  This paper presents an unobtrusive solution that can automatically identify
deep breath when a person is walking past the global depth camera. Existing
non-contact breath assessments achieve satisfactory results under restricted
conditions when human body stays relatively still. When someone moves forward,
the breath signals detected by depth camera are hidden within signals of trunk
displacement and deformation, and the signal length is short due to the short
stay time, posing great challenges for us to establish models. To overcome
these challenges, multiple region of interests (ROIs) based signal extraction
and selection method is proposed to automatically obtain the signal informative
to breath from depth video. Subsequently, graph signal analysis (GSA) is
adopted as a spatial-temporal filter to wipe the components unrelated to
breath. Finally, a classifier for identifying deep breath is established based
on the selected breath-informative signal. In validation experiments, the
proposed approach outperforms the comparative methods with the accuracy,
precision, recall and F1 of 75.5%, 76.2%, 75.0% and 75.2%, respectively. This
system can be extended to public places to provide timely and ubiquitous help
for those who may have or are going through physical or mental trouble.
