Algorithmic Principles of Camera-based Respiratory Motion Extraction
This work addresses the need for better validation in health monitoring technologies, though it is incremental as it focuses on benchmarking existing methods rather than introducing new ones.
The paper tackled the lack of rigorous benchmarks for camera-based respiratory motion extraction algorithms by designing a controllable physical phantom to test six algorithmic combinations, revealing their promises and limitations for measuring sub-pixel displacements.
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.