Stabilized Annotations for Mobile Remote Assistance

Abstract

Recent mobile technology has provided new opportunities for creating remote assistance systems. However, mobile support systems present a particular challenge: both the camera and display are held by the user, leading to shaky video. When pointing or drawing annotations, this means that the desired target often moves, causing the gesture to lose its intended meaning. To address this problem, this thesis investigates an annotation stabilization technique, which allows annotations to stick to their intended location. I studied two different forms of annotation systems, with both tablets and head-mounted displays. To differentiate my work from the prior research, I considered a number of task factors that might influence system performance in remote assistance scenarios. My analysis suggests that stabilized annotations and head-mounted displays are only beneficial in certain situations. I conclude with reflections on system limitations and potential future work.