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Commenced in January 2007 Frequency: Monthly Edition: International Publications Count: 31239

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Video-Based System for Support of Robot-Enhanced Gait Rehabilitation of Stroke Patients
We present a dedicated video-based monitoring system for quantification of patient’s attention to visual feedback during robot assisted gait rehabilitation. Two different approaches for eye gaze and head pose tracking are tested and compared. Several metrics for assessment of patient’s attention are also presented. Experimental results with healthy volunteers demonstrate that unobtrusive video-based gaze tracking during the robot-assisted gait rehabilitation is possible and is sufficiently robust for quantification of patient’s attention and assessment of compliance with the rehabilitation therapy.
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[1] B.Kollen, G.Kwakkel, and E. Lindeman, "Functional Recovery After Stroke: A Review of Current Developments in Stroke Rehabilitation Research,” Reviews on Recent Clinical Trials, 1, 2006, pp. 75-80.
[2] J.Mehrholz, C. Werner, J.Kugler, and M. Pohl, "Electromechanicalassisted training for walking after stroke,”Cochrane Database Syst. Rev., 17(4), 2007.
[3] M.J.Matarić, J. Eriksson, D.J.Feil-Seifer, and C.J.Winstein, "Socially assistive robotics for post-stroke rehabilitation,”J.Neuroeng.Rehabil., 4(5), 2007.
[4] R.Teasell, and L.Kalra, "What's new in stroke rehabilitation: Back to basics,” Stroke, 36, 2005, pp. 215-217.
[5] Project BETTER,, 2013.
[6] D.W. Hansen, and Q.Ji, "In the eye of the beholder: A survey of models for eyes and gaze,”IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol. 32, Iss. 3, 2010, pp. 478-500.
[7] E. Bagherian, and R.W.O.K.Rahmat, "Facial feature extraction for face recognition: a review,”International Symposium on Information Technology, Kuala Lumpur, Malaysia, 2008, pp. 1 – 9.
[8] W.K. Liao, D.Fidaleo, and G.Medioni, "Robust, real-time 3D face tracking from a monocular view,”EURASIP Journal on Image and Video Processing, Vol. 2010, article ID 183605, 2010.
[9] A. Poole, and L.J Ball, "Eye tracking in human-computer interaction and usability research: Current status and future”, Encyclopedia of Human- Computer Interaction, C. Ghaouli, Pennsylvania, Idea Group, 2005.
[10] Q.Ji, and X. Yang, "Real-time eye, gaze and face pose tracking for monitoring driver vigilance,”Real-Time Imaging, 8, 2002, pp. 357-377.
[11] L. Lang, and H. Qi, "The study of driver fatigue monitor algorithm combined PERCLOS and AECS”, Proc. Int. Conf. on Comp. Science and Software Eng., Vol. 1, 2008.
[12] Q.Ji, P.Lan, and C. A. Looney, "Probabilistic framework for modeling and real-time monitoring human fatigue”, IEEE Trans. on Systems, Man and Cyb., Vol. 36, Iss. 5, 2006, pp. 862-875.
[13] M. Bakker, F. P. de Lange, J. A. Stevens, I. Toni, and B. R. Bloem, "Motor imagery of gait: a quantitative approach”, Exp Brain Res, 179, 2007, pp. 497–504.
[14] OpenCV, Open source computer vision library,, 2014.
[15] J.Sivic, M.Everingham, and A.Zisserman, "Who are you? Learning person specific classifiers from video,” Proc. of IEEE Conference on Computer Vision and Pattern Recognition, 2009, pp. 1145-1152.
[16] G. Loy, and A. Zelinsky, "A Fast Radial Symmetry Transform for Detecting Points of Interest,”IEEE PAMI, 25 (8),2003, pp 959-973.
[17] M.Asadifard, and J.Shanbezadeh, "Automatic Adaptive Center of Pupil Detection Using Face Detection and CDF Analysis,”Proc. of IMECS 2010 conf., Vol. I, Hong Kong, 2010.
[18] A.H. Gee, and R. Cipolla, "Determining the gaze of faces in images,” Image and Vision Computing, 12, 1994, pp. 639-647.
[19] I. Matthews, J. Xiao, and S. Baker, "2D vs. 3D Deformable Face Models: Representational Power, Construction, and Real-Time Fitting,”Internat. J. of Comput. Vision, 75(1), 2007, pp. 93-113.
[20] R.Oostenveld, and P.Praamstrac, "The five percent electrode system for high-resolution EEG and ERP measurements,”Clinical Neurophysiology, 112, 2001, pp. 713-719.
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