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Eye Blink Detection and Alarm System to Reduce Symptoms of Computer Vision Syndrome

  • Atheer K. Alsaif (College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU)) ;
  • Abdul Rauf Baig (College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU))
  • Received : 2023.05.05
  • Published : 2023.05.30

Abstract

In recent years, and with the increased adoption of digital transformation and spending long hours in front of these devices, clinicians have observed that the prolonged use of visual display units (VDUs) can result in a certain symptom complex, which has been defined as computer vision syndrome (CVS). This syndrome has been affected by many causes, such as light refractive errors, poor computer design, workplace ergonomics, and a highly demanding visual task. This research focuses on eliminating one of CVSs, which is the eye dry syndrome caused by infrequent eye blink rate while using a smart device for a long time. This research attempt to find a limitation on the current tools. In addition, exploring the other use cases to utilize the solution based on each vertical and needs.

Keywords

References

  1. Hassanat, A. B., Albustanji, A., Tarawneh, A. S., Alrashidi, M., Alharbi, H., Alanazi, M., ..'. & Prasath, V. B. (2021). Deep learning for identification and face, gender, expression recognition under constraints. arXiv preprint arXiv:2111.01930.
  2. Dementyev, A., & Holz, C. (2017). DualBlink: A Wearable Device to Continuously Detect, Track, and Actuate Blinking For Alleviating Dry Eyes and Computer Vision Syndrome. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol., 1, 1:1-1:19. https://doi.org/10.1145/3053330
  3. George, S., Pai, M.M., Pai, R.M., & Praharaj, S.K. (2017). Eye blink count and eye blink duration analysis for deception detection. 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI), 223-229.
  4. Macdonald, A. (2022, August 26). Deep-learning advance improves facial recognition for people wearing veils. Biometric Update |. https://www.biometricupdate.com/202208/deep-learning-advance-improves-facial-recognition-for-people-wearing-veils
  5. Phatchuay, S., Yooyen, A., & Ketcham, M. (2016). The detection eye blink on Working production line in Industrial. 2016 13th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON), 1-5.
  6. Sikandar, T., Ghazali, K. H., Mohd, I. I., & Rabbi, M. F. (2017, July). Skin color pixel classification for face detection with hijab and niqab. In Proceedings of the International Conference on Imaging, Signal Processing and Communication (pp. 1-4).
  7. A Alashbi, A., Sunar, M. S., & Alqahtani, Z. (2022). Deep-Learning-CNN for Detecting Covered Faces with Niqab. Journal of Information Technology Management, 14(Special Issue: 5th International Conference of Reliable Information and Communication Technology (IRICT 2020)), 114-123.
  8. B. Zheng, N. J. Yuan, K. Zheng, X. Xie, S. W. Sadiq, and X. Zhou, "Approximate keyword search in semantic trajectory database," in 31st IEEE International Con- ference on Data Engineering, ICDE 2015, Seoul, South Korea, April 13-17, 2015, 2015, pp. 975-986.
  9. Cech, J., & Soukupova, T. (2016). Real-time eye blink detection using facial landmarks. Cent. Mach. Perception, Dep. Cybern. Fac. Electr. Eng. Czech Tech. Univ. Prague, 1-8.
  10. R. A. Johnson, D. W. Wichern et al., Applied multivariate statistical analysis. Prentice-Hall New Jersey, 2014, vol. 4.
  11. J. F. Hair, W. C. Black, B. J. Babin, R. E. Anderson, R. L. Tatham et al., Multivariate data analysis. Prentice hall Upper Saddle River, NJ, 1998, vol. 5, no. 3.
  12. Jebakani, S., Divya, J., Megha, S., & Santhosh, H. (2020). Eye blink to voice for paralyzed patients. International Journal of Information Technology (IJIT), 6(3).
  13. J. E. Barlett, J. W. Kotrlik, and C. C. Higgins, "Organizational research: Determin- ing appropriate sample size in survey research," Information technology, learning, and performance journal, vol. 19, no. 1, p. 43, 2001.
  14. M. S. Islam, C. Liu, and J. Li, "Efficient answering of why-not questions in similar graph matching," IEEE Trans. Knowl. Data Eng., vol. 27, no. 10, pp. 2672-2686, 2015. https://doi.org/10.1109/TKDE.2015.2432798
  15. M. S. Islam, R. Zhou, and C. Liu, "On answering why-not questions in reverse skyline queries," in 29th IEEE International Conference on Data Engineering, ICDE 2013, Brisbane, Australia, April 8-12, 2013, 2013, pp. 973-984.
