Going beyond the surface

Spindler, Martin; Martsch, Marcel; Dachselt, Raimund

doi:10.1145/2207676.2208583

Cited by 40 publications

(4 citation statements)

References 19 publications

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“…No compensation was provided. To answer the research questions presented above, the design of the task was based on a basic multi-layer information space alongside the participants' line of sight (see figure 3.3) consisting of randomized integer numbers (each layer displayed one number) similar to [Spindler, Martsch, and Dachselt, 2012]. The conditions varied the number of layers in the available interaction space (which directly correlates with the layers' thickness) as an independent variable with the values of 12, 24, 36, 48, 60 and 72.…”

Section: Methodsmentioning

confidence: 99%

Cloudbits

Müller

Günther

Nejad

et al. 2017

Proceedings of the 5th Symposium on Spatial User Interaction

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The contributions of this thesis are situated in the field of Human-Computer Interactions (HCIs), a field "concerned with the design, evaluation and implementation of interactive computing systems for human use and with the study of major phenomena surrounding them" [Hewett et al., 2014]. Each of the main contributions presented in this thesis is substantiated in one or more observational studies or controlled experiments. All of the studies and experiments were designed, conducted, and analyzed according to widely accepted standards of the HCI community [Lazar, J. H. Feng, and Hochheiser, 2010]. This section describes the general approach to the design and analysis, which applies to all studies presented. Any exceptions are mentioned and justified in the respective chapters. Study DesignIn this thesis, two general types of study designs are applied, controlled experiments and observational studies.

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Section: Methodsmentioning

confidence: 99%

Cloudbits

Müller

Günther

Nejad

et al. 2017

Proceedings of the 5th Symposium on Spatial User Interaction

View full text Add to dashboard Cite

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“…Therefore, it makes sense to constrain movement interactions, and the resolution with which positions are tracked can be reduced. Spindler et al [105] observed that people can reach up to 44 different vertical positions reasonably well. Moreover, they can consider movements with respect to 2D reference planes, usually the horizontal and vertical planes in front of them.…”

Section: Spatial Interaction Using Motion Sensorsmentioning

confidence: 98%

Mobile Data Visualization

Lee¹,

Dachselt²,

Isenberg³

et al. 2021

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Chapter 1 | An Introduction to Mobile Data VisualizationIn this chapter, several characteristics are proposed that help us identify and describe the scope of mobile data visualization, which stretches beyond an intuitive understanding of the term. The focus lies on those characteristics that, particularly in their extremes, differentiate mobile data visualization from other forms of data visualization. These characteristics give rise to dimensions of a design space for mobile data visualization, against which instances may be classified and positioned. The chapter discusses a number of examples to illustrate how the design space makes it possible to describe and compare mobile visualizations. Chapter 2 | Responsive Visualization Design for Mobile DevicesThe term responsive describes aspects of a visualization that automatically adapt to changes in, for example, device characteristics, environment, usage context, or data. The chapter discusses aspects of responsive mobile data visualization, and summarizes the types of change that a visualization must respond to and how they can be sensed on mobile devices. Ten responsive visualization design strategies are reviewed, including adaptations of scale, layout, and visual encoding, as well as attentional cues and specific interactions. The chapter concludes with future research directions pertaining to responsive visualization design for mobile devices and beyond. Chapter 3 | Interacting with Visualization on Mobile DevicesThis chapter characterizes how interacting with data visualization using mobile devices, specifically a phone or tablet, differs from analogous experiences using a PC. An overview of the topic is provided, which is organized by interaction modality, beginning with touch interaction and subsequently discussing instances of voice interaction and spatial interaction. As an outlook, the chapter envisions compelling opportunities for future mobile data visualization research, inspired by recent developments in the field of mobile human-computer interaction. Chapter 4 | 3D Mobile Data VisualizationThis chapter surveys the space of three-dimensional (3D) mobile visualizations, that is, 3D abstract or spatial data on mobile 2D displays or 3D head-mounted displays. As a playful "case study" a scenario from the film Aliens is used, where the marines are overrun by aliens in the ceiling, as their mobile visualization device fails to show them the height dimension of the space around them. This example is used to illustrate how different mobile and 3D interaction techniques could have prevented the misunderstanding, using both hypothetical descriptions of the improved movie action and a scientific discussion of these scenarios and their implications. Preface xix Chapter 9 | Reflections on Ubiquitous VisualizationThis chapter provides an outlook into the future of mobile visualization, where we anticipate to see a growing emphasis on ubiquitous visualization. An overview of research in ubiquitous visualization is synthesized from the interviews with four renowned...

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“…Another important stream of research are tools for making devices in interactive spaces spatially-aware [54,73,74,93] and enabling proxemic interactions [56,57]. This requires combinations of sensor hardware and processing software, based on different sensing technologies such as depth sensing cameras [94], radio triangulation [54,57], ultra-sound [47], IR reflectance of mobile screens [73], polarized light [72], or marker-based tracking with IR cameras [56,81,93]. The development, setup, calibration, and maintenance of these tracking systems is typically time consuming, especially since the vast majority is based on experimental prototypes from research labs and only few are available as open source (e.g., [56,73,94]).…”

Section: Tools For Proxemics and Spatially-aware Interactionsmentioning

confidence: 99%