Quantifying swimming activities using accelerometer signal processing and machine learning: A pilot study

Abstract

Aerobic exercises on land could be quantified and tracked objectively, but swimming style recognition has remained unexplored. Taking the advantages of signal processing and machine learning on acceleration signals, the purpose of this study was, by analyzing swimming accelerometer data, to explore a set of algorithm in tracking swimming activities, including recognizing swimming styles, counting time and counting strokes in each style. A total of 17 participants (9 females) from the swimming team of the Southeast University of China was recruited. They performed breaststroke, front crawl, backstroke and butterfly, four 50-meter-lap each, with an ActiGraph GT9X inertia measurement unit on wrist of their preferred side. Overall, 78.7 ± 14.6, 148.5 ± 21.7, 151.2 ± 14.4, 98 ± 16.3 strokes were performed and evaluated on breaststroke, front crawl, backstroke and butterfly, respectively. In classification, three classifiers were examined and the result showed that support vector machine (SVM) provided the best accuracy of classification (over 99%). In time counting, the accuracy was over 99% and in stroke counting, the overall single-lap accuracy rate was 93.3%. In conclusion, with a combination of an objective measure and machine-learning algorithm, tracking swimming activities, including swimming style classification, counting swimming time and strokes, by a accelerometer becomes possible.