Abstract
With the growing interest in the domain of human computer interaction these days, budding research professionals are coming up with novel ideas of develo** 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 chapter, we propose a novel application of gesture dependent e-learning of dance . This e-learning procedure may provide help to many dance enthusiasts who cannot learn the art because of scarcity of resources despite having great zeal. The chapter 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 kee** 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. To check the capability of the discussed method, it has been compared with various standard classifiers in terms of performance indices and in each case the proposed framework has surpassed or provided nearly equal performance as given by the other networks.
Contributed by Sriparna Saha, Rimita Lahiri and Amit Konar
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
D.S. Alexiadis, P. Kelly, P. Daras, N.E. O’Connor, T. Boubekeur, M. Ben Moussa, Evaluating a dancer’s performance using kinect-based skeleton tracking, in Proceedings of the 19th ACM international conference on Multimedia, 2011, pp. 659–662
S. Saha, S. Ghosh, A. Konar, A.K. Nagar, Gesture recognition from indian classical dance using kinect sensor, in Fifth International Conference on Computational Intelligence, Communication Systems and Networks (CICSyN), 2013, pp. 3–8
S.G. Wu, F.S. Bao, E. Y. Xu, Y.-X. Wang, Y.-F. Chang, Q.-L. **ang, A leaf recognition algorithm for plant classification using probabilistic neural network, in IEEE International Symposium on Signal Processing and Information Technology, 2007, pp. 11–16
L. Shang, D.-S. Huang, J.-X. Du, C.-H. Zheng, Palmprint recognition using fast ICA algorithm and radial basis probabilistic neural network. Neurocomputing 69(13), 1782–1786 (2006)
D.F. Specht, Probabilistic neural networks for classification, map**, or associative memory, in IEEE International Conference on Neural Networks, 1988, pp. 525–532
D.F. Specht, Probabilistic neural networks. Neural Netw. 3(1), 109–118 (1990)
S. Waldherr, R. Romero, S. Thrun, A gesture based interface for human-robot interaction. Auton. Robots 9(2), 151–173 (2000)
S. Essid, X. Lin, M. Gowing, G. Kordelas, A. Aksay, P. Kelly, T. Fillon, Q. Zhang, A. Dielmann, V. Kitanovski, A multi-modal dance corpus for research into interaction between humans in virtual environments. J. Multimodal User Interfaces 7(1–2), 157–170 (2013)
D. Xu, A neural network approach for hand gesture recognition in virtual reality driving training system of SPG, in 18th International Conference on Pattern Recognition, ICPR 2006, 2006, vol. 3, pp. 519–522
K. Murakami, H. Taguchi, Gesture recognition using recurrent neural networks, in Proceedings of the SIGCHI conference on Human factors in computing systems, 1991, pp. 237–242
K.Z. Mao, K.-C. Tan, W. Ser, Probabilistic neural-network structure determination for pattern classification. Neural Netw. IEEE Trans. 11(4), 1009–1016 (2000)
A. LaViers, Y. Chen, C. Belta, M. Egerstedt, Automatic sequencing of ballet poses. Robot. Autom. Mag. IEEE 18(3), 87–95 (2011)
F. Guo, G. Qian, Dance posture recognition using wide-baseline orthogonal stereo cameras, in 7th International Conference on Automatic Face and Gesture Recognition, FGR 2006, 2006, pp. 481–486
B. Peng, G. Qian, S. Rajko, View-invariant full-body gesture recognition via multilinear analysis of voxel data, in Third ACM/IEEE International Conference on Distributed Smart Cameras, ICDSC 2009, 2009, pp. 1–8
Y. Lee, K. Jung, Non-temporal mutliple silhouettes in hidden Markov model for view independent posture recognition, in International Conference on Computer Engineering and Technology, ICCET’09. 2009, vol. 1, pp. 466–470
