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Frank Beruscha

dblp:199/3095 · DBLP profile ↗
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2ranked-venue papers
0as first author
1since 2021 · last 2023
0000-0003-4989-7050ORCID · corroborated

Domains — the database's venue-derived domains; a paper can count in several

Human-computer interaction and ubiquitous computing · 2 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Human-computer interaction and pervasive computing
1 paper
Interaction techniques and input · 77% Usability and user experience research · 23%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Interaction techniques and input
in-vehicle interaction
0.312017
An Evaluation of Input Controls for In-Car Interactions · CHI 2017
Usability and user experience research › safety-critical interaction
driver distraction
0.112017
An Evaluation of Input Controls for In-Car Interactions · CHI 2017

Methods — techniques the papers use, named apart from their topics

on-road driving study · 0.3eye tracking · 0.3
YearPublicationVenuePosition
2023 Experience by Cohabitation: Living in a Smart Home Initiated by Your Partner
Annika Sabrina Schulz, Johanna Müller, Frank Beruscha
INTERACT (3)3
2017 An Evaluation of Input Controls for In-Car Interactions
abstract
The way drivers operate in-car systems is rapidly changing as traditional physical controls, such as buttons and dials, are being replaced by touchscreens and touch-sensing surfaces. This has the potential to increase driver distraction and error as controls may be harder to find and use. This paper presents an in-car, on the road driving study which examined three key types of input controls to investigate their effects: a physical dial, pressure-based input on a touch surface and touch input on a touchscreen. The physical dial and pressure-based input were also evaluated with and without haptic feedback. The study was conducted with users performing a list-based targeting task using the different controls while driving on public roads. Eye-gaze was recorded to measure distraction from the primary task of driving. The results showed that target accuracy was high across all input methods (greater than 94%). Pressure-based targeting was the slowest while directly tapping on the targets was the faster selection method. Pressure-based input also caused the largest number of glances towards to the touchscreen but the duration of each glance was shorter than directly touching the screen. Our study will enable designers to make more appropriate design choices for future in-car interactions.
Alexander Ng, Stephen A. Brewster, Frank Beruscha, Wolfgang Krautter
CHI3