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Objective: 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. Method: 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. Results: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 tidal volume and main effort scaling factors, all show the superior accuracy of the dPPG approach. Conclusion: 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. Significance: The proposed dPPG method remarkably drops the L25 error mean and standard deviation of FEF50%, FEF75%, FEF25-75%, IC, and ERV measures by half, compared to the single Kinect approach. These significant improvements establish the potential for unconstrained remote respiratory monitoring and diagnosis.

Depth-Based Whole Body Photoplethysmography in Remote Pulmonary Function Testing