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

TeethTap: Recognizing Discrete Teeth Gestures Using Motion and Acoustic Sensing on an Earpiece