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Monitoring the respiratory rate is crucial for helping us identifyrespiratory disorders. Devices for conventional respiratory monitoring areinconvenient and scarcely available. Recent research has demonstrated theability of non-contact technologies, such as photoplethysmography and infraredthermography, to gather respiratory signals from the face and monitorbreathing. However, the current non-contact respiratory monitoring techniqueshave poor accuracy because they are sensitive to environmental influences likelighting and motion artifacts. Furthermore, frequent contact between users andthe cloud in real-world medical application settings might cause servicerequest delays and potentially the loss of personal data. We proposed anon-contact respiratory rate monitoring system with a cooperative three-layerdesign to increase the precision of respiratory monitoring and decrease datatransmission latency. To reduce data transmission and network latency, ourthree-tier architecture layer-by-layer decomposes the computing tasks ofrespiration monitoring. Moreover, we improved the accuracy of respiratorymonitoring by designing a target tracking algorithm and an algorithm foreliminating false peaks to extract high-quality respiratory signals. Bygathering the data and choosing several regions of interest on the face, wewere able to extract the respiration signal and investigate how differentregions affected the monitoring of respiration. The results of the experimentindicate that when the nasal region is used to extract the respiratory signal,it performs experimentally best. Our approach performs better than rivalapproaches while transferring fewer data.

Collaborative Three-Tier Architecture Non-contact Respiratory Rate Monitoring using Target Tracking and False Peaks Eliminating Algorithms