HeadTrack: Real-Time Human–Computer Interaction via Wireless Earphones

Abstract

Accurate head movement tracking is crucial for virtual reality and Metaverse in ubiquitous human-computer interaction (HCI) applications. Existing works for head tracking with wearable VR kits and wireless signals require expensive devices and heavy algorithmic processing. To resolve this problem, we propose HeadTrack, a low-cost, high-precision head motion tracking system that uses commercially available wireless earphones to capture the user’s head motion in real-time. Head-Track uses smartphones as ’sound anchors’ and emits inaudible chirps picked up by the user’s wireless earphones. By measuring the time-of-flight of these signals from the smartphone to each microphone on the earphone, we can deduce the user’s face orientation and distance relative to the smartphone, enabling us to accurately track the user’s head movement. To realize HeadTrack, we use the cross-correlation method to optimize the Frequency Modulated Continuous Wave (FMCW) based acoustic ranging method, which solves the problem of insufficient wireless earphone bandwidth. Moreover, we solve the problems of asynchronous startup time between devices and the existence of sampling frequency offset. We conduct excessive experiments in real scenarios, and the results prove that HeadTrack can continuously track the direction of the user’s head, with an average error under 6.3° in pitch and 4.9° in yaw.