AUTHOR=Daneault Jean-Francois , Oubre Brandon , Miranda Jose Garcia Vivas , Lee Sunghoon Ivan TITLE=Understanding voluntary human movement variability through data-driven segmentation and clustering JOURNAL=Frontiers in Human Neuroscience VOLUME=17 YEAR=2023 URL=https://www.frontiersin.org/journals/human-neuroscience/articles/10.3389/fnhum.2023.1278653 DOI=10.3389/fnhum.2023.1278653 ISSN=1662-5161 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 elements are extracted (i.e., medio-lateral, antero-posterior, and vertical) affect the proportion of the movement elements matching the theoretical model. These results provide novel insight into how the nervous system controls voluntary movements and may use variability in movement element properties to explore the environment.