DLPrPPG: Development and Design of Deep Learning Platform for Remote Photoplethysmography

Bo Rong Yan, Edwin Arkel Rios, Wen Hsien Lee, Bo Cheng Lai

研究成果: Conference contribution同行評審

摘要

This paper presents a comprehensive neural network-based development platform for remote photoplethysmography (rPPG). rPPG is a growing and popular research area, especially with the introduction of deep learning methods that can significantly improve its signal quality and heart rate prediction reliability. However, there are still many problems with the experimental methods in current studies, such as non-standardized and private data, different pre-processing methods, and incomplete or irreproducible experiment methodologies, among others. These problems prevent methods from being compared fairly and lead to lower reliability of the proposed experimental results, hindering progress in this area. For these reasons, we propose an open-source framework to facilitate the design and experimentation of deep learning-based rPPG development, and it's made freely available on GitHub(DLPrPPG). Through our platform we provide ready-to-use implementations of CNN-AE, LSTM, GAN, and Transformer models, whose hyperparameters we can easily and quickly optimize, and efficiently compare in a fair fashion. From our experiments we show that if the parameters of different neural networks are optimized, the performance of older architectures can be on par or even outperform newer ones.

原文English
主出版物標題IEEE International Symposium on Circuits and Systems, ISCAS 2022
發行者Institute of Electrical and Electronics Engineers Inc.
頁面697-701
頁數5
ISBN(電子)9781665484855
DOIs
出版狀態Published - 2022
事件2022 IEEE International Symposium on Circuits and Systems, ISCAS 2022 - Austin, United States
持續時間: 27 5月 20221 6月 2022

出版系列

名字Proceedings - IEEE International Symposium on Circuits and Systems
2022-May
ISSN(列印)0271-4310

Conference

Conference2022 IEEE International Symposium on Circuits and Systems, ISCAS 2022
國家/地區United States
城市Austin
期間27/05/221/06/22

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