  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.
