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

Abstract

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.

The interaction between changes of muscle activation and cortical network dynamics during isometric elbow contraction: a sEMG and fNIRS study

“…Previous studies revealed the composition of EMG signals were the summation of a temporal superposition of the motor unit action potentials (MUAPs) sequence produced by the various motor units of the contracted muscles ( De Luca, 1984 ; Gazzoni et al, 2004 ; Chen and Zhou, 2016 ). As the task intensity increased, the neuromuscular control strategy adjusted and structure in motor execution increased, leading to more complicated muscle firing patterns ( Enders et al, 2015 ; Ma et al, 2017 ; Deng et al, 2021 ). As muscle power increases, more motor units are recruited, increasing the fApEn of the EMG signals ( Kamavuako et al, 2012 ; Chen et al, 2018 ).…”

“…Chen et al proposed fuzzy approximate entropy (fApEn) based on the entropy theory by introducing the idea of a fuzzy set ( Chen et al, 2007 ). Previous studies have shown that fApEn can be used as an indicator of changes in the complexity of sEMG signals to determine the alterations in motor control during task execution ( Ao et al, 2015 ; Deng et al, 2021 ). However, the relationship between changes in muscle complexity and brain network during task execution remains unclear.…”