Deep Neural Network model for convergence of Visual Fatigue and Computer Vision Disability
Jeevanandam Jotheeswaran1, Surbhi Jain2
1Jeevanandam Jotheeswaran*, ACOE, Amity University, Noida, India.
2Surbhi Jain, Univo EdTech LLP, Noida, India.
Manuscript received on January 26, 2020. | Revised Manuscript received on February 05, 2020. | Manuscript published on February 30, 2020. | PP: 2599-2604 | Volume-9 Issue-3, February 2020. | Retrieval Number: C6007029320/2020©BEIESP | DOI: 10.35940/ijeat.C6007.029320
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The expanded utilization of blue screens in the work environment and home has realized the advancement of various health concerns. Numerous people who uses blue screens such as Computers, Tablets, Mobiles and Etc., report an elevated level of occupation related grievances and side effects, including visual fatigue and stress. The complex of eye and vision issues identified with close to such usages are called as “computer vision syndrome”. In this research work, we study and understand the flow level of a user, while using a smart phone. The study of the flow level will majorly depend on the eye-activity of the user. The data mentioned below is carefully recorded after examining the activity of eyes including the size of the pupil, blink rate, and blink duration. The purpose of this study is to understand the connection between the flow level and the activity of the eyes. A clear understanding of this connection could prove to be very useful information in the computer vision field. Additionally, this can also help a lot to understand about Visual Fatigue caused by Digital Medium.
Keywords: Deep Neural Network. Visual Fatigue, Computer Vision Syndrome, Machine Learning, Deep Learning