A Preliminary Study on Attention U-Net based Skin Segmentation and Vital Parameters Monitoring for Enhanced Diver Safety

Diver safety remains a critical concern in underwater exploration, with monitoring physiological parameters being crucial for assessing diver health. This preliminary study explores the feasibility of utilizing deep learning techniques for skin segmentation and the estimation of vital parameters to enhance diver safety. We employed an Attention U-Net based network to segment regions of interest from video sequences collected at Y 40 The Deep Joy in Montegrotto Terme, Italy, before and after a 30 meter-depth dive. The deep network was trained on images from the Face and Skin Detection database for the segmentation task. We achieved an accuracy of 97 % and an intersection over union of 89 % on the test data. Additionally, we extracted imaging photoplethysmography (iPPG) signals from the selected skin area and estimated vital parameters. The proposed pipeline was tested on data acquired in an underwater test environment, with reference data collected via pulse oximeter. Preliminary results demonstrate the potential of the proposed network to improve diver safety by providing real-time insights into diver health.