Computational Analysis of Human Navigation Trajectories in a Spatial Memory Locomotor Task

Annaki, Ihababdelbasset; Rahmoune, Mohammed; Bourhaleb, Mohammed; Berrich, Jamal; Zaoui, Mohamed; Castilla, Alexander; Berthoz, Alain

doi:10.1007/978-3-030-73882-2_22

Cited by 7 publications

(2 citation statements)

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“…Machine learning [16] and artificial intelligence [17] spurred the search for classifiers to recognize patterns in human navigation. As in this paper, we focus mainly on using K-Means [1] and (HAC) [2] to analyze the VR Magic Carpet TM [3,18] output and dig further into the many classes acquired via an early kinematic participantbased data analysis [5].…”

Section: Introductionmentioning

confidence: 99%

“…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.…”

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confidence: 99%

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

Annaki¹,

Rahmoune²,

Bourhaleb³

et al. 2022

E3S Web Conf.

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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.

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