  16. Z. He and E. Lo, "Answering why-not questions on top-k queries," IEEE Trans. Knowl. Data Eng., vol. 26, no. 6, pp. 1300-1315, 2014. https://doi.org/10.1109/TKDE.2012.158
  17. T. Pelkonen, S. Franklin, P. Cavallaro, Q. Huang, J. Meza, J. Teller, and K. Veer- araghavan, "Gorilla: A fast, scalable, in-memory time series database," PVLDB, vol. 8, no. 12, pp. 1816-1827, 2015.
  18. J. J. Levandoski, M. Sarwat, A. Eldawy, and M. F. Mokbel, "Lars: A location-aware recommender system," in ICDE, 2012, pp. 450-461.
  19. W. G. Aref and H. Samet, "Efficient processing of window queries in the pyramid data structure," in PODS, 1990, pp. 265-272.
  20. A. Marian, N. Bruno, and L. Gravano, "Evaluating top-k queries over web-accessible databases," ACM Trans. Database Syst., vol. 29, no. 2, pp. 319-362, 2004. https://doi.org/10.1145/1005566.1005569
  21. R. Fagin, A. Lotem, and M. Naor, "Optimal aggregation algorithms for middleware," J. Comput. Syst. Sci., vol. 66, no. 4, pp. 614-656, 2003. https://doi.org/10.1016/S0022-0000(03)00026-6
  22. Pulli, K., Baksheev, A., Kornyakov, K., & Eruhimov, V. (2012). Real-time computer vision with OpenCV. Communications of the ACM, 55(6), 61-69. https://doi.org/10.1145/2184319.2184337
  23. Dewi, C., Chen, R. C., Jiang, X., & Yu, H. (2022). Adjusting eye aspect ratio for strong eye blink detection based on facial landmarks. PeerJ Computer Science, 8, e943.
  24. Nguyen, N. D., Quang, N. D., Tin, D. T., & Dinh, A. (2020). Detecting and Counting Eyes Blinking Using Haar Cascade-A Handy Way to Diagnose Dry Eyes Disease. In 7th International Conference on the Development of Biomedical Engineering in Vietnam (BME7) Translational Health Science and Technology for Developing Countries 7 (pp. 447-453). Springer Singapore.
  25. Kamarudin, N., Jumadi, N. A., Mun, N. L., Keat, N. C., Ching, A. H. K., Mahmud, W. M., ... & Mahmud, F. (2019). Implementation of haar cascade classifier and eye aspect ratio for driver drowsiness detection using raspberry Pi. Universal Journal of Electrical and Electronic Engineering, 6(12), 2019.
  26. Asmara, R. A., Ridwan, M., & Budiprasetyo, G. (2021, September). Haar Cascade and Convolutional Neural Network Face Detection in Client-Side for Cloud Computing Face Recognition. In 2021 International Conference on Electrical and Information Technology (IEIT) (pp. 1-5). IEEE.
  27. Vishesh, P., Raghavendra, S., Jankatti, S. K., & Rekha, V. (2021). Eye blink detection using CNN to detect drowsiness level in drivers for road safety. Indonesian Journal of Electrical Engineering and Computer Science, 22(1), 222-231. https://doi.org/10.11591/ijeecs.v22.i1.pp222-231
  28. Anas, E. R., Henriquez, P., & Matuszewski, B. J. (2017). Online Eye Status Detection in the Wild with Convolutional Neural Networks. In VISIGRAPP (6: VISAPP) (pp. 88-95).
  29. Kuwahara, A., Hirakawa, R., Kawano, H., Nakashi, K., & Nakatoh, Y. (2021, January). Eye fatigue prediction system using blink detection based on eye image. In 2021 IEEE International Conference on Consumer Electronics (ICCE) (pp. 1-3). IEEE.
  30. Ghourabi, A., Ghazouani, H., & Barhoumi, W. (2020, September). Driver drowsiness detection based on joint monitoring of yawning, blinking and nodding. In 2020 IEEE 16th International Conference on Intelligent Computer Communication and Processing (ICCP) (pp. 407-414). IEEE.
