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arXiv · 2604.17442

BreathAI: Transfer Learning-Based Thermal Imaging for Automated Breathing Pattern Recognition

Hamza Kheddar·Yassine Himeur·Abbes Amira
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Abstract

This study presents an Adaptive Transfer Learning and Thresholding-based Deep Learning Model (ATL-TDLM) for automated breathing pattern recognition using thermal imaging. Unlike conventional methods that rely on sound-based respiratory data, our approach leverages hierarchical deep feature extraction and adaptive multi-thresholding (AMT) to enhance feature segmentation. The model integrates knowledge distillation-based fine-tuning (KD-FT) to optimize learning transfer and contrastive representation learning (CRL) to improve inter-class separability between inhalation (INH) and exhalation (EXH) phases. The ATL-TDLM framework achieves an accuracy of 98.8%, significantly outperforming state-of-the-art models while ensuring computational efficiency. This approach has potential applications in respiratory disorder detection, including sleep apnea and asthma monitoring.

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Hamza Kheddar, Yassine Himeur, Abbes Amira. 2026-04-19. BreathAI: Transfer Learning-Based Thermal Imaging for Automated Breathing Pattern Recognition. https://doi.org/10.1109/icip55913.2025.11084681

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