BreathAI: Transfer Learning-Based Thermal Imaging for Automated Breathing Pattern Recognition
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866908978232950784 |
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| author | Kheddar, Hamza Himeur, Yassine Amira, Abbes |
| author_facet | Kheddar, Hamza Himeur, Yassine Amira, Abbes |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2604_17442 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | BreathAI: Transfer Learning-Based Thermal Imaging for Automated Breathing Pattern Recognition Kheddar, Hamza Himeur, Yassine Amira, Abbes Image and Video Processing 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. |
| title | BreathAI: Transfer Learning-Based Thermal Imaging for Automated Breathing Pattern Recognition |
| topic | Image and Video Processing |
| url | https://arxiv.org/abs/2604.17442 |