EarGate: Gait-based User Identification with In-ear Microphones
This work addresses user identification for earable device users, presenting a novel biometric method but is incremental as it builds on existing gait-based identification techniques.
The paper tackled the problem of user identification using gait sounds captured by in-ear microphones in ear-worn wearables, achieving up to 97.26% Balanced Accuracy with low false acceptance and rejection rates of 3.23% and 2.25%, respectively.
Human gait is a widely used biometric trait for user identification and recognition. Given the wide-spreading, steady diffusion of ear-worn 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.