Robotic estimation of single scuba diver respiration rate for safety in underwater human-robot collaboration

Abstract

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 scuba diver stress for underwater human-robot interaction (UHRI) scenarios is hitherto unexplored. In this paper, we introduce a novel approach for robotic estimation of HRR from underwater visual data by observing the volume of bubbles from exhalation states in scuba diving to measure the respiration rate. We propose and exploit a fuzzy labeling system that uses audio information to annotate exhalation and inhalation states based on the presence of bubbles. We collect an expansive audio and visual dataset of diver breathing from diverse geographical locations and water bodies, on which we compare four different methods for classifying the presence of bubbles in images. We then use a novel estimation algorithm to assess the diver respiration state from visual classification for computing the HRR in breaths per minute. Ultimately, we demonstrate the efficacy of our method in estimating HRR by comparing the respiration rate output against those measured by human analysts.