Datenbestand vom 20. Januar 2019

Warenkorb Datenschutzhinweis Dissertationsdruck Dissertationsverlag Institutsreihen     Preisrechner

aktualisiert am 20. Januar 2019

ISBN 9783843931632

Euro 84,00 inkl. 7% MwSt


978-3-8439-3163-2, Reihe Informatik

Philipp Mock
Using Low-Level Sensor Data to Improve Touchscreen Interaction

157 Seiten, Dissertation Eberhard-Karls-Universität Tübingen (2016), Softcover, B5

Zusammenfassung / Abstract

Within the last ten years, interactive surfaces have become a standard of human-computer-interaction. This is true for mobile devices, but also for collaborative applications on interactive tabletops or wall-type displays. Next to its commonly attributed ease of use, the major advantage of the technology is its flexibility. One touchscreen device can be configured for a broad variety of applications by just exchanging the software. For example, most multi-touch devices use a virtual keyboard instead of a dedicated physical variant. This allows to make the most of the limited available screen space, because a virtual keyboard can be hidden when no text input is necessary.

The large flexibility however also implicates some limitations. Despite various optimizations, for some applications, finger input still cannot reach the performance of specialized input devices such as a mouse or the above mentioned physical keyboard. A main reason for this is the lack of haptic feedback for interactive elements on the flat screen.

This thesis illustrates multiple different approaches to improve touchscreen ergonomics and thus make up for the lack of haptic sensation. The idea is to intelligently utilize the high resolution sensor data which modern touchscreens provide. Following this approach can improve existing interfaces and at the same time enable novel forms of interaction. The described research projects can be divided into two categories: first, detection of physical objects on the screen's surface by means of analyzing the sensor data. Second, intelligent software solutions which adapt to a person's individual interaction behavior using adaptive user models.