Deep-learning-based identification of individual motion characteristics from upper-limb trajectories towards disorder stage evaluation

TL;DR

This study demonstrates that deep learning can accurately identify individuals based on their upper-limb movement trajectories, aiding in assessing movement disorders and personal rehabilitation progress.

Contribution

It introduces a deep learning approach for differentiating individual motion patterns from 3D upper-limb trajectories, achieving high classification accuracy.

Findings

95% accuracy for nine individuals

78% accuracy for 31 individuals

Insights into separability of patient attributes

Abstract

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.