Sports Medicine

, Volume 39, Issue 1, pp 15–28

Constraints on the Complete Optimization of Human Motion

  • Paul S. Glazier
  • Keith Davids
Leading Article


In sport and exercise biomechanics, forward dynamics analyses or simulations have frequently been used in attempts to establish optimal techniques for performance of a wide range of motor activities. However, the accuracy and validity of these simulations is largely dependent on the complexity of the mathematical model used to represent the neuromusculoskeletal system. It could be argued that complex mathematical models are superior to simple mathematical models as they enable basic mechanical insights to be made and individual-specific optimal movement solutions to be identified. Contrary to some claims in the literature, however, we suggest that it is currently not possible to identify the complete optimal solution for a given motor activity. For a complete optimization of human motion, dynamical systems theory implies that mathematical models must incorporate a much wider range of organismic, environmental and task constraints. These ideas encapsulate why sports medicine specialists need to adopt more individualized clinical assessment procedures in interpreting why performers’ movement patterns may differ.


  1. 1.
    Miller DI.Modelling in biomechanics: an overview.Med Sci Sports 1979; 11: 115–22PubMedGoogle Scholar
  2. 2.
    Hatze H. Quantitative analysis, synthesis and optimization of human motion. Hum Mov Sci 1984; 3: 5–25CrossRefGoogle Scholar
  3. 3.
    Alexander RM. Simple models of walking and jumping. Hum Mov Sci 1992; 11: 3–9CrossRefGoogle Scholar
  4. 4.
    Yeadon MR. Computer simulation in sports biomechanics. In: Riehle HJ, Vieten MM, editors. Proceedings of the XVIth International Symposium on Biomechanics in Sports; 1998 Jul 21-25; Germany: University of Konstanz, 309–18Google Scholar
  5. 5.
    Pandy MG. Computer modeling and simulation of human movement. Annu Rev Biomed Eng 2001; 3: 245–73PubMedCrossRefGoogle Scholar
  6. 6.
    Newell KM. Constraints on the development of coordination. In: Wade MG, Whiting HTA, editors. Motor development in children: aspects of coordination and control. Dordrecht: Martinus Nijhoff; 1986: 341–60CrossRefGoogle Scholar
  7. 7.
    Hatze H. A mathematical model for the computational determination of parameter values of anthropometric segments. J Biomech 1980; 13: 833–43PubMedCrossRefGoogle Scholar
  8. 8.
    Hatze H. Computerized optimization of sports motions:an overview of possibilities, methods and developments. J Sports Sci 1983; 1: 2–12CrossRefGoogle Scholar
  9. 9.
    Hatze H. Dynamics of the musculoskeletal system. In: Perren SM, Schneider E, editors. Biomechanics: current interdisciplinary research. Dordrecht: Marjinus Nijhoff, 1985: 15–25CrossRefGoogle Scholar
  10. 10.
    Hatze H. Biomechanics of sports: selected examples of successful applications and future perspectives. In: Riehle HJ, Vieten MM, editors. Proceedings of the XVIth International Symposium on Biomechanics in Sports; 1998 Jul 21-25; Germany: University of Konstanz, 1998: 2–22Google Scholar
  11. 11.
    Hatze H. Myocybernetic control models of skeletal muscle: characteristics and applications. Pretoria: University of South Africa Press, 1981Google Scholar
  12. 12.
    Hatze H. The complete optimization of a human motion. Math Biosci 1976; 28: 99–135CrossRefGoogle Scholar
  13. 13.
    Yeadon MR, Challis JH. The future of performance-related sports biomechanics research. J Sports Sci 1994; 12: 3–32PubMedCrossRefGoogle Scholar
  14. 14.
    Hatze H. A comprehensive model for human motion simulation and its application to the take-off phase of the long jump. J Biomech 1981; 14: 135–42PubMedCrossRefGoogle Scholar
  15. 15.
    Bartlett RM. Sports biomechanics: reducing injury and improving performance. London: E&FN Sport, 1999CrossRefGoogle Scholar
  16. 16.
    Sprigings EJ. Sport biomechanics: data collection, modelling, and implementation stages of development. Can J Sport Sci 1988; 13: 3–7PubMedGoogle Scholar
