Author ORCID Identifier
https://orcid.org/0009-0003-5604-8556
Date of Award
2024
Document Type
Thesis (Ph.D.)
Department or Program
Computer Science
First Advisor
Xing-Dong Yang
Abstract
Text input constitutes a critical and frequent interaction with mobile computing devices. However, the experiences on mobile text interfaces remain significantly inferior in terms of speed and accuracy, as input noise, jitteriness, and spatial variability in human input signals result in reduced clarity of the input. This hinders the ability of the text input system to accurately transform them into the intended text. The compact form factors of modern mobile and wearable devices further complicate precise input, as the input space is highly limited. The inadequate clarity of human input on these devices, therefore, necessitates precisely formatted input from users, imposing a significant physical and mental burden on them. In this thesis, we explore two different methodologies to tackle this problem. Firstly, we design and optimize miniature interfaces to enhance the clarity of text input on compact AR/VR wearable devices. We showcase the development of fingertip keyboards, a miniature text interface designed for wearable devices, allowing users to type with micro-finger gestures in an eyes-free manner. In the second methodology, we leverage advanced language modeling to decode ambiguous input paradigms where clarity is further diminished in exchange for reduced user effort. We present non-delimited phrase gestures on smartphones, where users can swipe through all letters of the words in a phrase using a single, continuous gesture. This technique provides more flexibility for multi-word gestural input, as users do not need to lift their fingers after each word. We also present LLM-powered abbreviated writing on tablets, a more ambiguous input form where most characters in a phrase can be skipped when abbreviating. With an intuitive interface and a fine-tuned LLM decoder, users can save significant physical effort while maintaining competitive input performance.
Recommended Citation
Xu, Zheer, "Resolving Low-Clarity Text Input on Mobile Platforms" (2024). Dartmouth College Ph.D Dissertations. 308.
https://digitalcommons.dartmouth.edu/dissertations/308
