MOVEMENT CONTROL IN MANIPULATING DYNAMIC OBJECTS

每天，人类都会与挑战他们控制运动稳定性能力的物体进行交互，例如端着一杯热咖啡。 对于患有运动障碍的患者，例如与基底神经节或小脑疾病相关的患者，这些任务会显著影响他们执行日常任务的能力。然而，令人惊讶的是，很少有人知道中枢神经系统（CNS）如何控制运动时，与动态复杂的对象。 CNS使用物体机械特性的内部表示来操纵简单的刚性物体，并且还可以学习复杂外部力场的内部模型以进行伸展运动。 有人假设，受试者学习类似的内部表征的动态特性的复杂对象。我们实验室的初步实验表明，健康受试者在目标导向的到达任务中调整他们的运动以保持非刚性物体的稳定性。 拟议的研究将首先奋进确定这种适应是否发生作为对象动态的内部模型的发展的结果，其次，表征那些方面的适应，特别是与保持运动稳定性时，操纵复杂的对象。 实验将进行使用机器人manipulgiant，允许复杂的“虚拟对象”的内在动力学特性被精确定义。 在第一个实验中，受试者将被训练在操纵动态复杂的虚拟对象时进行点对点的移动。 动态和刚性物体运动之间的图形误差（量化运动学形状轮廓的差异）的比较将用于确定受试者是否正在学习物体动力学的内部模型，或者只是增加整体肢体刚度或记忆由操纵器施加的力的特定模式。 在第二个实验中，受试者将与动态对象进行连续的有节奏的运动。从非线性动力学的方法将被用来量化运动的维度（相关维度）和神经肌肉控制系统的灵敏度内部产生的局部扰动（李雅普诺夫指数）。 据推测，运动的维度是由手臂+物体的内在力学决定的，因此不会随着训练而显著变化，但中枢神经系统会调整其控制策略，使运动对局部扰动不那么敏感。 预计这些实验的结果将导致更好地了解对象操作的神经肌肉控制过程，并最终将有助于为开发功能逼真的虚拟交互运动康复提供更清晰的方向。

Every day, humans interact with objects that challenge their ability to control movement stability, such as carrying a cup of hot coffee. For patients with movement disorders, such as those associated with basal ganglia or cerebellar disease, such tasks can significantly impact their ability to perform everyday tasks. However, surprisingly little is known about how the central nervous system (CNS) controls movement when interacting with dynamically complex objects. The CNS uses internal representations of object mechanical properties to manipulate simple rigid objects, and can also learn internal models of complex external force fields for making reaching movements. It was hypothesized that subjects learn similar internal representations of the dynamical properties of complex objects. Preliminary experiments from our lab indicate that healthy subjects adapt their movements to maintain stability of non-rigid objects during a goal-directed reaching task. The proposed research will first endeavor to determine if this adaptation occurs as the result of the development of an internal model of object dynamics, and second, to characterize those aspects of adaptation that are specifically related to maintaining movement stability when manipulating complex objects. Experiments will be conducted using a robotic manipulandum that allows the intrinsic dynamical properties of complex "virtual objects" to be precisely defined. In the first experiment, subjects will be trained to make point-to-point reaching movements while manipulating a dynamically complex virtual object. A comparison of figural errors (which quantify differences in kinematic shape profiles) between movements made with dynamic and rigid objects will be used to determine if subjects are learning and internal model of the object dynamics, or are simply increasing overall limb stiffness or memorizing the specific patterns of forces imposed by the manipulandum. In the second experiment, subjects will make continuous rhythmic movements with the dynamic object. Methods from nonlinear dynamics will be used to quantify movement dimensionality (correlation dimensions) and the sensitivity of the neuromuscular control system to internally-generated local perturbations (Lyapunov exponents). It is hypothesized that the dimensionality of movement is determined by the intrinsic mechanics of the arm+object, and will therefore not change significantly as a function of training, but that the central nervous system adapts its control strategy to make movements less sensitive to local perturbations. It is anticipated that the results of these experiments will lead to a better understanding of the neuromuscular control processes underlying object manipulation and will eventually help provide clearer direction for developing functionally realistic virtual interactions for motor rehabilitation.