Clustering analysis of human navigation trajectories in a visuospatial memory locomotor task using K-Means and hierarchical agglomerative clustering
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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...
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Artificial intelligence , FOS: Mechanical engineering , Memorization , Activity Recognition in Pervasive Computing Environments , Activity Recognition , Pattern recognition (psychology) , Hierarchical clustering , Spatial Ability for STEM Domains , Wayfinding , Engineering , Cluster analysis , Cognition , Cognitive psychology , Psychology , GE1-350 , Human Activity Analysis , Mental Rotation , Computer science , Cognitive Maps , Environmental sciences , FOS: Psychology , Human Action Recognition and Pose Estimation , Computer Science , Physical Sciences , Automotive Engineering , Computer Vision and Pattern Recognition , Neuroscience