Remote Photoplethysmography Enhancement with Machine Leaning Methods

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...

Full description

Saved in:
Bibliographic Details
Main Authors: Wu, Bing-Fei, Huang, Po-Wei, He, Da-Hong, Lin, Chung-Han, Chen, Kuan-Hung
Format: Conference Proceeding
Language:English
Subjects:
Convolution
Heart rate
Motion artifacts
Spectrogram
Time-frequency analysis
Vehicles
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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].
ISSN:2577-1655
DOI:10.1109/SMC.2019.8914554