  Monitoring the respiratory rate is crucial for helping us identify
respiratory disorders. Devices for conventional respiratory monitoring are
inconvenient and scarcely available. Recent research has demonstrated the
ability of non-contact technologies, such as photoplethysmography and infrared
thermography, to gather respiratory signals from the face and monitor
breathing. However, the current non-contact respiratory monitoring techniques
have poor accuracy because they are sensitive to environmental influences like
lighting and motion artifacts. Furthermore, frequent contact between users and
the cloud in real-world medical application settings might cause service
request delays and potentially the loss of personal data. We proposed a
non-contact respiratory rate monitoring system with a cooperative three-layer
design to increase the precision of respiratory monitoring and decrease data
transmission latency. To reduce data transmission and network latency, our
three-tier architecture layer-by-layer decomposes the computing tasks of
respiration monitoring. Moreover, we improved the accuracy of respiratory
monitoring by designing a target tracking algorithm and an algorithm for
eliminating false peaks to extract high-quality respiratory signals. By
gathering the data and choosing several regions of interest on the face, we
were able to extract the respiration signal and investigate how different
regions affected the monitoring of respiration. The results of the experiment
indicate that when the nasal region is used to extract the respiratory signal,
it performs experimentally best. Our approach performs better than rival
approaches while transferring fewer data.
