Paper ID: 2412.12016 • Published Dec 16, 2024
Deep-learning-based identification of individual motion characteristics from upper-limb trajectories towards disorder stage evaluation
Tim Sziburis, Susanne Blex, Tobias Glasmachers, Ioannis Iossifidis
TL;DR
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The identification of individual movement characteristics sets the foundation
for the assessment of personal rehabilitation progress and can provide
diagnostic information on levels and stages of movement disorders. This work
presents a preliminary study for differentiating individual motion patterns
using a dataset of 3D upper-limb transport trajectories measured in task-space.
Identifying individuals by deep time series learning can be a key step to
abstracting individual motion properties. In this study, a classification
accuracy of about 95% is reached for a subset of nine, and about 78% for the
full set of 31 individuals. This provides insights into the separability of
patient attributes by exerting a simple standardized task to be transferred to
portable systems.