MINNEAPOLIS / ST. PAUL (07/27/2026) - University of Minnesota Twin Cities researchers have developed a first-of-its-kind AI system that allows underwater companion robots to monitor a diver's health in real-time, simply by "watching" their exhaled bubbles.
Published in The International Journal of Robotics Research, the paper marks the first time robotic vision has been used to estimate a diver's Human Respiration Rate (HRR).
Scuba diving, particularly in extreme environments, is inherently risky and places humans under intense physical stress — ranging from exhaustion to life-threatening respiratory distress. By tracking the frequency and volume of bubbles exhaled from a diver's regulator, camera-equipped robots can now detect signs of stress, hyperventilation or exhaustion in real-time.
This non-contact approach solves a long-standing challenge where traditional medical sensors and wearables often fail underwater because thick wetsuits or drysuits block the contact needed for accurate readings. Wireless data transmission through water is also severely limited.
"Our goal was to give divers a dedicated robotic safety partner to provide a second set of 'eyes' capable of reading physiological stress underwater," said Junaed Sattar, Associate Professor in the Department of Computer Science and Engineering and senior author on the paper. "This work is a first step towards assessing not just one but a group of divers in the robot's field of view."
To train the AI model, the research team developed a "fuzzy labeling" system. Because underwater footage can be murky, they manually categorized thousands of images while using synchronized audio cues made up of the distinct sound of regulator exhalations to teach the robot exactly what a breath looks like.
"While monitoring breathing is a standard vital sign on land, doing so underwater presents immense technical challenges," said Demetrious Kutzke, a Ph.D. student in the Robotics & Vision Laboratory at the University of Minnesota and the study's lead author.
To meet these challenges, the team compiled an extensive dataset of audio and visual recordings from various environments to ensure the AUV could operate in different water temperatures and levels of clarity. Data collection spanned locations from Lake Superior in Duluth, Minn. and Square Lake in Stillwater, Minn., to the Caribbean Sea off the coast of Barbados.
At the core of the field trials was a communication system called HREyes, where the robot could notify its human dive partner of their status, categorizing their breathing as "below-normal" (<14 breaths/min), "normal" (14–20 breaths/min) or "above-normal" (>20 breaths/min). By converting these visual observations into breaths-per-minute, the robot can determine if a diver is under duress.
Looking ahead, the team plans to pair breathing-rate data with the analysis of diver movement. Merging these metrics will provide a comprehensive "wellness profile" to ensure maximum safety during deep-sea explorations.
In addition to Sattar and Kutzke, the research team included Vennela Dupati, undergraduate student in the Department of Computer Science and Engineering and the Department of Electrical and Computer Engineering.
This research was supported in part by the Science, Mathematics, and Research for Transformation (SMART) Scholarship from the U.S. Department of Defense and the National Science Foundation.
Read the full paper entitled, "Robotic estimation of single scuba diver respiration rate for safety in underwater human-robot collaboration," on the Sage Journal's website.
