Data Mining and Knowledge Discovery

, Volume 31, Issue 6, pp 1678–1705 | Cite as

Activity recognition in beach volleyball using a Deep Convolutional Neural Network

Leveraging the potential of Deep Learning in sports
  • Thomas KautzEmail author
  • Benjamin H. Groh
  • Julius Hannink
  • Ulf Jensen
  • Holger Strubberg
  • Bjoern M. Eskofier
Part of the following topical collections:
  1. Sports Analytics


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.


Deep Learning Convolutional Neural Network Activity classification Wearable sensors Sports 



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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Copyright information

© The Author(s) 2017

Authors and Affiliations

  • Thomas Kautz
    • 1
    Email author
  • Benjamin H. Groh
    • 1
  • Julius Hannink
    • 1
  • Ulf Jensen
    • 1
  • Holger Strubberg
    • 2
  • Bjoern M. Eskofier
    • 3
  1. 1.Digital Sports GroupFriedrich-Alexander-Universität Erlangen-Nürnberg (FAU)ErlangenGermany
  2. 2.Zentrum für Hochschulsport (ZfH)Universität LeipzigLeipzigGermany
  3. 3.ErlangenGermany

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