Why the study?
Current remote photoplethysmography algorithms face challenges with illuminance invariance and motion, where varying light conditions degrade signal-to-noise ratio and measurement accuracy.
Does the proposed rPPG algorithm accurately measure pulse rate variability and respiration rate compared to clinical apparatus under different illuminance scenarios?
Does the proposed rPPG algorithm accurately measure pulse rate variability and respiration rate compared to clinical apparatus under different illuminance scenarios?
A novel remote photoplethysmography algorithm demonstrates acceptable accuracy for measuring pulse rate variability and respiration rate under varying illuminance conditions, though currently limited to offline processing.
Remote photoplethysmography (rPPG) is now becoming a new trend method to measure human physiological parameters. Especially due to it noncontact measurement which safe dan suitable to use in this new era condition. Pulse rate variability (PRV) and respiration rate (RR) included as parameters can be measured by using rPPG. PRV and RR are used to measure both physical and psychological wellness of the subject. However, current performance challenges in rPPG algorithm in measuring PRV and RR are illuminance invariant and motion. Especially in different light condition which represent real-life environment, signal-to-noise ratio (SNR) will be affected and directly reduce the measurement accuracy. Therefore in this study, we develop rPPG algorithm and then investigate the performance rPPG in different illuminance scenarios. We perform PRV and RR measurement under each scenario. On this study, for the pulse signal extraction, we were using algorithm is based on the modification of plane orthogonal-to-skin (POS) algorithm. While, for respiration signal extraction is done in CIE Lab color space. Our experimental results show the mean absolute error (MAE) of each measured parameters are 3.25 BPM and 2 BPM for PRV and RR respectively compared with clinical apparatus. The proposed method proved to be more reliable to use in real environments measurement. However, limitation of our proposed algorithm is still running in offline mode, hence for the future we want try to make our algorithm run in real time.
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Suryasari et al. (2023) studied this question.
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