Computational Analysis of Human Navigation in a VR Spatial Memory Locomotor Assessment Using Density-Based Clustering Algorithm of Applications with Noise DBSCAN

In this study, we explore human navigation as evaluated by the VR Magic Carpet TM (VMC) [1], a variation of the Corsi Block Tapping task (CBT) [2, 3], employing Density-based spatial clustering of applications with noise (DBSCAN) [4]. As a result of the VMC, we acquired raw spatial data in 3D space, which we processed, analyzed, and turned into trajectories before evaluating them from a kinematic standpoint. Our previous research [5] revealed three unique groupings. However, the categorization remained ambiguous, showing clusters with diverse people (patients and healthy). We utilized DBSCAN to compare patients’ navigation behavior to healthy individuals, highlighting the most notable differences and assessing our existing classifiers. Our research aims to produce insights that may help clinicians and neuroscientists adopt machine learning, especially clustering algorithms, to identify cognitive impairments and analyze human navigation behavior. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.