Human gait simulation with a neuromusculoskeletal model and evolutionary computation

This paper describes a human gait animation system with a precise neuromusculoskeletal model and evolutionary computation. The neuromusculoskeletal model incorporates 14 rigid bodies, 19 degrees of freedom, 60 muscular models, 16 pairs of the neural oscillators, and other neuronal systems. By changing the search parameters and the evaluative criteria of the evolutionary search process, we demonstrate various locomotive patterns, such as normal gait, pathological gait, running and ape-like walking. The proposed simulation system takes not only kinematic data but also in vivo dynamic data such as energy consumption information into consideration, so that the resultant locomotion patterns are natural and valid from a biomechanical point of view. Hence the simulation system can also be used for finding a biologically appropriate physical model to realize a desired gait by simultaneously modifying the body dynamics parameters with the neuronal parameters. This capability creates a novel application of human gait simulation systems, such as rehabilitation tool design and consultation for physically handicapped people. Copyright © 2003 John Wiley & Sons, Ltd.

[1]  Kazunori Hase,et al.  BIOMECHANICAL CRITERIA FOR DETERMINATION OF CADENCE AND STRIDE LENGTH IN FREE WALKING , 1992 .

[2]  Petros Faloutsos,et al.  Composable controllers for physics-based character animation , 2001, SIGGRAPH.

[3]  R. Crowninshield,et al.  A physiologically based criterion of muscle force prediction in locomotion. , 1981, Journal of biomechanics.

[4]  Karl Sims,et al.  Evolving virtual creatures , 1994, SIGGRAPH.

[5]  H. Hatze,et al.  Energy-optimal controls in the mammalian neuromuscular system , 1977, Biological Cybernetics.

[6]  H. Hatze,et al.  A myocybernetic control model of skeletal muscle , 1977, Biological Cybernetics.

[7]  Kiyotoshi Matsuoka,et al.  Sustained oscillations generated by mutually inhibiting neurons with adaptation , 1985, Biological Cybernetics.

[8]  John R. Koza,et al.  Genetic programming - on the programming of computers by means of natural selection , 1993, Complex adaptive systems.

[9]  G.L. Marseglia,et al.  Response to Long-Term hGH Therapy in Two Children with Schwachman-Diamond Syndrome Associated with GH Deficiency , 1998, Hormone Research in Paediatrics.

[10]  Joe Marks,et al.  Spacetime constraints revisited , 1993, SIGGRAPH.

[11]  Eugene Fiume,et al.  Limit cycle control and its application to the animation of balancing and walking , 1996, SIGGRAPH.

[12]  Gentaro Taga,et al.  Emergence of bipedal locomotion through entrainment among the neuro-musculo-skeletal system and the , 1994 .

[13]  W S Levine,et al.  An optimal control model for maximum-height human jumping. , 1990, Journal of biomechanics.

[14]  Atsuo Kawamura,et al.  Three dimensional digital simulation and autonomous walking control for eight-axis biped robot , 1995, Proceedings of 1995 IEEE International Conference on Robotics and Automation.

[15]  Vladimir M. Zatsiorsky,et al.  The Mass and Inertia Characteristics of the Main Segments of the Human Body , 1983 .

[16]  Jessica K. Hodgins,et al.  Simulation of human running , 1994, Proceedings of the 1994 IEEE International Conference on Robotics and Automation.

[17]  H. Preuschoft,et al.  Curvature of the lumbar spine as a consequence of mechanical necessities in Japanese macaques trained for bipedalism. , 1988, Folia primatologica; international journal of primatology.

[18]  H. Ralston Energetics of Human Walking , 1976 .

[19]  S. Grillner Neurobiological bases of rhythmic motor acts in vertebrates. , 1985, Science.

[20]  M. Kawato,et al.  Formation and control of optimal trajectory in human multijoint arm movement , 1989, Biological Cybernetics.

[21]  Sooyol Ok,et al.  Evolving bipedal locomotion with genetic programming - a preliminary report , 2001, Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546).

[22]  Kazunori Hase,et al.  Development of Three-Dimensional Musculoskeletal Model for Various Motion Analyses. , 1995 .

[23]  Dimitris N. Metaxas,et al.  Automating gait generation , 2001, SIGGRAPH.

[24]  Karl Sims,et al.  Evolving 3d morphology and behavior by competition , 1994 .

[25]  N Yamazaki,et al.  Biomechanical analysis of the development of human bipedal walking by a neuro-musculo-skeletal model. , 1996, Folia primatologica; international journal of primatology.

[26]  T. Flash,et al.  The coordination of arm movements: an experimentally confirmed mathematical model , 1985, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[27]  Nobutoshi Yamazaki,et al.  Generation of human bipedal locomotion by a bio-mimetic neuro-musculo-skeletal model , 2001, Biological Cybernetics.

[28]  John T. McConville,et al.  INVESTIGATION OF INERTIAL PROPERTIES OF THE HUMAN BODY , 1975 .

[29]  Auke Jan Ijspeert,et al.  Evolution and Development of a Central Pattern Generator for the Swimming of a Lamprey , 1999, Artificial Life.

[30]  M. Pandy,et al.  Dynamic optimization of human walking. , 2001, Journal of biomechanical engineering.

[31]  Jack M. Winters,et al.  Multiple Muscle Systems , 1990, Springer New York.

[32]  David E. Orin,et al.  Efficient Dynamic Computer Simulation of Robotic Mechanisms , 1982 .

[33]  Hiroshi Shimizu,et al.  Self-organized control of bipedal locomotion by neural oscillators in unpredictable environment , 1991, Biological Cybernetics.

[34]  Mamoru Akiyama,et al.  Simulation of Laminar Flow over a Backward-Facing Step Using the Lattice BGK Method. , 1997 .

[35]  Ken-ichi Anjyo,et al.  Fourier principles for emotion-based human figure animation , 1995, SIGGRAPH.

[36]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .