Skip to main content
Log in

Electromyography-signal-based muscle fatigue assessment for knee rehabilitation monitoring systems

  • Original Article
  • Published:
Biomedical Engineering Letters Aims and scope Submit manuscript

Abstract

This study suggested a new EMG-signal-based evaluation method for knee rehabilitation that provides not only fragmentary information like muscle power but also in-depth information like muscle fatigue in the field of rehabilitation which it has not been applied to. In our experiment, nine healthy subjects performed straight leg raise exercises which are widely performed for knee rehabilitation. During the exercises, we recorded the joint angle of the leg and EMG signals from four prime movers of the leg: rectus femoris (RFM), vastus lateralis, vastus medialis, and biceps femoris (BFLH). We extracted two parameters to estimate muscle fatigue from the EMG signals, the zero-crossing rate (ZCR) and amplitude of muscle tension (AMT) that can quantitatively assess muscle fatigue from EMG signals. We found a decrease in the ZCR for the RFM and the BFLH in the muscle fatigue condition for most of the subjects. Also, we found increases in the AMT for the RFM and the BFLH. Based on the results, we quantitatively confirmed that in the state of muscle fatigue, the ZCR shows a decreasing trend whereas the AMT shows an increasing trend. Our results show that both the ZCR and AMT are useful parameters for characterizing the EMG signals in the muscle fatigue condition. In addition, our proposed methods are expected to be useful for developing a navigation system for knee rehabilitation exercises by evaluating the two parameters in two-dimensional parameter space.

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

Access this article

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

Similar content being viewed by others

References

  1. Holliday RC, Antoun M, Playford ED. A survey of goal-setting methods used in rehabilitation. Neurorehabilit Neural Repair. 2005;19(3):227–31.

    Article  Google Scholar 

  2. White G, Cordato D, O’Rourke F, Mendis R, Ghia D, Chan D. Validation of the Stroke Rehabilitation Motivation Scale: a pilot study. Asian J Gerontol Geriatr. 2012;7(2):80–7.

    Google Scholar 

  3. Chen K-H, Chen P-C, Liu K-C, Chan C-T. Wearable sensor-based rehabilitation exercise assessment for knee osteoarthritis. Sensors-Basel. 2015;15(2):4193–211.

    Article  Google Scholar 

  4. Chen K-H, Tseng W-C, Liu K-C, Chan C-T. Using gyroscopes and accelerometers as a practical rehabilitation monitor system after total knee arthroplasty. 2015 International microwave workshop series on RF and wireless technologies for biomedical and healthcare applications (IMWS-BIO), 2015 IEEE MTT-S; 2015. p. 58–9.

  5. Taylor PE, Almeida GJ, Kanade T, Hodgins JK. Classifying human motion quality for knee osteoarthritis using accelerometers. In: 2010 Annual international conference of the IEEE engineering in medicine and biology; 2010. p. 339–43.

  6. Masdar A, Ibrahim B, Hanafi D, Jamil MMA, Rahman K. Knee joint angle measurement system using gyroscope and flex-sensors for rehabilitation. In: Biomedical engineering international conference (BMEiCON), 2013 6th.; 2013. p. 1–4.

  7. Milenkovic M, Jovanov E, Chapman J, Raskovic D, Price J. An accelerometer-based physical rehabilitation system. In: 2002 Proceedings of the thirty-fourth southeastern symposium on system theory; 2002. p. 57–60.

  8. Herzog W, Sokolosky J, Zhang Y, Guimarães A. EMG-force relation in dynamically contracting cat plantaris muscle. J Electromyogr Kinesiol. 1998;8(3):147–55.

    Article  Google Scholar 

  9. Liu MM, Herzog W, Savelberg HH. Dynamic muscle force predictions from EMG: an artificial neural network approach. J Electromyogr Kinesiol. 1999;9(6):391–400.

    Article  Google Scholar 

  10. Liu P, Martel F, Rancourt D, Clancy EA, Brown DR. Fingertip force estimation from forearm muscle electrical activity. 2014 IEEE international conference on acoustics, speech and signal processing (ICASSP); 2014. p. 2069–73.

  11. Weir JP, Wagner LL, Housh TJ. Linearity and reliability of the IEMG v torque relationship for the forearm flexors and leg extensors. Am J Phys Med Rehabil. 1992;71(5):283–7.

    Article  Google Scholar 

  12. Lykholt LE, Ganeswarathas S, Thota AK, Harreby KR, Jung R. Information on ankle angle from intramuscular EMG Signals during development of muscle fatigue in an open-loop functional electrical stimulation system in rats. In: Winnie J, Ole KA, Metin A, editors. Replace, repair, restore, relieve-bridging clinical and engineering solutions in neurorehabilitation. Berlin: Springer; 2014. p. 529–36.

    Google Scholar 

  13. Cifrek M, Medved V, Tonković S, Ostojić S. Surface EMG based muscle fatigue evaluation in biomechanics. Clin Biomech (Bristol, Avon). 2009;24(4):327–40.

    Article  Google Scholar 

  14. Allen DG, Lamb GD, Westerblad H. Skeletal muscle fatigue: cellular mechanisms. Physiol Rev. 2008;88(1):287–332.

    Article  Google Scholar 

  15. Amann M, Dempsey JA. Locomotor muscle fatigue modifies central motor drive in healthy humans and imposes a limitation to exercise performance. J Physiol. 2008;586(1):161–73.

