Advertisement

Redundant Muscular Force Analysis of Human Lower Limbs During Rising from a Squat

  • Yiyong Yang
  • Rencheng Wang
  • Ming Zhang
  • Dewen Jin
  • Fangfang Wu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4561)

Abstract

Muscular coordination analysis of lower limbs during rising from a squat is one of the important categories in rehabilitation engineering and gymnastic science. This paper describes an efficient biomechanical model of the human lower limb with the aim of simulating the real human rising from a squat with lifting. To understand how intermuscular control coordinates limb muscle excitations the optimal control technique is used to solve the muscle forces sharing problem. The validity of the model is assessed comparing the calculated muscle excitations with the registered surface electromyogramm (EMG) of the muscles. The results show that synergistic muscles are build up by the neural control signals using a minimum fatigue criterion during human squat lifting, with distinct phases that include the acceleration during the initial movement and the posture at the final specified position. Synergistic muscular groups can be used to simplify motor control, and are essential to reduce the number of controlled parameters and amount of information needing to be analyzed in the performance of any motor act.

Keywords

Redundant Muscular force Neural control analysis Human squat lifting 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Crowninshield, R.D, Brand, R.A.: A physiologically based criterion of muscle force prediction in locomotion. J. Biomech. 14, 793–801 (1981)CrossRefGoogle Scholar
  2. 2.
    Pandy, M.G.: Computer Modeling and Simulation of Human Move-ment. Ann. Rev. Biomed. Eng. 3, 245–273 (2001)CrossRefGoogle Scholar
  3. 3.
    Yang, Y.Y.: Motion synergy and Control of Human Neuromusculoskeletal System. Ph.D. thesis, Tsinghua University, Beijing, China, pp. 66–88 (2004)Google Scholar
  4. 4.
    Anderson, F.C, Pandy, M.G.: Static and Dynamic Optimization Solutions for Gait Are Practically Equivalent. J. Biomech. 34, 153–161 (2001)CrossRefGoogle Scholar
  5. 5.
    Pandy, M.G, Garner, B.A, Anderson, F.C.: Optimal Control of Non-ballistic Muscular Movements: A constraint-Based Performance Criterion for Rising From a Chair. J. Biomech. Eng. 117, 15–26 (1995)Google Scholar
  6. 6.
    Pedersen, D.R, Brand, R.A, Davy, D.T.: Pelvic Muscle and Acetabular Contact Forces During Gait. J. Biomech. 30, 959–965 (1997)CrossRefGoogle Scholar
  7. 7.
    Rab, G.T.: Muscle. Human Walking. In: Rose, J., Gamble, J.G. (eds.) Williams and Wilkins, Baltimore, pp. 103–121 (1994)Google Scholar
  8. 8.
    Taga, G.: A Model of the Neuromusculoskeletal System for Human Locomotion: Emergence of Basic Gait. Biol. Cybern. 73, 95–111 (1995)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Yiyong Yang
    • 1
  • Rencheng Wang
    • 2
  • Ming Zhang
    • 3
  • Dewen Jin
    • 2
  • Fangfang Wu
    • 2
  1. 1.School of Engineering and Technology, China University of Geosciences (Beijing), Beijing 100083China
  2. 2.Division of Intelligent and Biomechanical System, State Key Laboratory of Tribology, Tsinghua University, Beijing, 100084China
  3. 3.Jockey Club Rehabilitation Engineering Centre, The Hong Kong Polytechnic University, Hong KongChina

Personalised recommendations