Multi-Joint Dynamics: A Model for Discrete and Rhythmic Coordination Tasks

即使是看起来很简单的任务，比如伸手去拿一个物体，也需要大量关节和肌肉同时被逐步激活。该项目研究了这种多关节运动产生的原理，以及感知信息如何指导这些动作。鉴于目前许多理论方法都局限于有节奏或离散的运动，该项目将开发一个包含两种运动类型的综合框架，因此可以应用于复杂的运动。调查将从三个相互关联的方向进行。首先，理论工作将建立一个基于耦合动力系统网络的运动生成模型。其次，对人类受试者的实验将研究涉及多关节手臂运动的任务中运动轨迹的具体特征。第三，该模型将用于合成类人机械臂的运动，以便将这些模型驱动的运动与人类参与者的运动进行比较。通过这种方式，模型的有效性和鲁棒性将在真实的物理环境中得到检验。除了增加我们对复杂运动产生的基本理解外，研究结果还应适用于机器人系统或假肢等人工设备的控制，并可能有助于运动障碍的诊断和治疗。

Even apparently simple tasks like reaching for an object involve the simultaneous graded activation of a large number of joints and muscles. This project investigates the principles that underlie the generation of such multi-joint movements, and how perceptual information guides these actions. Whereas many of the current theoretical approaches are limited to either rhythmic or discrete movements, this project will develop an integrated framework that encompasses both movement types and therefore can be applied to complex movements. The investigation will proceed in three interrelated directions. First, theoretical work will develop a model for movement generation on the basis of a network of coupled dynamic systems. Second, experiments with human subjects will study the specific features of movement trajectories in tasks involving multi-joint arm movements. Third, the model will be used to synthesize movements of a human-like robot arm in order to compare these model-driven movements with the movements of the human participants. In this way the model's validity and robustness will be tested in a real physical environment. In addition to increasing our basic understanding of complex movement generation the results should be applicable to the control of artificial devices such as robotic systems or limb prostheses, and may also contribute to the diagnosis and treatment of movement disorders.