Skip to main content

Physiological Methods to Solve the Force-Sharing Problem in Biomechanics

  • Chapter
Multibody Dynamics

Part of the book series: Computational Methods in Applied Sciences ((COMPUTMETHODS,volume 12))

The determination of individual muscle forces has many applications including the assessment of muscle coordination and internal loads on joints and bones, useful, for instance, for the design of endoprostheses. Because muscle forces cannot be directly measured without invasive techniques, they are often estimated from joint moments by means of optimization procedures that search for a unique solution among the infinite solutions for the muscle forces that generate the same joint moments. The conventional approach to solve this problem, the static optimization, is computationally efficient but neglects the dynamics involved in muscle force generation and requires the use of an instantaneous cost function, leading often to unrealistic estimations of muscle forces. An alternative is using dynamic optimization associated with a motion tracking, which is, however, computationally very costly. Other alternative approaches recently proposed in the literature are briefly reviewed and two new approaches are proposed to overcome the limitations of static optimization delivering more realistic estimations of muscle forces while being computationally less expensive than dynamic optimization.

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

Access this chapter

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 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

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Ackermann M (2007) Dynamics and energetics of walking with prostheses. Ph.D. thesis, University of Stuttgart, Shaker Verlag, Aachen

    Google Scholar 

  2. Ackermann M, Gros H (2005) Measurements of human gaits. Internal Report ZB-144, Institute B of Mechanics, University of Stuttgart, Stuttgart

    Google Scholar 

  3. Ackermann M, Schiehlen W (2006) Dynamic analysis of human gait disorder and metabolical cost estimation. Arch Appl Mech 75:569–594

    Article  Google Scholar 

  4. Anderson FC, Pandy MG (1999) A dynamic optimization solution for vertical jumping. Comput Meth Biomech Biomed Eng 2:201–231

    Article  Google Scholar 

  5. Anderson FC, Pandy MG (2001) Dynamic optimization of human walking. J Biomech Eng 123:381–390

    Article  Google Scholar 

  6. Bhargava LJ, Pandy MG, Anderson FC (2004) A phenomenological model for estimating metabolic energy consumption in muscle contraction. J Biomech 37:81–88

    Article  Google Scholar 

  7. Chapra SC, Canale RP (1985) Numerical methods for engineers. McGraw-Hill, New York

    Google Scholar 

  8. Crowninshield RD, Brand RA (1981) Physiologically based criterion of muscle force prediction in locomotion. J Biomech 14:793–801

    Article  Google Scholar 

  9. Davy DT, Audu ML (1987) A dynamic optimization technique for the muscle forces in the swing phase of the gait. J Biomech 20:187–201

    Article  Google Scholar 

  10. Delp SL (1990) Surgery simulation: a computer graphics system to analyze and design musculoskeletal reconstructions of the lower limb. Ph.D. thesis, Department of Mechanical Engineering, Stanford University, Stanford, CA

    Google Scholar 

  11. Hatze H (1976) The complete optimization of a human motion. Math Biosci 28:99–135

    Article  MATH  MathSciNet  Google Scholar 

  12. Hatze H, Buys JD (1977) Energy-optimal controls in the mammalian neuromus-cular system. Biol Cybern 27:9–20

    Article  MATH  Google Scholar 

  13. He J, Levine WS, Loeb GE (1991) Feedback gains for correcting small perturbations to standing posture. IEEE T Automat Contr 36:322–332

    Article  MATH  Google Scholar 

  14. de Leva P (1996) Adjustments to Zatsiorsky-Seluyanov's segment inertia parameters. J Biomech 29:1223–1230

    Article  Google Scholar 

  15. Menegaldo LL, Fleury AT, Weber HI (2003) Biomechanical modeling and optimal control of human posture. J Biomech 36:1701–1712

    Article  Google Scholar 

  16. Menegaldo LL, Fleury AT, Weber HI (2006) A ‘cheap’ optimal control approach to estimate muscle forces in musculoskeletal systems. J Biomech 39:1787–1795

    Article  Google Scholar 

  17. Nagano A, Gerritsen KGM (2001) Effects of neuromuscular stregth training on vertical jumping performance — a computer simulation study. J App Biomech 17:113–128

