Immersion Measurement in Watching Videos Using Eye-tracking Data

Abstract

Immersion plays a crucial role in video watching, leading viewers to a positive experience, such as increased engagement and decreased fatigue. However, few studies measure immersion while watching videos, and questionnaires are typically used in the measurement of immersion for other applications. These methods may rely on the viewer's memory and cause biased results. Therefore, we propose an objective immersion detection model by leveraging people's gaze behavior while watching videos. In a lab study with 30 participants, an in-depth analysis is carried out on a number of gaze features and machine learning (ML) models to identify the immersion state. Several gaze features are highly indicative of immersion and ML models with these features are able to detect an immersion state of video watchers. Post-hoc interviews demonstrate that our approach is applicable to measure immersion in the middle of watching a video, where some practical issues are discussed as well.