Abstract
Rehabilitation plays a very important role to achieve highest possible level of self-dependence by people with disability or affected with stroke or injury/surgery to spine or brain. Several assistive technologies, including the popular computer vision-based technologies made this task simpler and easier and affordable. The main difficulty is to track eyes accurately of the person with involuntary head movements using computer vision based techniques, where eye-tracking is used as the basic and essential step towards rehabilitation. Majority of the works (reported in literature) do not intend for real-time application for rehabilitation as higher complexity, longer processing time, requirement of special or wearable hardware prevent them to be used for intended application of rehabilitation. The present research uses Haar-classifier for detection of face and eye from the cluttered background and then Improved Hough transform is applied for accurate eye centre tracking. Experiment has been carried out with a person having involuntary head movements in different difficult/critical environments. The average efficiency of detection for the most critical situation (during night for user with spectacles at a distance of 213 cm or 7 ft) has reached 99%, which establishes the acceptance of the method for day-and-night rehabilitation of people.
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Sarkar, A.R., Sanyal, G., Majumder, S. (2018). Eye Tracking with Involuntary Head Movements for a Vision-Based Rehabilitation System. In: Mandal, J., Sinha, D. (eds) Social Transformation – Digital Way. CSI 2018. Communications in Computer and Information Science, vol 836. Springer, Singapore. https://doi.org/10.1007/978-981-13-1343-1_26
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DOI: https://doi.org/10.1007/978-981-13-1343-1_26
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