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Towards Real-Time Multi-Sensor Golf Swing Classification Using Deep CNNs

Towards Real-Time Multi-Sensor Golf Swing Classification Using Deep CNNs

Libin Jiao, Hao Wu, Rongfang Bie, Anton Umek, Anton Kos
Copyright: © 2018 |Volume: 29 |Issue: 3 |Pages: 26
ISSN: 1063-8016|EISSN: 1533-8010|EISBN13: 9781522542261|DOI: 10.4018/JDM.2018070102
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MLA

Jiao, Libin, et al. "Towards Real-Time Multi-Sensor Golf Swing Classification Using Deep CNNs." JDM vol.29, no.3 2018: pp.17-42. http://doi.org/10.4018/JDM.2018070102

APA

Jiao, L., Wu, H., Bie, R., Umek, A., & Kos, A. (2018). Towards Real-Time Multi-Sensor Golf Swing Classification Using Deep CNNs. Journal of Database Management (JDM), 29(3), 17-42. http://doi.org/10.4018/JDM.2018070102

Chicago

Jiao, Libin, et al. "Towards Real-Time Multi-Sensor Golf Swing Classification Using Deep CNNs," Journal of Database Management (JDM) 29, no.3: 17-42. http://doi.org/10.4018/JDM.2018070102

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Abstract

In recent years, smart sports equipment and body sensor systems have become popular in professional and amateur sports. One of a few remaining problems in real-time applications is the discovery of knowledge from the embedded sensors data. In sports training, such knowledge helps accelerated motor learning. The authors start with exploring the possibilities of the classification of golf swing performance with the 1-D convolutional neural network (CNN) in real-time. They thoroughly investigate multiple golf swing data classifiers based on CNNs fed with multi-sensor signals. The authors test the possibilities of real-time performance of CNN methods on the multi-length sequences. In addition, they thoroughly evaluate the performance of their well-trained CNN-based classifier on the aforementioned test set in terms of common indicators. Experiments and corresponding results show that the authors' models can satisfy the real-time requirement of the accuracy of the classification and outperform support vector machine (SVM).

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