Abstract
Many injuries in sports are caused by overuse. These injuries are a major cause for reduced performance of professional and non-professional beach volleyball players. Monitoring of player actions could help identifying and understanding risk factors and prevent such injuries. Currently, time-consuming video examination is the only option for detailed player monitoring in beach volleyball. The lack of a reliable automatic monitoring system impedes investigations about the risk factors of overuse injuries. In this work, we present an unobtrusive automatic monitoring system for beach volleyball based on wearable sensors. We investigate the possibilities of Deep Learning in this context by designing a Deep Convolutional Neural Network for sensor-based activity classification. The performance of this new approach is compared to five common classification algorithms. With our Deep Convolutional Neural Network, we achieve a classification accuracy of 83.2%, thereby outperforming the other classification algorithms by 16.0%. Our results show that detailed player monitoring in beach volleyball using wearable sensors is feasible. The substantial performance margin between established methods and our Deep Neural Network indicates that Deep Learning has the potential to extend the boundaries of sensor-based activity recognition.
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According to the sensor data sheet (Bosch Sensortec 2014a), the sampling rate of the acceleration sensors in low-power mode is 40 Hz. However, our tests showed, that the actual sampling rate was only approximately 39 Hz.
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Acknowledgements
We would like to thank Markus N. Streicher, Alexander Stolpe, Christoph Eckelmann, Adib Taraben and David Reck for their valuable suggestions and their contributions to the development of the sensor hardware as well as the recording and labeling of the data. Bjoern Eskofier gratefully acknowledges the support of the German Research Foundation (DFG) within the framework of the Heisenberg professorship programme (grant number ES 434/8-1).
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Appendices
Appendix 1: Generic features
The following generic features were calculated for the classification with the NB, kNN, CART, SVM, RF and VOTE classifiers. The features with the highest ARI values that were used for the experiments with feature selection are printed in bold type.
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correlation between y-axis and z-axis
Appendix 2: Classification accuracy
The performance of the compared classification approaches is summarized in Table 4.
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Kautz, T., Groh, B.H., Hannink, J. et al. Activity recognition in beach volleyball using a Deep Convolutional Neural Network. Data Min Knowl Disc 31, 1678–1705 (2017). https://doi.org/10.1007/s10618-017-0495-0
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DOI: https://doi.org/10.1007/s10618-017-0495-0