Depth-Based Whole Body Photoplethysmography in Remote Pulmonary Function Testing

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 $L_2$ error mean and standard deviation of FEF$_{50%}$ , FEF$_{75%}$ , FEF$_{25-75%}$, 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.
Source: IEEE Transactions on Biomedical Engineering - Category: Biomedical Engineering Source Type: research