  Non-contact estimation of respiratory pattern (RP) and respiration rate (RR)
has multiple applications. Existing methods for RP and RR measurement fall into
one of the three categories - (i) estimation through nasal air flow
measurement, (ii) estimation from video-based remote photoplethysmography, and
(iii) estimation by measurement of motion induced by respiration using motion
detectors. These methods, however, require specialized sensors, are
computationally expensive and/or critically depend on selection of a region of
interest (ROI) for processing. In this paper a general framework is described
for estimating a periodic signal driving noisy LTI channels connected in
parallel with unknown dynamics. The method is then applied to derive a
computationally inexpensive method for estimating RP using 2D cameras that does
not critically depend on ROI. Specifically, RP is estimated by imaging the
changes in the reflected light caused by respiration-induced motion. Each
spatial location in the field of view of the camera is modeled as a
noise-corrupted linear time-invariant (LTI) measurement channel with unknown
system dynamics, driven by a single generating respiratory signal. Estimation
of RP is cast as a blind deconvolution problem and is solved through a method
comprising subspace projection and statistical aggregation. Experiments are
carried out on 31 healthy human subjects by generating multiple RPs and
comparing the proposed estimates with simultaneously acquired ground truth from
an impedance pneumograph device. The proposed estimator agrees well with the
ground truth device in terms of correlation measures, despite variability in
clothing pattern, angle of view and ROI.
