EarGate: Gait-based user identification with in-ear microphones

• Main Authors: FERLINI, Andrea, MA, Dong, MASCOLO, Cecilia
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2021
• Subjects: Human-centered computing; Ubiquitous and mobile computing systems and tools; Artificial Intelligence and Robotics
• Online Access: https://ink.library.smu.edu.sg/sis_research/6990; https://ink.library.smu.edu.sg/context/sis_research/article/7993/viewcontent/3447993.3483240.pdf
• Institution: Singapore Management University

Human gait is a widely used biometric trait for user identification and recognition. Given the wide-spreading, steady diffusion of earworn wearables (Earables) as the new frontier of wearable devices, we investigate the feasibility of earable-based gait identification. Specifically, we look at gait-based identification from the sounds induced by walking and propagated through the musculoskeletal system in the body. Our system, EarGate, leverages an in-ear facing microphone which exploits the earable’s occlusion effect to reliably detect the user’s gait from inside the ear canal, without impairing the general usage of earphones. With data collected from 31 subjects, we show that EarGate achieves up to 97.26% Balanced Accuracy (BAC) with very low False Acceptance Rate (FAR) and False Rejection Rate (FRR) of 3.23% and 2.25%, respectively. Further, our measurement of power consumption and latency investigates how this gait identification model could live both as a stand-alone or cloud-coupled earable system.