ELECTROMYOGRAPHY SIGNAL ON BICEPS MUSCLE IN TIME DOMAIN ANALYSIS

Authors

  • Abu Bakar Yahya Faculty of Electrical Engineering, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, 76100 Durian Tunggal, Melaka, Malaysia
  • Wan Mohd Bukhari Wan Daud Faculty of Electrical Engineering, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, 76100 Durian Tunggal, Melaka, Malaysia
  • Chong Shin Horng Faculty of Electrical Engineering, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, 76100 Durian Tunggal, Melaka, Malaysia
  • Rubita Sudirman Faculty of Electrical Engineering, Universiti Teknologi Malaysia, 81310 Skudai, Johor, Malaysia

DOI:

https://doi.org/10.15282/jmes.7.2014.17.0115

Keywords:

Electromyography (EMG); time domain; force estimation; non-invasive.

Abstract

Features extraction is important for electromyography (EMG) signal analysis. The paper’s objective is to evaluate the features extraction of the EMG signal. The experimental set-up for EMG signal acquisition followed the procedures recommended by Europe’s Surface Electromyography for Non-invasive Assessment of Muscle (SENIAM) project. The EMG signal’s data were analysed in the time domain to get the features. Four features were considered based on the analysis, which are IEMG, MAV, VAR and RMS. The average muscle force condition can be estimated by correlation between the EMG voltage amplitude with linear estimation with the full-wave rectification method. The R-squared value determined the correlation between the EMG voltage amplitude with the loads. IEMG was chosen as the reference feature for estimation of the muscle’s force due to its R-squared value equal to 0.997. By referring to the IEMG, the linear equation obtained from the correlation was used for estimation of the muscle’s force. These findings can be integrated to design a muscle force model based on the biceps muscle.

References

Ahmed S, Ahmad S, Faruqe MO, Islam MR. EMG signal decomposition using wavelet transformation with respect to different wavelet and a comparative study. Proceedings of the 2nd International Conference on Interaction Sciences: Information Technology, Culture and Human. Seoul, Korea: ACM; 2009. p. 730-5.

Magaswaran K, Phuman Singh AS, Hassan MZ. A new method in the identification of noise and vibration characteristics of automotive disk brakes in the low frequency domain. International Journal of Automotive and Mechanical Engineering. 2014;9:1564-77.

Merletti R, Parker PA. Electromyography: Physiology, Engineering, and Non- Invasive Applications. New York: John Wiley and Sons Inc.; 2004.

Erdemir A, McLean S, Herzog W, van den Bogert AJ. Model-based estimation of muscle forces exerted during movements. Clinical Biomechanics. 2007;22:131-54.

Hug F. Can muscle coordination be precisely studied by surface electromyography? Journal of Electromyography and Kinesiology. 2011;21:1- 12.

Ahmad Z, Taha Z, Hassan HA, Hisham MA, Johari NH, Kadirgama K. Biomechanics measurements in archery. Journal of Mechanical Engineering and Sciences. 2014;6:762-71.

Wan Daud WMB, Yahya AB, Chong SH, Sulaima MF, Sudirman R. Features extraction of electromyography signals in time domain on biceps Brachii muscle. International Journal of Modeling and Optimization. 2013;3:515-9.

Parsaei H, Stashuk DW, Rasheed S, Farkas C, Hamilton-Wright A. Intramuscular EMG Signal Decomposition. Critical Reviews™ in Biomedical Engineering. 2010;38:435-65.

Daud WMBW, Sudirman R. A wavelet approach on energy distribution of eye movement potential towards direction. Industrial Electronics & Applications (ISIEA), 2010 IEEE Symposium on Industrial Electronics and Applications. Penang, Malaysia2010. p. 181-5.

Abdul Rahman AG, Yee KS, Ismail Z, Kuan KK, Chao OZ, Tong CW, et al. Impact force identification using the modal transformation method in collocated and non-collocated cases. Journal of Mechanical Engineering and Sciences. 2014;6:968-74.

Jamil N, Yusoff AR, Mansor MH. Literature review of electromagnetic actuator force generation for dynamic modal testing applications. Journal of Mechanical Engineering and Sciences. 2012;3:311-9.

Hermens H, Merletti R, Freriks B. European activities on surface electromyography. Proceedings of the first general SENIAM (Surface EMG for Non Invasive Assessment of Muscles) workshop, September1996.

Daud WMBW, Sudirman R. Time frequency analysis of electrooculograph (EOG) signal of eye movement potentials based on wavelet energy distribution. Fifth Asia Modelling Symposium. 2011, p. 81-6.

Phinyomark A, Phukpattaranont P, Limsakul C. Feature reduction and selection for EMG signal classification. Expert Systems with Applications. 2012;39:7420- 31.

Darmakusuma R, Prihatmanto AS, Indrayanto A, Mengko TL. Bicep brachii's force estimation using MAV method on assistive technology application. Instrumentation, Communications, Information Technology, and Biomedical Engineering (ICICI-BME), 2011 2nd International Conference on2011. p. 288- 92.

Daud WMBW, Bukhari WM. Surface electromyography of eyes potential behaviour using wavelet transform analysis. Australian Journal of Basic and Applied Sciences. 2013;7:64–71.

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Published

2014-12-31

How to Cite

[1]
Abu Bakar Yahya, Wan Mohd Bukhari Wan Daud, Chong Shin Horng, and Rubita Sudirman, “ELECTROMYOGRAPHY SIGNAL ON BICEPS MUSCLE IN TIME DOMAIN ANALYSIS”, J. Mech. Eng. Sci., vol. 7, no. 1, pp. 1179–1188, Dec. 2014.

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