scholarly journals Deep Neural Network model for Convergence of Visual Fatigue and Computer Vision Disability

2020 ◽  
Vol 9 (3) ◽  
pp. 2599-2604
Keyword(s):  
Computer Vision ◽  
Research Work ◽  
Smart Phone ◽  
Clear Understanding ◽  
Blink Rate ◽  
Visual Fatigue ◽  
Digital Medium ◽  
Computer Vision Syndrome ◽  
Vision Disability ◽  
Numerous People

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

scholarly journals Computer Vision Syndrome and Blink Rate

2015 ◽  
pp. 1-2 ◽  
Author(s):  
Mark Rosenfield ◽  
Joan K. Portello
Keyword(s):  
Computer Vision ◽  
Blink Rate ◽  
Computer Vision Syndrome

scholarly journals Trataka Kriya in Individuals with Digital Eye Strain: A Pre–Post Experimental Design

2021 ◽  
Author(s):  
Dhanesh Kumar K. U. ◽  
Sparsha Shetty ◽  
Hrishikesh Amin ◽  
Rashmitha A. P. ◽  
Shilna Rani P.
Keyword(s):  
Computer Vision ◽  
Statistical Analysis ◽  
Single Point ◽  
Sampling Technique ◽  
P Value ◽  
Small Object ◽  
Visual Fatigue ◽  
Black Dot ◽  
Computer Vision Syndrome ◽  
Before And After

Abstract Introduction Digital eye strain is the physical discomfort felt after 2 or more hours in front of a digital screen, including cell phones. Digital eye strain is otherwise known as computer vision syndrome. Trataka is to look at or to gaze—it is a preliminary step for meditation that involves staring at a single point such as a small object, black dot, or candle flame. Objective This study aimed to analyze the effect of trataka kriya in the management of digital eye strain. Materials and Methods Thirty participants of the age group 18 to 40 years were recruited in the study. The study design was a pre–post experiment. A convenient sampling technique was used to recruit the participants. The study included participants who use laptops or smartphones for a minimum of 2 hours daily and also participants having eye strain, dry eyes, burning sensation in the eyes, headache, and eye fatigue. They performed trataka kriya exercises once a day on alternative days for 1 month. Outcome measures was a computer vision syndrome questionnaire to assess the visual fatigue experienced by the study participants and the Schrimer test to find out whether the eye produces enough tears to keep it moist. Statistical Analysis and Results Statistical analysis was done using SPSS version 16.0. To compare the computer vision syndrome questionnaire and Schrimer test before and after interventions paired t-test was used. A p-value of less than 0.05 is considered significant for the study. Conclusion The study concludes that there was an improvement in the subjects with digital eye strain after performing trataka kriya. The clinical implication of the study is that this method can be used as one of the nonpharmacological interventions for digital eye strain.

Blink Rate, Incomplete Blinks and Computer Vision Syndrome

2013 ◽  
Vol 90 (5) ◽  
pp. 482-487 ◽  
Author(s):  
Joan K. Portello ◽  
Mark Rosenfield ◽  
Christina A. Chu
Keyword(s):  
Computer Vision ◽  
Blink Rate ◽  
Computer Vision Syndrome

Study on Knowledge and Prevalence of Computer Vision Syndrome among Computer Operators in Nobel Medical College Teaching Hospital, Biratnagar, Nepal

2020 ◽  
Vol 9 (2) ◽  
pp. 45-49
Author(s):  
Pramod Sharma Gautam ◽  
Uday Chandra Prakash ◽  
Subreena Dangol
Keyword(s):  
Computer Vision ◽  
Teaching Hospital ◽  
Human Life ◽  
College Teaching ◽  
Extended Period ◽  
Visual Display ◽  
Work Place ◽  
Medical College ◽  
Computer Vision Syndrome ◽  
Department Of Ophthalmology