    Google Scholar 

  18. Neptune RR, van den Bogert AJ (1998) Standard mechanical energy analyses do not correlate with muscle work in cycling. J Biomech 31:239–245

    Article  Google Scholar 

  19. Neptune RR, Hull ML (1998) Evaluation of performance criteria for simulation of submaximal steady-state cycling using a forward dynamic model. J Biomech Eng 120:334–341

    Article  Google Scholar 

  20. Pandy MG, Anderson F, Hull DG (1992) A parameter optimization approach for the optimal control of large-scale musculoskeletal systems. J Biomech Eng 114:450–460

    Article  Google Scholar 

  21. Ralston HJ (1976) Energetics of human walking. In: Herman RM et al. (eds) Neural control of locomotion. Plenum, New York, pp 77–98

    Google Scholar 

  22. Riener R, Edrich T (1991) Identification of passive elastic joint moments in the lower extremities. J Biomech 32:539–544

    Article  Google Scholar 

  23. Schiehlen W (2006) Computational dynamics: theory and applications of multi-body systems. Eur J Mech A-Solid 25:566–594

    Article  MATH  MathSciNet  Google Scholar 

  24. da Silva MPT, Ambrosio JAC (2004) Human motion analysis using multi-body dynamics and optimization tools. Ph.D. thesis, Instituto de Engenharia Mecânica, Lisboa, Portugal

    Google Scholar 

  25. Spägele T (1998) Modellierung, Simulation und Optimierung menschlicher Bewegung (in German). Ph.D. thesis, Institute A of Mechanics, University of Stuttgart, Stuttgart

    Google Scholar 

  26. Stein RB, Lebiedowska MK, Popovic DB, Scheiner A, Chizeck HJ (1996) Estimating mechanical parameters of leg segments in individuals with and without physical disabilities. IEEE T Rehabil Eng 4:201–211

    Article  Google Scholar 

  27. Strobach D, Kecskemethy A, Steinwender G, Zwick B (2005) A simplified approach for rough identification of muscle activation profiles via optimization and smooth profile patches. In: Proceedings of MULTIBODY DYNAMICS 2005, ECCOMAS Thematic Conference, Madrid, Spain

    Google Scholar 

  28. Thelen DG, Anderson FC (2006) Using computed muscle control to generate forward dynamic simulations of human walking from experimental data. J Bio-mech 39:1107–1115

    Google Scholar 

  29. Thelen DG, Anderson FC, Delp SL (2003) Generating dynamic simulations of movement using computed muscle control. J Biomech 36:321–328

    Article  Google Scholar 

  30. Tsirakos D, Baltzopoulos V, Bartlett R (1991) Inverse optimization: functional and physiological considerations related to the force-sharing problem. Crit Rev Biomed Eng 25:371–407

    Google Scholar 

  31. Umberger BR, Gerritsen KGM, Martin PE (2003) A model of human muscle energy expenditure. Comput Meth Biomech Biomed Eng 6:99–111

    Article  Google Scholar 

  32. Zajac FE (1989) Muscle and tendon: properties, models, scaling, and application to biomechanics and motor control. Crit Rev Biomed Eng 19:359–411

    Google Scholar 

  33. Zajac FE, Neptune RR, Kautz SA (2003) Biomechanics and muscle coordination of human walking part II: lessons from dynamical simulations and clinical implications. Gait Posture 17:1–17

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Marko Ackermann .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2009 Springer Science + Business Media B.V

About this chapter

Cite this chapter

Ackermann, M., Schiehlen, W. (2009). Physiological Methods to Solve the Force-Sharing Problem in Biomechanics. In: Bottasso, C.L. (eds) Multibody Dynamics. Computational Methods in Applied Sciences, vol 12. Springer, Dordrecht. https://doi.org/10.1007/978-1-4020-8829-2_1

Download citation

  • DOI: https://doi.org/10.1007/978-1-4020-8829-2_1

  • Publisher Name: Springer, Dordrecht

  • Print ISBN: 978-1-4020-8828-5

  • Online ISBN: 978-1-4020-8829-2

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics