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

¹ Embedded Systems, Electronics, and IT team, Research Laboratory in Applied Sciences. National School of Applied Sciences, PB 669, Oujda 60000, Morocco
² Collège de France,CIRB. 11, place Marcelin-Berthelot 75231 Paris Cedex 05, France
³ Laboratoire de Psychologie du Développement et de l'Éducation de l'Enfant (LaPsyDÉ, UMR CNRS 8240), Université de Paris, France
⁴ Institut de Médecine expérimentale (IME). Paris. France
⁵ The Sorbonne University Faculty of Medicine, Saint Antoine Hospital, 27 Rue de Chaligny, 75012 Paris, France

^* Corresponding author: i.annaki@ump.ac.ma

Abstract

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.