Performance Comparison of EMG Signal Analysis for Manual Lifting using Spectrogram

Authors

  • T.N.S. T.Zawawi Faculty of Electrical Engineering, Universiti Teknikal Malaysia Melaka, 76100 Durian Tunggal, Melaka, Malaysia.
  • A.R. Abdullah Faculty of Electrical Engineering, Universiti Teknikal Malaysia Melaka, 76100 Durian Tunggal, Melaka, Malaysia.
  • E.F. Shair Faculty of Electrical Engineering, Universiti Teknikal Malaysia Melaka, 76100 Durian Tunggal, Melaka, Malaysia.
  • S.M. Saleh Faculty of Electrical Engineering, Universiti Teknikal Malaysia Melaka, 76100 Durian Tunggal, Melaka, Malaysia.

Keywords:

Electromyography (EMG) Signal, Manual Lifting, Spectrogram

Abstract

Electromyography (EMG) signal is non-stationary signal and highly complex time and frequency characteristics. Fast-Fourier transform common technique in signal processing involving EMG signal. However, this technique has a limitation to provide the time-frequency information for EMG signals. This paper presents the analysis of EMG signal of the variable lifting height and mass of load between the four subjects selected in manual lifting by using spectrogram. Spectrogram is one of the time-frequency representation (TFR) that represents the threedimensional of the signal with respect to time and frequency in magnitude presentations. The manual lifting tasks is based on manual lifting of 5 kg and 10 kg load that performed by the right biceps brachii at lifting height of 75 cm and 140 cm. Four from ten healthy volunteers in fresh condition is selected into this comparison of subject performance tasks with their raw data collections. The raw data of EMG signals were then analyzed using MATLAB 2011 to obtain the voltage in time and frequency information. This study obtained the mean instantaneous RMS Voltage (Vrms(t)) to visualize the strength of the subjects produced during the manual lifting tasks. Results of this study evince the physical details of the subjects would able to effect the performance of the lifting. Higher lifting height and the number of contraction, the better performance of the subjects. It concluded that the application of spectrogram is able to providing the performance of the subjects by time-frequency information for EMG signals.

References

I. Halim, A. R. Omar, A. M. Saman, and I. Othman, “Assessment of Muscle Fatigue Associated with Prolonged Standing in the Workplace,” Saf. Health Work, vol. 3, no. 1, p. 31, 2012.

I. Halim and A. R. Omar, “Development of prolonged standing strain index to quantify risk levels of standing jobs,” Int. J. Occup. Saf. Ergon., vol. 18, no. 1, pp. 85–96, 2012.

R. Maiti and T. P. Bagchi, “Effect of different multipliers and their interactions during manual lifting operations,” Int. J. Ind. Ergon., vol. 36, no. 11, pp. 991–1004, 2006.

M. S. Isa Halim, Rawaida, Kamat S. R, Rohana A., Adi Saptari, “Analysis of Muscle Activity using Surface Electromyography for Muscle Performance in Manual Lifting Task,” Appl. Mech. Mater., vol. 564, pp. 544–649, 2014.

A. Phinyomark, P. Phukpattaranont, and C. Limsakul, “Feature reduction and selection for EMG signal classification,” Expert Syst. Appl., vol. 39, no. 8, pp. 7420–7431, 2012.

E. Gokgoz and A. Subasi, “Comparison of decision tree algorithms for EMG signal classification using DWT,” Biomed. Signal Process. Control, vol. 18, pp. 138–144, 2015.

S. D. Ruchika, “An Explanatory Study of the Parameters to Be Measured From,” International Journal of Engineering And Computer Science, vol. 2, no. 1, 2013.

M. R. Canal, “Comparison of wavelet and short time Fourier transform methods in the analysis of EMG signals,” J. Med. Syst., vol. 34, no. 1, pp. 91–94., 2010.

