Skip to main content
SpringerLink
Log in

Comparative study of a muscle stiffness sensor and electromyography and mechanomyography under fatigue conditions

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

Abstract

This paper proposes the feasibility of a stiffness measurement for muscle contraction force estimation under muscle fatigue conditions. Bioelectric signals have been widely studied for the estimation of the contraction force for physical human–robot interactions, but the correlation between the biosignal and actual motion is decreased under fatigue conditions. Muscle stiffness could be a useful contraction force estimator under fatigue conditions because it measures the same physical quantity as the muscle contraction that generates the force. Electromyography (EMG), mechanomyography (MMG), and a piezoelectric resonance-based active muscle stiffness sensor were used to analyze the biceps brachii under isometric muscle fatigue conditions with reference force sensors at the end of the joint. Compared to EMG and MMG, the change in the stiffness signal was smaller (p < 0.05) in the invariable contraction force generation test until failure. In addition, in the various contraction level force generation tests, the stiffness signal under the fatigue condition changed <10 % (p < 0.05) compared with the signal under non-fatigue conditions. This result indicates that the muscle stiffness signal is less sensitive to muscle fatigue than other biosignals. This investigation provides insights into methods of monitoring and compensating for muscle fatigue.

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

Similar content being viewed by others

References

  1. Bien Z, Park K, Jung J, Do J (2005) Intention reading is essential in human-friendly interfaces for the elderly and the handicapped. IEEE Trans Ind Electron 52:1500–1505

    Article  Google Scholar 

  2. Dellon B, Matsuoka Y (2007) Prosthetics, exoskeletons, and rehabilitation [Grand Challenges of Robotics]. IEEE Robot Autom Mag 14:30–34

    Article  Google Scholar 

  3. Merletti R, Parker PA (2004) Electromyography: physiology, engineering, and noninvasive applications. Wiley-IEEE Press, New Jersy

    Book  Google Scholar 

  4. Lieber R (2002) Skeletal muscle structure function and plasticity: the physiological basis of rehabilitation. Lippincott Williams & Wilkins, London

    Google Scholar 

  5. Orizio C, Perini R (1989) Veicsteinas A. Muscular sound and force relationship during isometric contraction in man. Eur J Appl Physiol Occup Physiol 58:528–533

    Article  CAS  PubMed  Google Scholar 

  6. Smith TG, Stokes MJ (1993) Technical aspects of acoustic myography (AMG) of human skeletal muscle: contact pressure and force/AMG relationships. J Neurosci Meth 47:85–92

    Article  CAS  Google Scholar 

  7. Buchanan TS, Lloyd DG, Manal K, Besier TF (2004) Neuromusculoskeletal modeling: estimation of muscle forces and joint moments and movements from measurements of neural command. J Appl Biomech 20:367–395

    PubMed Central  PubMed  Google Scholar 

  8. Akataki K, Mita K, Watakabe M, Itoh K (2001) Mechanomyogram and force relationship during voluntary isometric ramp contractions of the biceps brachii muscle. Eur J Appl Physiol 84:19–25

    Article  CAS  PubMed  Google Scholar 

  9. Silva J, Heim W, Chau T (2005) A self-contained, mechanomyography-driven externally powered prosthesis. Arch Phys Med Rehab 86:2066–2070

    Article  Google Scholar 

  10. Zwarts MJ, Bleijenberg G, Van Engelen BGM (2008) Clinical neurophysiology of fatigue. Clin Neurophysiol 119:2–10

    Article  CAS  PubMed  Google Scholar 

  11. Bigland-Ritchie B, Jones DA, Hosking GP, Edwards RH (1978) Central and peripheral fatigue in sustained maximum voluntary contractions of human quadriceps muscle. Clin Sci Mol Med 54:609–614

    CAS  PubMed  Google Scholar 

  12. Yoshitake Y, Ue H, Miyazaki M, Moritani T (2001) Assessment of lower-back muscle fatigue using electromyography, mechanomyography, and near-infrared spectroscopy. Eur J Appl Physiol 84:174–179

    Article  CAS  PubMed  Google Scholar 

  13. Orizio C, Gobbo M, Diemont B, Esposito F, Veicsteinas A (2003) The surface mechanomyogram as a tool to describe the influence of fatigue on biceps brachii motor unit activation strategy. Historical basis and novel evidence. Eur J Appl Physiol 90:325–336

    Article  Google Scholar 

  14. Viitasalo JHT, Komi PV (1977) Signal characteristics of EMG during fatigue. Eur J Appl Physiol Occup Physiol 37:111–121

    Article  CAS  PubMed  Google Scholar 

  15. Madeleine P, Jrgensen L, Sgaard K, Arendt-Nielsen L, Sjgaard G (2002) Development of muscle fatigue as assessed by electromyography and mechanomyography during continuous and intermittent low-force contractions: effects of the feedback mode. Eur J Appl Physiol 87:28–37