  17. 17.
    Goffe WL, Ferrier GD, Rogers J. Global optimisation of statistical functions with simulated annealing. J Econometrics 1994; 60: 65–99CrossRefGoogle Scholar
  18. 18.
    Alexander RM. Simple models of human movement. Appl Mech Rev 1995; 48: 461–9CrossRefGoogle Scholar
  19. 19.
    Hubbard M. Computer simulation in sport and industry. J Biomech 1993; 26(1 Suppl.): 53–61PubMedCrossRefGoogle Scholar
  20. 20.
    Yamaguchi GT. Performing whole-body simulations of gait with 3D dynamic musculoskeletal models. In: Winters JM, Woo SL, editors. Multiple muscle systems. New York: Springer, 1990: 663–79CrossRefGoogle Scholar
  21. 21.
    Yeadon MR, King MA. Computer simulation modelling in sport. In: Payton CJ, Bartlett RM, editors. Biomechanical evaluation of movement in sport and exercise. London: Routledge, 2008: 176–205Google Scholar
  22. 22.
    Davids K, Button C, Bennett SJ. Dynamics of skill acquisition: a constraints-led approach. Champaign (IL): Human Kinetics, 2008Google Scholar
  23. 23.
    Staddon JER, Hinson JM. Optimization: a result or a mechanism? Science 1983; 221: 976–7PubMedCrossRefGoogle Scholar
  24. 24.
    Edelman GW, Gally JA. Degeneracy and complexity in biological systems. Proc Natl Acad Sci USA 2001; 98: 13763–8PubMedCrossRefGoogle Scholar
  25. 25.
    Bernstein NA. The coordination and regulation of movements. Oxford: Pergamon Press, 1967Google Scholar
  26. 26.
    Kujala T, Palva M, Salonen O, et al. The role of blind humans’ visual cortex in auditory change detection. Neurosci Lett 2005; 379: 127–31PubMedCrossRefGoogle Scholar
  27. 27.
    Kelso JAS, SchoÖner G. Self-organization of coordinative movement patterns. Hum Mov Sci 1988; 7: 27–46CrossRefGoogle Scholar
  28. 28.
    Kugler PN, Turvey MT. Self-organization flow fields and information. Hum Mov Sci 1988; 7: 97–129CrossRefGoogle Scholar
  29. 29.
    Kelso JAS. Dynamic patterns: the self-organization of brain and behavior. Cambridge (MA): MIT Press, 1995Google Scholar
  30. 30.
    Bernstein NA. Level of construction of movements. In: Latash ML, Turvey MT, editors. Dexterity and its development. Mahwah (NJ): Lawrence Erlbaum Associates, Inc., 1996: 115–70Google Scholar
  31. 31.
    Turvey MT, Carello CC. Dynamics of Bernstein’s level of synergies. In: Latash ML, Turvey MT, editors. Dexterity and its development. Mahwah (NJ): Lawrence Erlbaum Associates, Inc., 1996: 339–76Google Scholar
  32. 32.
    Gelfand IM, Gurfinkel VS, Tsetlin ML, et al. Some problems in the analysis of movements. In: Gelfand IM, Gurfinkel VS, Fomin SV, et al. editors. Models of the structural-functional organization of certain biological systems. Cambridge (MA): MIT Press, 1971: 329–45Google Scholar
  33. 33.
    Greene PH. Problems of organization of motor systems. In: Rosen R, Snell F, editors. Progress in theoretical biology (vol. 2). New York: Academic Press, 1972: 303–38Google Scholar
  34. 34.
    Turvey MT. Preliminaries to a theory of action with reference to vision. In: Shaw R, Bransford J, editors. Perceiving, acting, and knowing. Hillsdale (NJ): Lawrence Erlbaum Associates, Inc., 1977: 211–65Google Scholar
  35. 35.
    Tuller B, Turvey MT, Fitch H. The Bernstein perspective, II: the concept of muscle linkage or coordinative structure. In: Kelso JAS, editor. Human motor behavior: an introduction. Hillsdale (NJ): Lawrence Erlbaum Associates, Inc., 1982: 253–70Google Scholar
  36. 36.
    Turvey MT. Coordination. Am Psychol 1990; 45: 938–53PubMedCrossRefGoogle Scholar
  37. 37.
    Latash ML, Scholz JP, Schöner G. Motor control strategies revealed in the structure of motor variability. Exerc Sport Sci Rev 2002; 30: 26–31PubMedCrossRefGoogle Scholar
  38. 38.
    Kugler PN, Turvey MT. Information natural law, and the self-assembly of rhythmic movement. Hillsdale (NJ): Lawrence Erlbaum Associates, Inc.,1987Google Scholar
  39. 39.
    Kay B. The dimensionality of movement trajectories and the degrees of freedom problem: a tutorial. Hum Mov Sci 1988; 7: 343–64CrossRefGoogle Scholar
  40. 40.
