Continuous Authentication of Wearable Device Users from Heart Rate, Gait, and Breathing Data
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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 ± 0.06, F_1 score 0.93 ± 0.03, and false positive rate (FPR) below 0.08 at 50% level of confidence, which shows the promise of this work.
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