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互动，比如端着一杯热咖啡。对于患有运动障碍的患者，如与基底节或小脑疾病相关的患者，这样的任务可能会显著影响他们执行日常任务的能力。然而，令人惊讶的是，人们对中枢神经系统(CNS)在与动态复杂的物体互动时如何控制运动知之甚少。CNS使用物体力学性质的内部表示来操纵简单的刚性物体，还可以学习复杂外力场的内部模型以进行伸展运动。假设受试者学习复杂物体动力学性质的相似内部表征。我们实验室的初步实验表明，在目标导向的到达任务中，健康的受试者会调整自己的动作，以保持非刚性物体的稳定性。这项拟议的研究将首先努力确定这种适应是否是物体动力学内部模型发展的结果，其次，描述与操作复杂物体时保持运动稳定性特别相关的适应的那些方面。实验将使用机器人操纵器进行，这种机器人可以精确地定义复杂“虚拟物体”的内在动力学特性。在第一个实验中，受试者将被训练在操纵动态复杂的虚拟物体时做出点对点的到达动作。对动态物体和刚性物体的运动之间的图形误差(量化运动学形状轮廓的差异)的比较将被用来确定受试者是在学习和建立物体动力学的内部模型，还是仅仅是增加了整体肢体僵硬或记住了机械手施加的特定作用力模式。在第二个实验中，受试者将与动态物体一起进行连续的有节奏的运动。非线性动力学的方法将被用来量化运动维度(关联维度)和神经肌肉控制系统对内部产生的局部扰动的敏感性(Lyapunov指数)。假设运动的维度是由手臂+物体的内在机制决定的，因此不会随着训练而发生显著变化，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有助于更好地理解物体操作背后的神经肌肉控制过程，并最终有助于为运动康复开发功能逼真的虚拟交互。

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.