KINARM robotic exoskeleton & integrated eyetracker

KINARM 机器人外骨骼

• 批准号: 390657-2010
• 负责人: Gribble, Paul
• 金额: $ 10.65万
• 依托单位: University of Western Ontario
• 依托单位国家: 加拿大
• 项目类别: Research Tools and Instruments - Category 1 (<$150,000)
• 财政年份: 2009
• 资助国家: 加拿大
• 起止时间: 2009-01-01 至 2010-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=443253
• 关键词: KINARM; robotic; exoskeleton; integrated; eyetracker

One of the most interesting (and difficult) problems the brain faces in controlling parts of the body such as the arm has to do with compensating for changes in the mechanics and dynamics of motion - both short term changes that arise when we manipulate objects, and long-term effects due to changes in the size and shape of our own bodies. One of the main theoretical concepts that has been proposed to explain this flexibility in motor control is that the brain uses central representations or "internal models" of limb dynamics to achieve this compensation. However, little is known about how the brain establishes these internal models or how they are adapted over the course of motor learning.

大脑在控制手臂等身体部位时面临的最有趣（也是最困难）的问题之一，是如何补偿运动的力学和动力学变化--既包括我们操纵物体时产生的短期变化，也包括我们身体大小和形状变化带来的长期影响。已经提出来解释运动控制中的这种灵活性的主要理论概念之一是，大脑使用肢体动力学的中央表征或“内部模型”来实现这种补偿。然而，关于大脑如何建立这些内部模型，或者它们如何在运动学习过程中适应，我们知之甚少。