AquaScan : a sonar-based underwater sensing system for human activity monitoring

Abstract

Human activity monitoring in the water is essential for pool management and drowning prevention. Existing camera-based solutions pose significant concerns about privacy and extra installation costs. Although sonars have been widely used for underwater sensing in open aquatic environments such as oceans and lakes, monitoring human activities with sonars in a pool setup is still challenging. In this work, we propose AquaScan, the first scanning sonar-based underwater sensing system for human activity monitoring. To overcome the low frame rate due to the sonar's physical limitation, we propose a novel scanning strategy and apply an image reconstruction method to accelerate the scanning speed without compromising the performance of motion detection. To overcome the dynamic interferences in the underwater scenario, we develop a novel signal processing pipeline based on a physical model to remove noises and localize human subjects. We further extract features like motion, time, and spatial information from sonar images and develop a state-transfer-based activity recognition system to recognize five common water activities, i.e., swimming, motionless, splashing, struggling, and drowning. We have deployed AquaScan on three public swimming pools for a total period of 94 hours. The evaluation results show that AquaScan can successfully recognize the five activities in the water with around 91.5%.