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

arXiv: https://arxiv.org/abs/2108.12305 · HTML: https://mlsanity.com/paper/2108.12305/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Aug 2021.

## Built on by (most cited first, 4 of 4)

- [Large Model for Small Data: Foundation Model for Cross-Modal RF Human Activity Recognition](https://mlsanity.com/paper/2410.19766/) (2024)
- [OmniBuds: A Sensory Earable Platform for Advanced Bio-Sensing and On-Device Machine Learning](https://mlsanity.com/paper/2410.04775/) (2024)
- [Time-bound Contextual Bio-ID Generation for Minimalist Wearables](https://mlsanity.com/paper/2403.00889/) (2024)
- [AccLock: Unlocking Identity with Heartbeat Using In-Ear Accelerometers](https://mlsanity.com/paper/2605.11901/) (2026)

## Abstract (arXiv)

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.