Understanding voluntary human movement variability through data-driven segmentation and clustering.

ABSTRACT: Recently, we proposed a novel approach where movements are decomposed into sub-segments, termed movement elements. This approach, to date, provides a robust construct of how the brain may generate simple as well as complex movements. Here, we address the issue of motor variability during voluntary movements by applying an unsupervised clustering algorithm to group movement elements according to their morphological characteristics. We observed that most movement elements closely match the theoretical bell-shaped velocity profile expected from goal-directed movements. However, for those movement elements that deviate from this theoretical shape, a small number of defined patterns in their shape can be identified. Furthermore, we observed that the axis of the body from which the movement elem

SUBMITTER: Daneault JF 

PROVIDER: S-EPMC10713770 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature