Using 3D Scan to Determine Human Body Segment Mass in OpenSim Model

  • Jing Chang
  • Damien Chablat
  • Fouad Bennis
  • Liang Ma
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10917)


Biomechanical motion simulation and dynamic analysis of human joint moments will provide insights into Musculoskeletal Disorders. As one of the mainstream simulation tools, OpenSim uses proportional scaling to specify model segment masses to the simulated subject, which may bring about errors. This study aims at estimating the errors caused by the specifying method used in OpenSim as well as the influence of these errors on dynamic analysis. A 3D scan is used to construct subject’s 3D geometric model, according to which segment masses are determined. The determined segment masses data is taken as the yardstick to assess the errors of OpenSim scaled model. Then influence of these errors on the dynamic calculation is evaluated in the simulation of a motion in which the subject walks in an ordinary gait. Result shows that the mass error in one segment can be as large as 5.31% of overall body weight. The mean influence on calculated joint moment varies from 0.68% to 12.68% in 18 joints.

In conclusion, a careful specification of segment masses will increase the accuracy of the dynamic simulation. As far as estimating human segment masses, the use of segment volume and density data can be an economical choice apart from referring to population mass distribution data.


Musculoskeletal Disorders Biomechanical analysis Virtual human model OpenSim Body segment mass 



This work was supported by INTERWEAVE Project (Erasmus Mundus Partnership Asia-Europe) under Grants number IW14AC0456 and IW14AC0148, and by the National Natural Science Foundation of China under Grant numbers 71471095 and by Chinese State Scholarship Fund. The authors also thank D. Zhang Yang for his support.


  1. 1.
    Eurogip: Déclaration des maladies professionnelles: problématique et bonnes pratiques dans cinq pays européens, p. 44 (2015).
  2. 2.
    Chaffin, D., Andersson, G., Martin, B.: Occupational Biomechanics. Wiley, Hoboken (1999)Google Scholar
  3. 3.
    Delp, S.L., Anderson, F.C., Arnold, A.S., Loan, P., Habib, A., John, C.T., Guendelman, E., Thelen, D.G.: Opensim: open-source software to create and analyze dynamic simulations of movement. IEEE Trans. Biomed. Eng. 54(11), 1940–1950 (2007)CrossRefGoogle Scholar
  4. 4.
    Thelen, D.G., Anderson, F.C.: Using computed muscle control to generate forward dynamic simulations of human walking from experimental data. J. Biomech. 39(6), 1107–1115 (2006)CrossRefGoogle Scholar
  5. 5.
    Kim, H.K., Zhang, Y.: Estimation of lumbar spinal loading and trunk muscle forces during asymmetric lifting tasks: application of whole-body musculoskeletal modelling in opensim. Ergonomics 60(4), 563–576 (2017)CrossRefGoogle Scholar
  6. 6.
    Clauser, C.E., McConville, J.T., Young, J.W.: Weight, volume, and center of mass of segments of the human body. Technical report, Antioch coll yellow springs oh (1969)Google Scholar
  7. 7.
    De Leva, P.: Adjustments to zatsiorsky-seluyanov’s segment inertia parameters. J. Biomech. 29(9), 1223–1230 (1996)CrossRefGoogle Scholar
  8. 8.
    Okada, H.: Body segment inertia properties of Japanese elderly. Biomechinsm 13, 125–138 (1996)CrossRefGoogle Scholar
  9. 9.
    Durkin, J.L., Dowling, J.J., et al.: Analysis of body segment parameter differences between four human populations and the estimation errors of four popular mathematical models. Trans.-Am. Soc. Mech. Eng. J. Biomech. Eng. 125(4), 515–522 (2003)Google Scholar
  10. 10.
    Drillis, R., Contini, R., Bluestein, M.: Body segment parameters. School of Engineering and Science Research Division, New York University, NY (1966)Google Scholar
  11. 11.
    Delp, S.L., Loan, J.P., Hoy, M.G., Zajac, F.E., Topp, E.L., Rosen, J.M.: An interactive graphics-based model of the lower extremity to study orthopaedic surgical procedures. IEEE Trans. Biomed. Eng. 37(8), 757–767 (1990)CrossRefGoogle Scholar
  12. 12.
    John, C.T., Seth, A., Schwartz, M.H., Delp, S.L.: Contributions of muscles to mediolateral ground reaction force over a range of walking speeds. J. Biomech. 45(14), 2438–2443 (2012)CrossRefGoogle Scholar
  13. 13.
    Wei, C., Jensen, R.K.: The application of segment axial density profiles to a human body inertia model. J. Biomech. 28(1), 103–108 (1995)CrossRefGoogle Scholar
  14. 14.
    Lee, M.K., Le, N.S., Fang, A.C., Koh, M.T.: Measurement of body segment parameters using dual energy x-ray absorptiometry and three-dimensional geometry: An application in gait analysis. J. Biomech. 42(3), 217–222 (2009)CrossRefGoogle Scholar
  15. 15.
    Davidson, P.L., Wilson, S.J., Wilson, B.D., Chalmers, D.J.: Estimating subject-specific body segment parameters using a 3-dimensional modeller program. J. Biomech. 41(16), 3506–3510 (2008)CrossRefGoogle Scholar
  16. 16.
    Pearsall, D.J., Reid, G.: The study of human body segment parameters in biomechanics. Sports Med. 18(2), 126–140 (1994)CrossRefGoogle Scholar
  17. 17.
    Durkin, J.L., Dowling, J.J., Andrews, D.M.: The measurement of body segment inertial parameters using dual energy x-ray absorptiometry. J. Biomech. 35(12), 1575–1580 (2002)CrossRefGoogle Scholar
  18. 18.
    Lukaski, H.C.: Methods for the assessment of human body composition: traditional and new. Am. J. Clin. Nutr. 46(4), 537–556 (1987)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Jing Chang
    • 1
  • Damien Chablat
    • 2
  • Fouad Bennis
    • 1
  • Liang Ma
    • 3
  1. 1.Ecole Centrale de Nantes, Laboratoire des Sciences du Numérique de Nantes (LS2N), UMR CNRS 6004NantesFrance
  2. 2.CNRS, Laboratoire des Sciences du Numérique de Nantes (LS2N), UMR CNRS 6004NantesFrance
  3. 3.Department of Industrial EngineeringTsinghua UniversityBeijingPeople’s Republic of China

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