As VR expands into public spaces, new challenges emerge around spontaneous interactions between bystanders and unfamiliar VR users. While current VR systems often prioritize user awareness of their physical surroundings, they overlook the social dynamics affecting nearby bystanders. We conducted a deception-based study (N=80) examining how interface availability influences bystanders’ comfort, confidence, and hesitation when interrupting VR users. We compared traditional static interruption interfaces (e.g., button on screen) with a proactive proxy that actively approached bystanders upon detecting interruption intent. Static interfaces, due to insufficient cueing, frequently caused bystander discomfort, leading to hesitant physical interruptions or complete communication avoidance. In contrast, the proactive proxy implicitly conveyed social permission, significantly enhancing bystanders’ comfort and confidence. Our findings provide empirical insights into how bystanders assess availability and initiate interruptions with unfamiliar VR users in shared spaces, offering design implications for VR systems that support bystander agency and comfort during these interactions.

Live streaming has become increasingly popular, driven by the desire for direct and real-time interactions between streamers and viewers. However, current text-based interactions and pre-defined emojis limit expressiveness, especially when referring to specific stream moments. We propose ThingMoji, a type of user-captured cut-outs to enhance user expression and foster more effective communication between streamers and their audience in the comment section. ThingMojis are unique digital icons created by users by capturing snapshots and annotating specific areas at any point during the stream. We developed StreamThing, a live-streaming platform integrated with ThingMojis, to explore their use during object-focused live streaming contexts. In a user study with three in-the-wild deployments reveals the expressive use of ThingMojis in diverse live-streaming scenarios with rich visual contents. Our findings show that ThingMojis enable viewers to reference specific objects, express emotions, and create shared visual narratives. Streamers found ThingMojis valuable for facilitating on-the-fly communication around visual content and fostering playful interactions. The study also uncovered challenges in ThingMoji comprehension, issues for long-term uses of ThingMojis, and potential concerns regarding misuse. Based on these insights, we discussed new opportunities for supporting object-focused communication during live streaming environments.

Tasks that involve locating objects and then moving hands to those specific locations, such as using touchscreens or grabbing objects on a desk, are challenging for the visually impaired. Over the years, audio guidance and haptic feedback have been a staple in hand navigation based assistive technologies. However, these methods require the user to interpret the generated directional cues and then manually perform the hand motions. In this paper, we present automated hand-based spatial guidance to bridge the gap between guidance and execution, allowing visually impaired users to move their hands between two points automatically, without any manual effort. We implement this concept through FingerRover, an on-finger miniature robot that carries the user’s finger to target points. We demonstrate the potential applications that can benefit from automated hand-based spatial guidance. Our user study shows the potential of our technique in improving the interaction capabilities of people with visual impairments.

Wearable devices allow quick and convenient interactions for controlling mobile computers. However, these interactions are often device-dependent, and users cannot control devices in a way they are familiar with if they do not wear the same wearable device. This paper proposes a new method, UnifiedSense, to enable device-dependent gestures even when the device that detects such gestures is missing by utilizing sensors on other wearable devices. UnifiedSense achieves this without explicit gesture training for different devices, by training its recognition model while users naturally perform gestures. The recognizer uses the gestures detected on the primary device (i.e., a device that reliably detects gestures) as labels for training samples and collects sensor data from all other available devices on the user. We conducted a technical evaluation with data collected from 15 participants with four types of wearable devices. It showed that UnifiedSense could correctly recognize 5 gestures (5 gestures × 5 configurations) with an accuracy of 90.9% (SD = 1.9%) without the primary device present.

People often form small conversation groups during physical gatherings to have ad-hoc and informal conversations. As these groups are loosely defined, others can often overhear and join the conversation. However, current video-conferencing tools only allow for strict boundaries between small conversation groups, inhibiting fluid group formations and between-group conversations. This isolates small-group conversations from others and leads to inefficient transitions between conversations. We present FluidMeet, a virtual breakout meeting system that employs flexible conversation boundaries and cross-group conversation visualizations to enable fluid conversation group formations and ad-hoc, informal conversations. FluidMeet enables out-group members to overhear group conversations while allowing conversation groups to control their shared level of context. Users within conversation groups can also quickly switch between in-group and private conversations. A study of FluidMeet showed that it encouraged users to break group boundaries, made them feel less isolated in group conversations, and facilitated communication across different groups.