TY - GEN
T1 - DLPrPPG
T2 - 2022 IEEE International Symposium on Circuits and Systems, ISCAS 2022
AU - Yan, Bo Rong
AU - Rios, Edwin Arkel
AU - Lee, Wen Hsien
AU - Lai, Bo Cheng
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85142529472&partnerID=8YFLogxK
U2 - 10.1109/ISCAS48785.2022.9937698
DO - 10.1109/ISCAS48785.2022.9937698
M3 - Conference contribution
AN - SCOPUS:85142529472
T3 - Proceedings - IEEE International Symposium on Circuits and Systems
SP - 697
EP - 701
BT - IEEE International Symposium on Circuits and Systems, ISCAS 2022
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 27 May 2022 through 1 June 2022
ER -