Ph.D
Group : Human-Centered Computing

Designing and Combining Interaction Techniques in Large Display Environments

Starts on 01/10/2008
Advisor : BEAUDOUIN-LAFON, Michel

Funding : Digiteo
Affiliation : Université Paris-Saclay
Laboratory : LRI

Defended on 05/12/2012, committee :

Research activities :

Abstract :
Large display environments (LDEs) are interactive physical workspaces featuring one or more static large displays as well as rich interaction capabilities, and are meant to visualize and manipulate very large datasets. Research about mid-air interactions in such environments has emerged over the past decade, and a number of interaction techniques are now available for most elementary tasks such as pointing, navigating and command selection. However these techniques are often designed and evaluated separately on specific platforms and for specific use-cases or operationalizations, which makes it hard to choose, compare and combine them.
In this dissertation I propose a framework and a set of guidelines for analyzing and combining the input and output channels available in LDEs. I analyze the characteristics of LDEs in terms of (1) visual output and how it affects usability and collaboration and (2) input channels and how to combine them in rich sets of mid-air interaction techniques. These analyses lead to four design requirements intended to ensure that a set of interaction techniques can be used (i) at a distance, (ii) together with other interaction techniques and (iii) when collaborating with other users. In accordance with these requirements, I designed and evaluated a set of mid-air interaction techniques for panning and zooming, for invoking commands while pointing and for performing difficult pointing tasks with limited input requirements. For the latter I also developed two methods, one for calibrating high-precision techniques with two levels of precision and one for tuning velocity-based transfer functions. Finally, I introduce two higher-level design considerations for combining interaction techniques in input-constrained environments. Designers should take into account (1) the trade-off between minimizing limb usage and performing actions in parallel that affects overall performance, and (2) the decision and adaptation costs incurred by changing the resolution function of a pointing technique during a pointing task.

CAUSAL LEARNING FOR DIAGNOSTIC SUPPORT


CAUSAL UNCERTAINTY QUANTIFICATION UNDER PARTIAL KNOWLEDGE AND LOW DATA REGIMES


MICRO VISUALIZATIONS: DESIGN AND ANALYSIS OF VISUALIZATIONS FOR SMALL DISPLAY SPACES
The topic of this habilitation is the study of very small data visualizations, micro visualizations, in display contexts that can only dedicate minimal rendering space for data representations. For several years, together with my collaborators, I have been studying human perception, interaction, and analysis with micro visualizations in multiple contexts. In this document I bring together three of my research streams related to micro visualizations: data glyphs, where my joint research focused on studying the perception of small-multiple micro visualizations, word-scale visualizations, where my joint research focused on small visualizations embedded in text-documents, and small mobile data visualizations for smartwatches or fitness trackers. I consider these types of small visualizations together under the umbrella term ``micro visualizations.'' Micro visualizations are useful in multiple visualization contexts and I have been working towards a better understanding of the complexities involved in designing and using micro visualizations. Here, I define the term micro visualization, summarize my own and other past research and design guidelines and outline several design spaces for different types of micro visualizations based on some of the work I was involved in since my PhD.