Remote depth-based photoplethysmography in pulmonary function testing

This thesis introduces several novel, noninvasive lung function assessment approaches in which we incorporate computer vision techniques to remotely compute standard clinical Pulmonary Function Testing (PFT) measures. Using a single depth sensor, a dynamic 3-D model of a subject's chest is reconstructed and used to generate chest volume-time data by estimating the chest volume variation throughout a sequence. Following computation of multiple keypoints and calibration of volume-time data to present real volume of exchanged air, 7 Forced Vital Capacity (FVC) measures and 4 Slow Vital Capacity (SVC) measures are computed. Evaluation on a dataset of 85 patients (529 sequences), attending a respiratory outpatient service for spirometry, shows a high correlation between the proposed depth-based PFT measures and the measures from a spirometer. Trunk motion during PFT affects the accuracy of these results, so the natural reaction of the subject's body to maximal inhalation and exhalation, must be decoupled from the chest-surface breathing motion. We present an automatic, open source data acquisition and calibration pipeline in which two opposing depth sensors are calibrated and used to reconstruct a well-defined dynamic 3-D model of the trunk during PFT performance. Our proposed method is able to reconstruct dynamic 3-D models with accurate temporal frame synchronisation and spatial registration. Then, we propose a whole body depth-based photoplethysmography (dPPG) approach which allows subjects to perform PFT, as in routine spirometry, without restraining their natural trunk reactions. By decoupling the trunk movement and the chest-surface respiratory motion, dPPG obtains more accurate respiratory volume-time data which improves the accuracy of the estimated PFT measures. A dataset spanning 35 subjects (298 sequences) was collected and used to illustrate the superiority of the proposed dPPG method by comparing its measures to those provided by a spirometer and the single Kinect approach. Although dPPG is able to improve the PFT measures accuracy to a significant extent, it is not able to filter complex trunk motions, particularly at the deep forced inhalation-exhalation stage. To effectively correct trunk motion artifacts further, we propose an active trunk shape modelling approach by which the respiratory volume-time data is computed by performing principal component analysis on temporal 3-D geometrical features, extracted from the chest and posterior shape models in R3 space. We validate the method's accuracy at the signal level by computing several comparative metrics between the depth-based and spirometer volume-time data. Evaluating on the dPPG PFT dataset (300 PFT sequences), our trunk shape modelling approach outperforms the single Kinect and dPPG methods.