Performance analysis of remote photoplethysmography deep filtering using long short-term memory neural network. [PDF]
Biomed Eng Online, 2022 Botina-Monsalve D +2 more
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The reliability of remote photoplethysmography under low illumination and elevated heart rates. [PDF]
NPJ Digit MedAcharya B +3 more
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Remote photoplethysmography for cardiorespiratory self-monitoring: A qualitative study of usability, convenience, and patient confidence. [PDF]
Digit HealthWood KV, Moore A, Ahmad M, Bostanci DN.
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Evaluating remote photoplethysmography: A 10-minute video dataset in uncontrolled lighting. [PDF]
Data BriefRodrigues G, Garcia NM.
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The role of face regions in remote photoplethysmography for contactless heart rate monitoring. [PDF]
NPJ Digit MedBondarenko M, Menon C, Elgendi M.
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Advanced signal-processing framework for remote photoplethysmography-based heart rate measurement: Integrating adaptive Kalman filtering with discrete wavelet transformation. [PDF]
PLoS OneDebnath U, Kim S.
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A comprehensive review of heart rate measurement using remote photoplethysmography and deep learning. [PDF]
Biomed Eng OnlineDebnath U, Kim S.
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Remote Photoplethysmography Technology for Blood Pressure and Hemoglobin Level Assessment in the Preoperative Assessment Setting: Algorithm Development Study. [PDF]
JMIR Form ResTan SYL +6 more
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Low-Complexity Timing Correction Methods for Heart Rate Estimation Using Remote Photoplethysmography. [PDF]
Sensors (Basel)Chen CC, Lin SX, Jeong H.
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Integrating Remote Photoplethysmography and Machine Learning on Multimodal Dataset for Noninvasive Heart Rate Monitoring. [PDF]
Sensors (Basel)Buyung RA, Bustamam A, Ramazhan MRS.
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