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Swimming style recognition and lap counting using a smartwatch and deep learning

Human activity recognition from raw sensor data has enabled modern wearable devices to track and analyze everyday activities. However, when used in real world conditions, the performance of off-the-shelf devices is often insufficient. This paper tackles the problem of swimming style recognition and lap counting using sensor data from a single smartwatch. In total 17 hours of this data was collected from 40 swimmers of diverse backgrounds. The data was then used to train a convolutional neural network to recognize the four main swimming styles, transition periods and lap turns. Our method achieves an F1 score of 97.4% for style recognition and 99.2% for counting laps. To the best of our knowledge, these results are the first to enable accurate automatic swimming recognition in a realistic and completely uncontrolled environment.