Activity recognition in beach volleyball using a Deep Convolutional Neural Network

Beitrag in einer Fachzeitschrift
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Details zur Publikation

Autor(en): Kautz T, Groh B, Hannink J, Jensen U, Strubberg H, Eskofier B
Zeitschrift: Data Mining and Knowledge Discovery
Jahr der Veröffentlichung: 2017
Band: 31
Heftnummer: 6
Seitenbereich: 1678–1705
ISSN: 1573-756X
Sprache: Englisch


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.



FAU-Autoren / FAU-Herausgeber

Eskofier, Björn Prof. Dr.
Lehrstuhl für Informatik 14 (Maschinelles Lernen und Datenanalytik)
Groh, Benjamin
Lehrstuhl für Informatik 14 (Maschinelles Lernen und Datenanalytik)
Hannink, Julius
Stiftungs-Juniorprofessur für Sportinformatik (Digital Sports)
Jensen, Ulf
Stiftungs-Juniorprofessur für Sportinformatik (Digital Sports)
Kautz, Thomas
Stiftungs-Juniorprofessur für Sportinformatik (Digital Sports)


Zitierweisen

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.

BibTeX: 

Zuletzt aktualisiert 2018-19-04 um 04:04