  31. Ling, Y., Luo, R., Dong, X., & Weng, X. (2021). Driver eye location and state estimation based on a robust model and data augmentation. IEEE Access, 9, 67219-67231. https://doi.org/10.1109/ACCESS.2021.3076365
  32. Kim, J. A., Sung, J. Y., & Park, S. H. (2020, November). Comparison of Faster-RCNN, YOLO, and SSD for real-time vehicle type recognition. In 2020 IEEE international conference on consumer electronics-Asia (ICCE-Asia) (pp. 1-4). IEEE.
  33. Y. Tao, X. Xiao, and J. Pei, "Efficient skyline and top-k retrieval in subspaces," IEEE Trans. Knowl. Data Eng., vol. 19, no. 8, pp. 1072-1088, 2007. https://doi.org/10.1109/TKDE.2007.1051
  34. I. F. Ilyas, G. Beskales, and M. A. Soliman, "A survey of top-k query processing techniques in relational database systems," ACM Comput. Surv., vol. 40, no. 4, 2008.
  35. I. Muslea, "Machine learning for online query relaxation," in KDD, 2004, pp. 246-255.
  36. L. Pan, J. Luo, and J. Li, "Probing queries in wireless sensor networks," in ICDCS, 2008, pp. 546-553.
  37. Q. T. Tran and C.-Y. Chan, "How to conquer why-not questions," in SIGMOD Conference, 2010, pp. 15-26.
  38. C. Mishra and N. Koudas, "Interactive query refinement," in EDBT, 2009, pp. 862-873.
  39. C. Mishra, N. Koudas, and C. Zuzarte, "Generating targeted queries for database testing," in SIGMOD Conference, 2008, pp. 499-510.
  40. N. Bruno, S. Chaudhuri, and D. Thomas, "Generating queries with cardinality con- straints for dbms testing," IEEE Trans. Knowl. Data Eng., vol. 18, no. 12, pp. 1721- 1725, 2006. https://doi.org/10.1109/TKDE.2006.190
  41. A. Kadlag, A. V. Wanjari, J. Freire, and J. R. Haritsa, "Supporting exploratory queries in databases," in DASFAA, 2004, pp. 594-605.
  42. M. Vartak, V. Raghavan, and E. A. Rundensteiner, "Qrelx: generating meaning- ful queries that provide cardinality assurance," in SIGMOD Conference, 2010, pp. 1215-1218.
  43. N. Koudas, C. Li, A. K. H. Tung, and R. Vernica, "Relaxing join and selection queries," in VLDB, 2006, pp. 199-210.
  44. S. Chaudhuri and V. Narasayya, "Program for generating skewed data distributions for tpc-d," ftp://ftp.research.microsoft.com/users/surajitc/TPCDSkew/.
  45. K. C.-C. Chang and S. won Hwang, "Minimal probing: supporting expensive pred- icates for top-k queries," in SIGMOD Conference, 2002, pp. 346-357.
  46. M. Singh, A. Nandi, and H. V. Jagadish, "Skimmer: rapid scrolling of relational query results," in SIGMOD Conference, 2012, pp. 181-192.
  47. A. Motro, "Vague: A user interface to relational databases that permits vague queries," ACM Trans. Inf. Syst., vol. 6, no. 3, pp. 187-214, 1988. https://doi.org/10.1145/45945.48027
  48. U. Nambiar and S. Kambhampati, "Answering imprecise queries over autonomous web databases," in ICDE, 2006, p. 45.
  49. M. S. Islam, C. Liu, and R. Zhou, "A framework for query refinement with user feedback," Journal of Systems and Software, vol. 86, no. 6, pp. 1580-1595, 2013. https://doi.org/10.1016/j.jss.2013.01.069
  50. Bouafia, Y., & Guezouli, L. (2018). An Overview of Deep Learning-Based Object Detection Methods.
  51. R. Fagin, A. Lotem, and M. Naor, "Optimal aggregation algorithms for middleware," in PODS, 2001.
  52. U. Cetintemel, M. Cherniack, J. DeBrabant, Y. Diao, K. Dimitriadou, A. Kalinin, O. Papaemmanouil, and S. B. Zdonik, "Query steering for interactive data exploration," in CIDR, 2013.
  53. A. Telang, C. Li, and S. Chakravarthy, "One size does not fit all: Toward user- and query-dependent ranking for web databases," IEEE Trans. Knowl. Data Eng., vol. 24, no. 9, pp. 1671-1685, 2012. https://doi.org/10.1109/TKDE.2011.36
  54. P. E. Hart, N. J. Nilsson, and B. Raphael, "A formal basis for the heuristic deter- mination of minimum cost paths," IEEE Trans. Systems Science and Cybernetics, vol. 4, no. 2, pp. 100-107, 1968. https://doi.org/10.1109/TSSC.1968.300136
  55. T. Sellam and M. L. Kersten, "Meet charles, big data query advisor," in CIDR, 2013.
  56. A. Albarrak, T. Noboa, H. A. Khan, M. A. Sharaf, X. Zhou, and S. Sadiq, "Orange: Objective-aware range query refinement," in MDM, 2014.
