Paper Info
Title
Platform for Studying Self-Repairing Auto-Corrections in Mobile Text Entry based on Brain Activity, Gaze, and Context
Abstract
Auto-correction is a standard feature of mobile text entry. While the performance of state-of-the-art auto-correct methods is usually relatively high, any errors that occur are cumbersome to repair, interrupt the flow of text entry, and challenge the user's agency over the process. In this paper, we describe a system that aims to automatically identify and repair auto-correction errors. This system comprises a multi-modal classifier for detecting auto-correction errors from brain activity, eye gaze, and context information, as well as a strategy to repair such errors by replacing the erroneous correction or suggesting alternatives. We integrated both parts in a generic Android component and thus present a research platform for studying self-repairing end-to-end systems. To demonstrate its feasibility, we performed a user study to evaluate the classification performance and usability of our approach.
Year
DOI
Venue
2020
10.1145/3313831.3376815
CHI '20: CHI Conference on Human Factors in Computing Systems Honolulu HI USA April, 2020
DocType
ISBN
Citations 
Conference
978-1-4503-6708-0
1
PageRank 
References 
Authors
0.35
18
4
Name
Order
Citations
PageRank
Felix Putze120529.73
Tilman Ihrig210.35
T. Schultz32423252.72
Wolfgang Stuerzlinger41143101.60