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

• Published in: Sensors (Basel, Switzerland) Vol. 17; no. 6; p. 1261
• Main Authors: Kim, Dohyung, Kim, Dong-Hyeon, Kwak, Keun-Chang
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 01.06.2017; MDPI
• Subjects: Algorithms; Classification; Dance; Dancing; Data points; Discriminant Analysis; Human motion; Humans; Motion capture; Movement; Neural networks; Principal components analysis; Sensors; Support Vector Machine; Support vector machines; dimensionality reduction; fisherdance; K-pop dance movements; extreme learning machine; skeletal joint data
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s17061261

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.