论文标题
头部文本：通过在智能耳机上进行运动感测的头部手势探索免提文字输入
HeadText: Exploring Hands-free Text Entry using Head Gestures by Motion Sensing on a Smart Earpiece
论文作者
论文摘要
我们介绍了HeadText，这是一种智能耳机上的免提技术，可通过运动传感输入文本。用户输入文本仅利用7个头手势进行键选择，单词选择，单词承诺和单词取消任务。头部手势识别是通过在智能耳机上进行运动感测的支持，以捕获头部移动信号和机器学习算法（K-Nearest-neighbor（KNN），具有动态的时间扭曲（DTW）距离测量）。一项10个参与者的用户研究证明，头部文本可以识别出7个头手势，准确率为94.29％。之后，第二次用户研究表明，HeadText可以达到10.65 wpm的最大精度，文本输入的平均精度为9.84 wpm。最后，我们展示了在（a）的免提场景中的潜在应用。运动障碍者的文本输入，（b）。私人文本输入和（c）。社会可接受的文本条目。
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.
