ATTNFNET: feature aware depth-to-pressure translation with cGAN training
Neevkumar Manavar, Hanno Gerd Meyer, Joachim Waßmuth, Barbara Hammer, Axel Schneider · Frontiers in Medical Technology · 2025
Excessive pressure and shear forces on bedridden patients can lead to pressure injuries, particularly on those with existing ulcers. Monitoring pressure distribution is crucial for preventing such injuries by identifying high-risk areas. To address this challenge, we propose Attention Feature Network (AttnFnet), a self-attention-based deep neural network that generates pressure distribution maps from single-depth images using Conditional Generative Adversarial Network (cGAN) training. We introduce a mixed-domain SSIML2 loss function, combining structural similarity and pixel-level accuracy, along with adversarial loss, to enhance the prediction of pressure distributions for subjects lying in a bed. Evaluation results from the benchmark dataset demonstrate that the AttnFnet outperforms existing methods in terms of Structural Similarity Index Measure (SSIM) and quality analysis, providing accurate pressure distribution estimation from a single depth image.