EarPass : Continuous User Authentication with In-ear PPG

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review

1 Scopus Citations
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Author(s)

Related Research Unit(s)

Detail(s)

Original languageEnglish
Title of host publicationUbiComp/ISWC ’23 Adjunct
Subtitle of host publicationAdjunct Proceedings of the 2023 ACM International Joint Conference on Pervasive and Ubiquitous Computing & the 2023 ACM International Symposium on Wearable Computing
PublisherAssociation for Computing Machinery
Pages327-332
ISBN (print)9798400702006
Publication statusPublished - 2023

Publication series

NameUbiComp/ISWC Adjunct - Adjunct Proceedings of the ACM International Joint Conference on Pervasive and Ubiquitous Computing and the ACM International Symposium on Wearable Computing

Conference

Title2023 ACM International Joint Conference on Pervasive and Ubiquitous Computing & 2023 ACM International Symposium on Wearable Computing (UbiComp/ISWC 2023)
PlaceMexico
CityCancun
Period8 - 12 October 2023

Abstract

In the rapidly expanding universe of smart IoT, earable devices, such as smart headphones and hearing aids, are gaining remarkable popularity. As we anticipate a future where a myriad of sophisticated applications—interaction, communication, health monitoring, and fitness guidance—migrate to wearable devices handling sensitive and private information, the need for a robust, continuous authentication system for these devices becomes more critical than ever. Yet, current earable-based solutions, which rely predominantly on audio signals, are marred by inherent drawbacks such as privacy concerns, high costs, and noise interference. In light of these challenges, we investigate the potential of leveraging photoplethysmogram (PPG) sensors, which monitor key cardiac activities and reflect the uniqueness of an individual's cardiac system, for earable authentication. Our study presents EarPass, an innovative ear-worn system that introduces a novel pipeline for the extraction and classification of in-ear PPG features to enable continuous user authentication. Initially, we preprocess the input in-ear PPG signals to facilitate this feature extraction and classification. Additionally, we present a method for detecting and eliminating motion artifacts (MAs) caused by head motions. Through extensive experiments, we not only demonstrate the effectiveness of our proposed design, but also establish the feasibility of using in-ear PPG for continuous user authentication - a significant stride towards more secure and efficient earable technologies. © 2023 Copyright held by the owner/author(s).

Research Area(s)

  • earable authentication, machine learning, PPG sensing, wearable security

Citation Format(s)

EarPass: Continuous User Authentication with In-ear PPG. / Li, Jiao; Liu, Yang; Li, Zhenjiang et al.
UbiComp/ISWC ’23 Adjunct: Adjunct Proceedings of the 2023 ACM International Joint Conference on Pervasive and Ubiquitous Computing & the 2023 ACM International Symposium on Wearable Computing. Association for Computing Machinery, 2023. p. 327-332 (UbiComp/ISWC Adjunct - Adjunct Proceedings of the ACM International Joint Conference on Pervasive and Ubiquitous Computing and the ACM International Symposium on Wearable Computing).

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review