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arXiv · 2205.09978

HeadText: Exploring Hands-free Text Entry using Head Gestures by Motion Sensing on a Smart Earpiece

Songlin Xu·Guanjie Wang·Ziyuan Fang·Guangwei Zhang·Guangzhu Shang·Rongde Lu·Liqun He
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Abstract

We present HeadText, a hands-free technique on a smart earpiece for text entry by motion sensing. Users input text utilizing only 7 head gestures for key selection, word selection, word commitment and word cancelling tasks. Head gesture recognition is supported by motion sensing on a smart earpiece to capture head moving signals and machine learning algorithms (K-Nearest-Neighbor (KNN) with a Dynamic Time Warping (DTW) distance measurement). A 10-participant user study proved that HeadText could recognize 7 head gestures at an accuracy of 94.29%. After that, the second user study presented that HeadText could achieve a maximum accuracy of 10.65 WPM and an average accuracy of 9.84 WPM for text entry. Finally, we demonstrate potential applications of HeadText in hands-free scenarios for (a). text entry of people with motor impairments, (b). private text entry, and (c). socially acceptable text entry.

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Songlin Xu, Guanjie Wang, Ziyuan Fang, Guangwei Zhang, Guangzhu Shang, Rongde Lu, Liqun He. 2022-05-20. HeadText: Exploring Hands-free Text Entry using Head Gestures by Motion Sensing on a Smart Earpiece. https://arxiv.org/abs/2205.09978

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