While virtual reality (VR) technologies enable remote communication through the use of 3D avatars, it is often difficult to foster engaging group discussions without addressing the limitations to the non-verbal communication among distributed participants. In this paper, we discuss a technique to detect the intentions to speak in group discussions by tapping into intricate sensor data streams from VR headsets and hand-controllers. To this end, we developed a prototype VR group discussion app equipped with comprehensive sensor data-logging functions and conducted an experiment of VR group discussions (N = 24). We used the quantitative and qualitative experimental data to analyze participants' experiences of group discussions in relation to the temporal patterns of their different speaking intentions. We then propose a sensor-based mechanism for detecting speaking intentions by employing a sampling strategy that considers the temporal patterns of speaking intentions, and we verify the feasibility of our approach in group discussion settings.
Keywords: deep learning; group work; human–computer interaction; sensor; virtual reality.