EarPass: Continuous User Authentication with In-ear PPG

Li，Jiao1; Liu，Yang2; Li，Zhenjiang3; Zhang，Jin4

10.1145/3594739.3610670

2023-10-08

UbiComp/ISWC 2023 Adjunct - Adjunct Proceedings of the 2023 ACM International Joint Conference on Pervasive and Ubiquitous Computing and the 2023 ACM International Symposium on Wearable Computing

327-332

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 earable 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.

earable authentication machine learning PPG sensing wearable security

第一

英语

EI

20234515008302

Audio acoustics ; Audition ; Authentication ; Classification (of information) ; Extraction ; Hearing aids ; Wearable technology

Ergonomics and Human Factors Engineering:461.4 ; Rehabilitation Engineering and Assistive Technology:461.5 ; Information Theory and Signal Processing:716.1 ; Computer Software, Data Handling and Applications:723 ; Artificial Intelligence:723.4 ; Acoustic Waves:751.1 ; Acoustic Devices:752.1 ; Chemical Operations:802.3 ; Information Sources and Analysis:903.1 ; Acoustical Instruments:941.1

2-s2.0-85175488239

Scopus

1.Department of Computer Science and Engineering,Southern University of Science and Technology,Shen Zhen,China
2.Department of Computer Science and Technology,University of Cambridge,Cambridge,United Kingdom
3.Department of Computer Science,City University of Hong Kong,Hong Kong
4.Research Institute of Trustworthy Autonomous Systems,Department of Computer Science and Engineering,Southern University of Science and Technology,Shen Zhen,China

Li，Jiao,Liu，Yang,Li，Zhenjiang,et al. EarPass: Continuous User Authentication with In-ear PPG[C],2023:327-332.