Evaluation of muscle fatigue using infrared thermal imaging technique with assisted electromyography
DOI:
https://doi.org/10.11113/mjfas.v13n4-2.823Keywords:
Muscle fatigue, thermal imaging, muscle injury, thermoregulationAbstract
Muscle fatigue in sports science is an established research area where various techniques and types of muscles have been studied in order to understand the fatigue condition. It can be used as an indicator for predicting muscle injury and other muscle problems which can decrease athletes’ performance. Muscle fatigue usually occurs after a long lasting or repeated muscular activity. Electromyography (EMG) assessment method is a standard tool used to evaluate muscle fatigue based on the signals from the neuromuscular activation during fatigue condition. However, additional time for equipment set up such as placement of the electrodes and the use of multiple wires make this overall setting a bit complicated. In addition, the signal from EMG which possessed some noise, need to be filtered and post processing time is also required to obtain a reliable measurement signal. Therefore, researchers have explored the application of thermal imaging technique as one of the alternative methods for muscle fatigue assessment. The objective of this study is to investigate the correlation of muscle fatigue condition measured using a non-invasive infrared thermal imaging technique and a standard evaluation method, EMG. Five healthy men were selected to run on a treadmill for 30 minutes with a constant speed setting. Temperature and EMG signals were registered from gastrocnemius muscle of the subjects' dominant leg simultaneously. Result obtained shows that the average temperature of gastrocnemius muscle decrease as subjects start to exercise. Further temperature decrease along with exercise and increase in temperature were observed during the recovery period. Statistical analysis was performed and analyzed using both temperature and EMG parameters. Result shows a significant strong correlation with r = 0.7707 and p < 0.05 between temperature difference and median frequency (MDF) for all subjects compared to average temperature. Therefore, it is concluded that temperature difference extracted from thermal images can be used as an ideal parameter for muscle fatigue evaluation.
References
Al-Mulla, M. R., Sepulveda, F. and Colley, M. (2011). A review of non-invasive techniques to detect and predict localised muscle fatigue. Sensors, 11(4), pp. 3545–3594. doi: 10.3390/s110403545
Arfaoui, A., Bertucci, W., Letellier, T. and Polidori, G. (2014). Thermoregulation during incremental exercise in masters cycling, Journal of Science and Cycling, 3(1), pp. 33–41.
Bartuzi, P., Roman-Liu, D. and Wiśniewski, T. (2012). The influence of fatigue on muscle temperature. International Journal of Occupational Safety and Ergonomics, 18(2), pp. 233–243. doi: 10.1080/10803548.2012.11076931
Cardozo, A., Gonçalves, M., Hallal, C. and Marques, N. (2013). Age-related neuromuscular adjustments assessed by EMG. In Turker, H. (ed.), Electrodiagnosis in New Frontiers of Clinical Research, InTech. doi: 10.5772/55053
Cramer, M. N. and Jay, O. (2016). Biophysical aspects of human thermoregulation during heat stress. Autonomic Neuroscience: Basic & Clinical, 196, pp. 3–13. doi:10.1016/j.autneu.2016.03.001
Duc, S., Arfaoui, A., Polidori, G. and Bertucci, W. (2015). Efficiency and thermography in cycling during a graded exercise test. Journal of Exercise, Sports & Orthopedics, 2(2), pp. 1-8. Retrieved from https:// symbiosisonlinepublishing.com/exercise-sports-orthopedics/exercise-sports -orthopedics28.pdf
Fenner, K., Yoon, S., White, P., Starling, M., McGreevy, P. (2016). The effect of noseband tightening on horses’ behavior, eye temperature, and cardiac responses. PLoS ONE, 11(5). doi:10.1371/journal.pone.0154179
Fernández-Cuevas, I., Sillero-Quintana, M., Garcia-Concepcion, M. A., Serrano, J. R., Gomez-Carmona, P., Marins, J. C. (2014) Monitoring skin thermal response to training with infrared thermography. New Studies in Athletics, 29(1), pp. 57–71.
