Auto-Segmentation Analysis of EMG Signal for Lifting Muscle Contraction Activities
Keywords:Manual Lifting, Spectrogram, Segmentation, Instantaneous Energy, Electromyography,
AbstractTime-frequency representation of a signal has been widely used in various research areas to analyze non-stationary signals (ie. electromyography (EMG) signals). However, due to the high computational complexity of certain time-frequency distribution techniques, the application of these techniques in the analysis of long duration EMG signals is not suitable. To overcome this problem, muscle contraction segmentation is essential to process the existed EMG signals, since not all of the EMG signal contains valid information to be analyzed. Thus, this paper presents an algorithm to automatically detect and segment the muscle contractions existed in EMG signal during long duration recordings. Surface EMG signals were collected from biceps branchii muscle of ten subjects during manual lifting. Subjects were required to lift a 5 kg load mass with lifting height of 75 cm until experiencing fatigue. The utilization of instantaneous energy of EMG is used to estimate the presence of first muscle contraction, second muscle contraction and until the last muscle contraction. This instantaneous energy is obtained from spectrogram and a threshold value is set to differentiate between muscle contractions and noise. This research shows that the algorithm is able to automatically segment muscle contractions in EMG signal based on the signal instantaneous energy.
A. Merlo and I. Campanini, “Technical Aspects of Surface
Electromyography for Clinicians,” Open Rehabil. J., vol. 3, pp. 98–109, 2010.
L. H. Smith and L. J. Hargrove, “Comparison of Surface and Intramuscular EMG Pattern Recognition for Simultaneous Wrist/Hand Motion Classification,” in Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, 2013, pp. 4223–4226.
E. N. Kamavuako, J. C. Rosenvang, R. Horup, W. Jensen, D. Farina, and K. B. Englehart, “Surface Versus Untargeted Intramuscular EMG Based Classification of Simultaneous and Dynamically Changing Movements,” IEEE Trans. Neural Syst. Rehabil. Eng., vol. 21, no. 6, pp. 992–998, 2013.
H.-J. Shin and J.-Y. Kim, “Measurement of Trunk Muscle Fatigue During Dynamic Lifting and Lowering as Recovery Time Changes,” Int. J. Ind. Ergon., vol. 37, no. 6, pp. 545–551, 2007.
A. R. Abdullah, N. H. T. H. Ahmad, A. Z. Shameri, N. A. Abidullah, M. H. Jopri, and E. F. Shair, “Optimal Kernel Parameters of SmoothWindowed Wigner-Ville Distribution for ower Quality Analysis,” J. Basic Appl. Sci., vol. 2, no. 4, pp. 235–242, 2013.
W. El Falou, J. Duchêne, D. Hewson, M. Khalil, M. Grabisch, and F. Lino, “A Segmentation Approach to Long Duration Surface EMG Recordings,” J. Electromyogr. Kinesiol., vol. 15, no. 1, pp. 111–9, 2005.
S. A. Ahmad and P. H. Chappell, “Moving Approximate Entropy Applied to Surface Electromyographic Signals,” Biomed. Signal Process. Control, vol. 3, no. 1, pp. 88–93, 2008.
H. J. Hermens, B. Freriks, C. Disselhorst-Klug, and G. Rau, “Development of Recommendations for SEMG Sensors and Sensor Placement Procedures,” J. Electromyogr. Kinesiol., vol. 10, no. 5, pp. 361–374, 2000.
T. N. S. Tengku Zawawi, A. R. Abdullah, and E. F. Shair,
“Electromyography Signal Analysis Using Spectrogram,” in IEEE Student Conference on research and development (SCOReD), 2013, pp. 16–17.
T. N. S. Tengku Zawawi, A. R. Abdullah, E. F. Shair, I. Halim, and S. Mohamad Saleh, “EMG Signal Analysis of Fatigue Muscle Activity in Manual Lifting,” J. Electr. Syst., vol. 11, no. 3, pp. 319–325, 2015.
A. R. Abdullah, N. S. Ahmad, E. F. Shair, and A. Jidin, “Open Switch Faults Analysis in Voltage Source Inverter Using Spectrogram,” in 2013 IEEE 7th International Power Engineering and Optimization Conference (PEOCO), 2013, no. June, pp. 438–443.
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 Emergent Technologies (ISTMET), 2015, pp. 233–237.
M. B. Malarvilil, I. Kamarulafizam, S. Hussain, and D. Helmi, “Heart Sound Segmentation Algorithm Based on Instantaneous Energy of Electrocardiogram,” Comput. Cardiol. 2003, pp. 327–330, 2003.
Y. F. Chang, C. J. Lin, J. M. Chyan, I. M. Chen, and J. E. Chang, “Multiple Regression Models for the Lower Heating Value of Municipal Solid Waste in Taiwan,” J. Environ. Manage., vol. 85, no. 4, pp. 891–899, 2007.
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