Muscle activity detection in electromyograms recorded during periodic movements

Computers in Biology and Medicine

Volumn 47, Issue 1, 2014, Pages 93-103

  Špulák, Daniel ^a   Čmejla, Roman ^a   Bačáková, Radka ^b   Kračmar, Bronislav ^b   Satrapová, Lenka ^b   Novotný, Petr ^b  

^a CZECH TECHNICAL UNIVERSITY IN PRAGUE   (Czech Republic)

^b CHARLES UNIVERSITY   (Czech Republic)

Author keywords

Averaging; Electromyography; Kinesiology; Muscle activation pattern; Muscle activity detection; Periodic movements; Signal processing

Indexed keywords

AVERAGING; ELECTROMYOGRAPHY; KINESIOLOGY; MUSCLE ACTIVATION PATTERN; MUSCLE ACTIVITY DETECTION; PERIODIC MOVEMENTS; SIGNAL PROCESSING;

BICYCLING; COMPUTER SIMULATION; ELECTROMYOGRAPHY; HUMANS; MALE; MUSCLE CONTRACTION; MUSCLE, SKELETAL; SIGNAL PROCESSING, COMPUTER-ASSISTED; USER-COMPUTER INTERFACE;

EID: 84894347373     PISSN: 00104825     EISSN: 18790534     Source Type: Journal    
DOI: 10.1016/j.compbiomed.2014.01.013     Document Type: Article

References (40)

