Continuous Authentication of Wearable Device Users from Heart Rate, Gait, and Breathing Data

TL;DR

This paper proposes a lightweight, implicit wearable device authentication method using heart rate, gait, and breathing data, achieving high accuracy with simple models, addressing privacy and usability concerns.

Contribution

It introduces a novel soft-biometric-based authentication system for wearables utilizing heart rate, gait, and breathing signals, with effective lightweight classification models.

Findings

Achieves 93% accuracy with k-NN model

False positive rate below 8%

Effective soft-biometric authentication for wearables

Abstract

The security of private information is becoming the bedrock of an increasingly digitized society. While the users are flooded with passwords and PINs, these gold-standard explicit authentications are becoming less popular and valuable. Recent biometric-based authentication methods, such as facial or finger recognition, are getting popular due to their higher accuracy. However, these hard-biometric-based systems require dedicated devices with powerful sensors and authentication models, which are often limited to most of the market wearables. Still, market wearables are collecting various private information of a user and are becoming an integral part of life: accessing cars, bank accounts, etc. Therefore, time demands a burden-free implicit authentication mechanism for wearables using the less-informative soft-biometric data that are easily obtainable from modern market wearables. In this work, we present a context-dependent soft-biometric-based authentication system for wearables devices using heart rate, gait, and breathing audio signals. From our detailed analysis using the "leave-one-out" validation, we find that a lighter $k$-Nearest Neighbor ($k$-NN) model with $k = 2$ can obtain an average accuracy of $0.93 \pm 0.06$, $F_1$ score $0.93 \pm 0.03$, and {\em false positive rate} (FPR) below $0.08$ at 50\% level of confidence, which shows the promise of this work.