Augmented Reality (AR) is promising in providing individual learning support for students.However, it is currently unknown which display technology is appropriate to use in classroom settings.In this work, we investigate different AR displays and their usability

Gait is a distinctive behavioral trait, yet its vulnerability against imitation remains underexplored in immersive environments. We present a study investigating how real-time visual feedback in virtual reality (VR) influences a person’s ability to mimic another’s gait. Through two experiments, we first identify the most usable feedback design (N = 8), then evaluate its impact on imitation performance compared to a baseline without feedback (N = 18). We analyze positional and rotational similarity between participants and target avatars, examining the influence of avatar–user gender matching and repeated practice. Our findings reveal that visual feedback significantly improves rotational alignment and that practice leads to measurable improvements in mimicry accuracy. We discuss implications for avatar embodiment, personalization in VR applications, and potential considerations for behavioral biometric systems. We also contribute a publicly available dataset of gait mimicry in VR, supporting further research on motion learning and imitation.

Robots play a vital role in modern automation, with applications in manufacturing and healthcare. Collaborative robots integrate human and robot movements. Therefore, it is essential to ensure that interactions involve qualified, and thus identified, individuals. This study delves into a new approach: identifying individuals through robot arm movements. Different from previous methods, users guide the robot, and the robot senses the movements via joint sensors. We asked 18 participants to perform six gestures, revealing the potential use as unique behavioral traits or biometrics, achieving F1-score up to 0.87, which suggests direct robot interactions as a promising avenue for implicit and explicit user identification.

The global COVID-19 pandemic created a constant need for hand disinfection. While it is still essential, disinfection use is declining with the decrease in perceived personal risk (e.g., as a result of vaccination). Thus this work explores using different visual cues to act as reminders for hand disinfection. We investigated different public display designs using (1) paper-based only, adding (2) screen-based, or (3) projection-based visual cues. To gain insights into these designs, we conducted semi-structured interviews with passersby (N=30). Our results show that the screen- and projection-based conditions were perceived as more engaging. Furthermore, we conclude that the disinfection process consists of four steps that can be supported: drawing attention to the disinfection station, supporting the (subconscious) understanding of the interaction, motivating hand disinfection, and performing the action itself. We conclude with design implications for technology-supported disinfection.

This paper contributes to our understanding of user-centered attacks on smartphones. In particular, we investigate the likelihood of so-called shoulder surfing attacks during touch-based unlock events and provide insights into users' views and perceptions. To do so, we ran a two-week in-the-wild study (N=12) in which we recorded images with a 180-degree field of view lens that was mounted on the smartphone's front-facing camera. In addition, we collected contextual information and allowed participants to assess the situation. We found that only a small fraction of shoulder surfing incidents that occur during authentication are actually perceived as threatening. Furthermore, our findings suggest that our notions of (un)safe places need to be rethought. Our work is complemented by a discussion of implications for future user-centered attack-aware systems. This work can serve as a basis for usable security researchers to better design systems against user-centered attacks.