Skip to main content
Log in

Determination of the correlation between muscle forces obtained from OpenSim and muscle activities obtained from electromyography in the elderly

  • Scientific Paper
  • Published:
Physical and Engineering Sciences in Medicine Aims and scope Submit manuscript

Abstract

Measurement of muscle forces related to aging can help to better identify the gait impairment mechanisms in the elderly. To this end, musculoskeletal modeling has been developed to estimate muscle forces. This study aimed to check the validity of OpenSim modeling (i.e., computed muscle control) approach in elderly subjects. Kinematic and kinetic data and Electromyography (EMG) signals for four different muscles were collected in nine healthy elderly males during walking. Dynamic simulation was done within OpenSim. Correlation analysis was performed to quantitatively compare the maximum estimated muscle forces with maximum measured muscle activities during the first double limb support, single limb support, and the second double limb support phases. The area-time plots of OpenSim and EMG data during gait cycle were obtained for qualitative assessment. In quantitative assessment, a low to moderate correlation was observed for the peak of muscle force and muscle activation of four muscles during sub phases of gait. The muscle forces pattern from OpenSim was found to be relatively similar to the muscle activity pattern from EMG especially for Gastrocnemius Medialis. A low to moderate consistency between OpenSim and EMG in the elderly can be explained by using a single mathematical estimation approach.

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.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  1. Żuk M, Pezowicz C (2015) Kinematic analysis of a six-degrees-of-freedom model based on ISB recommendation: a repeatability analysis and comparison with conventional gait model. Appl Bionics Biomech. https://doi.org/10.1155/2015/503713

    Article  PubMed  PubMed Central  Google Scholar 

  2. Żuk M, Trzeciak M (2016) Anatomical protocol for gait analysis: joint kinematics measurement and its repeatability. J Theor Appl Mech 55:369–376. https://doi.org/10.15632/jtam-pl.55.1.369

    Article  Google Scholar 

  3. Hicks JL, Uchida TK, Seth A, Rajagopal A, Delp SL (2015) Is my model good enough? Best practices for verification and validation of musculoskeletal models and simulations of movement. J Biomech Eng 137:020905. https://doi.org/10.1115/1.4029304

    Article  PubMed  Google Scholar 

  4. Heller M, Bergmann G, Deuretzbacher G, Dürselen L, Pohl M, Claes L, Haas N, Duda G (2001) Musculo-skeletal loading conditions at the hip during walking and stair climbing. J Biomech 34:883–893. https://doi.org/10.1016/S0021-9290(01)00039-2

    Article  CAS  PubMed  Google Scholar 

  5. Erdemir A, McLean S, Herzog W, van den Bogert AJ (2007) Model-based estimation of muscle forces exerted during movements. Clin Biomech 22:131–154. https://doi.org/10.1016/j.clinbiomech.2006.09.005

    Article  Google Scholar 

  6. Żuk M, Pezowicz C (2016) The influence of uncertainty in body segment mass on calculated joint moments and muscle forces. In: Inf Technol Med Springer, pp. 349–359. https://doi.org/10.1007/978-3-319-39904-1_3.

  7. Čadová M, Gallo L (2013) Is OpenSim suitable for masticatory system analysis. Russian J Biomech 17:53–67. https://doi.org/10.5167/uzh-89161

    Article  Google Scholar 

  8. Heintz S, Gutierrez-Farewik EM (2007) Static optimization of muscle forces during gait in comparison to EMG-to-force processing approach. Gait Posture 26:279–288. https://doi.org/10.1016/j.gaitpost.2006.09.074

    Article  PubMed  Google Scholar 

  9. Scarton A, Jonkers I, Guiotto A, Spolaor F, Guarneri G, Avogaro A, Cobelli C, Sawacha Z (2017) Comparison of lower limb muscle strength between diabetic neuropathic and healthy subjects using OpenSim. Gait Posture 58:194–200. https://doi.org/10.1016/j.gaitpost.2017.07.117

    Article  PubMed  Google Scholar 

  10. Żuk M, Syczewska M, Pezowicz C (2018) Use of the surface electromyography for a quantitative trend validation of estimated muscle forces. Biocybern Biomed Eng 38:243–250. https://doi.org/10.1016/j.bbe.2018.02.001

