Cross-Domain Adaptation for Biometric Identification Using Photoplethysmogram

Eugene Lee, Annie Ho, Yi Ting Wang, Cheng Han Huang, Chen-Yi Lee

研究成果: Conference contribution同行評審

1 引文 斯高帕斯(Scopus)

摘要

The adoption of biomedical signals such as photoplethysmogram (PPG) and electrocardiogram (ECG) for health parameter estimation on wearable devices is growing in tandem with the increase of attention in mobile healthcare. In our work, we use PPG signals extracted from PPG sensors which are used for biometric identification. A challenge for biometric identification using PPG signal is the variation in domain (placement of sensors, wavelengths, device variation, etc.). In this work, we propose the use of both unsupervised and semi-supervised adversarial learning techniques for cross-domain adaptation. As such algorithm will be deployed on wearable devices, we propose a compact model meeting tight memory footprint limitation. All experiments will be simulated using a public dataset (TROIKA) and our in-house dataset. By introducing a cross-domain adaptation approach across sensors, we observe an accuracy gain of 4.15% on our in-house dataset. The proposed semi-supervised learning technique gives an additional accuracy boost of 2.02%.

原文English
主出版物標題2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Proceedings
發行者Institute of Electrical and Electronics Engineers Inc.
頁面1289-1293
頁數5
ISBN(電子)9781509066315
ISBN(列印)978-1-5090-6632-2
DOIs
出版狀態Published - 五月 2020
事件2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Barcelona, Spain
持續時間: 4 五月 20208 五月 2020

出版系列

名字ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
2020-May
ISSN(列印)1520-6149

Conference

Conference2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020
國家/地區Spain
城市Barcelona
期間4/05/208/05/20

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