Skip to main content
SpringerLink
Log in

Non-obstructive monitoring of muscle fatigue for low intensity dynamic exercise with infrared thermography technique

  • Original Article
  • Published:
Medical & Biological Engineering & Computing Aims and scope Submit manuscript

Abstract

Surface electromyography (sEMG) has been widely used in evaluating muscle fatigue among athletes where electrodes are attached on the skin during the activity. Recently, infrared thermography technique (IRT) has gain popularity and shown to be another preferred method in monitoring and predicting muscle fatigue non-obstructively. This paper investigates the correlation between surface temperature and muscle activation parameters obtained using both IRT and sEMG methods simultaneously. Twenty healthy subjects were required to perform a repetitive calf raise exercise with various loads attached around their ankle for 3 min to induce fatigue on the targeted gastrocnemius muscles. Average temperature and temperature difference information were extracted from thermal images, while root mean square (RMS) and median frequency (MF) were extracted from sEMG signals. Spearman statistical analysis performed shows that there is a significant correlation between average temperature with RMS and between temperature difference with MF values at p<0.05. While ANOVA test conducted shows that there is significant impact of loads on RMS and MF where F=12.61 and 3.59, respectively, at p< 0.05. This study suggested that skin surface temperature can be utilized in monitoring and predicting muscle fatigue in low intensity dynamic exercise and can be extended to other dynamic exercises.

Graphical abstract

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  1. Mitchell JH, Haskell W, Snell P, Van Camp SP (2005) Task Force 8: classification of sports. J Am Coll Cardiol 45:1364–1367. https://doi.org/10.1016/j.jacc.2005.02.015

    Article  PubMed  Google Scholar 

  2. Priego Quesada JI, Carpes FP, Bini RR, Salvador Palmer R, Pérez-Soriano P, Cibrián Ortiz de Anda RM (2015) Relationship between skin temperature and muscle activation during incremental cycle exercise. J Therm Biol 48:28–35. https://doi.org/10.1016/j.jtherbio.2014.12.005

    Article  PubMed  Google Scholar 

  3. Patel H, Alkhawam H, Madanieh R, Shah N, Kosmas CE, Vittorio TJ (2017) Aerobic vs anaerobic exercise training effects on the cardiovascular system. World J Cardiol 9:134–138. https://doi.org/10.4330/wjc.v9.i2.134

    Article  PubMed  PubMed Central  Google Scholar 

  4. Al-Mulla MR, Sepulveda F, Colley M (2011) A review of non-invasive techniques to detect and predict localised muscle fatigue. Sensors (Basel, Switzerland) 11:3545–3594. 11:3545–3594. https://doi.org/10.3390/s110403545

  5. Bangsbo J, Mohr M, Krustrup P (2006) Physical and metabolic demands of training and match-play in the elite football player. J Sports Sci 24:665–674. https://doi.org/10.1080/02640410500482529

    Article  PubMed  Google Scholar 

  6. Bogdanis GC (2012) Effects of physical activity and inactivity on muscle fatigue. Front Physiol 3:142. https://doi.org/10.3389/fphys.2012.00142

    Article  PubMed  PubMed Central  Google Scholar 

  7. Halder A, Kuklane K, Gao C et al (2017) Limitations of oxygen uptake and leg muscle activity during ascending evacuation in stairways. Appl Ergon 66:52–63

    Article  Google Scholar 

  8. Grassi B, Rossiter HB, Zoladz JA (2015) Skeletal muscle fatigue and decreased efficiency: two sides of the same coin? Exerc Sport Sci Rev 43:75–83. https://doi.org/10.1249/JES.0000000000000043

    Article  PubMed  Google Scholar 

  9. Borji R, Zghal F, Zarrouk N, Martin V, Sahli S, Rebai H (2017) Neuromuscular fatigue and recovery profiles in individuals with intellectual disability. J Sport Health Sci 8:242–248. https://doi.org/10.1016/j.jshs.2017.03.015

    Article  PubMed  PubMed Central  Google Scholar 

  10. McCrary JM, Ackermann BJ, Halaki M (2018) EMG amplitude, fatigue threshold, and time to task failure: a meta-analysis. J Sci Med Sport 21:736–741. https://doi.org/10.1016/j.jsams.2017.11.005