    Fitch H, Tuller B, Turvey MT. The Bernstein perspective, III: tuning of coordinative structures with special reference to perception. In: Kelso JAS, editor. Human motor behavior: an introduction. Hillsdale (NJ): Lawrence Erlbaum Associates, Inc., 1982: 271–81Google Scholar
  41. 41.
    Bingham GP. Task specific devices and the perceptual bottleneck. Hum Mov Sci 1988; 7: 225–64CrossRefGoogle Scholar
  42. 42.
    Higgins JR. Human movement: an integrated approach. St. Louis (MO): C.V. Mosby, 1977Google Scholar
  43. 43.
    Kugler PN, Kelso JAS, Turvey MT. On the concept of coordinative structures as dissipative structures: I. Theoretical lines of convergence. In: Stelmach GE, Requin J, editors. Tutorials in motor behavior. Amsterdam: North- Holland, 1980: 3–48CrossRefGoogle Scholar
  44. 44.
    Clark JE. On becoming skillful: patterns and constraints. Res Q Exerc Sport 1995; 66: 173–83PubMedGoogle Scholar
  45. 45.
    Newell KM, van Emmerik REA, McDonald PV. Biomechanical constraints and action theory: reaction to G.J. van Ingen Schenau (1989). Hum Mov Sci 1989; 8: 403–9CrossRefGoogle Scholar
  46. 46.
    Shemmell J, Tresilian JR, Riek S, et al. Musculoskeletal constraints on the acquisition of motor skills. In: Williams AM, Hodges NJ, editors. Skill acquisition in sport: research, theory and practice. London: Routledge, 2004: 390–408Google Scholar
  47. 47.
    Newell KM, Jordan K. Task constraints and movement organization: a common language. In: Davis WE, Broadhead GD, editors. Ecological task analysis and movement. Champaign (IL): Human Kinetics, 2007: 5–23Google Scholar
  48. 48.
    Maynard Smith J. Optimization theory in evolution. Annu Rev Ecol Syst 1978; 9: 31–56CrossRefGoogle Scholar
  49. 49.
    Mazur JE. Optimization: a result or a mechanism? Science 1983; 221: 977PubMedCrossRefGoogle Scholar
  50. 50.
    Chow JY, Davids K, Button C, et al. Variation in coordination of a discrete multiarticular action as a function of skill level. J Mot Behav 2007; 39: 463–79PubMedCrossRefGoogle Scholar
  51. 51.
    Newell KM. Coordination control and skill. In: Goodman D, Wilberg RB, Franks IM, editors. Differing perspectives in motor learning, memory and control. Amsterdam: North-Holland, 1985: 295–317CrossRefGoogle Scholar
  52. 52.
    Gould SJ, Lewontin RC. The spandrels of San Marco and the Panglossian Paradigm: a critique of the adaptationist programme. Proc R Soc London, Series B 1979; 205: 581–98CrossRefGoogle Scholar
  53. 53.
    Jenson RK. Estimation of the biomechanical properties of three body types using a photogrammetric method. J Biomech 1978; 11: 349–58CrossRefGoogle Scholar
  54. 54.
    Yeadon MR. The simulation of aerial movement, II: a mathematical inertia model of the human body. J Biomech 1990; 23: 67–74PubMedCrossRefGoogle Scholar
  55. 55.
    King MA, Yeadon MR. Determining subject-specific torque parameters for use in a torque driven simulation model of dynamic jumping. J Appl Biomch 2002; 18: 207–17Google Scholar
  56. 56.
    Yeadon MR, King MA, Wilson C. Modelling the maximum voluntary joint torque/,angular velocity relationship in human movement. J Biomech 2006; 39: 476–82PubMedCrossRefGoogle Scholar
  57. 57.
    Gruber K, Ruder H, Denoth J, et al. A comparative study of impact dynamics: wobbling mass model versus rigid models. J Biomech 1998; 31: 439–44PubMedCrossRefGoogle Scholar
  58. 58.
    Gittoes MJR, Kerwin DG. Component inertia modeling of segmental wobbling and rigid masses. J Appl Biomech 2006; 22: 148–54PubMedGoogle Scholar
  59. 59.
    Umberger BR. Constraints necessary to produce realistic simulations of countermovement vertical jumping and the effects on achieved jump heights. In:Proceedings of the 2005 International Symposium of Computer Simulation in Biomechanics; 2005 Jul 28-30; Cleveland (OH): Case Western Reserve University, 2005: 35–6Google Scholar
  60. 60.
    Wilson C, Yeadon MR, King MA. Considerations that affect simulation in a running jump for height. J Biomech 2007; 40: 3155–61PubMedCrossRefGoogle Scholar
  61. 61.
