Non-contact estimation of divers physiological indicators using iPPG and attention U-Net mechanism

Ensuring diver safety remains a major challenge in breath-hold diving due to physiological stressors such as hypoxia and increased hydrostatic pressure. Assessing physiological parameters before and after diving enables characterization of baseline and post-dive variations associated with these stressors. This study presents an imaging photoplethysmography (iPPG)-based method to estimate vital parameters, aiming to develop a non-invasive monitoring framework for diver safety. The proposed framework is based on an Attention U-Net architecture for segmenting regions of interest in video recordings of nine breath-hold divers acquired before and after a 30-meter dive in the controlled thermal pool at Y-40 The Deep Joy, Italy. The segmentation model was trained on the Face and Skin Detection (FSD) dataset. iPPG signals were derived from pulsatile skin regions to estimate heart rate (HR) and blood oxygen saturation (SpO₂). The pipeline was first validated on the Pulse Rate Detection (PURE) dataset and then evaluated on the acquired diver data. The segmentation model achieved an accuracy of 97% and an intersection over union of 89% on test data. HR and SpO₂ were estimated with mean absolute errors (MAEs) of 5 bpm and 4%, respectively, on the PURE dataset. The proposed pipeline further estimated pre-dive MAEs of 6 bpm for HR and 3% for SpO₂, while post-dive MAEs were 7 bpm for HR and 3% for SpO₂, indicating increased physiological variability following breath-hold diving. This study demonstrates the effects of breath-hold diving on physiological responses and establishes the feasibility of iPPG-based non-contact monitoring for detecting stress-related changes in divers.