Human Skeleton Matching for E-learning of Dance Using a Probabilistic Neural Network

Saha, S. and Lahiri, R. and Konar, A. and Banerjee, B. and Nagar, Atulya K. (2016) Human Skeleton Matching for E-learning of Dance Using a Probabilistic Neural Network. In: 2016 International Joint Conference on Neural Networks, 24-29 July 2016, Vancouver, Canada. (Accepted for Publication)

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5. IJCNN-17074-Human Skeleton Matching for E-learning of Dance Using A Probabilistic Neural Network.pdf

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Official URL: http://ieeexplore.ieee.org/document/7727411/

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.

Item Type: Conference or Workshop Item (Paper)
Additional Information and Comments: (c) 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works."
Keywords: human computer interaction; e-learning of dance; Kinect sensor; probabilistic neural network
Faculty / Department: Faculty of Science > Mathematics and Computer Science
Depositing User: Atulya Nagar
Date Deposited: 09 Jun 2016 11:40
Last Modified: 24 Nov 2016 12:13
URI: http://hira.hope.ac.uk/id/eprint/1448

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