Precise Embodied Data Selection in Room-scale Visualisations While Retaining View Context
 Shaozhang Dai - Monash University, Melbourne, Australia
 Yi Li - Monash University, Melbourne, Australia
 Barrett Ens - The University of British Columbia (Okanagan Campus), Kelowna, Canada
Lonni Besançon - Linköping University, Norrköping, Sweden
 Tim Dwyer - Monash University, Melbourne, Australia
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 Room: Bayshore II
2024-10-16T13:06:00ZGMT-0600Change your timezone on the schedule page
2024-10-16T13:06:00Z

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Keywords
immersive analytics, focus-and-context, remote interaction, portal, haptic feedback
Abstract
Room-scale immersive data visualisations provide viewers a wide-scale overview of a large dataset, but to interact precisely with individual data points they typically have to navigate to change their point of view. In traditional screen-based visualisations, focus-and-context techniques allow visualisation users to keep a full dataset in view while making detailed selections. Such techniques have been studied extensively on desktop to allow precise selection within large data sets, but they have not been explored in immersive 3D modalities. In this paper we develop a novel immersive focus-and-context technique based on a ``magic portal'' metaphor adapted specifically for data visualisation scenarios. An extendable-hand interaction technique is used to place a portal close to the region of interest.The other end of the portal then opens comfortably within the user's physical reach such that they can reach through to precisely select individual data points.Through a controlled study with 12 participants, we find strong evidence that portals reduce overshoots in selection and overall hand trajectory length, reducing arm and shoulder fatigue compared to ranged interaction without the portal.The portals also enable us to use a robot arm to provide haptic feedback for data within the limited volume of the portal region. In a second study with another 12 participants we found that haptics provided a positive experience (qualitative feedback) but did not significantly reduce fatigue. We demonstrate applications for portal-based selection through two use-case scenarios.