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

Throughout this study, we employed unsupervised machine learning clustering algorithms, namely K-Means [1] and hierarchical agglomerative (HAC) [2], to explore human locomotion wayfinding using a VR Magic Carpet (VMC) [3], table test version known as the Corsi Block Tapping task (CBT) [4]. This variation was carried out in context of virtual reality experimental setup. The participants were required memorize sequence target positions projected on rug walk each figuring displayed sequence. participant’s trajectory collected analyzed from kinematic perspective. An earlier study [5] identified three different categories, but classification remained ambiguous, implying that they include both kinds individuals (normal patients with cognitive spatial impairments). On basis, utilized HAC distinguish navigation behavior normal individuals, emphasizing most important discrepancies then delving deeper gain more insights.