Heart rate estimation by leveraging static and dynamic region weights

Liu, Lili; Xia, Zhaoqiang; Zhang, Xiaobiao; Peng, Jinye; Feng, Xiaoyi; Zhao, Guoying

doi:10.1117/1.jei.32.4.043037

Cited by 3 publications

(2 citation statements)

References 63 publications

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“… Publications by subject area [ 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , …”

Section: Figureunclassified

Non-Contact Vision-Based Techniques of Vital Sign Monitoring: Systematic Review

Saikevičius,

Raudonis,

Dervinis

et al. 2024

Sensors

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The development of non-contact techniques for monitoring human vital signs has significant potential to improve patient care in diverse settings. By facilitating easier and more convenient monitoring, these techniques can prevent serious health issues and improve patient outcomes, especially for those unable or unwilling to travel to traditional healthcare environments. This systematic review examines recent advancements in non-contact vital sign monitoring techniques, evaluating publicly available datasets and signal preprocessing methods. Additionally, we identified potential future research directions in this rapidly evolving field.

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Section: Figureunclassified

Non-Contact Vision-Based Techniques of Vital Sign Monitoring: Systematic Review

Saikevičius,

Raudonis,

Dervinis

et al. 2024

Sensors

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“…Wu et al 9 proposed a framework that incorporates an error compensation neural network after traditional signalbased rPPG convolution to improve measurement results. Liu et al 10 extracted high-quality rPPG signals from facial sub-regions using a method that combines static and dynamic weights with a CNN. Chromatic features from distinct regions of interest were employed for sub-region rPPG signal generation.…”

Section: Introductionmentioning

confidence: 99%

Standardized rPPG signal generation based on generative adversarial networks

Tu,

Liu,

Zhao

et al. 2024

J. Electron. Imag.

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Remote photoplethysmography (rPPG) is an optical technique that measures physiological signals from facial videos by analyzing subtle changes in the skin blood volume. However, rPPG signals generated in practical applications are easily affected by external environmental factors and the state of individuals, leading to irregular waveform variations that increase the difficulty in heart rate estimation. To improve the regularity of generated rPPG signals, we propose a standardized rPPG signal generation method. Specifically, facial videos are fed into the generator of a generative adversarial network (GAN) to predict a rough rPPG signal by supervised learning. In addition, a mathematical signal synthesizer model is used to generate noise-free standardized rPPG signals, which are subsequently fed into a discriminator along with the predicted signal for adversarial learning. This enables the generator to learn more standardized waveforms. As a result, the predicted signal waveform by the generator becomes closer to the waveform distribution of real rPPG signals. The proposed method is validated on the widely used MAHNOB-HCI, UBFC-rPPG, and MMSE-HR databases and shows significant improvements in the prediction accuracy and signal-to-noise ratio.

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