Effects of Future Information and Trajectory Complexity on Kinematic Signal and Muscle Activation during Visual-Motor Tracking

Visual-motor tracking movement is a common and essential behavior in daily life. However, the contribution of future information to visual-motor tracking performance is not well understood in current research. In this study, the visual-motor tracking performance with and without future-trajectories was compared. Meanwhile, three task demands were designed to investigate their impact. Eighteen healthy young participants were recruited and instructed to track a target on a screen by stretching/flexing their elbow joint. The kinematic signals (elbow joint angle) and surface electromyographic (EMG) signals of biceps and triceps were recorded. The normalized integrated jerk (NIJ) and fuzzy approximate entropy (fApEn) of the joint trajectories, as well as the multiscale fuzzy approximate entropy (MSfApEn) values of the EMG signals, were calculated. Accordingly, the NIJ values with the future-trajectory were significantly lower than those without future-trajectory (p-value < 0.01). The smoother movement with future-trajectories might be related to the increasing reliance of feedforward control. When the task demands increased, the fApEn values of joint trajectories increased significantly, as well as the MSfApEn of EMG signals (p-value < 0.05). These findings enrich our understanding about visual-motor control with future information.