Headar

Abstract

Nod and shake of one's head are intuitive and universal gestures in communication. As smartwatches become increasingly intelligent through advances in user activity sensing technologies, many use scenarios of smartwatches demand quick responses from users in confirmation dialogs, to accept or dismiss proposed actions. Such proposed actions include making emergency calls, taking service recommendations, and starting or stopping exercise timers. Head gestures in these scenarios could be preferable to touch interactions for being hands-free and easy to perform. We propose Headar to recognize these gestures on smartwatches using wearable millimeter wave sensing. We first surveyed head gestures to understand how they are performed in conversational settings. We then investigated positions and orientations to which users raise their smartwatches. Insights from these studies guided the implementation of Headar. Additionally, we conducted modeling and simulation to verify our sensing principle. We developed a real-time sensing and inference pipeline using contemporary deep learning techniques, and proved the feasibility of our proposed approach with a user study (n=15) and a live test (n=8). Our evaluation yielded an average accuracy of 84.0% in the user study across 9 classes including nod and shake as well as seven other signals -- still, speech, touch interaction, and four non-gestural head motions (i.e., head up, left, right, and down). Furthermore, we obtained an accuracy of 72.6% in the live test which reveals rich insights into the performance of our approach in various realistic conditions.