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Article

Classification of K-Pop Dance Movements Based on Skeleton Information Obtained by a Kinect Sensor

1
Electronics and Telecommunications Research Institute (ETRI), Daejeon 34129, Korea
2
Department of Control and Instrumentation Engineering, Chosun University, Gwangju 61452, Korea
*
Author to whom correspondence should be addressed.
Academic Editor: Vittorio M. N. Passaro
Sensors 2017, 17(6), 1261; https://doi.org/10.3390/s17061261
Received: 4 April 2017 / Revised: 27 May 2017 / Accepted: 30 May 2017 / Published: 1 June 2017
(This article belongs to the Section Physical Sensors)
This paper suggests a method of classifying Korean pop (K-pop) dances based on human skeletal motion data obtained from a Kinect sensor in a motion-capture studio environment. In order to accomplish this, we construct a K-pop dance database with a total of 800 dance-movement data points including 200 dance types produced by four professional dancers, from skeletal joint data obtained by a Kinect sensor. Our classification of movements consists of three main steps. First, we obtain six core angles representing important motion features from 25 markers in each frame. These angles are concatenated with feature vectors for all of the frames of each point dance. Then, a dimensionality reduction is performed with a combination of principal component analysis and Fisher’s linear discriminant analysis, which is called fisherdance. Finally, we design an efficient Rectified Linear Unit (ReLU)-based Extreme Learning Machine Classifier (ELMC) with an input layer composed of these feature vectors transformed by fisherdance. In contrast to conventional neural networks, the presented classifier achieves a rapid processing time without implementing weight learning. The results of experiments conducted on the constructed K-pop dance database reveal that the proposed method demonstrates a better classification performance than those of conventional methods such as KNN (K-Nearest Neighbor), SVM (Support Vector Machine), and ELM alone. View Full-Text
Keywords: dimensionality reduction; extreme learning machine; fisherdance; K-pop dance movements; skeletal joint data dimensionality reduction; extreme learning machine; fisherdance; K-pop dance movements; skeletal joint data
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MDPI and ACS Style

Kim, D.; Kim, D.-H.; Kwak, K.-C. Classification of K-Pop Dance Movements Based on Skeleton Information Obtained by a Kinect Sensor. Sensors 2017, 17, 1261. https://doi.org/10.3390/s17061261

AMA Style

Kim D, Kim D-H, Kwak K-C. Classification of K-Pop Dance Movements Based on Skeleton Information Obtained by a Kinect Sensor. Sensors. 2017; 17(6):1261. https://doi.org/10.3390/s17061261

Chicago/Turabian Style

Kim, Dohyung, Dong-Hyeon Kim, and Keun-Chang Kwak. 2017. "Classification of K-Pop Dance Movements Based on Skeleton Information Obtained by a Kinect Sensor" Sensors 17, no. 6: 1261. https://doi.org/10.3390/s17061261

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