Motion Capture of Human for Interaction with Service Robot

Alexander Gmiterko¹ and Tomáš Lipták^1,

¹Department of Applied Mechanics and Mechatronics, Faculty of Mechanical Engineering, Technical University of Košice, Košice, Slovakia

Cite this paper:
Alexander Gmiterko and Tomáš Lipták. Motion Capture of Human for Interaction with Service Robot. American Journal of Mechanical Engineering. 2013; 1(7):212-216. doi: 10.12691/ajme-1-7-12

Abstract

This article deals about the issue of the motion capture of human for interaction with service robots. In the framework of article were used the results of the Bachelor thesis, which dealt with this issue . Motion capture is a rapidly emerging technology, via which is possible quickly, easily and mainly highly detailed to record movements of the scanned subject and continue to work with them and then to control service robots. The article discusses the methods motion capture and design three variant solutions of motion capture, from which was selected optimal variant on the basis of selected criteria and for the chosen variant was created algorithm.

Keywords:
motion capture service robot algorithm MATLAB

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References:

[1]	Lipták, T., Motion capture of human for interaction with service robot, Bachelor Thesis, Technical University of Košice, Košice, 2011.
[2]	Tatsuya Harada, Taketoshi Mori, Tomomasa Sato, “Human Posture Probability Density Estimation Based on Actual Motion Measurement and Eigenpostures,” Systems, Man and Cybernetics, 2004 IEEE International Conference on (Volume:2 ), pp. 1595-1600, 10-13 Oct. 2004.
[3]	Jun Wang, Chongzhao Han, Yonggang Hu and Yanfeng Chen, “Markers Rectifying in the Motion Capture System,” Electronic and Mechanical Engineering and Information Technology (EMEIT), 2011 International Conference on (Volume:9 ), pp. 4554-4558, 12-14 Aug. 2011.
[4]	A. D. Young, “Use of Body Model Constraints to ImproveAccuracy of Inertial Motion Capture,” Body Sensor Networks (BSN), 2010 International Conference on, pp. 180-186, 7-9 June 2010.
[5]	Jian Li, Jiushan Yang, Zhanwang Xu, and Jingliang Peng, “Computer-assisted hand rehabilitation assessment using an optical motion capture system,” Image Analysis and Signal Processing (IASP), 2012 International Conference on, pp. 1-5, 9-11 Nov. 2012.
[6]	Masashi Uchinoumi, Joo Kooi, Tan Seiji Ishikawa, “A Simple-Structured Real-Time Motion Capture System Employing Silhouette Images,” Systems, Man and Cybernetics, 2004 IEEE International Conference on (Volume:4 ), pp. 3094-3098, 10-13 Oct. 2004.