Khatib O, Demircan E, De Sapio V, Sentis L, Besier T, Delp S
Artificial Intelligence Laboratory, Stanford University, Stanford, CA 94305, USA.
J Physiol Paris. 2009 Sep-Dec;103(3-5):211-9. doi: 10.1016/j.jphysparis.2009.08.004. Epub 2009 Aug 7.
The 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.
人体运动合成是一个复杂的过程，涉及运动序列的精确重建、肌肉骨骼运动学、动力学和驱动建模，以及可靠性能标准的表征。这些过程中的许多与机器人研究中发现的问题有很多共同之处。机器人技术中使用的基于任务的方法可用于提供新颖的肌肉骨骼建模方法和生理上准确的性能预测。在本文中，我们提出了：（i）一种使用直接标记跟踪实时重建人体运动轨迹的新方法；（ii）一种任务驱动的肌肉努力最小化标准；以及（iii）用于动态表征运动技能的新人体性能指标。动态运动重建是通过控制模拟人体模型实时跟踪捕获的标记轨迹来实现的。操作空间控制和实时模拟可在性能的任何配置下提供人体动力学。引入了一种新的肌肉努力最小化标准来分析人体静态姿势。进行了广泛的运动捕捉实验以验证新的最小化标准。最后，引入了新的人体性能指标来详细研究一项运动技能。这些指标包括技能执行过程中的努力消耗和操作空间加速度的可行集。动态表征考虑了骨骼运动学以及肌肉路径运动学和力产生能力。这些进展借鉴了先进的肌肉骨骼建模平台和面向任务的框架，以有效整合生物力学和机器人技术方法。
