Depth-Based Whole Body Photoplethysmography in Remote Pulmonary Function Testing

<italic>Objective:</italic> We propose a novel depth-based photoplethysmography (dPPG) approach to reduce motion artifacts in respiratory volume–time data and improve the accuracy of remote pulmonary function testing (PFT) measures. <italic>Method:</italic> Following spatial and temporal calibration of two opposing RGB-D sensors, a dynamic three-dimensional model of the subject performing PFT is reconstructed and used to decouple trunk movements from respiratory motions. Depth-based volume–time data is then retrieved, calibrated, and used to compute 11 clinical PFT measures for forced vital capacity and slow vital capacity spirometry tests. <italic>Results:</italic> A dataset of 35 subjects (298 sequences) was collected and used to evaluate the proposed dPPG method by comparing depth-based PFT measures to the measures provided by a spirometer. Other comparative experiments between the dPPG and the single Kinect approach, such as Bland–Altman analysis, similarity measures performance, intra-subject error analysis, and statistical analysis of <italic>tidal volume</italic> and <italic>main effort</italic> scaling factors, all show the superior accuracy of the dPPG approach. <italic>Conclusion</italic>: We introduce a depth-based whole body photoplethysmography approach, which reduces motion artifacts in depth-based volume–time data and highly improves the accuracy of depth-based computed measures. <italic>Significance:</italic> The proposed dPPG method remarkably drops the <inline-formula><tex-math notation="LaTeX">$L_2$</tex-math></inline-formula> error mean and standard deviation of <italic>FEF</italic><inline-formula><tex-math notation="LaTeX">$_{50\%}$</tex-math> </inline-formula>, <italic>FEF</italic><inline-formula><tex-math notation="LaTeX">$_{75\%}$</tex-math></inline-formula> , <italic>FEF</italic><inline-formula><tex-math notation="LaTeX">$_{25-75\%}$</tex-math></inline-formula>, <italic>IC </italic>, and <italic>ERV</italic> measures by half, compared to the single Kinect approach. These significant improvements establish the potential for unconstrained remote respiratory monitoring and diagnosis.

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