    Article  Google Scholar 

  16. Harput G, Kilinc HE, Ozer H, Baltaci G, Mattacola CG. Quadriceps and hamstring strength recovery during early neuromuscular rehabilitation after ACL hamstring-tendon autograft reconstruction. J Sport Rehabil. 2015;24(4):398–404.

    Article  Google Scholar 

  17. Hawley JA, Myburgh K, Noakes TD, Dennis S. Training techniques to improve fatigue resistance and enhance endurance performance. J Sports Sci. 1997;15(3):325–33.

    Article  Google Scholar 

  18. Hicks Audrey L, Kent-Braun Jane, Ditor David S. Sex differences in human skeletal muscle fatigue. Exerc Sport Sci Rev. 2001;9(3):109–12.

    Article  Google Scholar 

  19. Lindström B, Karlsson S, Gerdle B. Knee extensor performance of dominant and non-dominant limb throughout repeated isokinetic contractions, with special reference to peak torque and mean frequency of the EMG. Clin Physiol Funct Imaging. 1995;15(3):275–86.

    Article  Google Scholar 

  20. Vøllestad NK. Measurement of human muscle fatigue. J Neurosci Methods. 1997;74(2):219–27.

    Article  Google Scholar 

  21. Koike Y, Kawato M. Estimation of arm posture in 3D-space from surface EMG signals using a neural network model. IEICE Trans Inf Syst. 1994;77(4):368–75.

    Google Scholar 

  22. Christanell F, Hoser C, Huber R, Fink C, Luomajoki H. The influence of electromyographic biofeedback therapy on knee extension following anterior cruciate ligament reconstruction: a randomized controlled trial. BMC Sports Sci Med Rehabil. 2012;4(1):1.

    Article  Google Scholar 

  23. Draper V. Electromyographic biofeedback and recovery of quadriceps femoris muscle function following anterior cruciate ligament reconstruction. Phys Ther. 1990;70(1):11–7.

    Article  Google Scholar 

  24. Giggins OM, Persson UM, Caulfield B. Biofeedback in rehabilitation. J Neuroeng Rehabil. 2013;10(1):1.

    Article  Google Scholar 

  25. Holtermann A, Mork P, Andersen L, Olsen HB, Søgaard K. The use of EMG biofeedback for learning of selective activation of intra-muscular parts within the serratus anterior muscle: a novel approach for rehabilitation of scapular muscle imbalance. J Electromyogr Kinesiol. 2010;20(2):359–65.

    Article  Google Scholar 

  26. Middaugh S, Thomas KJ, Smith AR, McFall TL, Klingmueller J. EMG biofeedback and exercise for treatment of cervical and shoulder pain in individuals with a spinal cord injury: a pilot study. Top Spinal Cord Inj Rehabil. 2013;19(4):311.

    Article  Google Scholar 

  27. Sturma A, Göbel P, Herceg M, Gee N, Roche A, Fialka-Moser V, et al. Advanced rehabilitation for amputees after selective nerve transfers: EMG-guided training and testing. In: Winnie J, Ole KA, Metin A, editors. Replace, repair, restore, relieve-bridging clinical and engineering solutions in neurorehabilitation. Berlin: Springer; 2014. p. 169–77.

    Google Scholar 

  28. Holtermann A, Grönlund C, Karlsson JS, Roeleveld K. Motor unit synchronization during fatigue: described with a novel sEMG method based on large motor unit samples. J Electromyogr Kinesiol. 2009;19(2):232–41.

    Article  Google Scholar 

  29. Hunter SK, Enoka RM. Changes in muscle activation can prolong the endurance time of a submaximal isometric contraction in humans. J Appl Physiol. 2003;94(1):108–18.

    Article  Google Scholar 

  30. Lee S, Koo K, Lee Y, Lee J, Kim J. Spatiotemporal analysis of EMG signals for muscle rehabilitation monitoring system. In: 2013 IEEE 2nd global conference on consumer electronics (GCCE); 2013. p. 1–2.

  31. Enoka RM, Stuart DG. Neurobiology of muscle fatigue. J Appl Physiol. 1992;72(5):1631–48.

    Article  Google Scholar 

  32. Shin D, Kim J, Koike Y. A myokinetic arm model for estimating joint torque and stiffness from EMG signals during maintained posture. J Neurophysiol. 2009;101:387–401.

    Article  Google Scholar 

  33. Flores E, Tobon G, Cavallaro E, Cavallaro FI, Perry JC, Keller T. Improving patient motivation in game development for motor deficit rehabilitation. In: Proceedings of the 2008 international conference on advances in computer entertainment technology; 2008. p. 381–4.

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Jaehyo Kim.

Ethics declarations

Conflict of interest

The authors declare that there is no conflict of interest regarding the publication of this paper.

Ethical statement

All the subjects provided written informed consent prior to participation. The experimental protocol was approved by the ethics committees of Handong Global University and was conducted in accordance with the ethical standards outlined in the Declaration of Helsinki.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (DOCX 12 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kim, H., Lee, J. & Kim, J. Electromyography-signal-based muscle fatigue assessment for knee rehabilitation monitoring systems. Biomed. Eng. Lett. 8, 345–353 (2018). https://doi.org/10.1007/s13534-018-0078-z

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13534-018-0078-z

Keywords

Navigation