Evolution of Human Locomotion: A Computer Simulation Study

인류 보행의 진화: 컴퓨터 시뮬레이션 연구

  • Published : 2004.05.01

Abstract

This research was designed to investigate biomechanical aspects of the evolution based on the hypothesis of dynamic cooperative interactions between the locomotion pattern and the body shape in the evolution of human bipedal walking The musculoskeletal model used in the computer simulation consisted of 12 rigid segments and 26 muscles. The nervous system was represented by 18 rhythmic pattern generators. The genetic algorithm was employed based on the natural selection theory to represent the evolutionary mechanism. Evolutionary strategy was assumed to minimize the cost function that is weighted sum of the energy consumption, the muscular fatigue and the load on the skeletal system. The simulation results showed that repeated manipulations of the genetic algorithm resulted in the change of body shape and locomotion pattern from those of chimpanzee to those of human. It was suggested that improving locomotive efficiency and the load on the musculoskeletal system are feasible factors driving the evolution of the human body shape and the bipedal locomotion pattern. The hypothetical evolution method employed in this study can be a new powerful tool for investigation of the evolution process.

Keywords

References

  1. Ray, T. S., 'An approach to the synthesis of life,' in Artificial Life II, C. G. Langton et al. Eds., Addison Wesley, pp.371-408, 1992
  2. Toquenaga, Y., Ichinose, M., Hoshino, T. and Fujii, K., 'Contest and scramble competitions in an artificial world: Genetic analysis with genetic algorithms,' in Artificial Life III, C. G., Langton Ed., Addison Wesley, pp. 177-199, 1994
  3. Sims, K., 'Evolving 3D morphology and behavior by competition,' in Artificial Life IV, R. A. Brooks et al. Eds., The MIT Press, pp. 28-39, 1994
  4. Yamazaki, N., 'Biomechanical interrelationship among body proportions, posture, and bipedal walking,' in Topics in Primatology III, S. Matano et al. Eds., University of Tokyo Press, pp. 243-257, 1992
  5. Taga, G., Yamaguchi, Y. and Shimizu, H., 'Self-organized control of bipedal Iocomotion by neural oscillators in unpredictable environment,' Biol. Cybern., Vol. 65, pp. 147-159, 1991 https://doi.org/10.1007/BF00198086
  6. Taga, G., 'A model of the neuro-musculo-skeletal system for human locomotion. Ⅰ.Emergence of basic gait,' Biol. Cybern., Vol. 73, pp. 97-111, 1995 https://doi.org/10.1007/BF00204048
  7. Walker, M.W. and Orin, D.E., 'Efficient dynamic computer simulation of robotic mechanisms,' Trans. ASME J. Dynamic Systems, Measurement, and Control, Vol. 104, pp.205-211, 1982 https://doi.org/10.1115/1.3139699
  8. Fujimoto, Y. and Kawamura, A., 'Three dimensional digital simulation and autonomous walking control for eight-axis biped robot,' IEEE Int. Conf. on Robotics and Automation, pp. 2877-2884, 1995 https://doi.org/10.1109/ROBOT.1995.525692
  9. Hatze, H. and Buys, J.D., 'Energy-optimal controls in the mammalian neuromuscular system,' Biol. Cybern., Vol. 27, pp.9-20, 1977 https://doi.org/10.1007/BF00357705
  10. Crowninshield, R.D. and Brand, R.A., 'A physiologically based criterion of muscle force prediction in locomotion,' J. Biomechanics, Vol. 14, pp. 793-801, 1981 https://doi.org/10.1016/0021-9290(81)90035-X
  11. Goldberg, D.E., Genetic Algorithms in Search, Optimization and Machine Learning, Addison Wesley, 1989
  12. Gabrielle, G. and Karman, T.V., 'What price speed,' Mechanical Engineering, Vol.72, pp.775-781, 1950
  13. Yamazaki, N., 'Primate bipedal walking: computer simulation,' in Primate Morphophysiology Locomotor Analyses and Human Bipedalism, Univ. Tokyo Press, pp.105-130, 1985
