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Article

Badminton Activity Recognition Using Accelerometer Data

1
IDLab, Department of Information Technology, Ghent University-imec, 9000 Ghent, Belgium
2
WAVES, Department of Information Technology, Ghent University-imec, 9000 Ghent, Belgium
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(17), 4685; https://doi.org/10.3390/s20174685
Received: 8 July 2020 / Revised: 13 August 2020 / Accepted: 17 August 2020 / Published: 19 August 2020
(This article belongs to the Collection Sensor Technology for Sports Science)
A thorough analysis of sports is becoming increasingly important during the training process of badminton players at both the recreational and professional level. Nowadays, game situations are usually filmed and reviewed afterwards in order to analyze the game situation, but these video set-ups tend to be difficult to analyze, expensive, and intrusive to set up. In contrast, we classified badminton movements using off-the-shelf accelerometer and gyroscope data. To this end, we organized a data capturing campaign and designed a novel neural network using different frame sizes as input. This paper shows that with only accelerometer data, our novel convolutional neural network is able to distinguish nine activities with 86% precision when using a sampling frequency of 50 Hz. Adding the gyroscope data causes an increase of up to 99% precision, as compared to, respectively, 79% and 88% when using a traditional convolutional neural network. In addition, our paper analyses the impact of different sensor placement options and discusses the impact of different sampling frequenciess of the sensors. As such, our approach provides a low cost solution that is easy to use and can collect useful information for the analysis of a badminton game. View Full-Text
Keywords: badminton; activity recognition; accelerometer; gyroscope; DNN; CNN; neural network; machine learning badminton; activity recognition; accelerometer; gyroscope; DNN; CNN; neural network; machine learning
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MDPI and ACS Style

Steels, T.; Van Herbruggen, B.; Fontaine, J.; De Pessemier, T.; Plets, D.; De Poorter, E. Badminton Activity Recognition Using Accelerometer Data. Sensors 2020, 20, 4685. https://doi.org/10.3390/s20174685

AMA Style

Steels T, Van Herbruggen B, Fontaine J, De Pessemier T, Plets D, De Poorter E. Badminton Activity Recognition Using Accelerometer Data. Sensors. 2020; 20(17):4685. https://doi.org/10.3390/s20174685

Chicago/Turabian Style

Steels, Tim, Ben Van Herbruggen, Jaron Fontaine, Toon De Pessemier, David Plets, and Eli De Poorter. 2020. "Badminton Activity Recognition Using Accelerometer Data" Sensors 20, no. 17: 4685. https://doi.org/10.3390/s20174685

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