1. Frigo C., Crenna P. Multichannel SEMG in clinical gait analysis: a review and state-of-the-art. Clin. Biomech. 2009, 24(3):236-245.
2. Subasi A. Classification of EMG signals using PSO optimized SVM for diagnosis of neuromuscular disorders. Comput. Biol. Med. 2013, 43:576-586.
3. Arslan Y.Z., Adli M.A., Akan A., Baslo M.B. Prediction of externally applied forces to human hands using frequency content of surface EMG signals. Comput. Meth. Programs Biomed. 2010, 98:36-44.
4. Hu X., Wang Z., Ren X. Classification of surface EMG signal using relative wavelet packet energy. Comput. Meth. Programs Biomed. 2005, 79:189-195.
5. Kleissen R.F.M., Buurke J.H., Harlaar J., Zilvold G. Electromyography in the biomechanical analysis of human movement and its clinical application. Gait Posture 1998, 8:143-158.
6. Zazula D., Holobar A. An approach to surface EMG decomposition based on higher-order cumulants. Comput. Meth. Programs Biomed. 2005, 80(suppl. 1):S51-S60.
7. Staudenmann D., Kingma I., Daffertshofer A., Stegeman D.F., van Dieën J.H. Heterogenity of muscle activation in relation to force direction: a multi-channel surface electromyography study on the triceps surae muscle. J. Electromyogr. Kinesiol. 2009, 19:882-895.
8. Karlsson S., Yu J., Akay M. Time-frequency analysis of myoelectric signals during dynamic contractions: a comparative study. IEEE Trans. Biomed. Eng. 2000, 47(2):228-238.
9. Johnson T.D., Elashoff R.M., Harkema S.J., Bayesian A change-point analysis of electromyographic data: detecting muscle activation patterns and associated applications. Biostatistics 2003, 4(1):143-164.
10. Xu Q., Quan Y., Yang L., He J. An adaptive algorithm for the determination of the onset and offset of muscle contraction by EMG signal processing. IEEE Trans. Neural Syst. Rehabil. Eng. 2013, 21(1):65-73.
11. Lee J.H., Shim H., Lee H.S., Lee Y.H., Roon Y.R. Detection of onset and offset time of muscle activity in surface EMGs using the Kalman smoother. IFMBE Proc. 2007, 14/q:1103-1106.
12. Romero F., Alonso F.J., Cubero J., Font-Llagunes J.M., Lugrís U., Vinagre B., Hosseinia H. EMG signal smoothing using singular spectrum analysis. XXIX Congr. Anual Soc. Esp. Ing. Bioméd. 2011, 81-84.
13. Vaisman L., Zariffa J., Popovic M.R. Application of singular spectrum-based change-point analysis to EMG-onset detection. J. Electromyogr. Kinesiol. 2010, 20:750-760.
14. Vannozzi G., Conforto S., D'Alessio T. Automatic detection of surface EMG activation timing using a wavelet transform based method. J. Electromyogr. Kinesiol. 2010, 20:767-772.
15. Merlo A., Farina D., Merletti R. A fast and reliable technique for muscle activity detection from surface EMG signals. IEEE Trans. Biomed. Eng. 2003, 50(3):316-323.
16. J. Lee, H. Ko, S. Lee, H. Lee, Y. Yoon, Detection technique of muscle activation intervals for sEMG signals based on the Empirical Mode Decomposition, in: Proceedings of the 31st Annual International Conference of the IEEE EMBS, September 2-6, 2009, pp. 336-339.
17. Zhang X., Zhou P. Sample entropy analysis of surface EMG for improved muscle activity onset detection against spurious background spikes. J. Electromyogr. Kinesiol. 2012, 22:901-907.
18. Hodges P.W., Bui B.H. A comparison of computer-based methods for the determination of onset of muscle contraction using electromyography. Electroencephalogr. Clin. Neurophysiol. Electromyogr. Motor Control 1996, 101(6):511-519.
19. Micera S., Vannozzi G., Sabatini A.M., Dario P. Improving detection of muscle activation intervals. IEEE Eng. Med. Biol. 2001, 38-46.
20. Staude G., Flachenecker C., Daumer M., Wolf W. Onset detection in surface electromyographic signals: a systematic comparison of methods. EURASIP J. Appl. Signal Process. 2001, 2:67-81.
21. Soderberg G.L., Knutson L.M. A guide for use and interpretation of kinesiologic electromyographic data. Phys. Ther. 2000, 80(5):485-498.
22. Allison G.T. Trunk muscle onset detection technique for EMG signals with ECG artefact. J. Electromyogr. Kinesiol. 2003, 13:209-216.
23. Li X., Zhou P., Aruin A.S. Teager-Kaiser energy operation of surface EMG improves muscle activity onset detection. Ann. Biomed. Eng. 2007, 35(9):1532-1538.
24. Solnik S., Rider P., Steinweg K., DeVita P., Hortobágyi T. Teager-Kaiser energy operator signal conditioning improves EMG onset detection. Eur. J. Appl. Physiol. 2010, 110(3):489-498.
25. Billaut F., Basset F.A., Falgairette G. Mucsle coordination changes during intermittent cycling sprints. Neurosci. Lett. 2005, 380:265-269.
26. Gazendam M.G.J., Hof A.L. Averaged EMG profiles in jogging and running at different speeds. Gait Posture 2007, 25(4):604-614.
27. Wren T.A.L., Do K.P., Rethlefsen S.A., Healy B. Cross-correlation as a method for comparing dynamic electromyography signals during gait. J. Biomech. 2006, 39(14):2714-2718.
28. Hug F. Can muscle coordination be precisely studied by surface electromyography?. J. Electromyogr. Kinesiol. 2011, 21(1):1-12.
29. Campanini I., Merlo A., Degola P., Merletti R., Vezzosi G., Farina D. Effect of electrode location on EMG signal envelope in leg muscles during gait. J. Electromyogr. Kinesiol. 2007, 17:515-526.
30. Dorel S., Couturier A., Hug F. Intra-session repeatability of lower limb muscles activation pattern during pedaling. J. Electromyogr. Kinesiol. 2008, 18:857-865.
31. Jobson S.A., Hopker J., Arkesteijn M., Passfield L. Inter- and intra-session reliability of muscle activity patterns during cycling. J. Electromyogr. Kinesiol. 2012, , 〈http://dx.doi.org/10.1016/j.jelekin.2012.08.013〉.
32. D. Špulák, R. Čmejla, P. Mikulíková, J. Bezoušková Paulů, B. Kračmar, Muscle activity detection using EMG envelope thresholding: comparison of various approaches, in: Proceedings of the 20th Annual Conference Proceedings Technical Computing, 2012.
33. Thexton A.J. A randomisation method for discriminating between signal and noise in recordings of rhythmic electromyographic activity. J. Neurosci. Methods 1996, 93-98.
34. Prilutsky B., Zatsiorsky V.M. Optimization-based models of muscle coordination. Exercise Sport Sci. Rev. 2002, 1-13.
35. Hof A.L., Elzinga H., Grimmius W., Halbertsma J.P.K. Speed dependence of averaged EMG profiles in walking. Gait Posture 2002, 16(1):78-86.
36. Mesin L. Volume conductor models in surface electromyography: applications to signal interpretation and algorithm test. Comput. Biol. Med. 2013, 43:953-961.
37. Yu H., Alaqtash M., Spier E., Sarkodie-Gyan T. Analysis of muscle activity during gait cycle using fuzzy rule-based reasoning. Measurement 2010, 43:1106-1114.
38. Ivanenko Y.P., Poppele R.E., Lacquaniti F. Five basic muscle activation patterns account for muscle activity during human locomotion. J. Physiol. 2004, 556.1:267-282.
39. Wang W., Stefano A.De., Allen R. A simulation model of the surface EMG signal for analysis of muscle activity during the gait cycle. Comput. Biol. Med. 2006, 36:601-618.
40. D. Špulák, P. Mikulíková, Segmentation of electromyographic signal during periodic movements, POSTER 2012, in: 16th International Student Conference on Electrical Engineering Proceedings 2012.