The Use of Image Processing Methods to Improve the Detection of User’s Hand in Vision Based Games Used in Neurological Rehabilitation
Vision based games is a type of software that can become a promising, modern neurorehabilitation tool. This paper presents the possibilities offered for the implementation of this kind of software by the open source vision library. The methods and functions related to the aspect of image processing and analysis are presented in terms of their usefulness in creating programs based on the analysis of the images acquired from the camera. On the basis of the issues contained in the paper, the functionality of the library is presented in terms of the possibilities related primarily to the processing of video sequences, detection, tracking and analysis of the movement of objects.
As part of the work, the software that meets the requirements for modern neurorehablitation games has been implemented. Its main part is responsible for the identification of the current position of the user's hand and is based on the image captured from the webcam. Whereas the tasks set for the user used among others supporting visual-motor coordination.
The main subject of the research was the analysis of the impact of the applied methods of initial image processing on the correctness of the chosen tracking algorithm. It was proposed and experimentally examined the impact of operations such as morphological transformations or apply an additional mask on a functioning of the CamShift algorithm. And hence on the functioning of the whole game which analyzing the user's hand movement.
Allen G. J., Richard Xu Y. D., Jin J. S. (2004). Object Tracking Using CAMShift Algorithm and Multiple Quantized Feature Spaces, Proceedings of the Pan-Sydney area workshop on Visual information processing , Sydney, 3-7.
Bradski G., Kaehler A. (2008). Learning OpenCV. Computer Vision with the OpenCV Library, Sebastopol, CA: O'Reilly Media.
Buczyński P. (2005). Optymalna reprezentacja kolorów w analizie i przetwarzaniu obrazów komputerowych, Praca doktorska. Warszawa: Politechnika Warszawska.
Burke J. W., Morrow P.J., et al. (2008). Vision Based Games for Upper-Limb Stroke Rehabilitation, Machine Vision and Image Processing Conference, 159 - 164.
Burke J. W. McNeill M. D. J., et al. (2010). Designing engaging, playable games for rehabilitation”, International Conference Series On Disability, Virtual Reality and Associated Technologies (ICDVRAT), 195-202.
Cameirão M.S. , et al. (2010). Neurorehabilitation using the virtual reality based Rehabilitation Gaming System: methodology, design, psychometrics, usability and validation, Journal of NeuroEngineering and Rehabilitation, 7, 48.
Comaniciu D., Ramesh V., Meer P. (2003). Kernel-based object tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Transactions 2003, p. 564-577.
Derpanis K. G. (2005). Mean Shift Clustering, http://www.cse.yorku.ca/~kosta/ Comp-Vis_Notes/mean_shift.pdf
Di Loreto I., Gouaich A., Hocine N., (2011). Mixed reality serious games for post-stroke rehabilitation, Pervasive Computing Technologies for Healthcare , 5th International Conference on, 530-537.
Garcia-Marin J., Felix-Navarro K., Law-rence E. (2011). Serious games to Improve the Physical Health of the Elderly: A Categorization Scheme, Fourth International Conference on Advances in Human-oriented and Personalized Mechanisms, Technologies, and Services (CENTRIC 2011), 64-71.
Jog A., Halbe S. (2013). Multiple Objects Tracking Using CAMShift Algorithm and Implementation of Trip Wire, International Journal of Image, Graphics and Signal Processing, 43-48.
Joshi S., Gujarathi S., Mirgemoving A. (2014). Moving object tracking method using improved camshift with surf algorithm. International Journal of Advances in Science Engineering and Technology, 2(2), 14-19.
Laganière R. (2011). “OpenCV 2 Computer Vision Application Programming Cookbook”, Packt Publishing, 2011.
Lange B., Flynn S.M., Rizzo A. A., (2009). Game-based telerehabilitation, European Journal of Physical and Rehabilitation Medicine, 45(1), 143-151.
Rafajłowicz E, Rafajłowicz W. (2010). Wstęp do przetwarzania obrazów przemysłowych, Wrocław: Oficyna Wydawnicza Politechniki Wrocławskiej.
Rayavel P., Appasami G., Nakeeran R. (2011). Noise removal for object tracking based on HSV color space parameter using CAMSHIFT. International Journal of Computational Intelligence & Telecommunication Systems, 2(1), 39–45.
Yilmaz A., Javed O., Shah M. (2006). Object tracking: A survey, ACM Computing Surveys, 38(4), Article 13, 1-45.