Exploring Remote Photoplethysmography for Neonatal Pain Detection from Facial Videos
Ashutosh Dhamaniya, Anup Kumar Gupta, Trishna Saikia, Puneet Gupta

TL;DR
This paper presents a non-contact method using remote photoplethysmography (rPPG) from facial videos to detect neonatal pain, improving reliability and safety over traditional contact-based techniques.
Contribution
Introduces a novel rPPG-based approach for neonatal pain detection, incorporating quality and noise metrics to enhance signal accuracy from facial videos.
Findings
rPPG signals effectively indicate neonatal pain.
Blue colour channel yields the best rPPG signals.
Combining rPPG with audio features improves detection accuracy.
Abstract
Unaddressed pain in neonates can lead to adverse effects, including delayed development and slower weight gain, emphasising the need for more objective and reliable pain assessment methods. Hence, automated methods using behavioural and physiological pain indicators have been developed to aid healthcare professionals in the Neonatal ICU. Traditional contact-based methods for physiological parameter estimation are unsuitable for long-term monitoring and increase the risk of spreading diseases like COVID-19. We introduce a novel approach using remote photoplethysmography (rPPG) to estimate pulse signals in a non-contact manner and employ them for neonatal pain detection. The temporal signals acquired from regions-of-interest (ROIs) affected by skin deformations may exhibit lower quality and provide erroneous rPPG signals. Therefore, we incorporated a quality parameter to select the…
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