Sophstication of feedback control during voluntary behaviour

自愿行为期间反馈控制的复杂性

• 批准号: 227920-2010
• 负责人: Scott, Stephen
• 金额: $ 4.44万
• 依托单位: Queen's University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2013
• 资助国家: 加拿大
• 起止时间: 2013-01-01 至 2014-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=524149
• 关键词: Sophstication; feedback; control; during; voluntary

Humans have a remarkable ability to make smooth and graceful movements. Perhaps our ability to make quick and accurate corrections when we are accidentally bumped or rapidly make changes in our movements such as weaving through a crowded shopping mall. By comparison, robotic movements are nothing short of clumsy and lack the speed and agility we take for granted. My long-term goal is to understand how the motor system is able to coordinate and learn the broad range of sensorimotor skills we use to move and interact in the world. My model system is whole-arm motor tasks performed in the horizontal plane. These two-dimensional tasks possess most of the complexity inherent in body movements, including intersegmental dynamics and redundancy in how one moves to a spatial goal. The research program uses a unique robotic device I developed called KINARM (Kinesiological Instrument for Normal and Altered Reaching Movements) that both monitors motion at the shoulder and elbow joints and can apply mechanical loads at one or both joints. My present focus is on using the ideas of optimal feedback control as a conceptual framework for studying the voluntary motor system. These type of controllers use sensory feedback in a sophisticated way, such that errors that influence the goal of a motor task are corrected and errors that won't influence performance are ignored. The projects will examine how the motor system responds to mechanical perturbations to correct errors in performance while considering obstacles in the environment, whether the two limbs are working together (bimanual control) or independently (unimanual control), or learn to compensate for novel loads applied to the limb. Artificial neural networks that control a model of the limb will also be examined to explore whether they demonstrate the same principles associated with optimal feedback and to complement and interpret our human experimental work. These projects will provide important details on human motor control. Such knowledge forms the basis for examining and understanding motor dysfunctions in various patient groups as well as for understanding motor skill acquisition related to motor development and sports.

人类具有做出流畅、优雅动作的非凡能力。也许我们有能力在意外碰撞时做出快速准确的纠正，或者快速改变我们的动作，例如穿过拥挤的购物中心。相比之下，机器人的动作非常笨拙，缺乏我们认为理所当然的速度和敏捷性。 我的长期目标是了解运动系统如何协调和学习我们用来在世界上移动和互动的广泛感觉运动技能。我的模型系统是在水平面上执行的全臂运动任务。这些二维任务拥有身体运动固有的大部分复杂性，包括节间动力学和人们如何移动到空间目标的冗余。该研究项目使用了我开发的一种独特的机器人设备，称为 KINARM（正常和改变伸展运动的运动学仪器），它既可以监测肩关节和肘关节的运动，也可以在一个或两个关节上施加机械负载。我目前的重点是使用最优反馈控制的思想作为研究自愿运动系统的概念框架。这些类型的控制器以复杂的方式使用感觉反馈，从而纠正影响运动任务目标的错误，并忽略不会影响性能的错误。这些项目将检查运动系统如何响应机械扰动以纠正性能错误，同时考虑环境中的障碍，两个肢体是否一起工作（双手控制）或独立工作（单手控制），或者学习补偿施加到肢体的新负载。 还将检查控制肢体模型的人工神经网络，以探索它们是否表现出与最佳反馈相关的相同原理，并补充和解释我们的人体实验工作。这些项目将提供有关人类运动控制的重要细节。 这些知识构成了检查和理解不同患者群体的运动功能障碍以及理解与运动发育和运动相关的运动技能习得的基础。