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

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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.