FingerText: Exploring and Optimizing Performance for Wearable, Mobile and One-Handed Typing

DoYoung Lee, Jiwan Kim, Ian Oakley
CHI'21: ACM CHI Conference on Human Factors in Computing Systems
DOI: https://doi.org/10.1145/3411764.3445106

My work: Experiment work / Data analysis / Presenter at CHI 2021

Abstract

Typing on wearables while situationally impaired, such as while walking, is challenging. However, while HCI research on wearable typing is diverse, existing work focuses on stationary scenarios and fine-grained input that will likely perform poorly when users are on-the-go. To address this issue we explore single-handed wearable typing using inter-hand touches between the thumb and fingers, a modality we argue will be robust to the physical disturbances inherent to input while mobile. We first examine the impact of walking on performance of these touches, noting no significant differences in accuracy or speed, then feed our study data into a multi-objective optimization process in order to design keyboard layouts (for both five and ten keys) capable of supporting rapid, accurate, comfortable, and unambiguous typing. A final study tests these layouts against QWERTY baselines and reports performance improvements of up to 10.45% WPM and 39.44% WER when users type while walking.

Full presentation at CHI 2021

Short concept video