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

Ihababdelbasset Annaki, Mohammed Rahmoune, Mohammed Bourhaleb, Jamal Berrich, Mohamed Zaoui, Alexandre Castilla, Alain Berthoz, Bernard Cohen

Research output: Contribution to journalConference articlepeer-review

30 Scopus citations

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.

Original languageEnglish
Article number01042
JournalE3S Web of Conferences
Volume351
DOIs
StatePublished - 24 May 2022
Externally publishedYes
Event10th International Conference on Innovation, Modern Applied Science and Environmental Studies, ICIES 2022 - Istanbul, Turkey
Duration: 12 May 202214 May 2022

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