Abstract
Decomposition of motion data into task-relevant and task-irrelevant components is an effective way to clarify the diverse features involved in motor control and learning. Several previous methods have succeeded in this type of decomposition while focusing on the clear relation of motion to both a specific goal and a continuous outcome, such as a 10 mm deviation from a target or 1 m/s hand velocity. In daily life, it is vital to quantify not only continuous but also categorical outcomes. For example, in baseball, batters must judge whether the opposing pitcher will throw a fastball or a breaking ball; tennis players must decide whether an opposing player will serve out wide or down the middle. However, few methods have focused on quantifying categorical outcome; thus, how to decompose motion data into task-relevant and task-irrelevant components when the outcome is categorical rather than continuous remains unclear. Here, we propose a data-driven method to decompose motion data into task-relevant and task-irrelevant components when the outcome takes categorical values. We applied our method to experimental data where subjects were required to throw fastballs or breaking balls with a similar form. Our data-driven approach can be applied to the unclear relation between motion and outcome, and the relation can be estimated in a data-driven manner. Furthermore, our method can successfully evaluate how the task-relevant components are modulated depending on the task requirements.
Highlights
Decomposition of motion data into task-relevant and task-irrelevant components is an effective way to clarify the diverse features involved in motor control and learning
While the uncontrolled manifold (UCM) focuses on kinematic outcome, the goal equivalent manifold (GEM)[3] and noise-tolerant-covariance (TNC)[4] methods quantify the task-relevant and task-irrelevant motion components by explicitly defining the relations between kinematic parameters and task outcome
Along with recent data-driven approaches in biomechanics that focused on unsupervised methods[8,9], we rely on supervised methods to address the task-relevant and task-irrelevant components with categorical outcomes in a data-driven manner
Summary
Decomposition of motion data into task-relevant and task-irrelevant components is an effective way to clarify the diverse features involved in motor control and learning. In addition to the GEM and the TNC, our recent methods detect the task-relevant and task-irrelevant motion components after estimating the unknown relation between time-varying motion and task outcome in a data-driven manner[5,6]. Task-relevant and task-irrelevant motion components are more difficult to quantify when the outcomes are categorical, such as throwing a fastball or breaking ball, whether the action results in success or failure, serving wide or down the line in tennis, whether the subject is a healthy control or a patient, and whether the player is an amateur or professional. Along with our previous methods, we propose a unified data-driven approach to detect task-relevant and task-irrelevant motion components for multiple kinds of task outcomes
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