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

TL;DR

HeadText introduces a novel hands-free text entry method using head gestures detected by motion sensors on a smart earpiece, achieving high gesture recognition accuracy and practical text entry speeds through machine learning.

Contribution

This work presents a new head gesture-based text entry system on a smart earpiece, combining motion sensing and machine learning for accurate recognition and practical use.

Findings

Gesture recognition accuracy of 94.29%

Maximum text entry speed of 10.65 WPM

Average text entry speed of 9.84 WPM

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.