  Human respiration rate (HRR) is an important physiological metric for
diagnosing a variety of health conditions from stress levels to heart
conditions. Estimation of HRR is well-studied in controlled terrestrial
environments, yet robotic estimation of HRR as an indicator of diver stress in
underwater for underwater human robot interaction (UHRI) scenarios is to our
knowledge unexplored. We introduce a novel system for robotic estimation of HRR
from underwater visual data by utilizing bubbles from exhalation cycles in
scuba diving to time respiration rate. We introduce a fuzzy labeling system
that utilizes audio information to label a diverse dataset of diver breathing
data on which we compare four different methods for characterizing the presence
of bubbles in images. Ultimately we show that our method is effective at
estimating HRR by comparing the respiration rate output with human analysts.
