Blink Patterns Research Articles

A comparative study on the lipid layer thickness analysis of medical staff before and after work

Background & Aims To study the change of the lipid layer thickness analysis in medical staff (MS) before and after work, and to explore the significance of measuring lipid layer thickness (LLT) respectively in four quadrants. Methods Ocular Surface Disease Index (OSDI) questionnaire and video display terminal using time for 55 MS were collected (the informed consent was obtained from all patients). Noninvasive tear break-up time, LLT, tear meniscus height (TMH), meibomian glands (MG) dropout, and blink pattern before and after work (worked for more than 4 h) of 110 eyes were measured by Gaush iDea Ocular surface analyzer. Lid margin abnormalities were evaluated by the slit-lamp microscopy. Results The average OSDI score of 55 MS was 25.68 ± 14.91. The average LLT of 110 eyes after work (65.12 ± 3.63 nm) was significantly reduced compared to before work (66.54 ± 4.16 nm), p < 0.05. The LLT in the superior quadrant was significantly thinner than that in the other three quadrants, p < 0.01. The average LLT was positively correlated with the LLT in the inferior (r = 0.822, p < 0.001), nasal (r = 0.261, p < 0.001), and temporal quadrant (r = 0.372, p < 0.001), while was negatively correlated with the MG dropout in lower lid (r = −0.209, p = 0.002). There was a significant correlation between the LLT in the inferior quadrant and the VDTt (r = −0.173, p = 0.01). The LLT of inferior quadrant were positively related to the TMH (r = 0.149, p = 0.027) and negatively related to MG dropout in lower lid (r = −0.162, p = 0.017). Conclusion The LLT significantly decreases after work in MS. The distribution of the lipid layer on the ocular surface is uneven. It is unreasonable for current detection instruments to measure the inferior quadrant LLT alone to represent average LLT.

Alternative Emergency Vehicle Lighting Affects Traffic Behaviors

Emergency vehicle lightings (EVL) mitigate the risk of collision and make drivers move over. The present paper explored how new alternative EVL designs can improve driver behavior in different emergency vehicle interaction scenarios. From workshops with 14 emergency responders, three scenarios (Emergency driving, Police pull-over, Emergency vehicle alongside the road) and 19 EVL blink patterns were chosen. These alternative EVLs were presented in an online survey with 2627 Swedish respondents. Alternative EVL affected reported driving behavior. Drivers reported being most certain of what is expected from them in the emergency-driving scenario. It is important to ensure that the EVL is easy to interpret in more unfamiliar emergency-vehicle interactions such as police pull-over. Only a third of drivers had seen the pull-over EVL currently used in Sweden. The current pull-over EVL in combination with a stop sign increased the chance of drivers reporting that they would pull over.

Dry eye disease as an occupational risk factor in operating room nurses

ABSTRACT Clinical relevance Knowing occupational risks in a multifactorial disease such as dry eye disease is important for disease diagnosis and treatment. Background Dry eye disease (DED) is a multifactorial disease of the ocular surface characterised by tear film instability, adversely affecting visual function and quality of life. The operating room environment has many risk factors for DED such as air conditioning, constant humidity, constant room temperature, intense lighting, surgical smoke, anaesthetic gases and irritating chemicals, and prolonged mask use. This study investigates the ocular surface findings and blink patterns of operating room nurses at risk of DED. Methods A total of 68 nurses (operating room, n = 34; outpatient clinic, n = 34) were included in this study. The diagnosis of DED was assessed using a questionnaire, best-corrected visual acuity, tear break-up time, corneal fluorescein staining, meibomian gland assessment, Schirmer I test. All the tests were conducted in the same area. Results According to the ocular surface disease index, the rate of dry eye symptoms was significantly higher among operating room nurses than among outpatient nurses (70.5% vs 41.1%). Severe dry eye symptoms were more common in the operating room group. The meibomian gland score was higher in the operating room group (29.4% vs 5.9%). The numbers of blinks (30.91 ± 12.81 vs 21.05 ± 7.77) and incomplete blinks (8.76 ± 4.68 vs 5.76 ± 3.20) at rest were higher in operating room nurses. Conclusion The occupational risk for DED was high among operating room staff. National and international standards for the prevention and management of DED in personnel involved in health care services are needed.

