Digital Eye Strain and Fatigue Recognition Using Electrooculogram Signals and Ultrasonic Distance Measurements

Billones, Robert Kerwin C.; Bedruz, Rhen Anjerome; Arcega, Madon L.; Eustaqio, Gabriela A.; Guehring, Diana; Tupaz, Ramon P.; Valenzuela, Ira C.; Dadios, Elmer P.

doi:10.1109/hnicem.2018.8666298

Cited by 14 publications

(9 citation statements)

References 6 publications

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“…Similarly, we also recorded significantly higher refractive errors in the CVS group than the control group with a significant relationship between myopia, contact lens wearing, and CVS severity. Billones et al [ 16 ] concluded that to minimise CVS eye strain, the best distance associated with the least eye saccades and strain was 50–62.5 cm. Similarly, we found that eye strain decreased as the distance between the user and the screen increased.…”

Section: Discussionmentioning

confidence: 99%

“…Computer vision syndrome (CVS) is also called by other names as digital eye strain (DES) [3], occupational asthenopia [11], digital asthenopia [1], and video display terminal syndrome (VDTS) [12,13]. CVS ocular symptoms include visual blur with an underlying mechanism that is not fully understood [10], dry eye disease (DED) [3,14,15], eye redness and irritation, eyestrain, fatigue, discomfort [14,16], difficulty in refocusing the eyes, and diplopia [3,4]. CVS extraocular symptoms include headache; sleep disturbances; depression [7,8]; musculoskeletal aches, such as neck/ shoulder/back pain [2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17]; difficulty in writing or holding objects; and pain in thumbs, fingers, or wrists because of tendonitis and/or arthritis [18][19][20].…”

Section: Introductionmentioning

confidence: 99%

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Visual Sequelae of Computer Vision Syndrome: A Cross-Sectional Case-Control Study

Iqbal

Said

Ibrahim

et al. 2021

Journal of Ophthalmology

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Purpose. To assess the visual, ocular, extraocular, and multifocal electroretinography (mfERG) outcomes of computer vision syndrome (CVS) among medical students. Methods. This study was designed as a cross-sectional case-control study that included 733 medical students. All students completed a specially designed and validated CVS questionnaire survey (CVS-F3). Students from the control (No-CVS) and CVS groups underwent comprehensive ophthalmic examinations including the mfERG examinations. Our main outcome measures included uncorrected and corrected distance visual acuity (UDVA and CDVA, resp.) measurements, subjective and cycloplegic refractions, slit-lamp examination, intraocular pressure measurement, pupillary reflexes tests, ocular movements’ tests, dry eye disease tests, and fundus and mfERG examinations. Results. The CVS-F3 identified that 87.9% of students had complaints that might be related to CVS. We documented a 76% prevalence rate in students undergoing an ophthalmologic exam. The most common ocular and extraocular complaints included visual blur and headache (40.9% and 46.8%, resp.). Statistical logistic and linear regression analyses showed that refractive errors, prolonged screen-hours, close eye-screen distance, improper gaze angle, poor screen-resolution, and screen-glare were risk factors for developing CVS and influencing its severity. In the mfERG subgroup, 42.5% demonstrated reduced amplitudes of mfERG rings and quadrants, indicating reduced foveal responses. Conclusion. Surveys cannot yield an accurate CVS prevalence. However, they help to identify subjects at risk who should be comprehensively assessed to confirm or exclude CVS diagnosis. Smartphone misuse primarily caused CVS among users. Our mfERG findings might be a sign of potential CVS visual sequelae; however, future studies are warranted. Clinicians need to understand these sequelae to appropriately identify and treat CVS.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Visual Sequelae of Computer Vision Syndrome: A Cross-Sectional Case-Control Study

Iqbal

Said

Ibrahim

et al. 2021

Journal of Ophthalmology

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“…The smart glass industry has various outcomes in the social realm. First, human health (inclusive of eye health) is of utmost importance, and the smart glass industry negatively affects human health, particularly eye health [14,15,17]. Ophthalmic difficulties, such as strain, dry eyes, and changes in ocular vision score above other health-related side effects, such as behavioral changes, motor imbalance, dermatological infections, gastrointestinal problems (e.g., nausea, vomiting, and acidity), and neurological effects (e.g., headache, dizziness, seizures, dystonia, and loss of consciousness) [14,15,17].…”

Section: Social Implications Of the Smart Glass Industrymentioning

confidence: 99%

“…Visual acuity, contrast sensitivity, visual field testing, and color tests often reveal complications of the prolonged usage of smart glasses [14]. Before and after using smart glasses, users' visual acuity, contrast sensitivity, visual field parameters, and color distinction abilities show significant differences, indicating serious negative implications of the usage of smart glasses over a prolonged period [4,12,15,16].…”

Section: Introductionmentioning

confidence: 99%

Current and future perspectives on smart glasses

Kanupriya¹

2022

Med Hypothesis Discov Innov Optom

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“…However, in previous studies related to DES, the detection of this syndrome was concluded based on the results with questionnaires distributed to subjects [13]- [14]. In addition to using a questionnaire, this syndrome can also be identified and measured objectively, by directly observing eye blinks and reading the EOG chart [10]- [11], [15]- [17]. Moreover, the blink characteristic for example reduction in blink rate, due to frequent computer use, is increasingly being used as a measurement parameter in recent DES studies [18] and it is considered relevant to the symptoms associated with DES [19].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Digital Eye Strain Due to Online Learning Based on the Number of Blinks

Riandini

Aditya

Wardhani

et al. 2022

IJEEI

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Eye strain is a big concern, especially when it comes to continuous and prolonged online learning. If this is allowed to continue, it will result in Computer Vision Syndrome, also known as Digital Eye Strain (DES), which includes headaches, blurred vision, dry eyes, and even neck and shoulder pain. This condition can be observed either directly based on excessive eye blinking or indirectly based on observations of the electrical activity of eye movements or electrooculography (EOG). The observed blink signal from the EOG, as a representation of eye strain, is the focus of this study. Data acquisition was obtained using the EOG sensor and was carried out on the condition that the participants were conducting online learning activities. There are four different modes of observation taken in succession: when the eye is in a viewing state but without blinking, when the eye blinks intentionally, when the eye is closed, and finally when the eye sees naturally. Observation time is 10s, 20s and 30s, where each interval is performed three times for every mode. The obtained signal is processed by the proposed method. The resulting signal is then labeled as a Blinking signal. Determination of the number of blinks or CNT_PEAK is the result of training this signal by tunning its threshold and width. If the number of blinks is less than or more than 17 then the system will provide a prediction of eye status which is stated in two categories, the first is normal eye while the last is eye strain or fatigue.

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Digital Eye Strain and Fatigue Recognition Using Electrooculogram Signals and Ultrasonic Distance Measurements

Cited by 14 publications

References 6 publications

Visual Sequelae of Computer Vision Syndrome: A Cross-Sectional Case-Control Study

Visual Sequelae of Computer Vision Syndrome: A Cross-Sectional Case-Control Study

Current and future perspectives on smart glasses

Prediction of Digital Eye Strain Due to Online Learning Based on the Number of Blinks

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