P. Silapasuphakornwong, S. Phimoltares, C. Lursinsap, A. Hansuebsai, “Posture recognition invariant to background, cloth textures, body size, and camera distance using morphological geometry,” in International Conference on Machine Learning and Cybernetics (ICMLC), 2010, vol. 3, pp. 1130–1135
F. Buccolieri, C. Distante, A. Leone, Human posture recognition using active contours and radial basis function neural network, in IEEE Conference on Advanced Video and Signal Based Surveillance, AVSS 2005, 2005, pp. 213–218
N.M. Tahir, A. Hussain, S.A. Samad, H. Hussin, On the use of decision tree for posture recognition, in International Conference on Intelligent Systems, Modelling and Simulation (ISMS), 2010, pp. 209–214
A. Konar, Artificial Intelligence and Soft Computing: Behavioral and Cognitive Modeling of the Human Brain (vol. 1. CRC Press, Boca Raton, 1999)
A. Alvarez-Alvarez, G. Trivino, O. Cordón, Body posture recognition by means of a genetic fuzzy finite state machine, in IEEE 5th International Workshop on Genetic and Evolutionary Fuzzy Systems (GEFS), 2011, pp. 60–65
K.K. Htike, O.O. Khalifa, Comparison of supervised and unsupervised learning classifiers for human posture recognition, in International Conference on Computer and Communication Engineering (ICCCE), 2010, pp. 1–6
T. Shiratori, A. Nakazawa, K. Ikeuchi, Synthesizing dance performance using musical and motion features, in Proceedings 2006 IEEE International Conference on Robotics and Automation, ICRA 2006. 2006, pp. 3654–3659
L. Kovar, M. Gleicher, F. Pighin, Motion graphs, in ACM SIGGRAPH 2008 classes, 2008, p. 51
K. Pullen, C. Bregler, Motion capture assisted animation: Texturing and synthesis. ACM Trans. Graph. (TOG) 21(3), 501–508 (2002)
O. Arikan, D.A. Forsyth, Interactive motion generation from examples. ACM Trans. Graph. (TOG) 21(3), 483–490 (2002)
K. Grochow, S.L. Martin, A. Hertzmann, Z. Popović, Style-based inverse kinematics. ACM Trans. Graph. (TOG) 23(3), 522–531 (2004)
T. Kim, S. Il Park, S.Y. Shin, Rhythmic-motion synthesis based on motion-beat analysis. ACM Trans. Graph. (TOG) 22(3), pp. 392–401 (2003)
M. Stone, D. DeCarlo, I. Oh, C. Rodriguez, A. Stere, A. Lees, C. Bregler, Speaking with hands: Creating animated conversational characters from recordings of human performance. ACM Trans. Graph. 23(3), 506–513 (2004)
M.-H. Lee, U.-M. Kim, J.-I. Park, An analysis methodology on emotion of Korean traditional dance using a virtual reality system, in Second International Conference on Culture and Computing (Culture Computing), 2011, pp. 149–150
J.A. Russell, L.F. Barrett, Core affect, prototypical emotional episodes, and other things called emotion: dissecting the elephant. J. Pers. Soc. Psychol. 76(5), 805 (1999)
J. Posner, J.A. Russell, B.S. Peterson, The circumplex model of affect: an integrative approach to affective neuroscience, cognitive development, and psychopathology. Dev. Psychopathol. 17(3), 715–734 (2005)
J.A. Russell, M. Lewicka, T. Niit, A cross-cultural study of a circumplex model of affect. J. Pers. Soc. Psychol. 57(5), 848 (1989)
S. Saha, M. Pal, A. Konar, R. Janarthanan, Neural network based gesture recognition for elderly health care using kinect sensor, in Swarm, Evolutionary, and Memetic Computing, Springer, 2013, pp. 376–386
S. Saha, S. Datta, A. Konar, R. Janarthanan, A study on emotion recognition from body gestures using Kinect sensor, in International Conference on Communications and Signal Processing (ICCSP), 2014, pp. 56–60
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG
About this chapter
Cite this chapter
Konar, A., Saha, S. (2018). Probabilistic Neural Network Based Dance Gesture Recognition. In: Gesture Recognition. Studies in Computational Intelligence, vol 724. Springer, Cham. https://doi.org/10.1007/978-3-319-62212-5_6
Download citation
DOI: https://doi.org/10.1007/978-3-319-62212-5_6
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-62210-1
Online ISBN: 978-3-319-62212-5
eBook Packages: EngineeringEngineering (R0)