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We present MAF, a novel acoustic sensing approach that leverages the commodity hardware in bone conduction earphones for hand-to-face gesture interactions. Briefly, by shining audio signals with bone conduction earphones, we observe that these signals not only propagate along the surface of the human face but also dissipate into the air, creating an acoustic field that envelops the individual's head. We conduct benchmark studies to understand how various hand-to-face gestures and human factors influence this acoustic field. Building on the insights gained from these initial studies, we then propose a deep neural network combined with signal preprocessing techniques. This combination empowers MAF to effectively detect, segment, and subsequently recognize a variety of hand-to-face gestures, whether in close contact with the face or above it. Our comprehensive evaluation based on 22 participants demonstrates that MAF achieves an average gesture recognition accuracy of 92% across ten different gestures tailored to users' preferences.
Yang et al. (Sat,) studied this question.