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: 2014
• 资助国家: 加拿大
• 起止时间: 2014-01-01 至 2015-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=546958
• 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.

人类执行各种熟练的运动任务，从写签名到骑自行车，然而，了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重大挑战。大量的关节和肌肉为中枢神经系统（CNS）提供了多种运动和控制自由度；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，研究揭示了个体内部和个体之间一致的、刻板的运动和肌肉激活模式，但中枢神经系统如何控制这么多自由度的问题仍然是运动控制和感觉运动神经科学中尚未解决的关键问题。在本提案中，我们描述了旨在直接测试有关中枢神经系统如何在当前运动控制和运动学习的主要计算框架（即最优反馈控制（OFC））内控制运动的假设的研究。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机器人设备来测量和扰动手臂运动，与使用手臂神经肌肉系统的生理真实计算模型来生成特定预测的计算机模拟相结合。我们使用机器人在视觉引导的伸手运动期间向手臂提供精确的运动相关负载，并使用虚拟显示系统通过操纵看不见的手和代表手位置的光标之间的关系来提供视觉运动扰动。力场和视觉运动扰动的组合用于检验关于运动学习的假定成本函数的假设。我们还将通过使用呼出气体分析来测量运动学习过程中摄氧量的变化，实证检验中枢神经系统最大限度地减少代谢能量的假设。这项研究将扩展我们对如何计划和控制随意运动以及大脑如何实现运动学习的了解。这项工作的结果还有可能推动神经修复、人机界面和拟人机器人等应用工程和技术领域的发展。