    Article  PubMed  Google Scholar 

  11. Merletti R, Botter A, Troiano A, Merlo E, Minetto MA (2009) Technology and instrumentation for detection and conditioning of the surface electromyographic signal: state of the art. Clin Biomech 24:122–134. https://doi.org/10.1016/j.clinbiomech.2008.08.006

    Article  Google Scholar 

  12. Kallenberg LAC, Hermens HJ (2008) Behaviour of a surface EMG based measure for motor control: motor unit action potential rate in relation to force and muscle fatigue. J Electromyogr Kinesiol 18:780–788. https://doi.org/10.1016/j.jelekin.2007.02.011

    Article  CAS  PubMed  Google Scholar 

  13. Balaji AS, Makaram N, Balasubramanian S, Swaminathan R (2017) Analysis of pre- and post-fatigue thermal profiles of the dominant hand using infrared imaging. International Conference on Computational Biology and Bioinformatics, In, pp 53–57

    Google Scholar 

  14. Shakhih MFM, Wahab AA, Salim MIM (2019) Assessment of inspiration and expiration time using infrared thermal imaging modality. Infrared Phys Technol 99:129–139

    Article  Google Scholar 

  15. Cabizosu A, Carboni N, Martínez-Almagro Andreo A, Casu G, Ramón Sánchez C, Vegara-Meseguer JM (2020) Relationship between infrared skin radiation and muscular strength tests in patients affected by Emery-Dreifuss muscular dystrophy. Med Hypotheses 138:109592. https://doi.org/10.1016/j.mehy.2020.109592

    Article  CAS  PubMed  Google Scholar 

  16. Etehadtavakol M, Emrani Z, Ng EYK (2019) Rapid extraction of the hottest or coldest regions of medical thermographic images. Med Biol Eng Comput 57:379–388. https://doi.org/10.1007/s11517-018-1876-2

    Article  PubMed  Google Scholar 

  17. García A, Camargo C, Olguín J, Barreras JAL (2018) Analysis of risk for repetitive work using thermography sensory. Advances in Intelligent Systems and Computing, In, pp 239–248

    Google Scholar 

  18. Adamczyk JG, Krasowska I, Boguszewski D, Reaburn P (2016) The use of thermal imaging to assess the effectiveness of ice massage and cold-water immersion as methods for supporting post-exercise recovery. J Therm Biol 60:20–25. https://doi.org/10.1016/j.jtherbio.2016.05.006

    Article  PubMed  Google Scholar 

  19. Hildebrandt C, Zeilberger K, John Ring EF, Raschner C (2012) The application of medical infrared thermography in sports medicine. In: Zaslav KR (ed) An International Perspective on Topics in Sports Medicine and Sports Injury. InTech, pp 257–274

  20. Hamilton B, Alonso JM, Best TM (2017) Time for a paradigm shift in the classification of muscle injuries. J Sport Health Sci 6:255–261. https://doi.org/10.1016/j.jshs.2017.04.011

    Article  PubMed  PubMed Central  Google Scholar 

  21. Côrte AC, Pedrinelli A, Marttos A, Souza IFG, Grava J, José Hernandez A (2019) Infrared thermography study as a complementary method of screening and prevention of muscle injuries: pilot study. BMJ Open Sport Exercise Med 5:e000431. https://doi.org/10.1136/bmjsem-2018-000431

    Article  Google Scholar 

  22. Duc S, Arfaoui A, Polidori G, Bertucci W (2015) Efficiency and thermography in cycling during a graded exercise test. Journal of Exerecise, Sports & Orthopedics 1–8

  23. Ludwig N, Trecroci A, Gargano M, Formenti D, Bosio A, Rampinini E, Alberti G (2016) Thermography for skin temperature evaluation during dynamic exercise: a case study on an incremental maximal test in elite male cyclists. Appl Opt 55:1–5. https://doi.org/10.1364/AO.55.00D126

    Article  Google Scholar 

  24. Tanda G (2015) The use of infrared thermography to detect the skin temperature response to physical activity. J Phys Conf Ser:655. https://doi.org/10.1088/1742-6596/655/1/012062

  25. Merla A, Mattei PA, Di Donato L, Romani GL (2010) Thermal imaging of cutaneous temperature modifications in runners during graded exercise. Ann Biomed Eng 38:158–163. https://doi.org/10.1007/s10439-009-9809-8

