High-Reliability Non-Contact Photoplethysmography Imaging for Newborn Care by a Generative Artificial Intelligence

Abstract

Long-term wiring on a newborn patient could be a disguise scene for parents. Unobtrusive and reliable monitoring without wiring can be a euphoric alternative for newborns and parents in obstetrics and gynecology (OB/GYN) incubation rooms. However, reliable and continuous non-contact surveillance in an incubation room is challenging. Therefore, a novel photoplethysmography imaging (PPGi) is developed specifically for baby skins through predictive adversarial adaptation and risk-sensitive generative synchronizer. Our artificial intelligence approach does not take blind guesses from input-output pairs. We apply an intelligent step to decouple the influence of fluctuated illumination through a generative algorithm of artificial intelligence. To boost skin detection performance, we capture those pixels with periodic variations and maximize the coherence of the extraction algorithm by the generative synchronizer. The periodic variations are matched by a synthesized pulse from the output PPGi signals through the control of a risk-sensitive filter to not over-compensate the illuminate variation. Based on the sensed pulsation, we synthesize the corresponding pulsation signals on the flight to identify the living skin in a spatiotemporal image sequence. We find that our skin classifier in risk-sensitive generative synchronizer effectively improves the quality of the resulting non-contact PPGi signal. Our algorithm produces substantial accuracy in the performance of PPGi reconstruction in the critical environment of newborn care. In the limited illustration of the incubation room, our non-contact PPGi can still achieve an average accuracy of 96.62%.