Clustering analysis of human navigation trajectories in a visuospatial memory locomotor task using K-Means and hierarchical agglomerative clustering

• Published in: E3S Web of Conferences Vol. 351; p. 1042
• Main Authors: Annaki, Ihababdelbasset, Rahmoune, Mohammed, Bourhaleb, Mohammed, Berrich, Jamal, Zaoui, Mohamed, Castilla, Alexandre, Berthoz, Alain, Cohen, Bernard
• Format: Journal Article Conference Proceeding
• Language: English
• Published: Les Ulis EDP Sciences 01-01-2022
• Subjects: Algorithms; Cluster analysis; Clustering; Cognitive ability; Computer applications; Locomotion; Machine learning; Mental task performance; Navigation; Navigation behavior; Spatial memory; Trajectory analysis; Virtual reality; Wayfinding
• ISSN: 2267-1242; 2555-0403; 2267-1242
• DOI: 10.1051/e3sconf/202235101042

Throughout this study, we employed unsupervised machine learning clustering algorithms, namely K-Means [1] and hierarchical agglomerative clustering (HAC) [2], to explore human locomotion and wayfinding using a VR Magic Carpet (VMC) [3], a table test version known as the Corsi Block Tapping task (CBT) [4]. This variation was carried out in the context of a virtual reality experimental setup. The participants were required to memorize a sequence of target positions projected on the rug and walk to each target figuring in the displayed sequence. the participant’s trajectory was collected and analyzed from a kinematic perspective. An earlier study [5] identified three different categories, but the classification remained ambiguous, implying that they include both kinds of individuals (normal and patients with cognitive spatial impairments). On this basis, we utilized K-Means and HAC to distinguish the navigation behavior of patients from normal individuals, emphasizing the most important discrepancies and then delving deeper to gain more insights.