdahl, Ø.(2011). Resolving the limb position effect inmyoelectric pattern recognition. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 19(6), 644-651.
10. Geethanjali, P., & Ray, K. K. (2011). Identificationofmotion from multi-channel EMG signals for control ofprosthetic hand. Australiasian College of Physical Scientists and Engineers inMedicine, 34(3), 419-427.
11. Gitter, J. A., & Czerniecki, M. J. (1995). Fractal analysis ofthe electromyographic interference pattern. Journal of Neuroscience Methods, 58(1-2), 103-108.
12. Gupta, V., Suryanarayanan, S., & Reddy, N. P. (1997). Fractal analysis ofsurface EMG signals from the biceps. International Journal ofMedical Informatics, 45(3), 185-192. (Pubitemid 27375569)
13. Hariharan, M., Fook, C. Y., Sindhu, R., Ilias, B., & Yaacob, S. (2012). Acomparative study of wavelet families for classificationofwrist motions. Computers and Electrical Engineering, 38(6), 1798-1807.
14. Hudgins, B., Parker, P., & Scott, R. N. (1993). Anew strategy for multifunction myoelectric control. IEEE Transactions on Biomedical Engineering, 40(1), 82-94. (Pubitemid 24060692)
15. Jain, S., Singhal, G., Smith, R. J., Kaliki, R., & Thakor, N. (2012). Improving long term myoelectric decoding, using anadaptive classifierwith label correction. In Proceedings of 4th IEEE RAS/EMBS international conference onbiomedical robotics and biomechatronics (pp. 532-537).
16. Kamavuako, E. N., Farina, D., Yoshida, K., & Jensen, W. (2012). Estimation ofgrasping force from features ofintramuscular EMG signals with mirrored bilateral training. Annals of Biomedical Engineering, 40(3), 648-656.
17. Kaufmann, P., Englehart, K., & Platzner, M. (2010). Fluctuating EMG signals: investigating long-term effects ofpattern matching algorithms. In Proceedings of 32nd annual international conference ofthe IEEE engineering inmedicine and biology society (pp. 6357-6360).
18. Kendell, C., Lemaire, E. D., Losier, Y., Chan, A., & Hudgins, B. (2012). Anovel approach to surface electromyography:Anexploratory study ofelectrode-pair selection based on signal characteristics. Journal of NeuroEngineering and Rehabilitation, 9(24).
19. Khushaba, R. N., Al-Ani, A., & Al-Jumaily, A. (2010). Orthogonal fuzzy neighborhood discriminant analysis for multifunction myoelectric hand control. IEEE Transactions on Biomedical Engineering, 57(6), 1410-1419.
20. Kim, K. S., Choi, H. H., Moon, C. S., & Mun, C. W. (2011). Comparison of k-nearest neighbor, quadratic discriminant and linear discriminant analysis in classification ofelectromyogram signals based onthe wrist-motion directions. Current Applied Physics, 11(3), 740-745.
21. Liarokapis, M. V., Artemiadis, P. K., Katsiaris, P. T., Kyriakopoulos, K. J., & Manolakos, E. S. (2012). Learning human reach-to-grasp strategies:towards EMG-based control of robotic arm-hand systems. In Proceedings of IEEE international conference on robotics and automation (pp. 2287-2292).
22. Lorrain, T., Jiang, N., & Farina, D. (2011). Influenceofthe training set onthe accuracy of surface EMG classificationindynamic contractions for the control of multifunction prostheses. Journal of NeuroEngineering and Rehabilitation, 8(25).
23. Nazarpour, K., Al-Timemy, A. H., Bugmann, G., & Jackson, A. (2013). Anote onthe probability distribution function ofthe surface electromyogram signal. Brain Research Bulletin, 90, 88-91.
24. Oskoei, M. A., & Hu, H. (2007). Myoelectric control systems-Asurvey. Biomedical Signal Processing and Control, 2(4), 275-294.
25. Oskoei, M. A., & Hu, H. (2008). Support vector machine-based classificationscheme for myoelectric control applied toupper limb. IEEE Transactions onBiomedical Engineering, 55(8), 1956-1965. (Pubitemid 352013532)
26. Peerdeman, B., Boere, D., Witteveen, H., in'tVeld, R. H., Hermens, H., Stramigioli, S., et al.(2011). Myoelectric forearm prostheses:State ofthe art from auser- centered perspective. Journal of Rehabilitation Research and Development, 48(6), 719-737.
27. Phinyomark, A., Nuidod, A., Phukpattaranont, P., & Limsakul, C. (2012b). Feature extraction and reduction ofwavelet transform coefficientsfor EMG pattern classification. Elektronika ir Elektrotechnika, 122 (6), 27-32.
