Enhancing motor skill learning by manipulating extrinsic and intrinsic components of the motor task

这个项目的主要目标是确定可以用来提高学习运动任务速度的内在和外在因素。在我的计算模型成功地提高了等距任务中的学习速度之后，我现在将该模型应用于更复杂的动态到达任务。在该任务中，影响学习速度的因素是手臂的动态可操纵性椭圆和任务期间手臂状态的分布。这样就可以预测诸如手臂姿势和手臂质量等变量对学习动态任务速度的影响。同时，我还开发了一个系统，可以在虚拟抛光任务中通过肌电生物反馈来定向学习所需的肌肉激活。

The main objective of this project is to identify intrinsic and extrinsic factors that can be exploited to increase the speed of learning a motor task. Following the success of my computational model to enhance the speed of learning in an isometric task, I have now applied the model to a more complex dynamic reaching task. In this task, the factors that influence the speed of learning are the dynamic manipulability ellipse of the arm, and the distribution of arm states during the task. This allows to make predictions about the effect of variables such as arm posture and arm mass in the speed of learning a dynamic task. In parallel, I have also developed a system that allows the directed learning of desired muscle activations via EMG-biofeedback in a virtual polishing task.