Neural Enhanced Underwater SOS Detection

Every day, one person loses his life due to drowning in swimming pools, even with professional lifeguards present. Contrary to what the public might assume, drowning swimmers can hardly splash or yell for help. This life-threatening situation calls for a robust SOS channel between the swimmers and the lifeguards. This paper proposes Neusos, a neural-enhanced underwater SOS communication system based on commercial wearable devices and low-cost hydrophones deployed in the swimming pool. Specifically, we repurpose popular wearable devices (e.g., smartwatches) as SOS transmitters, which can send a distress signal when the user is in an emergency. In response, an underwater hydrophone in the swimming pool can detect SOS signals and make alerts immediately to facilitate a timely rescue. The main technical challenge lies in reliably detecting weak SOS signals in non-stationary underwater scenarios. To achieve so, we thoroughly characterize the properties of underwater channels and examine the limitations of the traditional correlation-based signal detection method in underwater communication scenarios. Based on our empirical findings, we developed a robust SOS detection method enhanced with deep learning. By fully embedding signal characteristics into networks, Neusos outperforms traditional signal processing-based underwater SOS detection methods. In particular, our experiments in a real swimming pool show that Neusos can detect SOS signals with a detection rate of 98.2% under various underwater conditions. Given the increasing popularity of smartwatches among swimmers, our system holds immense potential to enhance their safety in swimming pools.