Surface Electromyography Based Core Muscle Fatigue Analysis During Repetitive Plank Using Multivariate Dimensionality Reduction Methods in Boys Aged 12-14

Keywords: Myoelectric Signals, Heterogeneity, Agonist-Antagonist Co-Contraction, Discriminant analysis, Principal Component Analysis

Abstract

The aims of the study were: 1. To analyse the discriminative power of neuromuscular components for classifying the pre and post muscle fatigued states. 2. To examine whether the modification of neural recruitment strategies become more/less heterogeneous due to fatigue. 3. To research the effect of Erector Spinae (ES) muscle activity collectively with Rectus Abdominis (RA) and External Oblique (EO) muscle activity to identify the reduced spine stability during fatiguing Plank. 

Material and methods. Twelve boys (age – 12-14 years, height 148.75 ± 10 cm, body mass 38.9 ± 7.9 kg) participated in the study. Multivariate Discriminant Analysis (DA) and Principal Component Analysis (PCA) were applied to identify the changes in the pattern of the electromyographic signals during muscle fatigue. In DA the Wilks’ lambda, p-value, canonical correlation, classification percentage and structure matrix were used. To evaluate the component validity the standard limit for Kaiser-Meyer-Olkin (KMO) was set at ≥0.529 and the p-value of Bartlett’s test was ≤0.001. The eigenvalues ≥1 were used to determine the number of Principal Components (PCs). The satisfactory percentage of non-redundant residuals were set at ≤50% with standard value >0.05. The absolute value of average communality (x̄ h2) and component loadings were set at ≥0.6, ≥0.4 respectively. 

Results. Standardized canonical discriminant analysis showed that pre and post fatigued conditions were significantly different (p = 0.000, Wilks’ lambda = 0.297, χ2 = 24.914, df=3). The structure matrix showed that the parameter that correlated highly with the discriminant function was ES ARV (0.514). The results showed that the classification accuracy was 95.8% between fatigued conditions. In PCA the KMO values were reduced [0.547Pre fatigue vs. 0.264Post fatigue]; the value of Bartlett’s sphericity test was in pre χ2 = 90.72 (p = 0.000) and post fatigue χ2 = 85.32 (p = 0.000); The Promax criterion with Kaiser Normalization was applied because the component rotation was non-orthogonal [Component Correlation Matrix (rCCM) = 0.520 Pre fatigue >0.3Absolute<0.357Post fatigue]. In pre fatigue two PCs (cumulative s2 – 80.159%) and post fatigue three PCs (cumulative s2 – 83.845%) had eigenvalues ≥1. The x̄ h2 increased [0.802 Pre fatigue vs. 0.838 Post fatigue] and the percentage of nonredundant residuals reduced [50% Pre fatigue vs. 44% Post fatigue] from pre to post fatigue. 

Conclusions. The variability and heterogeneity increase in the myoelectric signals due to fatigue. The co-activity of antagonist ES muscle is significantly sensitive to identify the deteriorating spine stability during the fatiguing Plank. Highly correlated motor unit recruitment strategies between ES and RA, providing supportive evidence to the concept of shared agonist-antagonist motoneuron pool or “Common Drive” phenomenon during fatigue.

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Author Biographies

Abir Samanta, Lakshmibai National Institute of Physical Education

Department of Exercise Physiolog,
Shaktinagar, Mela Road, Gwalior, Madhya Pradesh, Pin Code-474002, India
abirphyyoga137@gmail.com

Sabyasachi Mukherjee, Lakshmibai National Institute of Physical Education

Vice Chancellor (Officiating),
Shaktinagar, Mela Road, Gwalior, Madhya Pradesh, Pin Code-474002, India
mukherjee.mukherjee37@gmail.com

References

Merletti, R., & Farina, D. (2016). Surface Electromyography: Physiology, Engineering, and Applications. John Wiley & Sons NJ, Inc. IEEE Press.

