Predicting spatial familiarity by exploiting head and eye movements during pedestrian navigation in the real world.

Spatial familiarity has seen a long history of interest in wayfinding research. To date, however, no studies have been done which systematically assess the behavioral correlates of spatial familiarity, including eye and body movements. In this study, we take a step towards filling this gap by reporting on the results of an in-situ, within-subject study with [Formula: see text] pedestrian wayfinders that combines eye-tracking and body movement sensors. In our study, participants were required to walk both a familiar route and an unfamiliar route by following auditory, landmark-based route instructions. We monitored participants' behavior using a mobile eye tracker, a high-precision Global Navigation Satellite System receiver, and a high-precision, head-mounted Inertial Measurement Unit. We conducted machine learning experiments using Gradient-Boosted Trees to perform binary classification, testing out different feature sets, i.e., gaze only, Inertial Measurement Unit data only, and a combination of the two, to classify a person as familiar or unfamiliar with a particular route. We achieve the highest accuracy of [Formula: see text] using exclusively Inertial Measurement Unit data, exceeding gaze alone at [Formula: see text], and gaze and Inertial Measurement Unit data together at [Formula: see text]. For the highest accuracy achieved, yaw and acceleration values are most important. This finding indicates that head movements ("looking around to orient oneself") are a particularly valuable indicator to distinguish familiar and unfamiliar environments for pedestrian wayfinders.