Authors:
Tadeja, Slawomir
Abstract:
Recent advancements and technological breakthroughs in the development of so-called immersive interfaces, such as augmented (AR), mixed (MR), and virtual reality (VR), coupled with the growing mass-market adoption of such devices has started to attract attention from academia and industry alike. Out of these technologies, VR offers the most mature option in terms of both hardware and software, as well as the best available range of different off-the-shelf offerings. VR is a term interchangeably used to denote both head-mounted displays (HMDs) a (...)
Recent advancements and technological breakthroughs in the development of so-called immersive interfaces, such as augmented (AR), mixed (MR), and virtual reality (VR), coupled with the growing mass-market adoption of such devices has started to attract attention from academia and industry alike. Out of these technologies, VR offers the most mature option in terms of both hardware and software, as well as the best available range of different off-the-shelf offerings. VR is a term interchangeably used to denote both head-mounted displays (HMDs) and fully immersive, bespoke 3D environments which these devices transport their users to. With modern devices, developers can leverage a range of different interaction modalities, including visual, audio, and even haptic feedback, in the creation of these virtual worlds. With such a rich interaction space it is thus natural to think of VR as a well-suited environment for interactive visualisation and analytical reasoning of complex multidimensional data. Research in \textit{visual analytics} (VA) combines these two themes, spanning the last one and a half decades, and has revealed a number of research findings. This includes a range of new advanced and effective visualisation and analysis tools for even more complex, more noisy and larger data sets. Furthermore, the extension of this research and the use of immersive interfaces to facilitate visual analytics has spun-off a new field of research: \textit{immersive analytics} (IA). Immersive analytics leverages the potential bestowed by immersive interfaces to aid the user in swift and effective data analysis. Some of the most promising application domains of such immersive interfaces in the industry are various branches of engineering, including aerospace design and in civil engineering. The range of potential applications is vast and growing as new stakeholders are adopting these immersive tools. However, the use of these technologies brings its own challenges. One such difficulty is the design of appropriate interaction techniques. There is no optimal choice, instead such a choice varies depending on available hardware, the user���s prior experience, their task at hand, and the nature of the dataset. To this end, my PhD work has focused on designing and analysing various interactive, VR-based immersive systems for engineering visual analytics. One of the key elements of such an immersive system is the selection of an adequate interaction method. In a series of both qualitative and quantitative studies, I have explored the potential of various interaction techniques that can be used to support the user in swift and effective data analysis. Here, I have investigated the feasibility of using techniques such as hand-held controllers, gaze-tracking and hand-tracking input methods used solo or in combination in various challenging use cases and scenarios. For instance, I developed and verified the usability and effectiveness of the AeroVR system for aerospace design in VR. This research has allowed me to trim the very large design space of such systems that have been not sufficiently explored thus far. Moreover, building on top of this work, I have designed, developed, and tested a system for digital twin assessment in aerospace that coupled gaze-tracking and hand-tracking, achieved via an additional sensor attached to the front of the VR headset, with no need for the user to hold a controller. The analysis of the results obtained from a qualitative study with domain experts allowed me to distill and propose design implications when developing similar systems. Furthermore, I worked towards designing an effective VR-based visualisation of complex, multidimensional abstract datasets. Here, I developed and evaluated the immersive version of the well-known Parallel Coordinates Plots (IPCP) visualisation technique. The results of the series of qualitative user studies allowed me to obtain a list of design suggestions for IPCP, as well as provide tentative evidence that the IPCP can be an effective tool for multidimensional data analysis. Lastly, I also worked on the design, development, and verification of the system allowing its users to capture information in the context of conducting engineering surveys in VR. Furthermore, conducting a meaningful evaluation of immersive analytics interfaces remains an open problem. It is difficult and often not feasible to use traditional A/B comparisons in controlled experiments as the aim of immersive analytics is to provide its users with new insights into their data rather than focusing on more quantifying factors. To this end, I developed a generative process for synthesising clustered datasets for VR analytics experiments that can be used in the process of interface evaluation. I further validated this approach by designing and carrying out two user studies. The statistical analysis of the gathered data revealed that this generative process for synthesising clustered datasets did indeed result in datasets that can be used in experiments without the datasets themselves being the dominant contributor of the variability between conditions.
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