TeethTap：使用耳机上的运动和声学传感来识别离散的牙齿姿势
TeethTap: Recognizing Discrete Teeth Gestures Using Motion and Acoustic Sensing on an Earpiece
摘要：
牙齿手势成为用于不同情况和可访问性目的的替代输入模态。在本文中，我们提出了TeethTap，一种新的眼睛和免提输入技术，它可以识别多达13个离散的牙齿敲击手势。TeethTap采用了可穿戴的3D打印耳机，在两个耳朵后面都有一个IMU传感器和一个接触式麦克风，它们协同工作，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将手势从噪声中分类，并使用运动数据实现具有动态时间规整（DTW）距离测量的K最近邻（KNN）用于手势分类。一项有11名参与者的用户研究表明，TeethTap可以识别13种手势，在实验室环境中的实时分类准确率为90.9%。我们进一步揭示了在单侧和双侧使用传感器时不同牙齿姿势的准确性差异。此外，我们还探索了在真实世界环境下的激活手势，包括吃，说话，走路和跳跃。基于我们的研究结果，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。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
