Journal article
(Original article)


Activity recognition in beach volleyball using a Deep Convolutional Neural Network


Publication Details
Author(s): Kautz T, Groh B, Hannink J, Jensen U, Strubberg H, Eskofier B
Publication year: 2017
Volume: 31
Journal issue: 6
Pages range: 1678–1705
ISSN: 1573-756X
Language: English

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.



How to cite
APA: Kautz, T., Groh, B., Hannink, J., Jensen, U., Strubberg, H., & Eskofier, B. (2017). Activity recognition in beach volleyball using a Deep Convolutional Neural Network. Data Mining and Knowledge Discovery, 31(6), 1678–1705. https://dx.doi.org/10.1007/s10618-017-0495-0

MLA: Kautz, Thomas, et al. "Activity recognition in beach volleyball using a Deep Convolutional Neural Network." Data Mining and Knowledge Discovery 31.6 (2017): 1678–1705.

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