LeakyFeeder: In-Air Gesture Control Through Leaky Acoustic Waves

Yongjie Yang, Tao Chen, Zhenlin An, Shirui Cao, Xiaoran Fan, Longfei Shangguan

Abstract: We present LeakyFeeder, a mobile application that explores the acoustic signals leaked from headphones to reconstruct gesture motions around the ear for fine-grained gesture control. To achieve this goal, LeakyFeeder repurposes the speaker and a single feedforward microphone on active noise cancellation (ANC) headphones as a SONAR system, using inaudible frequency-modulated continuous-wave (FMCW) signals to track gesture reflections for accurate sensing. Since this single-receiver SONAR system is unable to differentiate reflection angles and further disentangle signal reflections from different gesture parts, we draw on principles of multi-modal learning to frame gesture motion reconstruction as a multi-modal translation task and propose a deep learning-based approach to fill the information gap between low-dimensional FMCW ranging readings and high-dimensional 3D hand movements. We implement LeakyFeeder on a pair of Google Pixel Buds and conduct experiments to examine the efficacy and robustness of LeakyFeeder in various conditions. Experiments based on six gesture types inspired by Apple Vision Pro demonstrate that LeakyFeeder achieves a PCK performance of 89% at 3cm across ten users, with an average MPJPE and MPJRPE error of 2.71cm and 1.88cm, respectively.

External IDs:dblp:conf/sensys/00080ACFS25