Abstract
The aim of this paper is to propose and evaluate the novel method of template generation, matching, comparing and visualization applied to motion capture (kinematic) analysis. To evaluate our approach, we have used motion capture recordings (MoCap) of two highly-skilled black belt karate athletes consisting of 560 recordings of various karate techniques acquired with wearable sensors. We have evaluated the quality of generated templates; we have validated the matching algorithm that calculates similarities and differences between various MoCap data; and we have examined visualizations of important differences and similarities between MoCap data. We have concluded that our algorithms works the best when we are dealing with relatively short (2–4 s) actions that might be averaged and aligned with the dynamic time warping framework. In practice, the methodology is designed to optimize the performance of some full body techniques performed in various sport disciplines, for example combat sports and martial arts. We can also use this approach to generate templates or to compare the correct performance of techniques between various top sportsmen in order to generate a knowledge base of reference MoCap videos. The motion template generated by our method can be used for action recognition purposes. We have used the DTW classifier with angle-based features to classify various karate kicks. We have performed leave-one-out action recognition for the Shorin-ryu and Oyama karate master separately. In this case, actions were correctly classified. In another experiment, we used templates generated from Oyama master recordings to classify Shorin-ryu master recordings and vice versa. In this experiment, the overall recognition rate was , which is a very good result for this type of complex action.
Highlights
Human movements and actions analysis is a very interesting and up to date subject of wide research
Signal averaging and smoothing are common approaches. Those signal processing techniques are very often used in motion capture (MoCap) for missing sample complementing, averaging data from many captures of the same activity or removing the high frequency noises of the overall solution [14,15,16,17,18,19]
The aim of this paper is to introduce and evaluate the novel method of template generation, matching, comparing and visualization applied to motion capture analysis of motion capture data, which is a valuable extension of already published works [37]
Summary
Human movements and actions analysis is a very interesting and up to date subject of wide research. Human movements can be analyzed by various methods, for example by kinetic modeling [1,2,3], kinematic [3,4,5,6,7,8], electromyography [4,6,9,10,11], gait and posture analysis [12] and pattern recognition [13]. Quite often, those evaluations require statistical analysis of data from several measurements of the same action.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
