Head-mounted displays (HMDs) are being used for VR and AR applications and increasingly permeate our everyday life. At the same time, a detailed understanding of interruptions in settings where people wearing an HMD (HMD user) and people not wearing an HMD (bystander) is missing. We investigate (a) whether bystanders are capable of identifying when HMD users switch tasks by observing their gestures, and hence exploit opportune moments for interruptions, and (b) which strategies bystanders employ. In a lab study (N=64) we found that bystanders are able to successfully identify both task switches (83%) and tasks (77%) within only a few seconds of the task switch. Furthermore, we identified interruption strategies of bystanders. From our results we derive implications meant to support designers and practitioners in building HMD applications that are used in a co-located collaborative setting.

We performed a systematic evaluation of the shoulder surfing susceptibility of the Android pattern (un)lock. The results of an online study (n=298) enabled us to quantify the influence of pattern length, line visibility, number of knight moves, number of overlaps and number of intersections on observation resistance. The results show that all parameters have a highly significant influence, with line visibility and pattern length being most important. We discuss implications for real-world patterns and present a linear regression model that can predict the observability of a given pattern. The model can be used to provide proactive security measurements for (un)lock patterns, in analogy to password meters.