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- W4362549979 abstract "Assessing lung function and treating chronic pulmonary diseases require the estimation and monitoring of remote heart rate (HR). With the COVID-19 pandemic, the importance of early detection of changes in remote HR has become more apparent, especially in asymp-tomatic patients with clinical deterioration. However, traditional remote HR measurement methods rely on contact-based sensing, which can lead to cross-contamination and complications in patients with impaired limb perfusion. Pulse oximeters, which are commonly used for remote HR measurement, may also be unavailable in marginalized communities and underdeveloped countries. The system includes three main steps: 1) extraction of the region of interest (ROI), which includes the palm and back of the hand, from the smartphone-captured videos; 2) spatial averaging of the ROI to produce R, G, and B time series; and 3) feeding the time series into an optophysiology-inspired CNN for remote HR estimation. This method is more efficient and accurate for monitoring remote HR using videos captured from consumer-grade smartphones, which is particularly useful in telehealth and health screening settings. It can provide a comfortable and unobtrusive way to monitor remote HR without the risk of cross-contamination and the limitations of conventional methods, such as the availability of pulse oximeters." @default.
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- W4362549979 date "2023-04-04" @default.
- W4362549979 modified "2023-09-29" @default.
- W4362549979 title "Neural Network Interpretability for Facial rPPG Videos" @default.
- W4362549979 doi "https://doi.org/10.22541/au.168062642.29062359/v1" @default.
- W4362549979 hasPublicationYear "2023" @default.
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