Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

10-2021

Abstract

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.

Keywords

Human-centered computing, Ubiquitous and mobile computing systems and tools

Discipline

Artificial Intelligence and Robotics

Research Areas

Intelligent Systems and Optimization

Publication

Proceedings of the 27th Annual International Conference on Mobile Computing and Networking, New Orleans, 2021 October 25-29,

First Page

337

Last Page

349

ISBN

9781450383424

Identifier

10.1145/3447993.3483240

Publisher

ACM

City or Country

New Orleans, USA

Additional URL

https://dl.acm.org/doi/pdf/10.1145/3447993.3483240

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