Human skeleton matching for e-learning of dance using a probabilistic neural network

Abstract

Abstract-With the growing interest in the domain of human computer interaction (HCI) these days, budding research professionals are coming up with novel ideas of developing more versatile and flexible modes of communication between a man and a machine. Using the attributes of internet, the scientists have been able to create a web based social platform for learning any desired art by the subject himself/herself, and this particular procedure is termed as electronic learning or e-learning. In this paper, we propose a novel application of gesture dependent elearning of dance. This e-learning procedure may provide help to many dance enthusiasts who cannot learn the art because of the scarcity of resources despite having great zeal. The paper mainly deals with recognition of different dance gestures of a trained user such that after detecting the discrepancies between the gestures shown and actually performed by a novice; the user can rectify his faults. The elementary knowledge of geometry has been employed to introduce the concept of planes in the feature extraction stage. Actually, five planes have been constructed to signify major body parts while keeping the synchronous parts in one unit. Then four distances and four angular features have been obtained to provide entire positional information of the different body joints. Finally, using a probabilistic neural network the dance gestures have been classified after training the said network with sufficient amount of data recorded from numerous subjects to maintain generality.

Human–Machine Interaction Using Probabilistic Neural Network for Light Communication Systems

“…It was shown that a cross-validation type PNN produced better results (by a few percent), although the complexity order increases from O(n) to O(n 2 ). Dance routines were identified with 91.7% accuracy using captured Kinect video images by extracting features following the estimation of angles and displacement distances of limbs during movement [31,32]. It is beneficial to reduce the complexity by using an efficient representation of the data using as short a code word as possible.…”

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

“…In this paper, we focus on a markerless capture method based on the skeletal joint data of human motion utilizing a Kinect camera in a motion-capture studio environment for the classification of K-pop dance movements. The previous works have been focused on ballet analysis [ 16 , 17 ], video recommendation based on dance styles [ 18 ], dance pose estimation [ 19 , 20 ], dance animation [ 21 ], and e-learning of dance [ 22 ]. While some ballet movements and dance pose estimation have previously been studied in various aspects [ 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 ], nobody has yet performed research on K-pop dance movements using Kinect sensors to address the problem of dance plagiarism.…”