ORIGINAL RESEARCH article
Front. Med. Technol.
Sec. Medtech Data Analytics
Volume 7 - 2025 | doi: 10.3389/fmedt.2025.1621922
ATTNFNET: Feature Aware Depth-to-Pressure Translation with cGAN Training
Provisionally accepted
- 1Bielefeld University of Applied Sciences, Bielefeld, Germany
- 2Bielefeld University, Bielefeld, Germany
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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 Structural Similarity Index Measure L2 norm (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.
Keywords: Patient monitoring, Generative network, contact pressure prediction, image translation, Deep neural network, transformer
Received: 02 May 2025; Accepted: 26 Aug 2025.
Copyright: © 2025 Manavar, Meyer, Wassmuth, Hammer and Schneider. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Neevkumar Hareshbhai Manavar, Bielefeld University of Applied Sciences, Bielefeld, Germany
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