Towards A Lightweight Deep Learning Framework for Real-Time Vital Sign Prediction with RGB Cameras

Remote photoplethysmography (rPPG) is a technology that extracts blood volume pulse (BVP) signals by analyzing the wavelength differences of light reflected from a person's skin using devices such as RGB and infrared cameras. This technology enables the measurement and estimation of various phy...

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Vydáno v:The ... International Winter Conference on Brain-Computer Interface s. 1 - 5
Hlavní autoři: Kim, Seong-Wook, Park, Hyeong-Yeong, Lee, In-Gyu, Jang, Se-Na, Yu, Seong-Hyun, Oh, Jun-Young, Moon, Gee-Myung, Jeong, Ji-Hoon
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 24.02.2025
Témata:
Blood pressure
Deep learning
digital health care
Estimation
Fast Fourier transforms
Heart rate
Photoplethysmography
Physiology
real-time physiological signals prediction
Real-time systems
Remote photoplethysmography
Wearable devices
Webcams
ISSN:2572-7672
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Shrnutí:Remote photoplethysmography (rPPG) is a technology that extracts blood volume pulse (BVP) signals by analyzing the wavelength differences of light reflected from a person's skin using devices such as RGB and infrared cameras. This technology enables the measurement and estimation of various physiological signals, including heart rate (HR), systolic blood pressure (SBP), diastolic blood pressure (DBP), respiratory rate, and oxygen saturation. In this study, we overcame the limitations of existing rPPG methods by constructing a custom RGB-rPPG dataset. The dataset was collected from 14 subjects using an RGB camera (a standard webcam), wearable device, and sphygmomanometer. The leave-one-subject-out (LOSO) approach was applied for model training to predict SBP and DBP, while HR was estimated using a fast Fourier transform (FFT). The average mean absolute error for SBP/DBP prediction and HR estimation were 4.55 (±2.7660) mmHg, 1.84 (±1.6064) mmHg, and 6.91 (±8.0319) bpm, respectively. These results demonstrate that the constructed RGB-rPPG dataset and the proposed method are effective for predicting physiological signs.
ISSN:2572-7672
DOI:10.1109/BCI65088.2025.10931624