Ken Takiyama

The paper examines how gait speed and surface stiffness affect muscle and joint synergy recruitment during walking on compliant surfaces, finding independent effects on joint-level control and interactive effects on specific muscle synergies.

The paper compares motor control and learning by analyzing how participants adjust movement velocity and duration during self-paced tasks and visuomotor perturbations, finding distinct optimization strategies.

This study investigates how cortico-muscular coherence changes during split-belt locomotor adaptation, finding temporary decreases followed by increases in coherence with adaptation and de-adaptation.

This study shows that neural tuning shifts during sensorimotor learning alter learning ability in a spatial pattern predicted by computational models.

This study shows that Parkinson’s disease patients have a larger, but not better, implicit motor adaptation ability compared to healthy controls using a smart-device-based experiment.

This study shows that competition with risk-averse opponents reduces risk-seeking bias and promotes optimal motor decision-making in a competitive reaching game.

This paper uses tensor decomposition to quantify spatial and temporal modules and their task-dependent modulation in kinematics and EMG data for walking and running.

This paper introduces a machine learning method to quantify task-relevant and task-irrelevant motion variability and their modulation during motor adaptation in goal-directed and whole-body movements.

This study shows that motor learning effects are partially transferable between pro- and anti-reaching movements even when movement direction is the same, indicating that rule switching affects learning transfer.