Background: The eye and vision related problems that results from continuous use of computers and other visual display terminals for extended period of time leads to computer vision syndrome. Due to rapid digitalization in human life, the risk of developing it has also increased in many folds. So, with an aim of determining the prevalence and level of awareness of computer vision syndrome among computer users along with their attitude and practices to prevent it, this study was conducted in the office employees who use computer for a considerable period of time. Materials and Methods: A hospital based observational descriptive study was conducted in the out-patient department of Ophthalmology in Nobel Medical College and Teaching Hospital, Biratnagar, where 105 employees working in different work stations of same institution were enrolled. A questionnaire and the clinical findings were used to collect data. Results: About 80% of the employees were using computer for about (8-11) hours per day. Prevalence of computer vision syndrome noted was (92.4%) with low level of knowledge (85.7%) about it. About 45% of them wore glasses for their refractive errors but attitude and practices in work place to prevent the bad effects of using visual display terminals were found to be lacking (53.3%). Burning sensation in the eye, headache, ocular irritation and itching and neck, shoulder or back pain were the common symptoms. Around (60-70)% of the eyes tested positive for dry eye. Conclusion: Lack of awareness of computer vision syndrome and lack of personal protective measures were associated with its high level of prevalence.  

Accommodative and binocular disorders in preteens with computer vision syndrome: a cross‐sectional study

2020 ◽  
Author(s):  
Concepción De‐Hita‐Cantalejo ◽  
Ángel García‐Pérez ◽  
José‐María Sánchez‐González ◽  
Raúl Capote‐Puente ◽  
María Carmen Sánchez‐González
Keyword(s):  
Computer Vision ◽  
Cross Sectional Study ◽  
Sectional Study ◽  
Cross Sectional ◽  
Computer Vision Syndrome

scholarly journals Computer vision syndrome in presbyopic digital device workers and progressive lens design

2021 ◽  
Author(s):  
Mar Sánchez‐Brau ◽  
Begoña Domenech‐Amigot ◽  
Francisco Brocal‐Fernández ◽  
Mar Seguí‐Crespo
Keyword(s):  
Computer Vision ◽  
Lens Design ◽  
Digital Device ◽  
Computer Vision Syndrome

scholarly journals Computer vision syndrome in presbyopia and beginning presbyopia: effects of spectacle lens type

2015 ◽  
Vol 98 (3) ◽  
pp. 228-233 ◽  
Author(s):  
Wolfgang Jaschinski ◽  
Mirjam König ◽  
Tiofil M. Mekontso ◽  
Arne Ohlendorf ◽  
Monique Welscher
Keyword(s):  
Computer Vision ◽  
Spectacle Lens ◽  
Computer Vision Syndrome

Ophthalmic agents in the prevention and treatment of computer vision syndrome

Vrach ◽  
2021 ◽  
Vol 32 (7) ◽  
pp. 39-46
Author(s):  
T. Potupchik ◽  
E. Okladnikova ◽  
L. Evert ◽  
E. Belova ◽  
Yu. Kostyuchenko
Keyword(s):  
Computer Vision ◽  
Computer Vision Syndrome ◽  
Prevention And Treatment ◽  
Ophthalmic Agents

Anti-Raider ATM System Using Mobilenetv2

2022 ◽  
Vol 9 (1) ◽  
pp. 0-0
Keyword(s):  
Computer Vision ◽  
Research Work ◽  
Computation Time ◽  
Vending Machines ◽  
Google Drive ◽  
Crime Counts

Cash vending machines are ubiquitous and although their technology vouches for its security, they are erratically stormed by the raiders. Albeit the escalating crime counts, the raiders are fleeing from the justice by virtue of evidence lacking. This research work proposes a computer vision based Anti-Raider ATM system. The proposed approach models the image, acquired from the CCTVs against the raider images based on the computer vision and deduces the fact from the MobileNetv2 architecture. Once the model identifies the raider, the image is uploaded to the Google Drive, which serves as evidence for the judicial department. The proposed research is modeled against several optimizers and the result concludes that, among them Adam optimizer has excelled in both computation time and accuracy.

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