    Article  PubMed  Google Scholar 

  16. Beck TW, Housh TJ, Johnson GO, Weir JP, Cramer JT, Coburn JW, Malek MH (2004) Mechanomyographic amplitude and mean power frequency versus torque relationships during isokinetic and isometric muscle actions of the biceps brachii. J Electromyogr Kinesiol 14:555–564

    Article  PubMed  Google Scholar 

  17. Song JH, Jung JW, Bien Z (2006) Robust EMG pattern recognition to muscular fatigue effect for human-machine interaction. Lect Notes Artif Intell. 4293:1190–1199

    Google Scholar 

  18. Artemiadis PK, Kyriakopoulos KJ (2010) An EMG-based robot control scheme robust to time-varying EMG signal features. IEEE Trans Inf Technol Biomed 14:582–588

    Article  PubMed  Google Scholar 

  19. Murayama Y, Haruta M, Hatakeyama Shiina YT, Sakuma H, Takenoshita S, Omata S, Constantinou C (2008) Development of a new instrument for examination of stiffness in the breast using haptic sensor technology. Sens Actuators A 143(2):430–438

    Article  CAS  Google Scholar 

  20. Arokoski J, Surakka J, Ojala T, Kolari P, Jurvelin JS (2005) Feasibility of the use of a novel soft tissue stiffness meter. Physiol Meas 26:215

    Article  PubMed  Google Scholar 

  21. Han H, Kim J (2013) Active muscle stiffness sensor based on piezoelectric resonance for muscle contraction estimation. Sens Actuators A Phys 194:212–219

    Article  CAS  Google Scholar 

  22. Jalkanen V, Andersson BM, Bergh A, Ljungberg B, Lindahl OA (2008) Explanatory models for a tactile resonance sensor system elastic and density related variations of prostate tissue in vitro. Physiol Meas 29:729–745

    Article  PubMed  Google Scholar 

  23. Omata S, Terunuma Y (1992) New tactile sensor like the human hand and its applications. Sens Actuators A Phys 35:9–15

    Article  Google Scholar 

  24. Omata S, Murayama Y, Constantinou CE (2004) Real time robotic tactile sensor system for the determination of the physical properties of biomaterials. Sens Actuators A Phys 112:278–285

    Article  CAS  Google Scholar 

  25. Cavanagh PR, Komi PV (1979) Electromechanical delay in human skeletal muscle under concentric and eccentric contractions. Eur J Appl Physiol Occup Physiol 42:159–163

    Article  CAS  PubMed  Google Scholar 

  26. Norman RW, Komi PV (1979) Electromechanical delay in skeletal muscle under normal movement conditions. Acta Physiol Scand 106:241–248

    Article  CAS  PubMed  Google Scholar 

  27. Madeleine P, Bajaj P, Søqaard K, Arendt-Nielsen L (2001) Mechanomyography and electromyography force relationships during concentric, isometric and eccentric contractions. J Electromyogr Kinesiol 11(2):113–121

    Article  CAS  PubMed  Google Scholar 

  28. Krishna M, Rajanna K (2004) Tactile sensor based on piezoelectric resonance. IEEE Sens J 4(5):691–697

    Article  Google Scholar 

  29. Borg G (1998) Borg’s perceived exertion and pain scales. Human Kinetics, Champaign

    Google Scholar 

  30. Allison GT (2003) Trunk muscle onset detection technique for EMG signals with ECG artifact. J Electomyogr Kinesiol 13:209–216

    Article  CAS  Google Scholar 

  31. Orizio C, Solomonow M, Baratta R, Veicsteinas A (1992) Influence of motor units recruitment and firing rate on the soundmyogram and EMG characteristics in cat gastrocnemius. J Electomyogr Kinesiol 2(4):232–241

    Article  CAS  Google Scholar 

  32. Bland JM, Altman DG (1999) Measuring agreement in method comparison studies. Stat Methods Med Res 8:135–160

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgments

This research was supported by the Public welfare and Safety research program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT and Future Planning (2010-0020449), and supported by the Ministry of Education under Basic Science Research Program through the National Research Foundation of Korea (2013R1A1A2009378).

Author information

Authors and Affiliations

  1. Department Computer Science, Korea Advanced Institute of Science and Technology, Daejeon, South Korea

    Hyonyoung Han & Sungho Jo

  2. Department Mechanical Engineering, Korea Advanced Institute of Science and Technology, Daejeon, South Korea

    Jung Kim

Authors
  1. Hyonyoung Han
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sungho Jo
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jung Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Sungho Jo or Jung Kim.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Han, H., Jo, S. & Kim, J. Comparative study of a muscle stiffness sensor and electromyography and mechanomyography under fatigue conditions. Med Biol Eng Comput 53, 577–588 (2015). https://doi.org/10.1007/s11517-015-1271-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11517-015-1271-1

Keywords

Search

Navigation