Non Invasive Health Monitoring Respiratory Infections and Pattern Classification: A Literature Review

Abstract

COVID 19 is now considered as the emerging concern by WHO on January 30,2020. Signal monitoring based IR images is a new growing field of biomedical engineering. In this review, the methods of extracting the features for health monitoring like temperature and respiratory rate was studied. We searched many data source like IEEE, Elsevier, Springer, Sensors to find the literature on the facial feature extraction of forehead and nostril area carried out by the researches with the various deep learning, machine learning algorithms and signal processing filtering techniques with the datasets available at open-sites for thermal images, FLIR dataset or by directly capturing the images with the help of FLIR camera. After a wide research and findings, we traced 51 research articles and sites for the Non-Contact health monitoring systems and respiratory rate classification. This region of interest tracking system will be helpful for the first level of health screening for the public due to the worst cause of pandemic viral spread. From this review learnings, we inferred that, this real-time feature extraction with respect to signal processing will give results at greater success rate.

Keywords-FLIR Thermal images, Health screening, Feature extraction, Signal Processing, Evaluation, deep learning techniques, classification of pattern.