    Article  Google Scholar 

  11. Trinler U, Leboeuf F, Hollands K, Jones R, Baker R (2018) Estimation of muscle activation during different walking speeds with two mathematical approaches compared to surface EMG. Gait Posture. https://doi.org/10.1016/j.gaitpost.2018.06.115

    Article  PubMed  Google Scholar 

  12. Glitsch U, Baumann W (1997) The three-dimensional determination of internal loads in the lower extremity. J Biomech 30:1123–1131. https://doi.org/10.1016/S0021-9290(97)00089-4

    Article  CAS  PubMed  Google Scholar 

  13. Valente G, Pitto L, Stagni R, Taddei F (2015) Effect of lower-limb joint models on subject-specific musculoskeletal models and simulations of daily motor activities. J Biomech 48:4198–4205. https://doi.org/10.1016/j.jbiomech.2015.09.042

    Article  PubMed  Google Scholar 

  14. Lin Y-C et al (2012) Comparison of different methods for estimating muscle forces in human movement. Proc Inst Mech Eng H 226:103–112. https://doi.org/10.1177/0954411911429401

    Article  PubMed  Google Scholar 

  15. Thelen DG, Anderson FC, Delp SL (2003) Generating dynamic simulations of movement using computed muscle control. J Biomech 36:321–328. https://doi.org/10.1016/s0021-9290(02)00432-3

    Article  PubMed  Google Scholar 

  16. Hermens HJ, Freriks B, Disselhorst-Klug C, Rau G (2000) Development of recommendations for SEMG sensors and sensor placement procedures. J Electromyogr Kinesiol 10:361–374. https://doi.org/10.1016/S1050-6411(00)00027-4

    Article  CAS  PubMed  Google Scholar 

  17. Delp SL et al (2007) OpenSim: open-source software to create and analyze dynamic simulations of movement. IEEE Trans Biomed Eng 54:1940–1950. https://doi.org/10.1109/10.102791

    Article  PubMed  Google Scholar 

  18. Mukaka MM (2012) A guide to appropriate use of correlation coefficient in medical research. Malawi Med J 24:69–71

    CAS  PubMed  PubMed Central  Google Scholar 

  19. Perry J, Davids JR (1992) Gait analysis: normal and pathological function. J Pediatr Orthop 12:815

    Article  Google Scholar 

  20. Benjuya N, Melzer I, Kaplanski J (2004) Aging-induced shifts from a reliance on sensory input to muscle cocontraction during balanced standing. J Gerontol A 59:M166–M171. https://doi.org/10.1093/gerona/59.2.M166

    Article  Google Scholar 

  21. Hallal CZ, Marques NR, Vieira ER, Brunt D, Spinoso DH, Castro A, Cardozo AC, Gonçalves M (2013) Lower limb muscle coactivation levels in healthy younger and older adults during functional dual-task gait. Motriz Rev Educ Fís 19:620–626. https://doi.org/10.1590/S1980-65742013000300013

    Article  Google Scholar 

  22. Dillon CF, Rasch EK, Gu Q, Hirsch R (2006) Prevalence of knee osteoarthritis in the United States: arthritis data from the Third National Health and Nutrition Examination Survey 1991–94. J Rheumatol 33:2271–2279

    PubMed  Google Scholar 

  23. Ko SU, Ling SM, Schreiber C, Nesbitt M, Ferrucci L (2011) Gait patterns during different walking conditions in older adults with and without knee osteoarthritis—results from the Baltimore longitudinal study of aging. Gait Posture 33:205–210. https://doi.org/10.1016/j.gaitpost.2010.11.006

    Article  PubMed  Google Scholar 

Download references

Acknowledgements

The present study was driven from a dissertation submitted in partial fulfilment of the requirements for the Doctor of Philosophy in Orthotics and Prosthetics by the corresponding author.

Funding

No funding.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Fatemeh Hemmati.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

All procedures performed in this study were in accordance with the ethical standards of the University of Social Welfare and Rehabilitation Sciences committee, Ref number: IR.USWR.REC.1396.283) and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Informed consent

Informed consent was obtained from all participants included in the study.

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

Karimi, M.T., Hemmati, F., Mardani, M.A. et al. Determination of the correlation between muscle forces obtained from OpenSim and muscle activities obtained from electromyography in the elderly. Phys Eng Sci Med 44, 243–251 (2021). https://doi.org/10.1007/s13246-021-00973-9

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13246-021-00973-9

Keywords

Navigation