Using iBeacon to Detect User Behavior from Indoor Physical Movement

In this paper, we present our work on user interest modeling using spatial behavioral data collected from indoor environments. Predicting user interests from their physical movement has a lot of important applications in contextual computing. We have critically reviewed the iBeacon technology and argued for its unique affordance in addressing human factor challenges in indoor contextual computing. We successfully applied iBeacon to model user's spots of interest inside a physical building. In particular, we have developed an iBeacon-based mobile app to capture user's indoor physical movement and collect their temporal feedback for comparison. We evaluated our prediction method by comparing the spatial behavioral data against user explicit responses and had achieved an appreciable precision. We hope to solicit timely feedback from the research community in realizing our ultimate goal of developing indoor navigation aids for users with special needs in the 2020 Tokyo Olympic Game.