ArticleScientific reports2019
Decomposing motion that changes over time into task-relevant and task-irrelevant components in a data-driven manner: application to motor adaptation in whole-body movements.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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11 citing papers in PubMed.
- Adaptation-dependent modulation of coordinated muscle activities in human gait adaptation as revealed by tensor decomposition.iScience · 2026Article
- Regional differences in the biological variability of plantar pressure as a basis for refining diagnostic gait analysis.Scientific reports · 2024Article
- Speed-dependent modulations of asymmetric center of body mass trajectory in the gait of above-knee amputee subjects.Frontiers in sports and active living · 2023Article
- Transition between individually different and common features in skilled drumming movements.Frontiers in sports and active living · 2022Article
- Impaired feedforward control of movements in pianists with focal dystonia.Frontiers in neurology · 2022Article
- Detecting task-relevant spatiotemporal modules and their relation to motor adaptation.PloS one · 2022Article
- Proof of Concept of Novel Visuo-Spatial-Motor Fall Prevention Training for Old People.Geriatrics (Basel, Switzerland) · 2021Article
- Effort-dependent effects on uniform and diverse muscle activity features in skilled pitching.Scientific reports · 2021Article
- Examining modifications of execution strategies during a continuous task.Scientific reports · 2021Article
- Larger, but not better, motor adaptation ability inherent in medicated Parkinson's disease patients revealed by a smart-device-based study.Scientific reports · 2020Article
- Speed-dependent and mode-dependent modulations of spatiotem-poral modules in human locomotion extracted via tensor decom-position.Scientific reports · 2020Article
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2 authors.
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Abstract
Motor variability is inevitable in human body movements and has been addressed from various perspectives in motor neuroscience and biomechanics: it may originate from variability in neural activities, or it may reflect a large number of degrees of freedom inherent in our body movements. How to evaluate motor variability is thus a fundamental question. Previous methods have quantified (at least) two striking features of motor variability: smaller variability in the task-relevant dimension than in the task-irrelevant dimension and a low-dimensional structure often referred to as synergy or principal components. However, the previous methods cannot be used to quantify these features simultaneously and are applicable only under certain limited conditions (e.g., one method does not consider how the motion changes over time, and another does not consider how each motion is relevant to performance). Here, we propose a flexible and straightforward machine learning technique for quantifying task-relevant variability, task-irrelevant variability, and the relevance of each principal component to task performance while considering how the motion changes over time and its relevance to task performance in a data-driven manner. Our method reveals the following novel property: in motor adaptation, the modulation of these different aspects of motor variability differs depending on the perturbation schedule.
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