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In the 34 developed and 156 developing countries, there are about 132 milliondisabled people who need a wheelchair constituting 1.86% of the worldpopulation. Moreover, there are millions of people suffering from diseasesrelated to motor disabilities, which cause inability to produce controlledmovement in any of the limbs or even head.The paper proposes a system to aidpeople with motor disabilities by restoring their ability to move effectivelyand effortlessly without having to rely on others utilizing an eye-controlledelectric wheelchair. The system input was images of the users eye that wereprocessed to estimate the gaze direction and the wheelchair was movedaccordingly. To accomplish such a feat, four user-specific methods weredeveloped, implemented and tested; all of which were based on a benchmarkdatabase created by the authors.The first three techniques were automatic,employ correlation and were variants of template matching, while the last oneuses convolutional neural networks (CNNs). Different metrics to quantitativelyevaluate the performance of each algorithm in terms of accuracy and latencywere computed and overall comparison is presented. CNN exhibited the bestperformance (i.e. 99.3% classification accuracy), and thus it was the model ofchoice for the gaze estimator, which commands the wheelchair motion. The systemwas evaluated carefully on 8 subjects achieving 99% accuracy in changingillumination conditions outdoor and indoor. This required modifying a motorizedwheelchair to adapt it to the predictions output by the gaze estimationalgorithm. The wheelchair control can bypass any decision made by the gazeestimator and immediately halt its motion with the help of an array ofproximity sensors, if the measured distance goes below a well-defined safetymargin.

An Intelligent and Low-cost Eye-tracking System for Motorized Wheelchair Control