Skip to main content
Article | Green Intelligent Systems and Applications | Volume 2 - Issue 1 - 2022 | 42-52 | https://doi.org/10.53623/gisa.v2i1.78
© 2022 by the author(s), and is licensed under the Creative Commons Attribution 4.0 International (CC BY 4.0) License Creative Commons Attribution 4.0 International (CC BY 4.0) License

Sarawak Traditional Dance Motion Analysis and Comparison using Microsoft Kinect V2

Author(s): Michael-Lian Gau 1 , Huong Yong Ting 1 , Jackie Tiew-Wei Ting 1 , Marcella Peter 1 , Khairunnisa Ibrahim 2
Author(s) information:
1 Drone Research and Application Centre, School of Computing and Creative Media, University of Technology Sarawak, No. 1, Jalan Universiti, 96000 Sibu, Sarawak
2 Advanced Centre for Sustainable Socio-Economic and Technological Development, School of Computing and Creative Media, University of Technology Sarawak, No. 1, Jalan Universiti, 96000 Sibu, Sarawak

Corresponding author

Green Intelligent Systems and Applications

Volume 2 - Issue 1 - 2022

 About the journal
Submit your article
This research project aimed to develop a software program or an interactive dance motion analysis application that utilizes modern technology to preserve and maintain the Sarawak traditional dance culture. The software program employs the Microsoft Kinect V2 to collect the digital dance data. The proposed method analyses the collected dance data for comparison purposes only. The comparison process was executed by displaying a traditional dance on the screen where the user who wants to learn the traditional dance can follow it and obtain results on how similar the dance is compared to the recorded dance data. The comparison of the performed and recorded dance data was visualized in graph form. The comparison graph showed that the Microsoft Kinect V2 sensors were capable of comparing the dance motion but with minor glitches in detecting the joint orientation. Using better depth sensors would make the comparison more accurate and less likely to have problems with figuring out how the joints move.
Read Full-Text
Previous article
Next article

State statistics: Malays edge past Chinese in Sarawak. (accessed on 15 March 2022) Available online: https://www.theborneopost.com/2014/02/08/state-statistics-malays-edge-past-chinese-in-sarawak/#:~:text=KUCHING%3A%20The%20Malays%20have%20overtaken,the%20Chinese%20stood%20at%20596%2C100.

Liu, X. (2013). The Influence of Video Games on 21st Century Youth Identity. Master Thesis. University of Victoria, Canada.

Huang, Yu; Huang, T.S. (2002). Model-based human body tracking. 2002 International Conference on Pattern Recognition, 1, 552-555. https://doi.org/10.1109/ICPR.2002.1044791.

Patra, B.G.; Das, D.; Bandyopadhyay, S. (2013). Unsupervised approach to Hindi music mood classification. Lecture Notes in Computer Science; Heidelberg, Germany; pp. 62–69. https://doi.org/10.1007/978-3-319-03844-5_7.

Protopapadakis, E.; Voulodimos, A.; Doulamis, A.; Camarinopoulos, S. (2017). A Study on the Use of Kinect Sensor in Traditional Folk Dances Recognition via Posture Analysis. Proceedings of the 10th International Conference on Pervasive Technologies Related to Assistive Environments, 305–310. https://doi.org/10.1145/3056540.3076200.

Li, M.; Miao, Z.; Xu, W. (2021). CRNN-based attention-seq2seq model with fusion feature for automatic labanotation generation. Neurocomputing, 454, 430-440. https://doi.org/10.1016/j.neucom.2021.05.036.

Abror, B.; Paramadihanto, D. (2017). Dance Motion Pattern Planning for K.Mei as Dancing Humanoid Robot. International Conference on Robotics, Biomimetics, and Intelligent Computational Systems (Robionetics), 6-11. https://doi.org/10.1109/ROBIONETICS.2017.8203428.

Jang, M.; Kim D.; Kim Y.; Kim J. (2017). Automated dance motion evaluation using Dynamic Time Warping and Laban Movement Analysis. IEEE International Conference on Consumer Electronics (ICCE). https://doi.org/10.1109/ICCE.2017.7889261.

Aristidou, A; Stavrakis, E.; Papaefthimiou, M; Papagiannakis, G.; Chrysanthou, Y. (2018). Style-based motion analysis for dance composition. The Visual Computer, 34, 12, 1725-1737. https://doi.org/10.1007/s00371-017-1452-z.

Shotton, J.; Fitzgibbon, A.; Cook, M.; Sharp, T.; Finocchio, M.; Moore, R.; Blake, A. (2011). Real-time human pose recognition in parts from single depth images. CVPR 2011. https://doi.org/10.1109/CVPR.2011.5995316.

Hemed, A. (2012). Motion Comparison using Microsoft Kinect. Project Report, Monash University, Melbourne.

Nitin Bhatia, V. (2010). Survey of Nearest Neighbor Techniques. International Journal of Computer Science and Information Security, 9, 302-305 https://doi.org/10.48550/arXiv.1007.0085.

Farid, D.M.; Zhang L.; Rahman C.M.; Hossain M.A.; Strachan, R. (2014). Hybrid decision tree and naïve Bayes classifiers for multi-class classification tasks. Expert Systems with Applications, 41, 1937–1946. https://doi.org/10.1016/j.eswa.2013.08.089.

Rokach, L.; Schclar, A.; Itach, E. (2014). Ensemble methods for multi-label classification. Expert Systems with Applications, 41, 16: 7507–7523. https://doi.org/10.1016/j.eswa.2014.06.015.

Daffertshofer, A; Lamoth, C.J.; Meijer, O.G.; Beek, P.J. (2004). PCA in studying coordination and variability: A tutorial. Clinical Biomechanics, 19, 415-428. https://doi.org/10.1016/j.clinbiomech.2004.01.005.

Hollands, K.; Wing, A.; Daffertshofer, A. (2004). Principal components analysis of contemporary dance kinematics. Proceedings of the 3rd IEEE EMBSS UK & RI, University of Southampton, Southampton.

Müller, M. (2007). Dynamic time warping. Information retrieval for music and motion. Springer: Berlin, Germany; pp. 69-84. https://doi.org/10.1007/978-3-540-74048-3_4.

Mangal, N.K.; Tiwari, A.K. (2020). Kinect v2 tracked Body Joint Smoothing for Kinematic Analysis in Musculoskeletal Disorders. Annual International Conference of the IEEE Engineering in Medicine & Biological Society, 5769-5772. https://doi.org/10.1109/EMBC44109.2020.9175492.

Hanson, A.J. (2006). Visualizing Quaternions (The Morgan Kaufmann Series in Interactive 3D Technology), 1st Ed.; Morgan Kaufmann: Burlington, United States.

Read Full-Text
About this article

SUBMITTED: 24 March 2022
ACCEPTED: 12 April 2022
PUBLISHED: 17 April 2022
SUBMITTED to ACCEPTED: 20 days
DOI: https://doi.org/10.53623/gisa.v2i1.78

Cite this article
Gau, M.-L., Ting, H. Y., Ting, J. T.-W., Peter, M., & Ibrahim, K. (2022). Sarawak Traditional Dance Motion Analysis and Comparison using Microsoft Kinect V2. Green Intelligent Systems and Applications, 2(1), 42–52. https://doi.org/10.53623/gisa.v2i1.78
Keywords
Microsoft Kinect Sarawak Dance Motion Analysis Motion Comparison
Accessed
561
Citations
0
Share this article