Staudenmann, D., van Dieën, J. H., Stegeman, D. F., & Enoka, R. M. (2014). Increase in heterogeneity of biceps brachii activation during isometric submaximal fatiguing contractions: a multichannel surface EMG study. Journal of Neurophysiology, 111(5), 984-990. https://doi.org/10.1152/jn.00354.2013

Schoenfeld, B. J., Contreras, B., Tiryaki-Sonmez, G., Willardson, J. M., & Fontana, F. (2014). An electromyographic comparison of a modified version of the plank with a long lever and posterior tilt versus the traditional plank exercise. Sports biomechanics, 13(3), 296-306. https://doi.org/10.1080/14763141.2014.942355

Lee, N., Kang, H., & Shin, G. (2015). Use of antagonist muscle EMG in the assessment of neuromuscular health of the low back. Journal of Physiological Anthropology, 34(1), 18. https://doi.org/10.1186/s40101-015-0055-5

Duchateau, J., & Baudry, S. (2014). The neural control of coactivation during fatiguing contractions revisited. Journal of Electromyography and Kinesiology, 24(6), 780-788. https://doi.org/10.1016/j.jelekin.2014.08.006

Stokes, I. A., Henry, S. M., & Single, R. M. (2003). Surface EMG electrodes do not accurately record from lumbar multifidus muscles. Clinical biomechanics, 18(1), 9-13. https://doi.org/10.1016/s0268-0033(02)00140-7

Mullany, H., O’Malley, M., St Clair Gibson, A., & Vaughan, C. (2002). Agonist-antagonist common drive during fatiguing knee extension efforts using surface electromyography. Journal of Electromyography and Kinesiology, 12(5), 375-384. https://doi.org/10.1016/s1050-6411(02)00048-2

Chen, W. J., Chiou, W. K., Lee, Y. H., Lee, M. Y., & Chen, M. L. (1998). Myo-electric behavior of the trunk muscles during static load holding in healthy subjects and low back pain patients. Clinical biomechanics, 13(1), S9-S15. https://doi.org/10.1016/s0268-0033(98)80133-2

Samanta, A., & Mukherjee, S. (2021). Assessment of Myoelectric Manifestations of Muscle Fatigue During Repetitive Isometric Voluntary Contraction in Boys Aged 12-14. Teorìâ ta Metodika Fìzičnogo Vihovannâ, 21(1), 50-60. https://doi.org/10.17309/tmfv.2021.1.07

Ivashchenko, O. (2020). Research Program: Modeling of Motor Abilities Development and Teaching of School children. Teorìâ ta Metodika Fìzičnogo Vihovannâ, 20(1), 32-41. https://doi.org/10.17309/tmfv.2020.1.05

Rogers, D. R., & MacIsaac, D. T. (2011). EMG-based muscle fatigue assessment during dynamic contractions using principal component analysis. Journal of Electromyography and Kinesiology, 21(5), 811-818. https://doi.org/10.1016/j.jelekin.2011.05.002

Ivashchenko, O., Nosko, Yu., Bartik, P., & Makanin, O. (2020). Gender-Related Peculiarities of 7-Year-Old Schoolchildren’s Motor Fitness. Teorìâ ta Metodika Fìzičnogo Vihovannâ, 20(4), 228-233. https://doi.org/10.17309/tmfv.2020.4.05

Boonstra, T. W., Daffertshofer, A., van As E., van-der-Vlugt, S., & Beek, P. J. (2007). Bilateral motor unit synchronization is functionally organized. Experimental Brain Research, 178(1), 79–88. https://doi.org/10.1007/s00221-006-0713-2

Roy, S. H., & Oddsson, L. I. (1998). Classification of paraspinal muscle impairments by surface electromyography. Physical therapy, 78(8), 838-851. https://doi.org/10.1093/ptj/78.8.838

Naik, G. R., Selvan, S. E., Gobbo, M., Acharyya, A., & Nguyen, H. T. (2016). Principal Component Analysis Applied to Surface Electromyography: A Comprehensive Review. IEEE Access, 4, 4025-4037. https://doi.org/10.1109/ACCESS.2016.2593013

