Joint Unsupervised Deep Temporal Clustering for Modeling Human Behavior in Vestibular Dysfunction: A Study of Navigation Pattern

Abstract

Human behavior modeling aims to extract and understand patterns of behavior in one's daily life. A seemingly intuitive walk to a destination following a specific path raises questions about the existence of a model that can explain the choices and strategies predetermined by the brain through physical behavior. Tasks such as finding objects, achieving goals, avoiding obstacles, and returning home can be challenging when one lacks navigational abilities. In our previous work. We employed machine learning (ML) approaches to evaluate and classify cognitively challenged individuals to identify crucial markers that could be used to simulate human navigation behavior. However, the challenge lies in the possibility of missing key elements. Our proposed method utilizes deep time series clustering architectures, in which we employ auto-encoders to extract features and then cluster our data using unsupervised machine learning techniques. This approach will provide us with initial insights into human navigation patterns and meaningful clustering. Our developed framework combines unsupervised learning with a dataset obtained from a neuropsychological evaluation in a virtual reality setting called “The VR Magic Carpet”, a variant of “the Corsi block-tapping task”.