Feature Extractions of EMG Signals during a Constant Workload Pedaling Exercise

Authors: Bing-Wen Chen, Alvin W. Y. Su, Yu-Lin Wang

Abstract:

Electromyography (EMG) is one of the important indicators during exercise, as it is closely related to the level of muscle activations. This work quantifies the muscle conditions of the lower limbs in a constant workload exercise. Surface EMG signals of the vastus laterals (VL), vastus medialis (VM), rectus femoris (RF), gastrocnemius medianus (GM), gastrocnemius lateral (GL) and Soleus (SOL) were recorded from fourteen healthy males. The EMG signals were segmented in two phases: activation segment (AS) and relaxation segment (RS). Period entropy (PE), peak count (PC), zero crossing (ZC), wave length (WL), mean power frequency (MPF), median frequency (MDF) and root mean square (RMS) are calculated to provide the quantitative information of the measured EMG segments. The outcomes reveal that the PE, PC, ZC and RMS have significantly changed (p<.001); WL presents moderately changed (p<.01); MPF and MDF show no changed (p>.05) during exercise. The results also suggest that the RS is also preferred for performance evaluation, while the results of the extracted features in AS are usually affected directly by the amplitudes. It is further found that the VL exhibits the most significant changes within six muscles during pedaling exercise. The proposed work could be applied to quantify the stamina analysis and to predict the instant muscle status in athletes.

Keywords: EMG, feature extraction, muscle status, pedaling exercise, relaxation segment.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1127304

References:

[1] J. H. Song, J. W. Jung, Z. and Bien, "Robust EMG pattern recognition to muscular fatigue effect for human-machine interaction." MICAI 2006: Advances in Artificial Intelligence. Springer Berlin Heidelberg, 2006. 1190-1199.
[2] B. Fonda, A. Panjan, G. Markovic, N. Sarabon. "Adjusted saddle position counteracts the modified muscle activation patterns during uphill cycling." Journal of Electromyography and Kinesiology 21.5 (2011): 854-860.
[3] S. W. Chen, J. W. Liaw, H. L. Chan, Y. J. Chang, C. H. Ku. "A Real-Time Fatigue Monitoring and Analysis System for Lower Extremity Muscles with Cycling Movement." Sensors 14.7 (2014): 12410-12424.
[4] S. Minning, C. A. Eliot, T. L. Uhl, T. R. Malone. "EMG analysis of shoulder muscle fatigue during resisted isometric shoulder elevation." Journal of Electromyography and Kinesiology 17.2 (2007): 153-159.
[5] A. Meigal, S. Rissanen, M. P. Tarvainen, P. A. Karjalainen, I. A. Iudina-Vassel, O. Airaksinen, M. Kankaanpää. "Novel parameters of surface EMG in patients with Parkinson’s disease and healthy young and old controls." Journal of Electromyography and Kinesiology 19.3 (2009): e206-e213.
[6] D. Laplaud, F. Hug, and L. Grélot. "Reproducibility of eight lower limb muscles activity level in the course of an incremental pedaling exercise." Journal of Electromyography and Kinesiology 16.2 (2006): 158-166.
[7] G. Venugopal, M. Navaneethakrishna, S. Ramakrishnan. "Extraction and analysis of multiple time window features associated with muscle fatigue conditions using sEMG signals." Expert Systems with Applications 41.6 (2014): 2652-2659.
[8] T. Migita, and K. Hirakoba. "Effect of switching pedal rate model on slow component of oxygen uptake during heavy-cycle exercise." BIOLOGY OF SPORT 24.3 (2007): 191.
[9] M. Navaneethakrishna, S. Ramakrishnan, "Multiscale feature based analysis of surface EMG signals under fatigue and non-fatigue conditions." International Conference of Medicine and Biology Society (EMBC), pp. 4627 – 4630, 2014.
[10] M. Vitor-Costa, et al. "EMG spectral analysis of incremental exercise in cyclists and non-cyclists using Fourier and Wavelet transforms." Revista Brasileira de Cineantropometria & Desempenho Humano 14.6 (2012): 660-670.
[11] C. L. Camic, T. J. Housh, G. O. Johnson, C. R. Hendrix, J. M. Zuniga, M. Mielke, R. J. Schmidt. "An EMG frequency-based test for estimating the neuromuscular fatigue threshold during cycle ergometry." European journal of applied physiology 108.2 (2010): 337-345.
[12] H. J. Hermens, B. Freriks, C. Disselhorst-Klug, G. Rau. "Development of recommendations for SEMG sensors and sensor placement procedures." Journal of electromyography and Kinesiology 10.5 (2000): 361-374.
[13] AURA® PSG LITE, Astro-med Inc., Grass Technologies, West Warwick, RI, USA. http://www.grasstechnologies.com
[14] P. V. Komi, P. Tesch. "EMG frequency spectrum, muscle structure, and fatigue during dynamic contractions in man." European Journal of Applied Physiology and Occupational Physiology 42.1 (1979): 41-50.
[15] O. Dayan, S. Irina, E. Amir, G. Pantelis, B. Jeroen, M. Alison "Applying EMG spike and peak counting for a real-time muscle fatigue monitoring system." IEEE Conference of Biomedical Circuits and Systems (BioCAS), pp.41-44, 2012.
[16] T. R. Lee, Y. H. Kim, P.S. Sung. "Spectral and entropy changes for back muscle fatigability following spinal stabilization exercises." J Rehabil Res Dev 47.2 (2010): 133-42.