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AbstractThe synthesis of human motion is a complex procedure that involves accurate reconstruction of movement sequences, modeling of musculoskeletal kinematics, dynamics and actuation, and characterization of reliable performance criteria. Many of these processes have much in common with the problems found in robotics research. Task-based methods used in robotics may be leveraged to provide novel musculoskeletal modeling methods and physiologically accurate performance predictions. In this paper, we present (i) a new method for the real-time reconstruction of human motion trajectories using direct marker tracking, (ii) a task-driven muscular effort minimization criterion and (iii) new human performance metrics for dynamic characterization of athletic skills. Dynamic motion reconstruction is achieved through the control of a simulated human model to follow the captured marker trajectories in real-time. The operational space control and real-time simulation provide human dynamics at any configuration of the performance. A new criteria of muscular effort minimization has been introduced to analyze human static postures. Extensive motion capture experiments were conducted to validate the new minimization criterion. Finally, new human performance metrics were introduced to study in details an athletic skill. These metrics include the effort expenditure and the feasible set of operational space accelerations during the performance of the skill. The dynamic characterization takes into account skeletal kinematics as well as muscle routing kinematics and force generating capacities. The developments draw upon an advanced musculoskeletal modeling platform and a task-oriented framework for the effective integration of biomechanics and robotics methods.
CitationKhatib O, Demircan E, De Sapio V, Sentis L, Besier T, et al. (2009) Robotics-based synthesis of human motion. Journal of Physiology-Paris 103: 211–219. Available: http://dx.doi.org/10.1016/j.jphysparis.2009.08.004.
SponsorsThe financial support of the Simbios National Center for Biomedical Computing Grant (http://simbios.stanford.edu/, NIH GM072970), Honda Company and KAUST (King Abdullah University of Science and Technology) are gratefully acknowledged. Many thanks to Francois Conti and Jinsung Kwong for their valuable contributions to the preparation of this manuscript.
JournalJournal of Physiology-Paris
PubMed Central IDPMC2782476
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