Electrooculography based blink detection to prevent Computer Vision Syndrome

The present work proposes an artificial system capable of preventing Computer Vision Syndrome from the analysis of eye movements. Ocular data is recorded using an Electrooculogram signal acquisition system developed in the laboratory. Wavelet detail coefficients obtained using Haar and Daubechies order 4 mother wavelets are used as signal features. From the recorded data, blinks are classified from any other type of eye movements using Support Vector Machine (SVM) classifier with different kernel functions. We obtain a maximum average accuracy of 95.83% over all classes and participants using second order polynomial kernel SVM classifier. Then the trained classifier has been used in real time to detect blinks. The system is designed to count the number of blinks in a particular interval of time thereby reminding people working on a computer for long periods to rest and blink frequently in case of insufficient number of blinks. We validate the method using a study on ten participants in real time.

[1]  John B. Shoven,et al.  I , Edinburgh Medical and Surgical Journal.

[2]  A. Graser,et al.  Brain-Computer Interface for high-level control of rehabilitation robotic systems , 2007, 2007 IEEE 10th International Conference on Rehabilitation Robotics.

[3]  Tobias Wissel,et al.  Considerations on Strategies to Improve EOG Signal Analysis , 2011, Int. J. Artif. Life Res..

[4]  M H Cuypers,et al.  The EOG in Best's disease and dominant cystoid macular dystrophy (DCMD). , 1996, Ophthalmic genetics.

[5]  Jong-Soo Lee,et al.  Driver’s eye blinking detection using novel color and texture segmentation algorithms , 2012 .

[6]  Amit Konar,et al.  Single channel electrooculogram(EOG) based interface for mobility aid , 2012, 2012 4th International Conference on Intelligent Human Computer Interaction (IHCI).

[7]  Chun-Liang Hsu,et al.  EOG-based Human-Computer Interface system development , 2010, Expert Syst. Appl..

[8]  A. Djohan.  ECG COMPFtESSION USING DISCRETE TRANSFORM SYMMETRIC WAVELET , 1995 .

[9]  M. Ursino,et al.  Visual and computer-based detection of slow eye movements in overnight and 24-h EOG recordings , 2007, Clinical Neurophysiology.

[10]  A R Fielder,et al.  ERG and EOG abnormalities in carriers of X-linked retinitis pigmentosa , 1996, Eye.

[11]  Andrés Úbeda,et al.  Multimodal human-machine interface based on a brain-computer interface and an electrooculography interface , 2011, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[12]  C. Blehm,et al.  Computer vision syndrome: a review. , 2005, Survey of ophthalmology.

[13]  A. Udayashankar,et al.  Assistance for the Paralyzed Using Eye Blink Detection , 2012, 2012 Fourth International Conference on Digital Home.

[14]  Chusak Limsakul,et al.  Feature Extraction and Reduction of Wavelet Transform Coefficients for EMG Pattern Classification , 2012 .

[15]  Vandana Kaushik,et al.  Computer Vision Syndrome (CVS): Recognition and Control in Software Professionals , 2009 .

[16]  P. Corcoran,et al.  A Statistical Modeling based System for Blink Detection in Digital Cameras , 2008, 2008 Digest of Technical Papers - International Conference on Consumer Electronics.

[17]  Ferat Sahin,et al.  EOG controlled mobile robot using Radial Basis Function Networks , 2009, 2009 Fifth International Conference on Soft Computing, Computing with Words and Perceptions in System Analysis, Decision and Control.

[18]  J. S. Sahambi,et al.  Design and Development of a Novel EOG Biopotential Amplifier , 2005 .

[19]  Sarah C. Smith,et al.  Computer vision syndrome , 2015, Glavvrač (Chief Medical Officer).

[20]  M. Simonetta,et al.  Abnormal ocular movements in Parkinson's disease. Evidence for involvement of dopaminergic systems. , 1989, Brain : a journal of neurology.

[21]  R. Panda,et al.  Classification of EEG signal using wavelet transform and support vector machine for epileptic seizure diction , 2010, 2010 International Conference on Systems in Medicine and Biology.

[22]  Thorsten Joachims,et al.  Text Categorization with Support Vector Machines: Learning with Many Relevant Features , 1998, ECML.

[23]  L. Young,et al.  Survey of eye movement recording methods , 1975 .

[24]  Manuel Mazo,et al.  EOG guidance of a wheelchair using neural networks , 2000, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000.

[25]  Eugene Coyle,et al.  On Improving Electrooculogram-based Computer Mouse Systems: the Accelerometer Trigger , 2011 .

[26]  R. Brereton,et al.  Support vector machines for classification and regression. , 2010, The Analyst.