A Low Cost System for 3D Motion Analysis Using Microsoft Kinect

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Many motion analysis systems which have been introduced in the past few years are currently receiving interests from researchers and developers due to their usefulness and wide application capability in the future. However, many of those systems meet with difficulties for the real applications because of high cost for the implementation and less accuracy. This paper introduces a new 3D motion analysis system which can be implemented at a lower cost and acceptable accuracy for various applications. The key component of our new system is the use of the MSK (Microsoft Kinect) sensor system which is equipped with both visual camera and infrared camera. It can provide the color image, the 3D depth image and the 3D skeleton data without wearing any marker device on the human body while it can provide acceptable accuracy in 3D motion trace at low cost. Our system can be exploited for a base framework for various 3D motion-based applications such as physical rehabilitation support, sport motion analysis and biomechanical applications.

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January 2013

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© 2013 Trans Tech Publications Ltd. All Rights Reserved

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[1] D. Zetu, P. Banerjee and D. Thompson: Extended-range hybrid tracker and applications to motion and camera tracking in manufacturing systems, Robotics and Automation, Vol. 16, no. 3 (2000), pp.281-293.

DOI: 10.1109/70.850646

Google Scholar

[2] M. S. Balteanu: Professional Risk Management Using Computerized Monitoring, Risk Management, Assessment & Mitigation Conference (2010).

Google Scholar

[3] J. R. Humm, et al: A biomechanical analysis of ballet dancers on pointe, Proceedings of the 16th Annual International Conference of the IEEE , Vol. 1 (1994), pp.374-375.

Google Scholar

[4] R. Wang, W. K. Leow and H. W. Leong: 3D-2D Spartiotemporal Registration for Sport Motion Analysis, Computer Vision and Pattern Recognition (2008), pp.1-8.

DOI: 10.1109/cvpr.2008.4587528

Google Scholar

[5] D. Fitzgerald, et al: Development of a Wearable Motion Capture Suit and Virtual Reality Biofeedback System for the Instruction and Analysis of Sports Rehabilitation Exercises, Proceeding of the 29th Annual International Conference of the IEEE EMBS (2007).

DOI: 10.1109/iembs.2007.4353431

Google Scholar

[6] S. You and U. Neumann: Fusion of Vision and Gyro Tracking for Robust Augmented Reality Registration, Virtual Reality (2001).

DOI: 10.1109/vr.2001.913772

Google Scholar

[7] C. Tomasi and T. Kanade: Detection and Tracking of Point Features, International Journal of Computer Vision (1991).

Google Scholar

[8] D. G. Lowe: Distinctive Image Features from Scale-Invariant Keypoints, International Journal of Computer Vision (2004).

DOI: 10.1023/b:visi.0000029664.99615.94

Google Scholar

[9] M. Liu, C. Wu and Y. Zhang: Multi-resolution optical flow tracking algorithm based on multi-scale Harris corner points feature, Control and Decision Conference (2008), pp.5287-5291.

DOI: 10.1109/ccdc.2008.4598340

Google Scholar

[10] P. Azad, T. Asfour and R. Dillmann: Combining Harris interest points and the SIFT descriptor for fast scale-invariant object recognition, Intelligent Robots and Systems (2009), pp.4275-4280.

DOI: 10.1109/iros.2009.5354611

Google Scholar

[11] N. Nordin, U. Soori, M. R. Arshad: 3D human motion sensing from multiple cameras, Intelligent and Advanced Systems (2007), pp.325-329.

DOI: 10.1109/icias.2007.4658400

Google Scholar

[12] N. Plokas, et al: Rigid and Non-rigid 3D Motion Estimation from Multi View Image Sequences, Signal Processing: Image Communication, Vol. 18 (2003), pp.185-202.

DOI: 10.1016/s0923-5965(02)00131-5

Google Scholar

[13] O. Akman: Multi-Camera Visual Surveillance for Motion Detection, Occlusion Handling, Tracking and Event Recoginition, Workshop on Multi-camera and Multi-modal Sensor Fusion Algorithms and Applications, (2008), pp.1-12.

Google Scholar

[14] K. Khoshelham and S. J. O. Elberink: Accuracy and resolution of Kinect depth data for indoor mapping applications, Sensors : journal on the science and technology of sensors and biosensors (2012), pp.1437-1454.

DOI: 10.3390/s120201437

Google Scholar

[15] P. M. Shilpa and N. T. Sanjay: Dynamic Motion Detection Technique for Fast and Efficient Video Coding, TENCON 2008 - 2008 IEEE Region 10 Conference (2008), pp.1-5.

DOI: 10.1109/tencon.2008.4766715

Google Scholar

[16] M.I.A. Lourakis, A.A. Argyros and S.C. Orphanoudakis: Independent 3D motion detection using residual parallax normal flow fields, Computer Vision (1998), pp.1012-1017.

DOI: 10.1109/iccv.1998.710840

Google Scholar

[17] A. A. Argyros, M.I.A. Lourakis, P.E. Trahanias and S.C. Orphanoudakis: Qualitative detection of 3D motion discontinuities, Intelligent Robots and Systems '96, vol. 3(1996), pp.1630-1637.

DOI: 10.1109/iros.1996.569030

Google Scholar