Camera-based respiration rate monitoring using informative frame extraction

Camera-based monitoring shows potential for continuous respiration rate measurement in clinical settings. The main cause of errors in camera-based respiration rate measurement using optical flow is non-breathing motion, which disturbs the measurement of the breathing motion. To prevent this, uninformative video frames containing non-breathing motion should be detected and removed from consideration for respiration rate extraction. Currently, signal quality metrics or global motion detection are used to detect non-breathing motion, but these methods have limitations when applied in clinical settings. Therefore, in this research, we propose a new informativeness metric for optical flow based respiration rate monitoring. This metric exploits the residual error of the optical flow fit for determining the presence of non-breathing motion on a per breath basis. This new metric is evaluated on a clinical dataset that contains RGB videos of 25 ICU patients. We find that a 3 breaths/min agreement of 98.7% and a mean absolute error of 0.21 breaths/min is achieved across all patients. These results show that using our new informativeness metric, we can achieve highly accurate camera-based respiration rate monitoring without relying on assumptions on signal quality or waveform morphology. • Propose a novel informativeness metric using optical flow error for respiration monitoring. • Accurately detects and excludes non-breathing motion on a per-breath basis. • No assumptions are made on waveform morphology or regularity of the respiration. • Evaluated on RGB video recordings from 25 ICU patients in a real clinical setting. • Achieves 98.7% 3 breaths/min agreement and mean absolute error of 0.21 breaths/min.