Non-contact Respiratory Flow Extraction from Infrared Images Using Balanced Data Classification
Abstract: The COVID-19 pandemic has emphasized the need for non-contact ways of measuring vital signs. However, collecting respiratory signals can be challenging due to the transmission risk and physical discomfort of spirometry devices. This is problematic in places like schools and workplaces where monitoring health is crucial. Infrared fever meters are not accurate enough since fever is not the only symptom of these diseases. The objective of our study was to develop a non-contact method for obtaining Respiratory Flow (RF) from infrared images. We recorded infrared images of three subjects at a distance of 1 m while they breathed through a spirometry device. We proposed a method called Balanced Data Classification to distribute frames equally into several classes and then used the DenseNet-121 Convolutional Neural Network Model to predict RF signals from the infrared images. Our results showed a high correlation of 97% and a RMSE of 5%, which are significant compared to other studies. Our method is fully non-contact and involves standing at a distance of 1 m from the subjects. In conclusion, our study demonstrates the feasibility of using infrared images to extract RF.
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