Computational Analysis of Human Navigation Trajectories in the VR Magic Carpet ™ Using K-Means

Abstract

In this research, we use unsupervised machine learning clustering techniques, notably K-means (Jain in Pattern Recogn Lett 31:651–666, 2010 [1]), to explore human navigation using the VR Magic Carpet (Berthoz and Zaoui in Dev Med Child Neurol 57:15–20, 2015 [2]). This is a variant of the Corsi Block Tapping task (CBT) (Corsi in Human memory and the medial temporal region of the brain. McGill University, 1972 [3]) that was carried out within the experimental framework of virtual reality. The participant’s trajectory was captured as raw spatial data and afterward kinematically evaluated. Our previous research (Annaki et al. in Digital technologies and applications. ICDTA 2021. Lecture notes in networks and systems, vol 211. Springer, Cham, 2021 [4]) found three distinct groups. However, the classification remained unclear, suggesting that they include both types of people (ordinary and patients with cognitive spatial impairments). Based on this premise, we used K-means to distinguish patients’ navigation behavior from that of healthy people, highlighting the most significant differences and validating the feature on which our previous analysis was based.