Detection of eye strain through blink rate and sclera area using raspberry-pi

References

• Available from: https://www.commonsensemedia.org/sites/default/files/uploads/research/0-8_executivesummary_release_final_1.pdf
   Google Scholar
• Available from: https://www.itu.int/osg/spu/ni/futuremobile/Youth.pdf
   Google Scholar
• Available from: https://www.rfsafe.com/study-cell-phone-radiation-can-damage-eyes-cause-early-cataracts/
   Google Scholar
• Lee EC, Park KR, Whang M, et al. Measuring the degree of eyestrain caused by watching LCD and PDP devices. Int J Ind Ergon. 2009;39(5):798–806. doi: 10.1016/j.ergon.2009.02.008
   Web of Science ®Google Scholar
• Available from: http://www.gmanetwork.com/news/lifestyle/healthandwellness/296961/more-kids-getting-dry-eyes-due-to-electronic-gadget-use/story/
   Google Scholar
• Available from: http://www.allaboutvision.com/cvs/children-computer-vision-syndrome.htm.
   Google Scholar
• Song K, Shen F, Liu Z, Liu Z. Eye detection and recognition in the fatigue warning system. IEEE International Conference on Intelligent Networks and Intelligent Systems (ICINIS), Shenyang, China, 2010 3rd; 2010. p. 36–38.
   Google Scholar
• Murawski K, Sondej T, Rozanowski K, et al. The contactless active optical sensor for vehicle driver fatigue detection. SENSORS, 2013 IEEE. (pp. 1–4)
   PubMedGoogle Scholar
• Rani PS, Subhashree P, Devi NS. Computer vision based gaze tracking for accident prevention. IEEE World Conference on Futuristic Trends in Research and Innovation for Social Welfare, Coimbatore, India; 2016. p. 1–6.
   Google Scholar
• Patel SN, Prakash V. Autonomous camera based eye controlled wheelchair system using raspberry-pi. IEEE International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS2015), Coimbatore, India; 2015. pp. 1–6.
   Google Scholar
• Stern JA, Boyer D, Schroeder DJ. Blink rate as a measure of fatigue: a review. Hum Factors J Hum Factors Ergon Soc. 1994;36(2):285–297. doi: 10.1177/001872089403600209
   PubMed Web of Science ®Google Scholar
• Do HC, You JY, Chien SI. Skin color detection through estimation and conversion of illuminant color using sclera region of eye under varying illumination. IEEE 18th International Conference on Pattern Recognition, (ICPR 2006), Hong Kong, China; Vol. 1; 2006. p. 327–330.
   Google Scholar
• Pardas M. Extraction and tracking of the eyelids. Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP'00, Istanbul, Turkey. Vol. 4. 2000.
   Google Scholar
• Chen BC, Wu PC, Chien SY. Real-time eye localization, blink detection, and gaze estimation system without infrared illumination. IEEE International Conference on Image Processing (ICIP). Quebec City, Canada: IEEE; 2015. p. 715–719.
   Google Scholar
• Pimplaskar D, Nagmode MS, Borkar A. Real time eye blinking detection and tracking using opencv. J Technol. 2015;13(14):15.
   Google Scholar
• Bhagirathi D, Malhan A. Human face, eye and iris detection in real-time using image processing. Int J Eng Res Appl. 2014;4(5):27–31.
   Google Scholar
• Kurylyak Y, Lamonaca F, Mirabelli G. Detection of the eye blinks for human's fatigue monitoring. IEEE International Symposium on Medical Measurements and Applications Proceedings (MeMeA2012), Budapest, Hungary; 2012. p. 1–4.
   Google Scholar
• Tafreshi M, Fotouhi AM. A fast and accurate algorithm to distinguish between open and closed eye by efficient combining of texture and appearance features. IEEE 22nd Iranian Conference on Electrical Engineering (ICEE2014), Tehran, Iran; 2014. p. 1013–1017.
   Google Scholar
• Gao XY, Zhang YF, Zheng WL, Lu BL. Evaluating driving fatigue detection algorithms using eye tracking glasses. 7th International IEEE/EMBS Conference on Neural Engineering (NER2015); 2015. p. 767–770.
   Google Scholar
• Mandal B, Li L, Wang GS, et al. Towards detection of bus driver fatigue based on robust visual analysis of eye state. IEEE Trans Intell Transp Syst. 2017;18(3):545–557. doi: 10.1109/TITS.2016.2582900
   Web of Science ®Google Scholar
• Alkassar S, Woo WL, Dlay SS, Chambers JA. Efficient eye corner and gaze detection for sclera recognition under relaxed imaging constraints. IEEE 24th European Signal Processing Conference (EUSIPCO2016); 2016. p. 1965–1969.
   Google Scholar
• Zhang F, Su J, Geng L, et al. Driver fatigue detection based on eye state recognition. IEEE International Conference on Machine Vision and Information Technology (CMVIT), Singapore; 2017. p. 105–110.
   Google Scholar
• Available from: http://www.intelligentappsinc.com/iDetox/
   Google Scholar
• Available from: http://www.cateye.com/en/products/detail/MSC-GC100/
   Google Scholar
• Wu J, Trivedi MM. An eye localization, tracking and blink pattern recognition system: algorithm and evaluation. ACM Trans (TOMM). 2010;6:599–601.
   Google Scholar
• Villanueva A, Ponz V, Sesma-Sanchez L, et al. Hybrid method based on topography for robust detection of iris center and eye corners. ACM Trans (TOMM). 2013;9:25.
   Google Scholar
• Zhu N, Wang G, Yang G, Dai W. A fast 2d Otsu thresholding algorithm based on improved histogram. Chinese Conference on Pattern Recognition (CCPR2009), Nanjing, China; 2009. p. 1–5.
   Google Scholar
• Viola P, Jones MJ. Robust real-time face detection. Int J Comput Vision. 2004;57(2):137–154. doi: 10.1023/B:VISI.0000013087.49260.fb
   Web of Science ®Google Scholar
• Kaehler A, Bradski G. Learning openCV 3: computer vision in C++ with the openCV library. O'Reilly Media; 2016.
   Google Scholar