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

Abstract

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.

Dancing Salsa with Machines—Filling the Gap of Dancing Learning Solutions

“…Currently, there are multiple dancing ITSs [6,7,8,9,10,17,18]; however, to the best of our knowledge each of the systems is designed to support just one specific dance style such as the system described in Reference [17] that exclusively supports Korean pop dancing or the system in Reference [18] that exclusively supports Forró. With this common approach, to build a new ITSs for dancing one has to start from scratch.…”

3D Analysis of Upper Limbs Motion During Rehabilitation Exercises Using the Kinect^TM Sensor: Development, Laboratory Validation and Clinical Application

“…In total 19 local coordinate systems (LCS), following ISB recommendations [27] for axis orientation, were located in the origins indicated by numbers (1)(2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19) in Figure 1. Then 33 LCSs origin motions relative to parent LCSs (Table 1) Each of 33 trajectory plots was processed to assess different properties of the shape created by hodograph.…”

“…motion analysis, posture analysis, feedback during rehabilitation exercises,etc.) [1][2][3][4][5][6][7].…”

Fruit Morphological Measurement Based on Three-Dimensional Reconstruction

“…The recent success of RGB-D cameras such as the Kinect sensor depicts a broad prospect of three-dimensional data-based computer applications [32]. In recent years, with the development of low-cost sensors such as Microsoft Kinect and Intel RealSense, RGB-D images have been widely used in face database [33][34][35], hand gesture [36][37][38][39][40], head pose [41][42][43], and skeleton information [44][45][46]. Compared with the RGB image, the RGB-D image can provide additional information about the object's three-dimensional geometric structure and provide more effective information for object localization and 3D measurement.…”