17 March 2017 Initial proposition of kinematics model for selected karate actions analysis
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Proceedings Volume 10341, Ninth International Conference on Machine Vision (ICMV 2016); 103410N (2017) https://doi.org/10.1117/12.2268402
Event: Ninth International Conference on Machine Vision, 2016, Nice, France
The motivation for this paper is to initially propose and evaluate two new kinematics models that were developed to describe motion capture (MoCap) data of karate techniques. We decided to develop this novel proposition to create the model that is capable to handle actions description both from multimedia and professional MoCap hardware. For the evaluation purpose we have used 25-joints data with karate techniques recordings acquired with Kinect version 2. It is consisted of MoCap recordings of two professional sport (black belt) instructors and masters of Oyama Karate. We have selected following actions for initial analysis: left-handed furi-uchi punch, right leg hiza-geri kick, right leg yoko-geri kick and left-handed jodan-uke block. Basing on evaluation we made we can conclude that both proposed kinematics models seems to be convenient method for karate actions description. From two proposed variables models it seems that global might be more useful for further usage. We think that because in case of considered punches variables seems to be less correlated and they might also be easier to interpret because of single reference coordinate system. Also principal components analysis proved to be reliable way to examine the quality of kinematics models and with the plot of the variable in principal components space we can nicely present the dependences between variables.
© (2017) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Tomasz Hachaj, Tomasz Hachaj, Katarzyna Koptyra, Katarzyna Koptyra, Marek R. Ogiela, Marek R. Ogiela, } "Initial proposition of kinematics model for selected karate actions analysis", Proc. SPIE 10341, Ninth International Conference on Machine Vision (ICMV 2016), 103410N (17 March 2017); doi: 10.1117/12.2268402; https://doi.org/10.1117/12.2268402


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