    Article  PubMed  Google Scholar 

  26. Arfaoui A, Bertucci WM, Letellier T (2014) Thermoregulation during incremental exercise in masters cycling. J Sci Cycling 3:32–40

    Google Scholar 

  27. Bartuzi P, Roman-Liu D, Wiśniewski T (2012) The influence of fatigue on muscle temperature. Int J Occup Saf Ergon 18:233–243. https://doi.org/10.1080/10803548.2012.11076931

    Article  PubMed  Google Scholar 

  28. 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. J Therm Biol 59:58–63. https://doi.org/10.1016/j.jtherbio.2016.04.013

    Article  PubMed  Google Scholar 

  29. Formenti D, Merla A, Priego Quesada JI (2017) The use of infrared thermography in the study of sport and exercise physiology. Application of Infrared Thermography in Sports Science, In, pp 111–136

    Google Scholar 

  30. Hamner SR, Seth A, Delp SL (2010) Muscle contributions to propulsion and support during running. J Biomech 43:2709–2716. https://doi.org/10.1016/j.jbiomech.2010.06.025

    Article  PubMed  PubMed Central  Google Scholar 

  31. El Ahrache K, Imbeau D, Farbos B (2006) Percentile values for determining maximum endurance times for static muscular work. Int J Ind Ergon 36:99–108. https://doi.org/10.1016/j.ergon.2005.08.003

    Article  Google Scholar 

  32. Garg A, Hegmann KT, Schwoerer BJ, Kapellusch JM (2002) The effect of maximum voluntary contraction on endurance times for the shoulder girdle. Int J Ind Ergon 30:103–113. https://doi.org/10.1016/S0169-8141(02)00078-1

    Article  Google Scholar 

  33. Ma L, Chablat D, Bennis F, Zhang W (2009) Dynamic muscle fatigue evaluation in virtual working environment. Int J Ind Ergon 1:211–220. https://doi.org/10.1016/j.ergon.2008.04.004

    Article  Google Scholar 

  34. Shi J, Zheng YP, Chen X, Huang QH (2007) Assessment of muscle fatigue using sonomyography: muscle thickness change detected from ultrasound images. Med Eng Phys 29:472–479. https://doi.org/10.1016/j.medengphy.2006.07.004

    Article  CAS  PubMed  Google Scholar 

  35. Kramer CGS, Hagg T, Kemp B (1987) Real-time measurement of muscle fatigue related changes in surface EMG. Med Biol Eng Comput 25:627–630. https://doi.org/10.1007/BF02447329

    Article  CAS  PubMed  Google Scholar 

  36. Štirn I, Jarm T, 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 14:28–38

    Google Scholar 

  37. Chang J, Chablat D, Bennis F, Ma L (2016) Estimating the EMG response exclusively to fatigue during sustained static maximum voluntary contraction. Advances Intelligent Syst Comput 489:29–39. https://doi.org/10.1007/978-3-319-41694-6_4

    Article  Google Scholar 

  38. Cardozo A, Gonçalves M, Halla C, Marques N (2013) Age-related neuromuscular adjustments assessed by EMG. Electrodiagnosis New Frontiers Clinical Res:18. https://doi.org/10.5772/55053129

  39. Priego Quesada JI (2017) Application of Infrared Thermography in Sports Science, 1st edn. Springer International Publishing

  40. Becher C, Springer J, Feil S, Cerulli G, Paessler HH (2008) Intra-articular temperatures of the knee in sports - An in-vivo study of jogging and alpine skiing. BMC Musculoskelet Disord 9:1–7. https://doi.org/10.1186/1471-2474-9-46

    Article  Google Scholar 

  41. Mito K, Kitahara S, Tamura T, Kaneko K, Sakamoto K, Shimizu Y (2007) Effect of skin temperature on RMS amplitude of electromyogram and mechanomyogram during voluntary isometric contraction. Electromyogr Clin Neurophysiol 47:153–160

    PubMed  Google Scholar 

  42. Pääsuke M, Rannama L, Ereline J, Gapeyeva H, Oöpik V (2007) Changes in soleus motoneuron pool reflex excitability and surface EMG parameters during fatiguing low- vs. high-intensity isometric contractions. Electromyogr Clin Neurophysiol 47:341–350

