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

即使是像伸手去拿物体这样看似简单的任务，也涉及大量关节和肌肉的同时分级激活。 这个项目研究了产生这种多关节运动的原理，以及感知信息如何指导这些动作。 鉴于目前的许多理论方法仅限于节奏或离散运动，本项目将开发一个综合框架，包括两种运动类型，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，理论工作将开发一个模型的基础上的一个网络的耦合动态系统的运动生成。 其次，与人类受试者的实验将研究涉及多关节手臂运动的任务中的运动轨迹的具体特征。 第三，该模型将用于合成类人机器人手臂的运动，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一个真实的物理环境中进行测试。 除了增加我们对复杂运动生成的基本理解外，研究结果还应适用于人工设备的控制，如机器人系统或假肢，也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.