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.

翻译：人类呼吸率（HRR）是诊断从压力水平到心脏状况等多种健康状态的重要生理指标。在受控的陆地环境中，HRR的估计已得到充分研究，然而，在水下人机交互（UHRI）场景中，通过机器人估计HRR作为潜水员压力指标的研究，据我们所知尚未探索。我们提出了一种新颖的系统，利用水肺潜水呼气周期中的气泡来计时呼吸率，从而从水下视觉数据中估计HRR。我们引入了一种模糊标记系统，利用音频信息对多样化潜水员呼吸数据集进行标记，并在此数据集上比较了四种描述图像中气泡存在性的不同方法。最终，通过将呼吸率输出与人类分析师的结果进行比较，我们证明了该方法在估计HRR方面的有效性。