Considerations of handheld respiratory rate estimation via a stabilized Video Magnification approach

Respiratory rate can be a vital indicator of illness; however, tracking this is a non-trivial process. Phase-based Eulerian Video Magnification (EVM) is an exciting spatiotemporal video processing approach able to reveal subtle breathing motions within video sequences; however, its results are variant to large motions and camera blur. In the case of camera motion, a compensation strategy of stabilizing (without smoothing) the video has the may reduce estimation error in handheld cases. This work explores the extent of removing motion artefacts and its impact on identifying subtle breathing motions. Tests across six indoor scenes show a reduction mean breathing estimate error for 4 of 6 cases and highlights the sensitivity of this approach to unwanted body movements. The results of this project suggest the plausibility that non-smoothing video amplification processes can be an effective method to track breathing motion and that implementing correction techniques which may allow a smartphone to provide a compact, non-invasive, online breathing monitor.