HeadFi

Xiaoran Fan, Longfei Shangguan, Siddharth Rupavatharam, Yanyong Zhang, Jie Xiong, Yunfei Ma, Richard Howard
2021 Proceedings of the 27th Annual International Conference on Mobile Computing and Networking  
Headphones continue to become more intelligent as new functions (e.g., touch-based gesture control) appear. These functions usually rely on auxiliary sensors (e.g., accelerometer and gyroscope) that are available in smart headphones. However, for those headphones that do not have such sensors, supporting these functions becomes a daunting task. This paper presents HeadFi, a new design paradigm for bringing intelligence to headphones. Instead of adding auxiliary sensors into headphones, HeadFi
more » ... rns the pair of drivers that are readily available inside all headphones into a versatile sensor to enable new applications spanning across mobile health, user-interface, and context-awareness. HeadFi works as a plug-in peripheral connecting the headphones and the pairing device (e.g., a smartphone). The simplicity (can be as simple as only two resistors) and small form factor of this design lend itself to be embedded into the pairing device as an integrated circuit. We envision HeadFi can serve as a vital supplementary solution to existing smart headphone design by directly transforming large amounts of existing "dumb" headphones into intelligent ones. We prototype HeadFi on PCB and conduct extensive experiments with 53 volunteers using 54 pairs of non-smart headphones under the institutional review board (IRB) protocols. The results show that HeadFi can achieve 97.2%-99.5% accuracy on user identification, 96.8%-99.2% accuracy on heart rate monitoring, and 97.7%-99.3% accuracy on gesture recognition. CCS CONCEPTS • Human-centered computing → Mobile computing; • Hardware → Emerging interfaces.
doi:10.1145/3447993.3448624 fatcat:qv5vhsnoefgajoaiogrckgw36m