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Every year, an average of 3,536 people die from drowning in America. The significant factors that cause unintentional drowning are people’s lack of water safety awareness and swimming proficiency. Current industry and research trends regarding swimming activity recognition and commercial motion sensors focus more on lap swimming utilized by expert swimmers and do not account for freeform activities. Enhancing swimming education through wearable technology can aid people in learning efficient and effective swimming techniques and water safety. We developed a novel wearable system capable of storing and processing sensor data to categorize competitive and survival swimming activities on a mobile device in real-time. This paper discusses the sensor placement, the hardware and app design, and the research process utilized to achieve activity recognition. For our studies, the data we have gathered comes from various swimming skill levels, from beginner to elite swimmers. Our wearable system uses angle-based novel features as inputs into optimal machine learning algorithms to classify flip turns, traditional competitive strokes, and survival swimming strokes. The machine-learning algorithm was able to classify all activities at .935 of an F-measure. Finally, we examined deep learning and created a CNN model to classify competitive and survival swimming strokes at 95% ac- curacy in real-time on a mobile device.
Powell, Larry; Polsley, Seth; Casey, Drew; and Hammond, Tracy
"The Real-Time Classification of Competency Swimming Activity Through Machine Learning,"
International Journal of Aquatic Research and Education: Vol. 14:
1, Article 6.
Available at: https://scholarworks.bgsu.edu/ijare/vol14/iss1/6
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