PulseGAN: Learning to Generate Realistic Pulse Waveforms in Remote Photoplethysmography

PulseGAN: Learning to Generate Realistic Pulse Waveforms in Remote Photoplethysmography

Remote photoplethysmography (rPPG) is a non-contact technique for measuring cardiac signals from facial videos. High-quality rPPG pulse signals are urgently demanded in many fields, such as health monitoring and emotion recognition. However, most of the existing rPPG methods can only be used to get...

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Bibliographic Details
Main Authors: Chen, H. (Author), Chen, X. (Author), Cheng, J. (Author), Li, C. (Author), Liu, Y. (Author), Song, R. (Author)
Format: Article
Language:English
Published: Institute of Electrical and Electronics Engineers Inc. 2021
Subjects:
Adversarial networks
algorithm
Algorithms
Article
Biomedical signal processing
breathing rate
classification algorithm
Database systems
electroencephalography
Emotion recognition
error
Errors
eye movement
face
Face
feature extraction
generative adversarial network
head movement
Health monitoring
Heart
heart rate
Heart Rate
Heart rate estimation
heart rate variability
Heart rate variability
human
Humans
image quality
image segmentation
mathematical analysis
mathematical model
Mean absolute error
Non-contact techniques
photoelectric plethysmography
Photoplethysmography
pulse waveform
remote photoplethysmography
RR interval
signal noise ratio
signal processing
Signal Processing, Computer-Assisted
Standard deviation
Time frequency characteristics
time series analysis
training
videorecording
visual stimulation
waveform
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