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- W4285251001 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." @default.
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- W4285251001 date "2022-01-01" @default.
- W4285251001 modified "2023-10-01" @default.
- W4285251001 title "Clustering analysis of human navigation trajectories in a visuospatial memory locomotor task using K-Means and hierarchical agglomerative clustering" @default.
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- W4285251001 doi "https://doi.org/10.1051/e3sconf/202235101042" @default.
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