Abstract
Target-directed elbow movements are essential in daily life; however, how different task demands affect motor control is seldom reported. In this study, the relationship between task demands and the complexity of kinematics and electromyographic (EMG) signals on healthy young individuals was investigated. Tracking tasks with four levels of task demands were designed, and participants were instructed to track the target trajectories by extending or flexing their elbow joint. The actual trajectories and EMG signals from the biceps and triceps were recorded simultaneously. Multiscale fuzzy entropy was utilized to analyze the complexity of actual trajectories and EMG signals over multiple time scales. Results showed that the complexity of actual trajectories and EMG signals increased when task demands increased. As the time scale increased, there was a monotonic rise in the complexity of actual trajectories, while the complexity of EMG signals rose first, and then fell. Noise abatement may account for the decreasing entropy of EMG signals at larger time scales. This study confirmed the uniqueness of multiscale entropy, which may be useful in the analysis of electrophysiological signals.
Highlights
Target-directed tasks have been widely adopted in previous studies to investigate the relationship between motor performance and external factors such as task orientation [1], target size [2], visual information [3], and error tolerance [4]
Investigated the ability of normal individuals when generating a two-finger force under different task demands and environmental information, and confirmed an increased force variability towards higher error tolerance and lower feedback frequency [4]
MSFuzzyEn was utilized to analyze the complexity of movement trajectories and EMG signals during elbow tracking tasks across four levels of task demands for healthy young individuals
Summary
Target-directed tasks have been widely adopted in previous studies to investigate the relationship between motor performance and external factors such as task orientation [1], target size [2], visual information [3], and error tolerance [4]. Maill et al [7] observed the tracking performance of normal individuals at different frequencies and found that movement variability decreased when movement frequency decreased. Investigated the ability of normal individuals when generating a two-finger force under different task demands (error tolerance) and environmental information (visual feedback frequency), and confirmed an increased force variability towards higher error tolerance and lower feedback frequency [4]. Hong et al suggested that there was compensation for the complexity between task, environment, and motor performance. As Morrison et al reported, there was compensation of the complexity between postural sway and EMG signals towards different task demands [8]. Barbado et al observed a contrary result between the complexity of postural sway and EMG signals during different standing balance tasks [9]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
