Swimming style recognition and lap counting using a smartwatch and deep learning
Gino Brunner, Darya Melnyk, Birkir Sigfússon, Roger P. Wattenhofer · 2019
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.