    Hatze H. A myocybernetic control model of skeletal muscle. Biol Cybern 1977; 25: 103–19PubMedCrossRefGoogle Scholar
  62. 62.
    Hatze H. A general myocybernetic control model of skeletal muscle. Biol Cybern 1978; 28: 143–57PubMedCrossRefGoogle Scholar
  63. 63.
    Müller W, Platzer D, Schmolzer B. Dynamics of human flight on skis: improvements in safety and fairness in ski jumping. J Biomech 1996; 29: 1061–8PubMedCrossRefGoogle Scholar
  64. 64.
    Wright IC, Neptune RR, van den Bogert AJ, et al. Passive regulation of impact forces in heel-toe running. Clin Biomech 1998; 13: 521–31CrossRefGoogle Scholar
  65. 65.
    Hiley MJ, Yeadon MR. The margin for error when releasing the asymmetric bars for dismounts. J Appl Biomech 2005; 21: 223–35PubMedGoogle Scholar
  66. 66.
    Beek PJ, Dessing JC, Peper CE, et al. Modelling the control of interceptive actions. Phil Trans R Soc Lond B 2003; 358: 1511–23CrossRefGoogle Scholar
  67. 67.
    Beek PJ, Beek WJ. Stability and flexibility in the temporal organisation of movements: reaction to G.J. van Ingen Schenau (1989). Hum Movement Sci 1989; 8: 347–56CrossRefGoogle Scholar
  68. 68.
    Engelbrecht SE.Minimum principles in motor control. J Math Psychol 2001; 45: 497–542PubMedCrossRefGoogle Scholar
  69. 69.
    Prilutsky BI, Zatsiorsky VM. Optimization-based models of muscle coordination. Exerc Sport Sci Rev 2002; 30: 32–8PubMedCrossRefGoogle Scholar
  70. 70.
    Todorov E. Optimality principles in sensorimotor. Nat Neurosci 2004; 7: 907–15PubMedCrossRefGoogle Scholar
  71. 71.
    Sparrow WA, editor. Energetics of human activity. Champaign (IL): Human Kinetics, 2000Google Scholar
  72. 72.
    Yeadon MR. What are the limitations of experimental and theoretical approaches in sports biomechanics? In: McNamee M, editor. Philosophy and the sciences of exercise,health and sport: critical perspectives on research methods. London: Routledge, 2005: 133–43Google Scholar
  73. 73.
    Thelen E. Motor development: a new synthesis. Am Psychol 1995; 50: 79–95PubMedCrossRefGoogle Scholar
  74. 74.
    Newell KM. On task and theory specificity. J Mot Behav 1989; 21: 92–6PubMedGoogle Scholar
  75. 75.
    McGinnis PM, Newell KM. Topological dynamics: a framework for describing movement and its constraints. Hum Mov Sci 1982; 1: 289–305CrossRefGoogle Scholar
  76. 76.
    Davids K, Handford C, Williams AM. The natural physical alternative to cognitive theories of motor behaviour: an invitation for interdisciplinary research in sports science? J Sports Sci 1994; 12: 495–528PubMedCrossRefGoogle Scholar
  77. 77.
    Glazier PS, Davids K, Bartlett RM. Dynamical systems theory: a relevant framework for performance-oriented sports biomechanics research. Sportscience 2003 [online]. Available from URL: [Accessed 2007 May 29]
  78. 78.
    Glazier PS, Wheat JS, Pease DL, et al. The interface of biomechanics and motor control: dynamic systems theory and the functional role of movement variability. In: Davids K, Bennett SJ, Newell KM, editors. Movement system variability. Champaign (IL): Human Kinetics, 2006: 49–69Google Scholar
  79. 79.
    Davids K, Glazier PS, Araújo D, et al. Movement systems as dynamical systems: the role of functional variability and its implications for sportsmedicine. Sports Med 2003; 33: 245–60PubMedCrossRefGoogle Scholar
  80. 80.
    West BJ, editor. Where medicine went wrong: rediscovering the path to complexity: studies in nonlinear phenomena. Vol. 11. Hackensack (NJ): World Scientific Publishing Co., 2006Google Scholar
  81. 81.
    Davids K, Bennett SJ, Newell KM, et al. Movement system variability. Champaign (IL): Human Kinetics, 2006Google Scholar
  82. 82.
    Latash ML, Anson JG. What are ‘normal movements’: in atypical populations? Behav Brain Sci M, 1996; 19: 55–106CrossRefGoogle Scholar

Copyright information

© Adis Data Information BV. 2009

Authors and Affiliations

  • Paul S. Glazier
    • 1
  • Keith Davids
    • 1
  1. 1.School of Human Movement StudiesQueensland University of Technology, Victoria Park RoadKelvin GroveAustralia

Personalised recommendations