Challenges in mobile multi-device ecosystems

Grubert, Jens; Kranz, Matthias; Quigley, Aaron

doi:10.1186/s13678-016-0007-y

Cited by 43 publications

(30 citation statements)

References 88 publications

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“…Existing cross-device research has highlighted challenges in adapting DUIs for collaborative environments in real-time, including problems in testing multi-device experiences [3], user interface widget adoption [11], functional UI coordination [46], component role allocation [50], spatial awareness [42] and changes in related parallel use [19]. Addressing these challenges has given rise to the approach taken here.…”

Section: Cross Device User Interfacesmentioning

confidence: 99%

AdaM

Park

Gebhardt

Rädle

et al. 2018

Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems

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Optimization & Real-time Adaptation Figure 1. Given a graphical user interface (left), AdaM automatically decides which UI elements should be displayed on each device in real-time. Our optimization is designed for multi-user scenarios and considers user roles and preferences, device access restrictions and device characteristics. ABSTRACTDeveloping cross-device multi-user interfaces (UIs) is a challenging problem. There are numerous ways in which content and interactivity can be distributed. However, good solutions must consider multiple users, their roles, their preferences and access rights, as well as device capabilities. Manual and rule-based solutions are tedious to create and do not scale to larger problems nor do they adapt to dynamic changes, such as users leaving or joining an activity. In this paper, we cast the problem of UI distribution as an assignment problem and propose to solve it using combinatorial optimization. We present a mixed integer programming formulation which allows realtime applications in dynamically changing collaborative settings. It optimizes the allocation of UI elements based on device capabilities, user roles, preferences, and access rights. We present a proof-of-concept designer-in-the-loop tool, allowing for quick solution exploration. Finally, we compare our approach to traditional paper prototyping in a lab study.

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Section: Cross Device User Interfacesmentioning

confidence: 99%

AdaM

Park

Gebhardt

Rädle

et al. 2018

Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems

Self Cite

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“…Additionally, they defined three levels of decisions for choosing the device in a particular situation [24]. Despite this existing work, Grubert et al argue that HCI has just begun to explore this area and that the understanding of the design of multidevice applications and implications from multi-device use in social settings are still "not well researched in the community" [14]. Grubert et al consider them as fundamental challenges in HCI and prompt for opportunities to further investigate micro-mobility for co-located interactions.…”

Section: Multi-device and Cross-device Interactionmentioning

confidence: 99%

Is Two Enough?

Plank

Jetter

Rädle

et al. 2017

Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems

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A sizable part of HCI research on cross-device interaction is driven by the vision of users conducting complex knowledge work seamlessly across multiple mobile devices. This is based on the Weiserian assumption that people will be inclined to distribute their work across multiple "pads" if such are available. We observed that this is not the reality today, even when devices were in abundance. We present a study with 24 participants in 12 dyads completing a collaborative visualization task with up to six tablets. They could choose between three different visualization types to answer questions about economic data. Tasks were designed to afford simultaneous use of tablets, either with linked or independent views. We found that users typically utilized only one tablet per user. A quantitative and qualitative analysis revealed a "legacy bias" that introduced barriers for using more tablets and reduced the overall benefit of multi-device visualization.

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“…It allows the developer to have solid guidelines for the smallest features (eg, text size) that will be visible given the hardware. An analogy is responsive design, where designers use different size and placement rules when targeting interfaces across desktop, tablet, and phone screens . The experiment on legibility specifies the minimum character height in pixels that is needed for the text to be comfortably legible.…”

Section: Impactmentioning

confidence: 99%

“…An analogy is responsive design, where designers use different size and placement rules when targeting interfaces across desktop, tablet, and phone screens. 43,44 The experiment on legibility specifies the minimum character height in pixels that is needed for the text to be comfortably legible. Eventually, the technology may advance to the point where making the top tier system is possible and then detail size is limited by the visual system.…”

Section: Impactmentioning

confidence: 99%

Aligning content rendering resolution and feature size with display capability in near‐eye display systems

et al. 2019

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In order to create a satisfying experience with near‐eye displays, the content must be adapted to be legible on the display used. New displays are using subpixel arrangements that can limit the minimum resolvable feature size to something greater than with the conventional RGB stripe arrangement. We conducted an experiment to measure the minimum and preferred size of text in two virtual reality (VR) displays systems and find that the text size is display limited. We then measure several displays with different pixel arrangements to determine whether the subpixel arrangement could impact legibility. We propose several Fourier metrics that can be computed from the measured data to categorize the capability of the display and describe a framework for selecting the appropriate content from a set of discrete tiers.

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Challenges in mobile multi-device ecosystems

Cited by 43 publications

References 88 publications

AdaM

AdaM

Is Two Enough?

Aligning content rendering resolution and feature size with display capability in near‐eye display systems

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