2022/05/20 by Songlin Xu, Guanjie Wang, Xu, Songlin +11
Computer Science · Neuroscience · Psychology · #FOS: Computer and information sciences #Hand Gesture Recognition Systems #Hearing Impairment and Communication #Human-Computer Interaction (cs.HC) #Machine Learning (cs.LG) #Tactile and Sensory Interactions
paper · pdf · doi:10.48550/arxiv.2205.09978
openalex publication_date 2022/05/20 · openalex created_date 2022/05/26 · openalex updated_date 2026/07/28
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.