Skip to main content
SpringerLink
Log in

Creating Personalized Dynamic Models

  • Chapter
  • First Online:
Book cover Biomechanics of Anthropomorphic Systems

Part of the book series: Springer Tracts in Advanced Robotics ((STAR,volume 124))

  • 954 Accesses

Abstract

In human motion science, the dynamics plays an important role. It relates the movement of the human to the forces necessary to achieve this movement. It also relates the human and its environment through interaction forces. Estimating subject-specific dynamic models is a challenging problem, due to the need for both accurate measurement and modeling formalisms. In the past decade, we have developed solutions for the computation of the dynamic quantities of humans, based on individual (subject specific) models, inspired largely by Robotics geometric and dynamic calibration. In this chapter, we will present the state of the art and our latest advances in this area and show examples of applications to both humans and humanoid robots. With these research results we hope to contribute beyond the field of robotics to the fields of biomechanics and ergonomics, by providing accurate dynamic models of beings.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Siciliano, B., Khatib, O. (eds.): Springer Handbook of Robotics. Springer, Berlin (2016)

    Google Scholar 

  2. Ayusawa, K., Venture, G., Nakamura, Y.: Identifiability and identification of inertial parameters using the underactuated base-link dynamics for legged multibody systems. Int. J. Rob. Res. 33(3), 446–468 (2013). https://doi.org/10.1177/0278364913495932

    Article  Google Scholar 

  3. Venture, G., Ayusawa, K., Nakamura, Y.: Real-time identification and visualization of human segment parameters. Conf. Proc. Int. Conf. IEEE Eng. Med. Biol. Soc. 2009, 3983–3986 (2009). https://doi.org/10.1109/IEMBS.2009.5333620

    Article  Google Scholar 

  4. Ayusawa, K.: Scaling Kinematic Chains in the Air–Identification of Floating Systems Using Dynamics Constraint of the Baselink without Force Measurement, pp. 19–25 (2011)

    Google Scholar 

  5. de Leva, P.: Adjustments to zatsiorsky-seluyanov’s segment inertia parameters. J. Biomech. 29(9), 1223–1230 (1996)

    Article  Google Scholar 

  6. Dumas, R., Cheze, L., Verriest, J.-P.: Adjustments to McConville et al. and Young et al. body segment inertial parameters. J. Biomech. 40(3), 543–553 (2007)

    Google Scholar 

  7. Zatsiorsky, V.: The mass and inertia characteristics of the main segments of the human body. Biomechanics, 1152–1159 (1983)

    Google Scholar 

  8. Andersen, M.S., Benoit, D.L., Damsgaard, M., Ramsey, D.K., Rasmussen, J.: Do kinematic models reduce the effects of soft tissue artefacts in skin marker-based motion analysis? An in vivo study of knee kinematics. J. Biomech. 43, 268–273 (2010)

    Article  Google Scholar 

  9. Peters, A., Galna, B., Sangeux, M., Morris, M., Baker, R.: Quantification of soft tissue artifact in lower limb human motion analysis: a systematic review. Gait Posture 31, 1–8 (2010)

    Article  Google Scholar 

  10. Zemp, R., List, R., Gülay, T., Elsig, J.P., Naxera, J., Taylor, W.R., Lorenzetti, S.: Soft tissue artefacts of the human back: Comparison of the sagittal curvature of the spine measured using skin markers and an open upright MRI. Plos One 9 (2014)

    Google Scholar 

  11. Hara, R., Sangeux, M., Baker, R., McGinley, J.: Quantification of pelvic soft tissue artifact in multiple static positions. Gait Posture 39, 712–717 (2014)

    Article  Google Scholar 

  12. Bonnet, V., Richard, V., Camomilla, V., Venture, G., Cappozzo, A., Dumas, R.: Multi-body optimisation and extended Kalman filter embedding a kinematic-driven STA model. J. Biomech (2017) (in press)

    Google Scholar 

  13. Futamure, S., Bonnet, V., Dumas, R., Kulic, D., Venture, G.: Dynamically consistent inverse kinematics framework using optimizations for human motion analysis. In: IEEE-RAS International Conference on Humanoid Robot, pp. 436–441 (2016). https://doi.org/10.1109/humanoids.2016.7803312

  14. Kulić, D., Venture, G., Yamane, K., Demircan, E., Mizuuchi, I., Mombaur, K.: Anthropomorphic movement analysis and synthesis: a survey of methods and applications. IEEE Trans. Robo 32 (2016)

    Google Scholar 

  15. Mandery, C., Borràs, J., Jöchner, M., Asfour, T.: Analyzing whole-body pose transitions in multi-contact motions. In: International Conference on Humanoid Robots, pp. 1020–1027 (2015)

    Google Scholar 

  16. Kolev, S., Todorov, E.: Physically consistent state estimation and system identification for contacts. In: International Conference on Humanoid Robots, pp. 1036–1043 (2015)

    Google Scholar 

  17. Leardini, A., Chiari, L., Croce, U.D., Cappozzo, A.: Human movement analysis using stereophotogrammetry Part 3. Soft tissue artifact assessment and compensation. Gait Posture 21, 212–225 (2005)

    Article  Google Scholar 

  18. Lee, J., Flashner, H., McNitt-Gray, J.L.: Estimation of multibody kinematics using position measurements. J. Comput. Nonlinear Dyn. 6, 1–9 (2011)

    Article  Google Scholar 

  19. Lu, T.-W, O’Connor, J.J.: Bone position estimation from skin marker co-ordinates using global optimisation with joint constraints. J. Biomech. Eng. 32, 129–134 (1999)

