EarGate: Gait-based User Identification with In-ear Microphones

TL;DR

EarGate demonstrates a novel gait-based user identification method using in-ear microphones, achieving high accuracy without disrupting earphone use, suitable for integration into wearable devices.

Contribution

This work introduces EarGate, a new in-ear gait recognition system leveraging occlusion effects for accurate user identification from ear-worn devices.

Findings

Achieves up to 97.26% balanced accuracy

Low false acceptance and rejection rates

Feasible for both stand-alone and cloud-based systems

Abstract

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.