Optimal feedback control of goal-directed arm movements

描述（由申请人提供）：我们建议进一步发展我们的运动协调的最佳反馈控制理论，并利用其潜力来解释已知的现象，以及在2D和3D中目标导向手臂运动的新实验结果。我们将使用该理论来阐明感觉运动控制中的几个重要问题：运动学和肌肉激活，任务特定阻抗和对扰动的反应，运动变异的起源和结构，以及眼手协调模式。除基础研究外，该项目还涉及直接应用于健康的大量生物工程部分。我们将建立详细的人体手臂肌肉骨骼模型，并开发分层控制算法，能够使模型手臂在真实的时间完成行为目标。然后，这种算法可以用于控制功能性电刺激器和机器人假肢，从而恢复运动功能并改善残疾患者的生活质量。公共卫生相关性：我们将开发一个关于大脑如何控制手臂运动的通用数学理论。对运动功能的更好的理论理解可以促进运动障碍的新治疗方法的设计。我们还将开发自动控制算法，可以使用从大脑中提取的信号来使假肢完成预期的运动目标。

DESCRIPTION (provided by applicant): We propose to further develop our optimal feedback control theory of motor coordination, and utilize its potential to explain known phenomena and as well as novel experimental results on goal-directed arm movements in 2D and 3D. We will use the theory to shed new light on several important issues in sensorimotor control: regularities in kinematics and muscle activations, task-specific impedance and responses to perturbations, origins and structure of motor variability, and eye-hand coordination patterns. In addition to basic research, the project involves a substantial bio-engineering component with direct applications to health. We will construct detailed musculoskeletal models of the human arm and develop hierarchical control algorithms capable of making the model arm accomplish behavioral goals in real time. Such algorithms can then be used to control functional electric stimulators and robotic prostheses, and thereby restore motor function and improve the quality of life of disabled patients. PUBLIC HEALTH RELEVANCE: We will develop a general mathematical theory of how the brain controls arm movements. Better theoretical understanding of motor function can facilitate the design of new treatments for movement disorders. We will also develop automatic control algorithms that can use signals extracted from the brain to make a prosthetic arm accomplish desired movement goals.