Teeth gestures become an alternative input modality for different situations and accessibility purposes. In this paper, we present TeethTap, a novel eyes-free and hands-free input technique, which can recognize up to 13 discrete teeth tapping gestures. TeethTap adopts a wearable 3D printed earpiece with an IMU sensor and a contact microphone behind both ears, which works in tandem to detect jaw movement and sound data, respectively. TeethTap uses a support vector machine to classify gestures from noise by fusing acoustic and motion data, and implements K-Nearest-Neighbor (KNN) with a Dynamic Time Warping (DTW) distance measurement using motion data for gesture classification. A user study with 11 participants demonstrated that TeethTap could recognize 13 gestures with a real-time classification accuracy of 90.9% in a laboratory environment. We further uncovered the accuracy differences on different teeth gestures when having sensors on single vs. both sides. Moreover, we explored the activation gesture under real-world environments, including eating, speaking, walking and jumping. Based on our findings, we further discussed potential applications and practical challenges of integrating TeethTap into future devices.

中文翻译：

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TeethTap：使用听筒上的运动和声音感应识别离散的牙齿手势

牙齿手势成为针对不同情况和可访问性目的的替代输入方式。在本文中，我们介绍了TeethTap，这是一种新颖的免眼睛和免提输入技术，可以识别多达13种离散的牙齿敲击手势。TeethTap采用可穿戴3D打印耳机，在两只耳朵后方带有IMU传感器和接触式麦克风，可协同工作以分别检测下颌运动和声音数据。TeethTap使用支持向量机通过融合声学和运动数据来从噪声中对手势进行分类，并使用运动数据对手势进行分类，并通过动态时间规整（DTW）距离测量实现K最近邻（KNN）。一项包含11名参与者的用户研究表明，TeethTap在实验室环境中可以识别13个手势，实时分类准确度为90.9％。我们进一步发现了在单侧和两侧都有传感器时，不同牙齿手势的精度差异。此外，我们探索了在现实环境中的激活手势，包括吃饭，说话，走路和跳跃。根据我们的发现，我们进一步讨论了将TeethTap集成到未来设备中的潜在应用和实际挑战。