Remote photoplethysmography enhancement with machine leaning methods

Bing Fei Wu, Po Wei Huang, Da Hong He*, Chung Han Lin, Kuan Hung Chen

*此作品的通信作者

研究成果: Conference contribution同行評審

2 引文 斯高帕斯(Scopus)

摘要

Driver's physiological state is highly correlated to the traffic safety. An affordable and convenient way to monitor driver's physiological state is remote Photoplethysmography (rPPG). Earlier algorithms achieved high accuracy on measuring rPPG signals in stationary case. But in real cases, such as driving, rPPG signals might be corrupted with interference. To obtain higher Signal-to-Noise-Ratio (SNR) rPPG signals, three algorithms are proposed. The PCA spectral subtraction (PCA-SS) considers the spectrum of the environmental noise and utilizes the energy subtraction to reduce the noise. The machine learning methods, convolution autoencoder (CAE) and multi-channel convolution autoencoder (Multi-CAE), are adopted in order to enhance the rPPG signal. The test data we used are 187 videos recorded in stationary case, passenger case, and real driving situation. In driving situation, the Multi-CAE method, in comparison with the original method provided by W. Wang et al. [1] and G. De Haan et al. [2], achieves 33% & 35% reduction in MAE, RMSE respectively, and 11% improvement in success rate [3].

原文English
主出版物標題2019 IEEE International Conference on Systems, Man and Cybernetics, SMC 2019
發行者Institute of Electrical and Electronics Engineers Inc.
頁面2466-2471
頁數6
ISBN(電子)9781728145693
DOIs
出版狀態Published - 10月 2019
事件2019 IEEE International Conference on Systems, Man and Cybernetics, SMC 2019 - Bari, Italy
持續時間: 6 10月 20199 10月 2019

出版系列

名字Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
2019-October
ISSN(列印)1062-922X

Conference

Conference2019 IEEE International Conference on Systems, Man and Cybernetics, SMC 2019
國家/地區Italy
城市Bari
期間6/10/199/10/19

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