  57. S. Chaudhuri, "Generalization and a framework for query modification," in Proceedings of the Sixth International Conference on Data Engineering, February 5-9, 1990, Los Angeles, California, USA, 1990, pp. 138-145.
  58. https://datareportal.com/reports/digital-2021-saudi-arabia.
  59. Ashwini, D. L., Ramesh, S. V., Nosch, D., & Wilmot, N. (2021). Efficacy of blink software in improving the blink rate and dry eye symptoms in visual display terminal users-A single-blinded randomized control trial. Indian Journal of Ophthalmology, 69(10), 2643.
  60. Soorjoo, Martin. 2009. The Black Book of Lie Detection : Creative Commons Organization.
  61. Nugroho, R. H., Nasrun, M., & Setianingsih, C. (2017, September). Lie detector with pupil dilation and eye blinks using hough transform and frame difference method with fuzzy logic. In 2017 International Conference on Control, Electronics, Renewable Energy and Communications (ICCREC) (pp. 40-45). IEEE.
  62. George, S., Pai, M. M., Pai, R. M., & Praharaj, S. K. (2017, September). Eye blink count and eye blink duration analysis for deception detection. In 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI) (pp. 223-229). IEEE.
  63. Salehifar, H., Bayat, P., & Majd, M. A. (2019). Eye gesture blink password: a new authentication system with high memorable and maximum password length. Multimedia Tools and Applications, 78(12), 16861-16885. https://doi.org/10.1007/s11042-018-7043-9
  64. Phatchuay, S., Yooyen, A., & Ketcham, M. (2016, June). The detection eye blink on Working production line in Industrial. In 2016 13th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON) (pp. 1-5). IEEE.
  65. Wang, Y., Wang, L., Lin, S., Cong, W., Xue, J., & Ochieng, W. (2021). Effect of Working Experience on Air Traffic Controller Eye Movement. Engineering, 7(4), 488-494. https://doi.org/10.1016/j.eng.2020.11.006
  66. Li, X., Xia, J., Cao, L., Zhang, G., & Feng, X. (2021). Driver fatigue detection based on convolutional neural network and face alignment for edge computing device. Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering, 235(10-11), 2699-2711. https://doi.org/10.1177/0954407021999485
  67. Kathpal, K., Negi, S., & Sharma, S. (2021, September). iChat: Interactive Eyes for Specially Challenged People Using OpenCV Python. In 2021 9th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO) (pp. 1-5). IEEE.
  68. Guo, F., Cao, Y., Ding, Y., Liu, W., & Zhang, X. (2015). A multimodal measurement method of users' emotional experiences shopping online. Human Factors and Ergonomics in Manufacturing & Service Industries, 25(5), 585-598. https://doi.org/10.1002/hfm.20577
  69. Kamangar, A. (2020). A literature review of customer behaviour patterns on e-commerce websites using an eye tracker. The Marketing Review, 20(1-2), 73-91. https://doi.org/10.1362/146934720X15929907504102
  70. Ishiguro, T., Suzuki, C., Nakakoji, H., Funagira, Y., & Takao, M. (2019). Immersive experience influences eye blink rate during virtual reality gaming. Polish Psychological Bulletin, 50(1)
  71. https://www.michalsons.com/blog/biometrics-and-data-protection-law-around-the-world/42094
  72. Chen, L., Xin, G., Liu, Y., & Huang, J. (2021). Driver fatigue detection based on
  73. Aravind, A., Agarwal, A., Jaiswal, A., Panjiyara, A., & Shastry, M. (2019). Fatigue detection system based on eye blinks of drivers. Int. J. Eng. Adv. Technol, 8, 72-75. https://doi.org/10.35940/ijeat.E1015.0585S19
  74. https://jmlr.org/beta/papers/v10/king09a.html
  75. https://gist.github.com/bilalkhann16/404d5a7ac7a3b73046ee74d18ec47954#file-face_eye_detection-py
  76. https://www.etutorialspoint.com/index.php/324-eye-detection-program-in-python-opencv