  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.