    Google Scholar 

  20. Chèze, L., Fregly, B.J., Dimnet, J.: A solidification procedure to facilitate kinematic analyses based on video system data. J. Biomech. 28(7), 879–884 (1995)

    Article  Google Scholar 

  21. Söderkvist, I., Wedin, P.A.: Determining the movements of the skeleton using well-configured markers. J. Biomech. 26(12), 1473–1477 (1993)

    Article  Google Scholar 

  22. Bonnet, V., Daune, G., Joukov, V., Dumas, R., Fraisse, P., Kulić, D., Seilles, A., Andary, S., Venture, G.: A constrained extended Kalman Filter for dynamically consistent inverse kinematics and inertial parameters identification. In: International Conference on Biomedical Robotics and Biomechatronics (2016)

    Google Scholar 

  23. Bonnet, V., Azevedo Coste, C., Robert, T., Fraisse, P., Venture, G.: Optimal external wrench distribution during a multi-contact sit-to-stand task. IEEE Trans. Neural Syst. Rehabil. Eng. 25 (2017). https://doi.org/10.1109/tnsre.2017.2676465

  24. Topper, A.K., Maki, B.E., Holliday, P.J.: Are activity-based assessments of balance and gait in the elderly predictive of risk of falling and/or type of fall ? J. Am. Geriatr. Soc. 41, 479–487 (1993)

    Article  Google Scholar 

  25. Papa, E., Cappozzo, A.: Sit-to-stand motor strategies investigated in able-bodied young and elderly subjects. J. Biomech. 33, 1113–1122 (2000)

    Article  Google Scholar 

  26. Rodosky, M.W., Andriacchi, T.P., Andersson, G.B.J.: The influence of chair height on lower-limb mechanics during rising. J. Orthop. Res. 7, 266–271 (1989)

    Article  Google Scholar 

  27. Cheng, P.T., Liaw, M.Y., Wong, M.K., Tang, F.T., Lee, M.Y., Lin, P.S.: The sit-to-fast deterstand movement in stroke patients and its correlation with falling. Arch. Phys. Med. Rehab. 79, 1043–1046 (1998)

    Article  Google Scholar 

  28. Millor, N., Lecumberri, P., Gomez, M., Martinez-Ramirez, A., Izquierdo, M.: Kinematic parameters to evaluate functional performance of sit-to-stand and stand-to-sit transitions using motion sensor devices: a systematic review. IEEE Trans. Neural. Syst. Rehab. Eng. 22, 926–936 (2014)

    Article  Google Scholar 

  29. Riley, P.O., Schenkman, M.L., Mann, R.W., Hodge, R.A.: Mechanics of a constrained chair rise. J. Biomech. 24, 77–85 (1991)

    Article  Google Scholar 

  30. Scarborough, M.D., Krebs, D.E., Harris, B.A.: Quadriceps muscle strength and dynamic stability in elderly persons. Gait Posture 10, 10–20 (1999)

    Article  Google Scholar 

  31. Bonnet, V., Venture, G.: Fast determination of the planar body segment inertial parameters using affordable sensors. IEEE Trans. Neural Syst. Rehabil. Eng. 23, 628–635 (2015)

    Google Scholar 

  32. Lin, J.F.-S., Bonnet, V., Panchea, A.M., Ramdani, N., Venture, G., Kulic, D.: Human motion segmentation using cost weights recovered from inverse optimal control. In: IEEE-RAS International Conference on Humanoid Robot, pp. 1107–1113. Cancun, Mexico (2016)

    Google Scholar 

  33. Alexander, R.M.: The gaits of bipedal and quadrupedal animals. Int. J. Robot. Res. 3, 49–59 (1984)

    Article  Google Scholar 

  34. Flash, T., Hogan, N.: The coordination of arm movements: an experimentally confirmed mathematical model. J. Neurosci. 5, 1688–1703 (1985)

    Article  Google Scholar 

  35. Mombaur, K., et al.: From human to humanoid locomotion an inverse optimal control approach. Auton Robot 28, 369–383 (2010)

    Article  Google Scholar 

  36. Todorov, E.: Optimality principles in sensorimotor control. Nat. Neurosci. 7, 907–915 (2004)

    Article  Google Scholar 

  37. Bonnet, V., et al.: A least-squares identification algorithm for estimating squat exercise mechanics using a single inertial measurement unit. J. Biomech. 45, 1472–1477 (2012)

    Article  Google Scholar 

  38. Berret, B., et al.: Evidence for composite cost functions in arm movement planning: an inverse optimal control approach. PLoS Comput. Biol. 7, 1–18 (2011)

    Article  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Tokyo University of Agriculture and Technology, 2-24-16 Nakacho, Koganei, Tokyo, Japan

    G. Venture

  2. University of Paris-Est, Créteil, France

    V. Bonnet

  3. University of Waterloo, 200 University Avenue West, Waterloo, ON, Canada

    D. Kulic

Authors
  1. G. Venture
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. V. Bonnet
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. D. Kulic
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to G. Venture .

Editor information

Editors and Affiliations

  1. Tokyo, Japan

    Gentiane Venture

  2. LAAS-CNRS , Toulouse, France

    Jean-Paul Laumond

  3. Toulouse, France

    Bruno Watier

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer International Publishing AG, part of Springer Nature

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Venture, G., Bonnet, V., Kulic, D. (2019). Creating Personalized Dynamic Models. In: Venture, G., Laumond, JP., Watier, B. (eds) Biomechanics of Anthropomorphic Systems. Springer Tracts in Advanced Robotics, vol 124. Springer, Cham. https://doi.org/10.1007/978-3-319-93870-7_5

Download citation

Publish with us

Policies and ethics

Search

Navigation