Locomotion is a key factor in virtual reality (VR) navigation but remains underexplored in asymmetric setups combining VR and mixed reality (MR). Our study investigates whether visualized teleportation can serve as a middle ground, balancing the social connection afforded by real walking with the efficiency of teleportation within asymmetric VR-MR setups. We conducted a mixed-design study (N = 24, 12 pairs) to examine how locomotion methods (teleport, real walking, visualized teleport), platform (VR vs. MR), and task type (social vs. navigation) affect user experience and performance. Results show that locomotion technique significantly influences both user experience and navigation effectiveness. Our findings highlight visualized teleport as a promising compromise, offering improved presence and user experience without imposing physical demand, providing practical guidance for designing locomotion in asymmetric mixed reality environments.
Interaction in Augmented Reality primarily relies on raycast pointing and mid-air touch. An alternative consists of using the non-dominant hand as a touch-sensitive surface, enabling more comfortable, less fatiguing input. AR UI design guidelines have so far discouraged this alternative because of poor hand tracking performance when the hands overlap, favoring touchpads in the air near the hand, rather than on the hand. But significant improvements to the hand tracking capabilities of recent commodity headsets suggest that on-hand pads may now be feasible. We develop an on-hand touchpad prototype and conduct two studies that involve both discrete input and continuous control tasks. The first study compares such on-hand pads to baseline in-air and on-object pads, showing comparable performance despite some limitations in tracking accuracy. The second study quantifies the advantage of on-hand and in-air pads over on-object pads during transitions between touchpad input and other physical hand activities.
Smartphones allow users to control the sharing of their data with apps according to their privacy preferences. Yet, users struggle to enact their privacy preferences via the available permission settings. To understand whether these difficulties result from inaccurate understanding and/or suboptimal interface design of the permissions manager, we designed and administered an online questionnaire to smartphone users from the United States (n = 151). We asked the participants to rate and rank the importance of the permissions commonly available on smartphones and to describe their understanding of what each setting controls. We found that a majority of users deem some permissions as important or unimportant, with the importance of other permissions varying across users based on use and privacy concerns. Our findings indicate that users misunderstand several permissions and express unfamiliarity with how some of them operate. We apply the insight from our study to derive suggestions to enhance smartphone permission managers by promoting personalized and efficient user interaction and more accurate user comprehension of functional operation.
Interaction mining is a popular technique for capturing design and interaction data while a mobile app is being used. Over the years, researchers have leveraged interaction mining systems to build large repositories of interaction data, enabling novel, ML-based tools for UX practitioners, designers, and programmers. Existing interaction mining systems range from simple screen recorders — which are easy to use but capture sparse, unstructured data — to complex installations requiring dedicated hardware and custom OS forks — which yield rich, high-fidelity traces but are difficult to deploy outside of a lab environment. This paper presents ODIM, an on-device framework for mobile interaction mining that produces detailed trace metadata. The framework is reified in an Android implementation based on a simple APK that users can install on their personal devices. The paper outlines ODIM’s design principles, describes its implementation, evaluates the system on traces collected from 100 popular apps on the Google Play Store, and discusses future avenues for scaling the utility and impact of interaction mining systems. The ODIM software, source code, and online trace repository are all freely available at interactionmining.org, for anyone to use and contribute to.
Experiential accounts of personal informatics are important as they inform us about users’ lived experiences with tracking technologies. However, these accounts describe experience at a meta-level and are not specifically related to how personal informatics is often composed of a collection of interconnected artefacts—what we call an ecology. To design personal informatics artefacts, we need a thorough understanding of how they are experienced as ecologies in practice. To this end, we interviewed 12 users of these ecologies using an interpretative phenomenological analysis approach. Our results show that users experience these ecologies through four aspects: effort minimising, sustainable tracking, performance explainability, and disconnection. We conceptualised these aspects as an automation experience—where technologies work collectively to minimise user effort while maximising insight. This conceptualisation provides a novel lens for analysis that can inform the design of more integrated and user-centered personal informatics ecologies.
