# BreathAI: Transfer Learning-Based Thermal Imaging for Automated Breathing Pattern Recognition Source: [BreathAI: Transfer Learning-Based Thermal Imaging for Automated Breathing Pattern Recognition]() Collection index: [llms.txt]() Data updated: 2026-04-22T05:22:24.044Z Authors: Hamza Kheddar, Yassine Himeur, Abbes Amira Published: 2026-04-19T13:44:51.000Z ## 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Breathing pattern classification on ITI dataset (test)](): Breathing pattern classification; ITI dataset (test); Accuracy: 98.8. ## Original sources and implementation [Original paper]() [PDF]()