STARE: Augmented Reality Data Visualization for Explainable Decision Support in Smart Environments
Mengya Zheng, Xingyu Pan, Nestor Velasco Bermeo, Rosemary J. Thomas, David Coyle, G. M. P. O’Hare, Abraham G. Campbell
Abstract
The Internet of Things (IoT) provides unprecedented opportunities for the access to and conflation of a myriad of heterogeneous data to support real-time decision-making within smart environments. Augmented Reality (AR) is on cusp of becoming mainstream and will allow for the ubiquitous visualization of IoT derived data. Such visualization will simultaneously permit the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">cognitive and visual binding</i> of information to the physical object(s) to which they pertain. Important questions exist as to how one can efficiently filter, prioritize, determine relevance and adjudicate on individual information needs in support of real-time decision making. To this end, this paper proposes a novel AR decision support framework ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">STARE</i> ) to support immediate decisions within a smart environment by augmenting the user’s focal objects with assemblies of semantically relevant IoT data and corresponding suggestions. In order to evaluate this technique, a remote user study was undertaken within a simulated smart home environment. The evaluation results demonstrate that the proposed Semantic Augmented Reality decision support framework leads to a reduction in information overloading and enhanced effectiveness, both in terms of IoT data interpretation and decision support.