Abstract
The monitoring of respiratory parameters is important across many areas of care within the hospital. Here we report on the performance of a depth-sensing camera system for the continuous non-contact monitoring of Respiratory Rate (RR) and Tidal Volume (TV), where these parameters were compared to a ventilator reference. Depth sensing data streams were acquired and processed over a series of runs on a single volunteer comprising a range of respiratory rates and tidal volumes to generate depth-based respiratory rate (RR(depth)) and tidal volume (TV(depth)) estimates. The bias and root mean squared difference (RMSD) accuracy between RR(depth) and the ventilator reference, RR(vent), across the whole data set was found to be -0.02 breaths/min and 0.51 breaths/min respectively. The least squares fit regression equation was determined to be: RR(depth) = 0.96 × RR(vent) + 0.57 breaths/min and the resulting Pearson correlation coefficient, R, was 0.98 (p < 0.001). Correspondingly, the bias and root mean squared difference (RMSD) accuracy between TV(depth) and the reference TV(vent) across the whole data set was found to be - 0.21 L and 0.23 L respectively. The least squares fit regression equation was determined to be: TV(depth) = 0.79 × TV(vent)-0.01 L and the resulting Pearson correlation coefficient, R, was 0.92 (p < 0.001). In conclusion, a high degree of agreement was found between the depth-based respiration rate and its ventilator reference, indicating that RR(depth) is a promising modality for the accurate non-contact respiratory rate monitoring in the clinical setting. In addition, a high degree of correlation between depth-based tidal volume and its ventilator reference was found, indicating that TV(depth) may provide a useful monitor of tidal volume trending in practice. Future work should aim to further test these parameters in the clinical setting.