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

Area of Science:

• Neuroscience
• Biomechanics
• Human Movement Science

Background:

• Visual-motor tracking is crucial for daily activities.
• The role of predictive information in motor control is not fully understood.
• Current research lacks insight into how future trajectory data influences tracking performance.

Purpose of the Study:

• To compare visual-motor tracking performance with and without future trajectory information.
• To investigate the impact of varying task demands on motor control.
• To elucidate the contribution of predictive cues to movement execution.

Main Methods:

• Eighteen healthy participants performed elbow joint tracking tasks.
• Kinematic (joint angle) and electromyographic (EMG) signals were recorded.
• Movement smoothness (NIJ) and complexity (fApEn, MSfApEn) were analyzed.

Main Results:

• Future trajectory information significantly reduced normalized integrated jerk (NIJ), indicating smoother movements (p < 0.01).
• Increased task demands led to significant increases in joint trajectory fuzzy approximate entropy (fApEn) and EMG multiscale fuzzy approximate entropy (MSfApEn) (p < 0.05).

Conclusions:

• Predictive information enhances motor control by promoting smoother movements, suggesting greater reliance on feedforward mechanisms.
• Task complexity influences movement control strategies, affecting both kinematic and neuromuscular signal dynamics.
• Findings contribute to a deeper understanding of predictive visual-motor control.