Depth-based Whole Body Photoplethysmography in Remote Pulmonary Function Testing

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 3-D 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 (FVC) and slow vital capacity (SVC) 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 L2 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.