Drowsy Driver Detection System

Abstract: Drowsy driver detection system is a safety technology designed to identify the potential signs of fatigue or drowsiness of the driver to prevent accidents caused as a result of not being alert. It is a significant risk factor for traffic accidents which has led to thousands of accidents, injuries, and fatalities each year. In this system we have used some techniques to monitor the driver’s behavior or state. This involves tracking facial features, eye movements or blink patterns and yawning. The system identifies signs of fatigue, such as slow eyelid closure, increased blink duration and yawn which often indicate drowsiness. As technology advances, these systems are becoming more accurate, efficient and integrated with other vehicle safety features. The continued development of drowsy driver detection technology holds significant promise for reducing traffic accidents and improving overall road safety.

Deep fakes Detection Using Human Eye Blinking Pattern: Deep Vision

Abstract: Deepfake technology is developing rapidly and has the ability to create fake videos that pose a threat to people. Modern depth detection methods often rely on facial disparity, but as technology advances these methods become obsolete. In this study, we propose a new method for deepfake detection based on observation of human eye blink patterns. Human eye blinking is a natural and involuntary action that is difficult to accurately replicate in deepfake video. In our study, we used the unique characteristics of individual blink patterns, which are influenced by many factors such as genetics, muscle tone and unconscious reflexes. We use computer vision and machine learning techniques to extract and identify these patterns from video clips. Our preliminary tests show good results in detecting deepfakes with high accuracy. We are focused on continuing to support the fight against the spread of fraud by focusing on a part of human behaviour that is difficult to replicate. This approach has the potential to improve existing tools for in-depth discovery and increase the overall security and reliability of multimedia content in the digital age. This research opens new avenues for the development of more robust, reliable and flexible deep recognition technologies. This represents a significant step forward in the ongoing fight against malicious misuse of electronic devices.

DROWSINESS AND ALCOHOL DETECTION USING ARDUINO

Accidents on road ways are increasing in our day to day lives. These accidents pose significant threats to human safety and well-being. There are several ways for reduction of accidents. Some of them are Drowsiness detection and Alcoholic detection. By using sensors, communication devices and algorithms, the system aims to identify potential hazards. This abstract presents a comprehensive approach to accident reduction using Internet of Things (IoT) technology. Drowsiness Detection System aims to mitigate the risks of drowsy driving and enhance road safety. By utilizing an Arduino microcontroller and various sensors, this system continuously monitors the driver’s vital signs and facial expressions. It analyzes parameters such as eye blink patterns, head movements, and heart rate to accurately identify signs of drowsiness. Upon detecting drowsiness, the system promptly alerts the driver through alarms or visual cues, prompting them to take necessary actions to prevent accidents. This project serves as a vital tool in reducing road accidents caused by driver drowsiness, ensuring safer journeys for all. This project presents the design and implementation of an Alcohol Detection with Engine Locking for cars using Arduino UNO as the MCU (Master Control Unit). The system will continuously monitor level of alcohol concentration in alcohol detection sensor and thus turn off the engine of vehicle if the alcohol concentration is above threshold level. The model will also send the message of whereabouts of the vehicle through SIM900A. The project provides an efficient solution to control accidents due to drunk driving Key Words: Arduino UNO, Sensor Fusion, Real time monitoring, MQ-3 sensor, GPS module, GSM module, Telematics.

Advancements in Drowsiness Detection: A Review

This review study looks at current advancements in sleepiness detection technologies to address the urgent problem of driving while sleepy leading to accidents. In order to prevent future accidents, it analyzes several approaches and technology and emphasizes how crucial it is to correctly recognize drowsy conditions in drivers. Important results highlight the effectiveness of methods for real-time drowsiness detection such as convolutional neural networks (CNNs), eye aspect ratio (EAR) analysis, and deep learning integration. Additionally, improvements in computer vision algorithms make it possible to precisely analyze facial landmarks and eye blinking patterns, which improves the accuracy of detection systems. In order to protect driver comfort and system reliability, the evaluation emphasizes the importance of non-intrusive fatigue detection techniques while also emphasizing the necessity to reduce false warnings. Remarkably, certain models detect drowsy-eye with an accuracy of above 95%. All things considered, the review emphasizes how important cutting-edge technology is in reducing the risks related to drunk driving, especially deep learning and computer vision algorithms. It promotes the deployment of reliable sleepiness detection technologies in order to increase road safety and reduce accidents brought on by fatigued drivers. Key Words: Drowsiness detection, Eye blink duration, Facial landmark detection, Deep learning algorithms, Computer vision, Eye aspect ratio, Road safety.