28. Phinyomark, A., Phothisonothai, M., Phukpattaranont, P., & Limsakul, C. (2011). Critical exponent analysis applied tosurface electromyography (EMG)signals for gesture recognition. Metrology and Measurement Systems, 18(4), 645-658.
29. Phinyomark, A., Phukpattaranont, P., & Limsakul, C. (2012a). Investigating long- term effects offeature extraction methods for continuous EMG pattern classification. Fluctuation and Noise Letters, 11(4), 1250028.
30. Phinyomark, A., Phukpattaranont, P., & Limsakul, C. (2012c). Feature reduction and selection for EMG signal classification. Expert Systems with Applications, 39(8), 7420-7431.
31. Phinyomark, A., Phukpattaranont, P., & Limsakul, C. (2012d). Fractal analysis features for weak and single-channel upper-limb EMG signals. Expert Systems with Applications, 39(12), 11156-11163.
32. Richman, J. S., & Moorman, J. R. (2000). Physiological time-series analysis using approximate entropy and sample entropy. American Journal of Physiology, 278(6), H2039-H2049. (Pubitemid 30439218)
33. Saponas, T. S., Tan, D. S., Morris, D., Balakrishnan, R., Turner, J., & Landay, J. A. (2009). Enabling always-available input with muscle-computer interfaces. In Proceedings of 22nd annual ACM symposium onuser interface software and technology (pp. 167-176).
34. Talebinejad, M., Chan, A. D. C., Miri, A., & Dansereau, R. M. (2009). Fractal analysis of surface electromyography signals:anovel power spectrum-based method. Journal of Electromyography and Kinesiology, 19(5), 840-850.
35. Tkach, D., Huang, H., & Kuiken, T. A. (2010). Study ofstability oftime-domain features for electromyographic pattern recognition. Journal of NeuroEngineering and Rehabilitation, 7(21).
36. van den Broek, E. L., Schut, M. H., Westerink, J. H. D. M., van Herk, J., & Tuinenbreijer, K. (2006). Computing emotion awareness through facial electromyography. In Proceedings of international conference oncomputer vision inhuman-computer interaction (pp. 52-63). (Pubitemid 44059047)
37. Yang, D., Zhao, J., Jiang, L., & Liu, H. (2012). Dynamic hand motion recognition based on transient and steady-state EMG signals. International Journal of Humanoid Robotics, 9(1), 1250007.
38. Young, A. J., Hargrove, L. J., & Kuiken, T. A. (2011). The effects ofelectrode size and orientation on the sensitivity ofmyoelectric pattern recognition systems to electrode shift. IEEE Transactions onBiomedical Engineering, 58(9), 2537-2544.
39. Young, A. J., Hargrove, L. J., & Kuiken, T. A. (2012). Improving myoelectric pattern recognition robustness toelectrode shift bychanging interelectrode distance and electrode configuration. IEEE Transactions onBiomedical Engineering, 59(3), 645-645.
40. Zardoshti-Kermani, M., Wheeler, B. C., Badie, K., & Hashemi, R. M. (1995). EMG feature evaluation for movement control ofupper extremity prostheses. IEEE Transactions on Rehabilitation Engineering, 3(4), 324-333.
41. Zecca, M., Micera, S., Carrozza, M. C., & Dario, P. (2002). Control ofmultifunctional prosthetic hands by processing the electromyographic signal. Critical Reviews in Biomedical Engineering, 30(4-6), 459-485. (Pubitemid 36528604)
42. Zhang, X., Chen, X., Zhao, Z. Y., Li, Q., Yang, J. H., Lantz, V., Wang, K. Q. (2007). An adaptive feature extractor for gesture SEMG recognition. In Proceedings of first international conference on medical biometrics (pp. 83-90).
43. Zhang, X., & Zhou, P. (2012). Sample entropy analysis ofsurface EMG for improved muscle activity onset detection against spurious background spikes. Journal of Electromyography and Kinesiology, 22(6), 901-907.
44. Zhao, J., Jiang, L., Cai, H., Liu, H., & Hirzinger, G. (2006a). Anovel EMG motion pattern classifier based onwavelet transform and nonlinearlity analysis method. In Proceedings of IEEE international conference onrobotics and biomimetics (pp. 1494-1499).
45. Zhao, J., Xie, Z., Jiang, L., Cai, H., Liu, H., & Hirzinger, G. (2006b). EMG control for a five-fingered underactuated prosthetic hand based onwavelet transform and sample entropy. In Proceedings of IEEE/RSJ international conference onintelligent robots and systems (pp. 3215-3220).