Cowleya, J. C., & Gates, D. H. (2017). Inter-joint coordination changes during and after muscle fatigue. Human Movement Science, 56, 109-118. https://doi.org/10.1016/j.humov.2017.10.015

Farina, D., Negro, F., Gizzi, L., & Falla, D. (2012). Low-frequency oscillations of the neural drive to the muscle are increased with experimental muscle pain. Journal of neurophysiology, 107(3), 958-965. https://doi.org/10.1152/jn.00304.2011

Ortega-Auriol, P.A., Besier, T.F., Byblow, W.D., & McMorland, A.J.C. (2018). Fatigue Influences the Recruitment, but Not Structure, of Muscle Synergies. Frontiers in Human Neuroscience, 12, 217. https://doi.org/10.3389/fnhum.2018.00217

McManus, L., Lowery, M., Merletti, R., Søgaard, K., Besomi, M., Clancy, E. A. et al. (2021). Consensus for experimental design in electromyography (CEDE) project: Terminology matrix. Journal of electromyography and kinesiology: official journal of the International Society of Electrophysiological Kinesiology, 59, 102565. https://doi.org/10.1016/j.jelekin.2021.102565

Verma, J. P. (2013). Data Analysis in Management with SPSS Software. (1st Ed.). Springer India. https://doi.org/10.1007/978-81-322-0786-3

Iermakov, S., Ivashchenko, O., Khudolii, O., Chernenko, S., Veremeenko, V., & Zelenskyi, B. (2021). Pattern Recognition: Impact of Exercises Modes on Developing a Small Ball Throwing Skill in Boys Aged 8. Teorìâ ta Metodika Fìzičnogo Vihovannâ, 21(1), 77-83. https://doi.org/10.17309/tmfv.2021.1.10

Broen, M. P., Moonen, A. J., Kuijf, M. L., Dujardin, K., Marsh, L., Richard, I. H., et al. (2015). Factor analysis of the Hamilton Depression Rating Scale in Parkinson’s disease. Parkinsonism & related disorders, 21(2), 142-146. https://doi.org/10.1016/j.parkreldis.2014.11.016

Kaiser, H. F., & Rice, J. (1974). Little Jiffy Mark IV. Educational and Psychological Measurement, 34(1), 111-117. https://doi.org/10.1177/001316447403400115

Watkins, M. W. (2021). A Step-by-Step Guide to Exploratory Factor Analysis with SPSS. (1st Ed.). Routledge NY.

Ivanenko, Y. P., Poppele, R. E., & Lacquaniti, F. (2004). Five basic muscle activation patterns account for muscle activity during human locomotion. The Journal of physiology, 556(1), 267-282. https://doi.org/10.1113/jphysiol.2003.057174

Gajewski, J., & Viitasalo, J.T. (1994). Does the level of adaptation to a heavy physical effort influence fatigue-induced changes in tremor amplitude? Human Movement Science, 13(2), 211-220. https://doi.org/10.1016/0167-9457(94)90037-X

Allen, D. G., & Westerblad, H. (2001). Role of phosphate and calcium stores in muscle fatigue. The Journal of physiology, 536(3), 657-665. https://doi.org/10.1111/j.1469-7793.2001.t01-1-00657.x

Dideriksen, J. L., Holobar, A., & Falla, D. (2016). Preferential distribution of nociceptive input to motoneurons with muscle units in the cranial portion of the upper trapezius muscle. Journal of Neurophysiology, 116(2), 611-618. https://doi.org/10.1152/jn.01117.2015

Falla, D., & Farina, D. (2008). Non-uniform adaptation of motor unit discharge rates during sustained static contraction of the upper trapezius muscle. Experimental Brain Research, 191(3), 363-370. https://doi.org/10.1007/s00221-008-1530-6

