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<p>The COVID-19 pandemic has highlighted the need for efficient and non-contact health screening methods. Signal-based infrared imaging is an emerging field in biomedical engineering that enables remote monitoring of vital signs. While fever is a common symptom, respiratory abnormalities often appear earlier, necessitating advanced screening systems that monitor both body temperature and respiratory patterns. This research presents an artificial intelligence-based screening device for health that identifies human respiratory patterns based on a deep learning model. The device is built with a Convolutional Neural Network (CNN) to extract features and a Long Short-Term Memory (LSTM) network to classify time-series patterns. The Softmax classifier accurately classifies respiratory patterns. It is learned on a specialized dataset of six breathing signal patterns, making it an effective model for real-time public health surveillance. The experimental result demonstrates that the proposed CNN-LSTM model achieves 91% accuracy, 90% precision, 93% recall, and an F1-score of 91%. It can be scaled up even further for medical real-time applications, paves the way to even greater future advancements in automated health surveillance.</p>

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Non-invasive respiratory infection monitoring using AI-driven thermal imaging and signal classification

  • 期刊论文

  • 发表日期：

    2025年5月12日

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  • Abisha D

摘要

The COVID-19 pandemic has highlighted the need for efficient and non-contact health screening methods. Signal-based infrared imaging is an emerging field in biomedical engineering that enables remote monitoring of vital signs. While fever is a common symptom, respiratory abnormalities often appear earlier, necessitating advanced screening systems that monitor both body temperature and respiratory patterns. This research presents an artificial intelligence-based screening device for health that identifies human respiratory patterns based on a deep learning model. The device is built with a Convolutional Neural Network (CNN) to extract features and a Long Short-Term Memory (LSTM) network to classify time-series patterns. The Softmax classifier accurately classifies respiratory patterns. It is learned on a specialized dataset of six breathing signal patterns, making it an effective model for real-time public health surveillance. The experimental result demonstrates that the proposed CNN-LSTM model achieves 91% accuracy, 90% precision, 93% recall, and an F1-score of 91%. It can be scaled up even further for medical real-time applications, paves the way to even greater future advancements in automated health surveillance.