Abstract
This study design a system prototype to control a mouse cursor's movement on a computer using an electrooculogram (EOG) signal. The EOG signal generated from eye movement was processed utilizing a microcontroller with an analog to the digital conversion process, which communicates with the computer through a USB port. The signal was decomposed using continuous wavelet transform (CWT), followed by feature extraction processes using statistic calculation, and then classified using K-Nearest Neighbors (k-NN) to decide the movement and direction of the mouse cursor. The test was carried out with 110 EOG signals then separated, 0.5 as training data and 0.5 as test data with eight categories of directional movement patterns, including up, bottom, right, left, top right, top left, bottom right bottom left. The highest accuracy that can be achieved using CWT-bump and kurtosis is 100%, while the time needed to translate the eye movement to the cursor movement is 1.9792 seconds. It is hoped that the proposed system can help assistive devices, particularly for Amyotrophic Lateral Sclerosis (ALS) sufferers.
Highlights
Modern technology in the health sector in monitoring and as a tool for bodily functions makes it very easy for its users
The EOG signal generated from eye movement was processed utilizing a microcontroller with an analog to the digital conversion process, which communicates with the computer through a USB port
It is hoped that the proposed system can help assistive devices, for Amyotrophic Lateral Sclerosis (ALS) sufferers
Summary
Modern technology in the health sector in monitoring and as a tool for bodily functions makes it very easy for its users. The application of the HCI system based on eye movements as a human-computer interaction communication was applied to patients with Amyotrophic Lateral Sclerosis (ALS) or other diseases that experience paralysis of the hands [3]– [6]. This study proposes a mouse cursor control system using EOG signals. The raw EOG signal was decomposed using a wavelet transform and calculating the statistical features into a feature vector that becomes the classification algorithm's input. This system was designed to move the mouse cursor, including up, bottom, right, left, top right, top left, bottom right, and bottom left. The final section briefly describes the conclusions and implications of this study
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