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.

• Matjaž Divjak
• Simon Zelič
• Aleš Holobar

• Video-based attention monitoring
• gaze estimation
• stroke rehabilitation
• user compliance.

[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, http://www.car.upm-csic.es/bioingenieria/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, http://opencv.org/, 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.