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

TL;DR

This paper introduces a novel deep learning framework using transfer learning and adaptive thresholding on thermal images to accurately recognize breathing patterns, outperforming existing methods.

Contribution

The study proposes an innovative ATL-TDLM model combining knowledge distillation and contrastive learning for improved thermal imaging-based breathing analysis.

Findings

Achieved 98.8% accuracy in breathing pattern recognition.

Outperformed state-of-the-art models in thermal imaging analysis.

Demonstrated potential for respiratory disorder monitoring.

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.