Remote pulmonary function testing using a depth sensor

We propose a remote non-invasive approach to Pulmonary Function Testing using a time-of-flight depth sensor (Microsoft Kinect V2), and correlate our results to clinical-standard spirometry results. Given point clouds, we approximate 3D models of the subject's chest, estimate the volume throughout a sequence and construct volume-time and flow-time curves for two prevalent spirometry tests: Forced Vital Capacity and Slow Vital Capacity. From these, we compute clinical measures, such as FVC, FEV1, VC and IC. We correlate automatically extracted measures with clinical spirometry tests on 40 patients in an outpatient hospital setting. These demonstrate high within-test correlations.

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