    PubMed  Google Scholar 

  43. Bertmaring I, Babski-Reeves K, Nussbaum MA (2008) Infrared imaging of the anterior deltoid during overhead static exertions. Ergonomics 51:1606–1619. https://doi.org/10.1080/00140130802216933

    Article  PubMed  Google Scholar 

  44. Kenny GP, Reardon FD, Zaleski W, Reardon ML, Haman F, Ducharme MB (2003) Muscle temperature transients before, during, and after exercise measured using an intramuscular multisensor probe. J Appl Physiol 94:2350–2357. https://doi.org/10.1152/japplphysiol.01107.2002

    Article  CAS  PubMed  Google Scholar 

  45. Felici F, Quaresima V, Fattorini L, Sbriccoli P, Filligoi GC, Ferrari M (2009) Biceps brachii myoelectric and oxygenation changes during static and sinusoidal isometric exercises. J Electromyogr Kinesiol 19:e1–e11. https://doi.org/10.1016/j.jelekin.2007.07.010

    Article  PubMed  Google Scholar 

  46. Charkoudian N (2016) Human thermoregulation from the autonomic perspective. Autonomic Neurosci Basic Clinical 196:1–2. https://doi.org/10.1016/j.autneu.2016.02.007

    Article  Google Scholar 

  47. Kuiken TA, Lowery MM, Stoykov NS (2003) The effect of subcutaneous fat on myoelectric signal amplitude and cross-talk. Prosthetics Orthot Int 27:48–54. https://doi.org/10.3109/03093640309167976

    Article  CAS  Google Scholar 

  48. Chudecka M, Lubkowska A, Kempińska-Podhorodecka A (2014) Body surface temperature distribution in relation to body composition in obese women. J Therm Biol 43:1–6. https://doi.org/10.1016/j.jtherbio.2014.03.001

    Article  PubMed  Google Scholar 

  49. Savastano DM, Gorbach AM, Eden HS, Brady SM, Reynolds JC, Yanovski JA (2009) Adiposity and human regional body temperature. Am J Clin Nutr 90:1124–1131. https://doi.org/10.3945/ajcn.2009.27567

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Hillen B, Pfirrmann D, Nägele M, Simon P (2020) Infrared Thermography in Exercise Physiology: The Dawning of Exercise Radiomics. Sports Med (Auckland, NZ) 50:263–282. https://doi.org/10.1007/s40279-019-01210-w

    Article  PubMed  Google Scholar 

Download references

Funding

Authors would like to express gratitude to the Ministry of Higher Education Malaysia and Universiti Teknologi Malaysia for supporting this research under the Institutional Research Grant Vote Number 15J89 and 07G22.

Author information

Authors and Affiliations

  1. School of Biomedical Engineering and Health Sciences, Faculty of Engineering, Universiti Teknologi Malaysia, 81310, Johor Bahru, Johor, Malaysia

    Muhammad Faiz Md Shakhih, Nursyazana Ridzuan, Asnida Abdul Wahab, Laila Fadhillah Ulta Delestri, Anis Suzziani Rosslan & Mohammed Rafiq Abdul Kadir

  2. Medical Devices and Technology Center (MEDITEC), Universiti Teknologi Malaysia, 81310, Johor Bahru, Johor, Malaysia

    Asnida Abdul Wahab

  3. Faculty of Sports Science and Recreation, Universiti Teknologi MARA, Perlis Branch, Arau Campus, 02600, Arau, Perlis, Malaysia

    Nurul Farha Zainuddin

  4. Sports Innovation Technology Centre (SITC), Universiti Teknologi Malaysia, 81310, Johor Bahru, Johor, Malaysia

    Mohammed Rafiq Abdul Kadir

Authors
  1. Muhammad Faiz Md Shakhih
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Nursyazana Ridzuan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Asnida Abdul Wahab
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Nurul Farha Zainuddin
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Laila Fadhillah Ulta Delestri
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Anis Suzziani Rosslan
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Mohammed Rafiq Abdul Kadir
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Asnida Abdul Wahab.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Shakhih, M.F.M., Ridzuan, N., Wahab, A.A. et al. Non-obstructive monitoring of muscle fatigue for low intensity dynamic exercise with infrared thermography technique. Med Biol Eng Comput 59, 1447–1459 (2021). https://doi.org/10.1007/s11517-021-02387-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11517-021-02387-x

Keyword

Search

Navigation