  14. Ackley, D. and Littman, M., 'Interactions between learning and evolution,' in Artificial Life II, C. G. Langton et al. Eds., Addison Wesley, pp. 487-509, 1992
  15. The Ministry of Health and Welfare, Ed., Recommended Dietary Allowances for the Japaness, Daiichi Shuppan, 1994
  16. Winter, J.M. and Woo, S.L., 'Multiple muscle system,' Springer Verlag, 1990
  17. Alexander, R.M., 'Locomotion of animals,' Blackie & Sons, 1982
  18. Hase, K. and Yamazaki, N., 'Computer simulation study of human locomotion with a three-dimensional entire-body neuro-musculo-skeletal model Ⅰ.acquisition of normal walking,' JSME International Journal, Ser. C, Vol. 45, pp.1040-1050, 2002 https://doi.org/10.1299/jsmec.45.1040
  19. Raibert, M. H., 'Hopping in leg systems - modeling and simulation for the two-dimensional on-leg case,' IEEE Trans. System, Man and Cybernetics, SMC-14, pp.451-463, 1984 https://doi.org/10.1109/TSMC.1984.6313238
  20. Taga, G., 'A model of the neuro-musculo-skeletal system for anticipatory adjustment of human locomotion during obstacle avoidance,' Biological Cybernetics, Vol. 78, pp.9-17, 1998 https://doi.org/10.1007/s004220050408
  21. Winters, J.M. and Stark, L., 'Muscle models: What is gained and what is lost by varying model complexity,' Biological Cybernetics, Vol.55, pp.403-420, 1987 https://doi.org/10.1007/BF00318375
  22. 엄광문, 강곤, 이정한, Hoshimiya, N., '전기자국을 이용한 상실된 운동기능의 회복: 기능적 전기자극(FES),' 한국 정밀공학회지, Vol. 20, No. 1, pp.26-35, 2003
  23. Sipper, M., 'Fifty years of research on self-replication: an overview,' Artif Life, Vol. 4, pp.237-257, 1998 https://doi.org/10.1162/106454698568576
  24. Gilchrist, L.A. and Winter, D.A., 'A two-part, viscoelastic foot model for use in gait simulations,' J. Biomehanics, Vol. 29, pp.795-798, 1996 https://doi.org/10.1016/0021-9290(95)00141-7
  25. Gilchrist, L.A. and Winter, D.A., 'A multisegment computer simulation of normal human gait,' IEEE Trans. Rehabil. Eng., Vol. 5, pp.290-9, 1997 https://doi.org/10.1109/86.650281
  26. Oatis, C.A., 'The use of a mechanical model to describe the stiffness and damping characteristics of the knee joint in healthy adults,' Phys. Ther., Vol.73, pp.740-9, 1993 https://doi.org/10.1093/ptj/73.11.740
  27. Lewis, M.A., Etienne-Cummings, R., Hartmann, M.J., Xu, Z. R. and Cohen, A. H., 'An in silico central pattern generator: silicon oscillator, coupling, entrainment and physical computation,' Biol. Cybern., Vol.88, pp.137-151, 2003 https://doi.org/10.1007/s00422-002-0365-7
  28. Ito, S., Yuasa, H., Luo, Z.W., Ito, M. and Yanagihara, D., 'A mathemtical model of adaptive behavior in quarduped locomotion,' Biol. Cybern., Vol.78, pp.337-47, 1998 https://doi.org/10.1007/s004220050438
  29. Pribe, C., Grossberg, S., Cohen, M.A., 'Neural control of interlimb oscillations. II. Biped and quadruped gaits and bifurcations,' Biol. Cybern., Vol. 77, pp.141-52, 1997 https://doi.org/10.1007/s004220050375
  30. Ogihara, N. and Yamazaki, N., 'Generation of human bipedal locomotion by a bio-mimetic neuro-musculo-skeletal model,' Biol. Cybern., Vol.84, pp.1-11, 2001 https://doi.org/10.1007/PL00007977
  31. DeJong, K.,'An analysis of the behavior of a class of genetic adaptive systems,' Ph.D. Thesis, University of Michigan, 1975
  32. 北野宏明, '遺專的アルゴリズム,' ISBN:4782851367, 産業圖緖, 1993
  33. Jang, J.S.R., Sun, C.T. and Mizutani, E., 'Neuro Fuzzy and Soft Computing,' ISBN: 0132874679, Prentice Hall, 1997
  34. Wang, W.J., Crompton, R.H., Li, Y. and Gunther, M.M., 'Energy transformation during erect and 'bent-hip, bent-knee' walking by humans with implications for the evolution of bipedalism,' J. Hum. Evol., Vol.44, pp.563-579, 2003 https://doi.org/10.1016/S0047-2484(03)00045-9