Control of Human Arm Movement

人体手臂运动的控制

• 批准号: 238338-2013
• 负责人: Gribble, Paul
• 金额: $ 2.91万
• 依托单位: University of Western Ontario
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2016
• 资助国家: 加拿大
• 起止时间: 2016-01-01 至 2017-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=594140
• 关键词: Control; Human; Arm; Movement

Humans perform a wide variety of skilled motor tasks, from writing an autograph to riding a bicycle, yet, understanding how humans plan and control even simple movements is still a major chal- lenge in neuroscience. A large number of joints and muscles provides the central nervous system (CNS) with many degrees of freedom in movement and control; movements can be achieved by an infinite number of joint rotations and muscle activation patterns. Surprisingly, research has revealed consistent, stereotypical patterns of movement and muscle activation, both within and across individuals, but the question of how the CNS controls these many degrees of freedom is still a key unresolved problem in motor control and sensory-motor neuroscience. In this proposal we describe studies aimed at directly testing hypotheses about how the CNS controls movement, within the current major computational framework of motor control and motor learning, namely optimal feedback control (OFC). Our approach combines human empirical work using custom-designed, high performance robotic devices to both measure and perturb arm movements, with computer simulations using physiologically realistic computational models of the arm neuromuscular system to generate specific predictions. We use the robots to deliver precise motion-dependent loads to the arm during visually-guided reaching movements, and a virtual display system to deliver visuo-motor perturbations, by manipulating the relationship between the unseen hand and a cursor representing hand position. A combination of force-fields and visuo-motor perturbations are used to test hypothesis about the putative cost function for motor learning. We will also empirically test the hypothesis that metabolic energy is minimized by the CNS, by using expired gas analysis to measure VO2 changes over the course of motor learning. This research will expand our knowledge of how voluntary movements are planned and controlled, and how motor learning is achieved by the brain. The results of this work also has the potential to advance fields in applied engineering and technology such as neural prosthetics, human-machine interfaces and anthropomorphic robotics.

人类执行各种各样的熟练运动任务，从写签名到骑自行车，然而，理解人类如何计划和控制即使是简单的运动仍然是神经科学的一个主要挑战。大量的关节和肌肉为中枢神经系统（CNS）提供了许多运动和控制的自由度;运动可以通过无限数量的关节旋转和肌肉激活模式来实现。令人惊讶的是，研究已经揭示了一致的，刻板的运动和肌肉激活模式，无论是在个人内部还是在个人之间，但是中枢神经系统如何控制这些自由度的问题仍然是运动控制和感觉运动神经科学中一个关键的未解决的问题。在这个建议中，我们描述的研究，旨在直接测试假设中枢神经系统如何控制运动，在当前的主要计算框架内的运动控制和运动学习，即最佳反馈控制（OFC）。我们的方法结合了人类的经验工作，使用定制设计的，高性能的机器人设备来测量和扰动手臂运动，与计算机模拟使用生理上逼真的计算模型的手臂神经肌肉系统，以产生特定的预测。我们使用的机器人提供精确的运动相关的负载的手臂在视觉引导达到运动，和一个虚拟显示系统提供视觉运动扰动，通过操纵看不见的手和光标代表手的位置之间的关系。力场和视觉运动扰动的组合被用来测试假设的运动学习的成本函数。我们还将通过使用呼出气体分析来测量运动学习过程中的VO 2变化，以经验性地检验CNS使代谢能量最小化的假设。这项研究将扩大我们对自主运动是如何计划和控制的，以及大脑如何实现运动学习的知识。这项工作的结果也有可能推动应用工程和技术领域的发展，如神经假肢，人机界面和拟人机器人。