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Human respiration rate (HRR) is an important physiological metric fordiagnosing a variety of health conditions from stress levels to heartconditions. Estimation of HRR is well-studied in controlled terrestrialenvironments, yet robotic estimation of HRR as an indicator of diver stress inunderwater for underwater human robot interaction (UHRI) scenarios is to ourknowledge unexplored. We introduce a novel system for robotic estimation of HRRfrom underwater visual data by utilizing bubbles from exhalation cycles inscuba diving to time respiration rate. We introduce a fuzzy labeling systemthat utilizes audio information to label a diverse dataset of diver breathingdata on which we compare four different methods for characterizing the presenceof bubbles in images. Ultimately we show that our method is effective atestimating HRR by comparing the respiration rate output with human analysts.

Robotic Detection and Estimation of Single Scuba Diver Respiration Rate from Underwater Video