Explaining to novice users how to interact in immersive VR applications may be challenging. This is in particular due to the fact that the learners are isolated from the real world, and they are asked to manipulate hardware and software objects they are not used to. Consequently, the onboarding phase, which consists in teaching the user how to interact with the application is particularly crucial. In this paper, we aim at giving a better understanding of current VR onboarding methods, their benefits and challenges. We performed 21 VR tutorial ergonomic reviews and 15 interviews with VR experts with experience in VR onboarding. Building on the results, we propose a conceptual framework for VR onboarding and discuss important research directions to explore the design of future efficient onboarding solutions adapted to VR.
Head-worn augmented reality (AR) is a hotly pursued and increasingly feasible contender paradigm for replacing or complementing smartphones and watches for continual information consumption. Here, we compare three different AR navigation aids (on-screen compass, on-screen radar and in-world vertical arrows) in a wide-area outdoor user study (n=24) where participants search for hidden virtual target items amongst physical and virtual objects. We analyzed participants’ search task performance, movements, eye-gaze, survey responses and object recall. There were two key findings. First, all navigational aids enhanced search performance relative to a control condition, with some benefit and strongest user preference for in-world arrows. Second, users recalled fewer physical objects than virtual objects in the environment, suggesting reduced awareness of the physical environment. Together, these findings suggest that while navigational aids presented in AR can enhance search task performance, users may pay less attention to the physical environment, which could have undesirable side-effects.
Optical see-through (OST) head-mounted displays (HMDs) enable users to experience Augmented Reality (AR) support in the form of helpful real-world annotations. Unfortunately, the blend of the environment with virtual augmentations due to semitransparent OST displays often deteriorates the contrast and legibility of annotations. View management algorithms adapt the annotations' layout to improve legibility based on real-world information, typically captured by built-in HMD cameras. However, the camera views are different from the user's view through the OST display which decreases the final layout quality. We present eye-perspective view management that synthesizes high-fidelity renderings of the user’s view to optimize annotation placement. Our method significantly improves over traditional camera-based view management in terms of annotation placement and legibility. Eye-perspective optimizations open up opportunities for further research on use cases relying on the user's true view through OST HMDs.
Motion sickness is a problem for many in everyday travel and will become more prevalent with the rise of automated vehicles. Virtual Reality (VR) headsets have shown significant promise in-transit, enabling passengers to engage in immersive entertainment and productivity experiences. In a controlled multi-session motion sickness study using an actuated rotating chair, we examine the potential of multi-sensory visual and auditory motion cues, presented during a VR reading task, for mitigating motion sickness. We found that visual cues are most efficient in reducing symptoms, with auditory cues showing some beneficial effects when combined with the visual. Motion sickness had negative effects on presence as well as task performance, and despite the cognitive demand and multi-sensory cues, motion sickness still reached problematic levels. Our work emphasises the need for effective mitigations and the design of stronger multi-sensory motion cues if VR is to fulfil its potential for passengers.
About one-third of autistic people are nonspeaking, and most are never provided access to an effective alternative to speech. Thoughtfully designed AR applications could provide members of this population with structured learning opportunities, including training on skills that underlie alternative forms of communication. A fundamental step toward creating such opportunities, however, is to investigate nonspeaking autistic people's ability to tolerate a head-mounted AR device and to interact with virtual objects. We present the first study to examine the usability of an interactive AR-based application by this population. We recruited 17 nonspeaking autistic subjects to play a HoloLens 2 game we developed that involved holographic animations and buttons. Almost all subjects tolerated the device long enough to begin the game, and most completed increasingly challenging tasks that involved pressing holographic buttons. Based on the results, we discuss best practice design and process recommendations. Our findings contradict prevailing assumptions about nonspeaking autistic people and thus open up exciting possibilities for AR-based solutions for this understudied and underserved population.
For non-technical domain experts and designers it can be a substantial challenge to create designs that meet domain specific goals. This presents an opportunity to create specialized tools that produce optimized designs in the domain. However, implementing domain-specific optimization methods requires a rare combination of programming and domain expertise. Creating flexible design tools with re-configurable optimizers that can tackle a variety of problems in a domain requires even more domain and programming expertise. We present OPTIMISM, a toolkit which enables programmers and domain experts to collaboratively implement an optimization component of design tools. OPTIMISM supports the implementation of metaheuristic optimization methods by factoring them into easy to implement and reuse components: objectives that measure desirable qualities in the domain, modifiers which make useful changes to designs, design and modifier selectors which determine how the optimizer steps through the search space, and stopping criteria that determine when to return results. Implementing optimizers with OPTIMISM shifts the burden of domain expertise from programmers to domain experts.