TeethFa: Real-Time, Hand-Free Teeth Gestures Interaction Using Fabric Sensors

The interaction mode of smart eyewear has garnered significant research attention. Most smart eyewear relies on touchpads for user interaction. This article identifies a drawback arising from the use of touchpads, which can be obtrusive and unfriendly to users. In this article, we propose TeethFa, a novel fabric sensor-based system for recognizing teeth gestures. TeethFa serves as a hands-free interaction method for smart eyewear. TeethFa utilizes fabric sensors embedded in the glasses frame to capture pressure changes induced by facial muscle movements linked to teeth movements. This enables the identification of subtle teeth gestures. To detect teeth gestures, TeethFa designs a novel template-based signal segmentation method to determine the boundary of teeth gestures from fabric sensors, even in the presence of motion interference. To improve TeethFa's generalization, we employ a meta-learning technique based on generalization adjustment to extend the model to new users. We conduct extensive experiments to assess TeethFa's performance on 30 volunteers. The results demonstrate that our system accurately identifies five different teeth gestures with an average accuracy of 93.57%, and even for new users, the accuracy can reach 89.58%. TeethFa shows promise in offering a new interaction paradigm for smart eyewear in the future.