M. B. I. Reaz, M. S. Hussain, and F. Mohd-Yasin, “Techniques of EMG signal analysis: detection, processing, classification and applications,” Biol. Proced. Online, vol. 8, no. 1, pp. 11–35, 2006.

D. C. R. E. Bekka, “Effect of the window length on the EMG spectral estimation through the Blackman-Tukey method,” Signal Process. Its Appl., vol. 2, pp. 17–20, 2003.

M. H. Abdullah, A.R.; Norddin, N.; Abidin, N.Q.Z.; Aman, A.; Jopri, “Leakage current analysis on polymeric and non-polymeric insulating materials using time-frequency distribution,” Power Energy (PECon), 2012 IEEE Int. Conf., no. December, pp. 2–5, 2012.

A. Andreotti, A. Bracale, P. Caramia, and G. Carpinelli, “Adaptive prony method for the calculation of power-quality indices in the presence of nonstationary disturbance waveforms,” IEEE Trans. Power Deliv., vol. 24, no. 2, pp. 874–883, 2009.

Ahsan, “Advances in Electromyogram Signal Classification to Improve the Quality of Life for the Disabled and Aged People,” J. Comput. Sci., vol. 6, no. 7, pp. 706–715, 2010.

T. Srividya and A. M. Sankar, “Power Quality Analysis Using DSP Techniques,” ITSI Trans. Electr. Electron. Eng., pp. 80–86, 2013.

and M. E. H. Joshi, Deepak, Bryson H. Nakamura, “High energy spectrogram with integrated prior knowledge for EMG-based locomotion classification,” Med. Eng. Phys., no. 5, pp. 518–524, 2015.

J. Kilby and K. Prasad, “Analysis of Surface Electromyography Signals Using Discrete Fourier Transform Sliding Window Technique.,” Int. J. Comput. Theory …, vol. 5, no. 2, pp. 321–325, 2013.

F. Faul, E. Erdfelder, A. Buchner, and A.-G. Lang, “Statistical power analyses using G*Power 3.1: tests for correlation and regression analyses.,” Behav. Res. Methods, vol. 41, no. 4, pp. 1149–60, 2009.

F. Faul, E. Erdfelder, A.-G. Lang, and A. Buchner, “G*Power 3: a flexible statistical power analysis program for the social, behavioral, and biomedical sciences.,” Behav. Res. Methods, vol. 39, no. 2, pp. 175–91, 2007.

S. M. S. T.N.S.Tengku Zawawi, A.R.Abdullah, E.F. Shair, I. Halim, “EMG Signal Analysis of Fatigue Muscle Activity in Manual Lifting,” J. Electr. Syst., vol. 11, no. 3, pp. 319–325, 2015.

T.N.S.Tengku Zawawi, A.R.Abdullah, E. F. Shair, I. Halim, and R. O, “Electromyography Signal Analysis Using Spectrogram,” IEEE Student Conf. Res. Dev., no. December, pp. 16–17, 2013.

E. F. Shair, T. N. S. T. Zawawi, A. R. Abdullah, and N. H. Shamsudin, “sEMG Signals Analysis Using Time-Frequency Distribution for Symmetric and Asymmetric Lifting,” in 2015 International Symposium on Technology Management and Emerging Technologies (ISTMET), August 25 - 27, 2015, Langkawi, Kedah, Malaysia, 2015, pp. 233–237

N. Q. Z. Abidin, A. R. Abdullah, N. B. Norddin, and A. Aman, “Online surface condition monitoring system using time-frequency analysis technique on high voltage insulators,” Power Eng. Optim. Conf. (PEOCO), 2013 IEEE 7th Int., vol. 7, no. 11, pp. 513–517, 2013.

Downloads

Published

2016-10-01

How to Cite

T.Zawawi, T., Abdullah, A., Shair, E., & Saleh, S. (2016). Performance Comparison of EMG Signal Analysis for Manual Lifting using Spectrogram. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 8(7), 29–34. Retrieved from https://jtec.utem.edu.my/jtec/article/view/1274