Description
The measurement and monitoring of respiratory rates is vital for the well-being of the individ-ual and is an excellent indicator for the diagnosis of related disease. Thus, it should be a rou-tine and everyday task not dependent on a clinical setting for the health of individuals. In this thesis, studies on respiratory monitoring were conducted with the aim of easy of field deploy-ment and non-contact monitoring without sacrificing accuracy. Specifically, deep learning methods were applied to video data to predict the respiratory rate of the subject. The content of the study is as follows. 1) Two datasets consisting of infrared video-imagery data and RGB video-imagery data were acquired. 2) Feature extraction was utilized to extract regions of in-terest and a filter was used to reduce noise and enhance performance. 3) Two methods that monitor respiratory rates based on video imagery and Deep Learning method were proposed and analyzed. Two other method were also used as non-deep learning counterparts to the above two methods. 4) Finally, metrics regarding the performance of the deep learning mod-els and the root mean square error of the predicted respiratory rate was calculated.| Period | 20 Jul 2023 |
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