Control of Human Arm Movements

我的研究重点是研究自主肢体运动的中枢控制信号组织，以及运动学习和感觉-运动适应的神经、认知和感觉机制。结合实证研究和模型研究来检验关于多关节肢体运动是如何控制和运动学习是如何实现的假设。本提案中概述的研究分为两个主要主题：(1)伴随着学习与物体互动的感觉变化（体感和视觉）。我们之前的研究记录了体感敏锐度（对手位置变化的敏感性）和偏倚（手的感知位置）是如何在学习接触新力量时被调节的。在这里，我们建议将这项工作扩展到一个新的方向，即研究在学习与环境中的物体（包括目标和障碍物）相互作用时，如何调节体感和视觉敏锐度和偏差。(2)我们提出了一套新颖的研究，利用新的3-DOF机器人外骨骼，来测试如何计划和控制多关节手臂运动的模型。新的IMT2外骨骼机器人（由NSERC RTI奖资助给申请人）在世界上是独一无二的，因为它允许完全仪器化和控制，肩关节，肘关节和手腕关节在水平面上的运动。新机器人将允许我们直接测试运动规划和控制模型（例如，不受控制流形理论，最小干预模型，最优控制模型等），通过在水平面上利用肩部/肘部/手腕系统的运动学冗余。例如，我们将能够在保持端点位置不受干扰的情况下干扰关节水平的肢体运动，从而使我们能够研究在运动过程中控制哪些变量。类似的方法将使我们能够研究系统如何响应注入不同关节或关节组合的噪声和可变性，以及这些响应在运动学习过程中如何变化。这项研究的结果将大大增加我们对自主运动是如何计划和控制的知识，以及运动和感觉系统是如何相互作用的。

The focus of my research program is to study the organization of central control signals for voluntary limb movement, and the neural, cognitive and sensory mechanisms underlying motor learning and sensory-motor adaptation. A combination of empirical and modelling studies are used to test hypotheses about how multi-joint limb movements are controlled and how motor learning is achieved. The studies outlined in this proposal are organized in two major themes: (1) Sensory changes (somatosensory and visual) that accompany learning to interact with objects. Our previous studies document how both somatosensory acuity (sensitivity to changes in hand position) and bias (sensed position of the hand) are modulated when learning to reach in the presence of novel forces. Here, we propose to extend this work in a novel direction by studying how both somatosensory and visual acuity and bias are modulated when learning to interact with objects in the environment, both targets and obstacles. (2) We propose a novel set of studies that take advantage of a new 3-DOF robotic exoskeleton, to test models of how multi-joint arm movements are planned and controlled. The new IMT2 exoskeleton robot (funded by an NSERC RTI award to the applicant) is unique in the world, in that it allows for fully instrumented, and controlled, motion in shoulder, elbow AND wrist joints, in a horizontal plane. The new robot will allow us to directly test models of movement planning and control (e.g. the Uncontrolled Manifold theory, the Minimal Intervention model, Optimal Control models, etc), by capitalizing on the kinematic redundancy in the shoulder/elbow/wrist system in the horizontal plane. For example we will be able to perturb limb movements at the joint level while keeping the endpoint position unperturbed, thereby allowing us to study what variables are controlled during movement. Similar approaches will allow us to study how the system responds to noise and variability injected at different joints, or combinations of joints, and how these responses change over the course of motor learning. The results of this research will add significantly to our knowledge of how voluntary movements are planned and controlled, and how motor and sensory systems interact.