CC BYIhababdelbasset AnnakiMohammed BourhalebMohammed RahmouneJamal BerrichMohamed ZaouiAlexandre CastillaAlain BerthozBernard Cohen2025-06-172025-06-172022-01-012267-124210.1051/e3sconf/20223510104210.60692/fnj7w-3wh8110.60692/4ysz1-nmr48https://ror.circle-u.eu/handle/123456789/740917<jats:p>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.</jats:p>OPENArtificial intelligenceFOS: Mechanical engineeringMemorizationActivity Recognition in Pervasive Computing EnvironmentsActivity RecognitionPattern recognition (psychology)Hierarchical clusteringSpatial Ability for STEM DomainsWayfindingEngineeringCluster analysisCognitionCognitive psychologyPsychologyGE1-350Human Activity AnalysisMental RotationComputer scienceCognitive MapsEnvironmental sciencesFOS: PsychologyHuman Action Recognition and Pose EstimationComputer SciencePhysical SciencesAutomotive EngineeringComputer Vision and Pattern RecognitionNeurosciencepublication0501 psychology and cognitive sciences05 social sciencesdoi_dedup___:7773e9982a1d3895069733603934b4c1