Virtual reality (VR) has increasingly been used in many areas, and the need to deliver notifications in VR is also expected to increase accordingly. However, untimely interruptions could largely impact the experience in VR. Identifying opportune times to deliver notifications to users allows for notifications to be scheduled in a way that minimizes disruption. We conducted a study to investigate the use of sensor data available on an off-the-shelf VR device and additional contextual information, including current activity and engagement of users, to predict opportune moments for sending notifications using deep learning models. Our analysis shows that using mainly sensor features could achieve 72% recall, 71% precision and 0.86 area under receiver operating characteristic (AUROC); performance can be further improved to 81% recall, 82% precision, and 0.93 AUROC if information about activity and summarized user engagement is included.
The ACM CHI Conference on Human Factors in Computing Systems (https://chi2022.acm.org/)