Formenti, D., Ludwig, N., Trecroci, A., Gargano, M., Michielon, G., Caumo, A., Alberti, G. (2016). Dynamics of thermographic skin temperature response during squat exercise at two different speeds. Journal of Thermal & Biology, 59, pp. 58–63. doi:10.1016/j. jtherbio.2016.04.013
Gillis, D. J., Barwood, M. J., Newton, P. S., House, J. R., Tipton, M. J. (2016). The influence of a menthol and ethanol soaked garment on human temperature regulation and perception during exercise and rest in warm, humid conditions. Journal of Thermal Biology, 58, pp. 99–105. doi:10.1016/j.jtherbio.2016.04.009
González-Alonso, J. (2012) Human thermoregulation and the cardiovascular system. Experimental Physiology, 97(3), pp. 340–346. doi: 10.1113/ expphysiol.2011.058701
Hadžić, V., Širok, B., Malneršič, A. and Čoh, M. (In Press). Can infrared thermography be used to monitor fatigue during exercise? A case study. Journal of Sport and Health Science, doi: 10.1016/j.jshs.2015.08.002
Hildebrandt, C., Zeilberger, K., Ring, E. F. J. and Raschner, C. (2012). The application of medical infrared thermography in sports medicine. In Zaslav, K. R. (ed.), An International Perspective on Topics in Sports Medicine and Sports Injury, InTech (14), pp.257–275. doi: 10.5772/28383
Priego Quesada, J. I. (Ed.) (2017), Application of infrared thermography in Sports Science, Biological and Medical Physics, Biomedical Engineering, Springer. doi: 10.1007/978-3-319-47410-6_1
Kallenberg, L. A. C. and Hermens, H. J. (2008). Behaviour of a surface EMG based measure for motor control: Motor unit action potential rate in relation to force and muscle fatigue. Journal of Electromyography and Kinesiology, 18(5), pp.780–788. doi: 10.1016/j.jelekin.2007.02.011
Lim, C. L., Byrne, C., Lee, J. K. (2008) Human thermoregulation and measurement of body temperature in exercise and clinical settings. Annals of the Academy of Medicine Singapore, 37(4), pp. 347–353.
Merla, A., Mattei, P. A., Di Donato, L. and Romani, G. L. (2010). Thermal imaging of cutaneous temperature modifications in runners during graded exercise. Annals of Biomedical Engineering, 38(1), pp. 158–163. doi: 10.1007/s10439-009-9809-8
Priego Quesada, J. I., Carpes, F. P., Bini, R. R., Salvador Palmer, R., Pérez-Soriano, P., Cibrián Ortiz de Anda. R. M. (2015) Relationship between skin temperature and muscle activation during incremental cycle exercise. Journal of Thermal Biology, 48, pp. 28–35. doi:10.1016/ j.jtherbio.2014.12.005
Shi, J., Zheng, Y. P., Chen, X. and Huang, Q. H. (2007). Assessment of muscle fatigue using sonomyography: Muscle thickness change detected from ultrasound images. Medical Engineering and Physics, 29(4), pp. 472–479. doi: 10.1016/j.medengphy.2006.07.004
Stirn, I., Jarm, T. and Strojnik, V. (2008). Evaluation of the mean power frequency of the EMG signal power spectrum at endurance levels during fatiguing isometric muscle contractions. Kinesiologia Slovenica, 14(1), pp. 28-38.
Thongpanja, S., Phinyomark, A., Phukpattaranont, P., and Limsakul, C. (2013). Mean and median frequency of EMG signal to determine muscle force based on time-dependent power spectrum. Elektronika ir Elektrotechnika, 19(3), pp. 51-56. doi: 10.5755/j01.eee.19.3.3697
Medved, V. and Cifrek, M. (2011). Kinesiological electromyography. In Klika, V. (Ed.) Biomechanics in Applications, InTech, doi: 10.5772/21282
Wallin, B. G. (1990). Neural control of human skin blood flow. Journal of the Autonomic Nervous System, 30(Suppl.): S185–S190.
