E3S Web Conf.
Volume 351, 202210th International Conference on Innovation, Modern Applied Science & Environmental Studies (ICIES’2022)
|Number of page(s)||5|
|Published online||24 May 2022|
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: email@example.com
Throughout this study, we employed unsupervised machine learning clustering algorithms, namely K-Means  and hierarchical agglomerative clustering (HAC) , to explore human locomotion and wayfinding using a VR Magic Carpet (VMC) , a table test version known as the Corsi Block Tapping task (CBT) . 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  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.
© The Authors, published by EDP Sciences, 2022
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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