The recent proliferation of immersive technology has led to the rapid adoption of consumer-ready hardware for Augmented Reality (AR) and Virtual Reality (VR). While this increase has resulted in a variety of platforms that can offer a richer interactive experience, the advances in technology bring more variability in display types, interaction sensors and use cases. This provides a spectrum of device-specific interaction possibilities, with each offering a tailor-made solution for delivering immersive experiences to users, but often with an inherent lack of standardisation across devices and applications. To address this, a systematic review and an evaluation of explicit, task-based interaction methods in immersive environments are presented in this paper. A corpus of papers published between 2013 and 2020 is reviewed to thoroughly explore state-of-the-art user studies, which investigate input methods and their implementation for immersive interaction tasks (pointing, selection, translation, rotation, scale, viewport, menu-based and abstract). Focus is given to how input methods have been applied within the spectrum of immersive technology (AR, VR, XR). This is achieved by categorising findings based on display type, input method, study type, use case and task. Results illustrate key trends surrounding the benefits and limitations of each interaction technique and highlight the gaps in current research. The review provides a foundation for understanding the current and future directions for interaction studies in immersive environments, which, at this pivotal point in XR technology adoption, provides routes forward for achieving more valuable, intuitive and natural interactive experiences.
Since the emergence of COVID-19 in late 2019, there has been a significant disturbance in human-to-human interaction that has changed the way we conduct user studies in the field of Human-Computer Interaction (HCI), especially for extended (augmented, mixed, and virtual) reality (XR). To uncover how XR research has adapted throughout the pandemic, this paper presents a review of user study methodology adaptations from a corpus of 951 papers. This corpus of papers covers CORE 2021 A* published conference submissions, from Q2 2020 through to Q1 2021 (IEEE ISMAR, ACM CHI, IEEE VR). The review highlights how methodologies were changed and reported; sparking discussions surrounding how methods should be conveyed and to what extent research should be contextualised, by drawing on external topical factors such as COVID-19, to maximise usefulness and perspective for future studies. We provide a set of initial guidelines based on our findings, posing key considerations for researchers when reporting on user studies during uncertain and unprecedented times.
Text selection is a common and essential activity during text interaction in all interactive systems. As Augmented Reality (AR) head-mounted displays (HMDs) become more widespread, they will need to provide effective interaction techniques for text selection that ensure users can complete a range of text manipulation tasks (e.g., to highlight, copy, and paste text, send instant messages, and browse the web). As a relatively new platform, text selection in AR is largely unexplored and the suitability of interaction techniques supported by current AR HMDs for text selection tasks is unclear. This research aims to fill this gap and reports on an experiment with 12 participants, which compares the performance and usability (user experience and workload) of four possible techniques (Hand+Pinch, Hand+Dwell, Head+Pinch, and Head+Dwell). Our results suggest that Head+Dwell should be the default selection technique, as it is relatively fast, has the lowest error rate and workload, and has the highestrated user experience and social acceptance.
A key factor encompassing immersive technologies is interaction, and the input methods employed to manipulate an interface. Nevertheless, barriers surrounding interaction and usability continue to hinder the development and potential of immersive technologies, with researchers having difficulties taking applications from laboratory environments to real use cases [5]. Therefore, the proposed PhD research aims to identify how natural input methods are best implemented in regards to commonplace tasks in immersive environments, and uncover how user approaches adapt depending on changes in environmental factors, the context of interaction and the hardware employed.
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