Remote, Depth-Based Lung Function Assessment

Objective: We propose a remote, noninvasive approach to develop pulmonary function testing (PFT) using a depth sensor. Method: After generating a point cloud from scene depth values, we construct a three-dimensional model of the subject's chest. Then, by estimating the chest volume variation throughout a sequence, we generate volume-time and flow-time data for two prevalent spirometry tests: forced vital capacity (FVC) and slow vital capacity (SVC). Tidal volume and main effort sections of volume- time data are analyzed and calibrated separately to remove the effects of a subject's torso motion. After automatic extraction of keypoints from the volume-time and flow- time curves, seven FVC (FVC, FEV1, PEF, FEF
_25%
, FEF
_50%
, FEF
_75%
, and FEF
_25-75%
) and four SVC measures (VC, IC, TV, and ERV) are computed and then validated against measures from a spirometer. A dataset of 85 patients (529 sequences in total), attending respiratory outpatient service for spirometry, was collected and used to evaluate the proposed method. Results: High correlation for FVC and SVC measures on intra-test and intra-subject measures between the proposed method and the spirometer. Conclusion: Our proposed depth-based approach is able to remotely compute eleven clinical PFT measures, which gives highly accurate results when evaluated against a spirometer on a dataset comprising 85 patients. Significance: Experimental results computed over an unprecedented number of clinical patients confirm that chest surface motion is linearly related to the changes in volume of lungs, which establishes the potential toward an accurate, low-cost, and remote alternative to traditional cumbersome methods, such as spirometry.