Contactless optical respiration rate measurement for a fast triage of SARS-CoV-2 patients in hospitals

Especially in hospital entrances, it is important to spatially separate potentially SARS-CoV-2 infected patients from other people to avoid further spreading of the disease. Whereas the evaluation of conventional laboratory tests takes too long, the main symptoms, fever and shortness of breath, can indicate the presence of a SARS-CoV-2 infection and can thus be considered for triage. Fever can be measured contactlessly using an infrared sensor, but there are currently no systems for measuring the respiration rate in a similarly fast and contactless way. Therefore, we propose an RGB-camera-based method to remotely determine the respiration rate for the triage in hospitals. We detect and track image features on the thorax, band-pass filter the trajectories and further reduce noise and artefacts by applying a principal component analysis. Finally, the respiration rate is computed using Welch's power spectral density estimate. Our contactless approach is focused on a fast measurement and computation. It is especially adapted to the use case of the triage in hospitals by comprising a face detection which is robust against partial occlusion allowing the patients to wear face masks. Moreover, we show that our method is able to correctly determine the respiration frequency for standing patients despite considerable body sway.