Farina, D., Leclerc, F., Arendt-Nielsen, L., Buttelli, O., & Madeleine, P. (2008). The change in spatial distribution of upper trapezius muscle activity is correlated to contraction duration. Journal of Electromyography and Kinesiology, 18(1), 16-25. https://doi.org/10.1016/j.jelekin.2006.08.005

Kiryu, T., Takahashi, K., & Ogawa, K. (1997). Multivariate Analysis of Muscular Fatigue during Bicycle Ergometer Exercise. IEEE Transactions on Biomedical Engineering, 44(8), 665-672. https://doi.org/10.1109/10.605423

Ivashchenko, O., Prykhodko, V., & Cieslicka, M. (2018). Movement Coordination: Factor Structure of Development in 5th-7th Grade Girls. Teorìâ ta Metodika Fìzičnogo Vihovannâ, 18(1), 38-49. https://doi.org/10.17309/tmfv.2018.1.05

Dien, J. (2010). Evaluating two-step PCA of ERP data with Geomin, Infomax, Oblimin, Promax, and Varimax rotations. Psychophysiology, 47(1), 170-83. https://doi.org/10.1111/j.1469-8986.2009.00885.x

Barroso, F. O., Torricelli, D., Moreno, J. C., Taylor, J., Gomez-Soriano, J., Bravo-Esteban, E., et al. (2014). Shared muscle synergies in human walking and cycling. Journal of Neurophysiology, 112(8), 1984-1998. https://doi.org/10.1152/jn.00220.2014

Fan, C., Fede, C., Gaudreault, N., Porzionato, A., Macchi, V., DE Caro, R., & Stecco, C. (2018). Anatomical and functional relationships between external abdominal oblique muscle and posterior layer of thoracolumbar fascia. Clinical anatomy, 31(7), 1092-1098. https://doi.org/10.1002/ca.23248

Czaplicki, A., Śliwa, M., Szyszka, P., & Sadowski, J. (2017). Biomechanical assessment of Strength and Jumping Ability in Male Volleyball Players during the Annual Training Macrocycle. Polish Journal of Sport and Tourism, 24, 221-227. https://doi.org/10.1515/pjst-2017-0021

Ivashchenko, O.V., Khudolii, O.M., Iermakov, S.S., Prykhodko, V.V., & Cieslicka, M. (2018). Movement Coordination: Identification of Age-Related Dynamics of its Development in Girls Aged 11-13. Teorìâ ta Metodika Fìzičnogo Vihovannâ, 18(2), 93-101. https://doi.org/10.17309/tmfv.2018.2.06

Vila-Chã, C., Falla, D., & Farina, D. (2010). Motor unit behavior during submaximal contractions following six weeks of either endurance or strength training. Journal of Applied Physiology, 109(5), 1455-1466. https://doi.org/10.1152/japplphysiol.01213.2009

Araujo, R. C., Duarte, M., & Amadio, A. C. (2000). On the inter- and intra-subject variability of the electromyographic signal in isometric contractions. Electromyography and Clinical Neurophysiology, 40(4), 225-229. PMID: 10907600

Martinez-Valdes, E., Negro, E., Falla, D., De-Nunzio, A. M., & Farina, D. (2018). Surface electromyographic amplitude does not identify differences in neural drive to synergistic muscles. Journal of Applied Physiology, 124(4), 1071-1079. https://doi.org/10.1152/japplphysiol.01115.2017

Prasad, S., & Bruce, L. M. (2008). Limitations of Principal Components Analysis for Hyperspectral Target Recognition. IEEE Geoscience and Remote Sensing Letters, 5(4), 625-629. https://doi.org/10.1109/LGRS.2008.2001282
Published
2021-09-25
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How to Cite
Samanta, A., & Mukherjee, S. (2021). Surface Electromyography Based Core Muscle Fatigue Analysis During Repetitive Plank Using Multivariate Dimensionality Reduction Methods in Boys Aged 12-14. Teorìâ Ta Metodika Fìzičnogo Vihovannâ, 21(3), 253-263. https://doi.org/10.17309/tmfv.2021.3.09
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