Identifying Successful Motor Task Completion via Motion-Based Performance Metrics

Objective assessment of skill is important in understanding the human performance of precision motor control tasks; however, outcome-based performance measures are most commonly used to provide feedback after the task is completed. In this paper, we propose the use of motion-based performance metrics in order to determine successful task completion strategies for a complex and unconstrained dynamic task. We developed a novel motor control task in a virtual environment and identified two motion-based metrics (mean absolute jerk and average frequency) that correlate with the successful performance of the task measured as a function of completion time and acquired targets. Our methodologies provide insight into the movement strategies used during successful trials of the motor control task, and could be used to provide specific feedback and coaching aimed at altering the task completion strategy and boosting the participants’ performance.