  Measuring the respiratory signal from a video based on body motion has been
proposed and recently matured in products for video health monitoring. The core
algorithm for this measurement is the estimation of tiny chest/abdominal
motions induced by respiration, and the fundamental challenge is motion
sensitivity. Though prior arts reported on the validation with real human
subjects, there is no thorough/rigorous benchmark to quantify the sensitivities
and boundary conditions of motion-based core respiratory algorithms that
measure sub-pixel displacement between video frames. In this paper, we designed
a setup with a fully-controllable physical phantom to investigate the essence
of core algorithms, together with a mathematical model incorporating two motion
estimation strategies and three spatial representations, leading to six
algorithmic combinations for respiratory signal extraction. Their promises and
limitations are discussed and clarified via the phantom benchmark. The insights
gained in this paper are intended to improve the understanding and applications
of camera-based respiration measurement in health monitoring.
