| dc.description.abstract | Swimming is a complex and dangerous sport. A recent study found that swimming is the third leading cause of death among children in the world each year. A significant factor contributing to these statistics may be the limitations of current approaches to water-based education. As such, the Red Cross and Bangladesh have started investing in research into water-based education. Current technology, monitors only the main swim styles backstroke, breaststroke, butterfly, and freestyle. These existing systems are missing additional activities, such as rest (treading water), transitions (flip turns), and low energy strokes (sidestroke). These additional activities have an effect on a person’s swimming ability, and they form the baseline for what is taught by the Red Cross, Bangladesh, and the military. We developed and tested an aqua-tracker system for monitoring swimmers in all forms of activities expected from a swimming-based training session. Our system uses a waterproof mobile device to capture a swimmer’s flip-turns, ability to tread water, sidestroke, freestyle, backstroke, breaststroke, and butterfly strokes. Activities are recognized using a sliding-window framework, comparing both a deep learning and a feature-based recognition system. Our tracker has shown that the system can accurately detect each of the activities, from beginner to expert level, with an f-measure of .94. Equipped with the capabilities provided by our aqua-tracker system, people can monitor their own swimming ability, parents can monitor their children while they are in the water, and lifeguards and swimmers taking proficiency exams will be able to perform the exams without the needs of a proctor. | |
