Abstract Summary
We propose a data-driven technique to discuss the task-relevant and task-irrelevant movement variabilities while estimating the relevance of the motion sequence to task performance. Our method can unify the advantages and overcome the disadvantages of previous methods. Further, we reveal a novel feature of motor variability based on our method.
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