Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

Research on Skeleton Data Compensation of Gymnastics based on Dynamic and Static Two-dimensional Regression using Kinect

Research on Skeleton Data Compensation of Gymnastics based on Dynamic and Static Two-dimensional... AbstractThe intelligent training and assessment of gymnastics movements require studying motion trajectory and reconstructing the character animation. Microsoft Kinect has been widely used due to its advantages of low price and high frame rate. However, its optical characteristics are inevitably affected by illumination and occlusion. It is necessary to reduce data noise via specific algorithms. Most of the existing research focuses on local motion but lacks consideration of the whole human skeleton. Based on the analysis of the spatial characteristics of gymnastics and the movement principle of the human body, this paper proposes a dynamic and static two-dimensional regression compensation algorithm. Firstly, the constraint characteristics of human skeleton motion were analyzed, and the maximum constraint table and Mesh Collider were established. Then, the dynamic acceleration of skeleton motion and the spatial characteristics of static limb motion were calculated based on the data of adjacent effective skeleton frames before and after the collision. Finally, using the least squares polynomial fitting to compensate and correct the lost skeleton coordinate data, it realizes the smoothness and rationality of human skeleton animation. The results of two experiments showed that the solution of the skeleton point solved the problem caused by data loss due to the Kinect optical occlusion. The data compensation time of an effective block skeleton point can reach 180 ms, with an average error of about 0.1 mm, which shows a better data compensation effect of motion data acquisition and animation reconstruction. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Measurement Science Review de Gruyter

Research on Skeleton Data Compensation of Gymnastics based on Dynamic and Static Two-dimensional Regression using Kinect

Measurement Science Review , Volume 22 (6): 10 – Dec 1, 2022
10 pages

Loading next page...
 
/lp/de-gruyter/research-on-skeleton-data-compensation-of-gymnastics-based-on-dynamic-nZ70ZOuOVZ

References

References for this paper are not available at this time. We will be adding them shortly, thank you for your patience.

Publisher
de Gruyter
Copyright
© 2022 Gang Zhao et al., published by Sciendo
ISSN
1335-8871
eISSN
1335-8871
DOI
10.2478/msr-2022-0036
Publisher site
See Article on Publisher Site

Abstract

AbstractThe intelligent training and assessment of gymnastics movements require studying motion trajectory and reconstructing the character animation. Microsoft Kinect has been widely used due to its advantages of low price and high frame rate. However, its optical characteristics are inevitably affected by illumination and occlusion. It is necessary to reduce data noise via specific algorithms. Most of the existing research focuses on local motion but lacks consideration of the whole human skeleton. Based on the analysis of the spatial characteristics of gymnastics and the movement principle of the human body, this paper proposes a dynamic and static two-dimensional regression compensation algorithm. Firstly, the constraint characteristics of human skeleton motion were analyzed, and the maximum constraint table and Mesh Collider were established. Then, the dynamic acceleration of skeleton motion and the spatial characteristics of static limb motion were calculated based on the data of adjacent effective skeleton frames before and after the collision. Finally, using the least squares polynomial fitting to compensate and correct the lost skeleton coordinate data, it realizes the smoothness and rationality of human skeleton animation. The results of two experiments showed that the solution of the skeleton point solved the problem caused by data loss due to the Kinect optical occlusion. The data compensation time of an effective block skeleton point can reach 180 ms, with an average error of about 0.1 mm, which shows a better data compensation effect of motion data acquisition and animation reconstruction.

Journal

Measurement Science Reviewde Gruyter

Published: Dec 1, 2022

Keywords: Azure Kinect; motion capture; motion tracking; motion compensation

There are no references for this article.