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Abstract Drowning is the major cause of death in self-contained underwater breathing apparatus (SCUBA) diving. This study proposes an embedded system with a live and light-weight algorithm which detects the breathing of divers through the analysis of the intermediate pressure (IP) signal of the SCUBA regulator. A system composed mainly of two pressure sensors and a low-power microcontroller was designed and programmed to record the pressure sensors signals and provide alarms in absence of breathing. An algorithm was developed to analyze the signals and identify inhalation events of the diver. A waterproof case was built to accommodate the system and was tested up to a depth of 25 m in a pressure chamber. To validate the system in the real environment, a series of dives with two different types of workload requiring different ranges of breathing frequencies were planned. Eight professional SCUBA divers volunteered to dive with the system to collect their IP data in order to participate to validation trials. The subjects underwent two dives, each of 52 min on average and a maximum depth of 7 m. The algorithm was optimized for the collected dataset and proved a sensitivity of inhalation detection of 97.5% and a total number of 275 false positives (FP) over a total recording time of 13.9 h. The detection algorithm presents a maximum delay of 5.2 s and requires only 800 bytes of random-access memory (RAM). The results were compared against the analysis of video records of the dives by two blinded observers and proved a sensitivity of 97.6% on the data set. The design includes a buzzer to provide audible alarms to accompanying dive buddies which will be triggered in case of degraded health conditions such as near drowning (absence of breathing), hyperventilation (breathing frequency too high) and skip-breathing (breathing frequency too low) measured by the improper breathing frequency. The system also measures the IP at rest before the dive and indicates with flashing light-emitting diodes and audible alarm the ...

In order to obtain robust and measurable data from the marine environment, citizen science projects need user-friendly tools that, during dives, autonomously gather information in an easy, low-cost and integrated way. Data collected from different devices and by different untrained divers must have good estimation of the position where the information has been acquired. With this goal, in the context of the Green Bubbles project, a novel platform, called DocuScooter has been designed and developed. DocuScooter implements all the algorithms to filter and merge data with correct timestamp and position preparing the complete mission report. After the mission, the diver can upload the report on an appropriate web service to produce a 3D documentation. In order to obtain the underwater position during the dive, a tailored device compatible with the platform is also presented. Result of the prototype of the platform and the first results of the position estimation algorithm are presented and discussed. ; This paper has received funding from the European Union (EU)'s H2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 643712 to the project Green Bubbles RISE for sustainable diving (Green Bubbles). This paper reflects only the authors' view. The Research Executive Agency is not responsible for any use that may be made of the information it contains. © 2017 Panebianco et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and build upon your work non-commercially.