Continuous User Authentication using IoT Wearable Sensors

TL;DR

This paper introduces a novel continuous user authentication method using ECG signals from a wearable chest strap, employing advanced signal processing and machine learning to verify user identity with high accuracy.

Contribution

It presents the first continuous authentication approach using a genuine wearable device with a new algorithm combining QRS detection, DCT, and SVM for user verification.

Findings

Zero intrusion attempts were successful on 33 subjects.

The method achieved high accuracy in continuous authentication.

It demonstrates feasibility of wearable ECG-based user verification.

Abstract

Over the past several years, the electrocardiogram (ECG) has been investigated for its uniqueness and potential to discriminate between individuals. This paper discusses how this discriminatory information can help in continuous user authentication by a wearable chest strap which uses dry electrodes to obtain a single lead ECG signal. To the best of the authors' knowledge, this is the first such work which deals with continuous authentication using a genuine wearable device as most prior works have either used medical equipment employing gel electrodes to obtain an ECG signal or have obtained an ECG signal through electrode positions that would not be feasible using a wearable device. Prior works have also mainly dealt with using the ECG signal for identification rather than verification, or dealt with using the ECG signal for discrete authentication. This paper presents a novel algorithm which uses QRS detection, weighted averaging, Discrete Cosine Transform (DCT), and a Support Vector Machine (SVM) classifier to determine whether the wearer of the device should be positively verified or not. Zero intrusion attempts were successful when tested on a database consisting of 33 subjects.