Facet Dependent Photoluminescence Blinking from Perovskite Nanocrystals.

Photoluminescence (PL) blinking of nanoparticles, while detrimental to their imaging applications, may benefit next-generation displays if the blinking is precisely controlled by reversible electron/hole injections from an external source. Considerable efforts are made to create well-characterized charged excitons within nanoparticles through electrochemical charging, which has led to enhanced control over PL-blinking in numerous instances. Manipulating the photocharging/discharging rates in nanoparticles by surface engineering can represent a straightforward method for regulating their blinking behaviors, an area largely unexplored for perovskite nanocrystals (PNCs). This work shows facet engineering leading to different morphologies of PNCs characterized by distinct blinking patterns. For instance, examining the PL intensity trajectories of single PNCs, representing the instantaneous photon count rate over time, reveals that the OFF-state population significantly increases as the number of facets increases from six to twenty-six. This study suggests that extra-faceted PNCs, owing to their polar facets and expanded surface area, render them more susceptible to photocharging, which results in larger OFF-state populations. Furthermore, the fluorescence correlation spectroscopy (FCS) study unveils that the augmented propensity for photocharging in extra-faceted PNCs can also originate from their greater tendency to form complexes with neighboring molecules.

Comparison of deep learning-assisted blinking analysis system and Lipiview interferometer in dry eye patients: a cross-sectional study

BackgroundAbnormal blinking pattern is associated with ocular surface diseases. However, blink is difficult to analyze due to the rapid movement of eyelids. Deep learning machine (DLM) has been proposed as an optional tool for blinking analysis, but its clinical practicability still needs to be proven. Therefore, the study aims to compare the DLM-assisted Keratograph 5M (K5M) as a novel method with the currently available Lipiview in the clinic and assess whether blinking parameters can be applied in the diagnosis of dry eye disease (DED).MethodsThirty-five DED participants and 35 normal subjects were recruited in this cross-sectional study. DED questionnaire and ocular surface signs were evaluated. Blinking parameters including number of blinks, number of incomplete blinking (IB), and IB rate were collected from the blinking videos recorded by the K5M and Lipiview. Blinking parameters were individually collected from the DLM analyzed K5M videos and Lipiview generated results. The agreement and consistency of blinking parameters were compared between the two devices. The association of blinking parameters to DED symptoms and signs were evaluated via heatmap.ResultsIn total, 140 eyes of 70 participants were included in this study. Lipiview presented a higher number of IB and IB rate than those from DLM-assisted K5M (P ≤ 0.006). DLM-assisted K5M captured significant differences in number of blinks, number of IB and IB rate between DED and normal subjects (P ≤ 0.035). In all three parameters, DLM-assisted K5M also showed a better consistency in repeated measurements than Lipiview with higher intraclass correlation coefficients (number of blinks: 0.841 versus 0.665; number of IB: 0.750 versus 0.564; IB rate: 0.633 versus 0.589). More correlations between blinking parameters and DED symptoms and signs were found by DLM-assisted K5M. Moreover, the receiver operating characteristic analysis showed the number of IB from K5M exhibiting the highest area under curve of 0.773.ConclusionsDLM-assisted K5M is a useful tool to analyze blinking videos and detect abnormal blinking patterns, especially in distinguishing DED patients from normal subjects. Large sample investigations are therefore warranted to assess its clinical utility before implementation.

A Flexible Wearable Sensor Based on Laser-Induced Graphene for High-Precision Fine Motion Capture for Pilots.

There has been a significant shift in research focus in recent years toward laser-induced graphene (LIG), which is a high-performance material with immense potential for use in energy storage, ultrahydrophobic water applications, and electronic devices. In particular, LIG has demonstrated considerable potential in the field of high-precision human motion posture capture using flexible sensing materials. In this study, we investigated the surface morphology evolution and performance of LIG formed by varying the laser energy accumulation times. Further, to capture human motion posture, we evaluated the performance of highly accurate flexible wearable sensors based on LIG. The experimental results showed that the sensors prepared using LIG exhibited exceptional flexibility and mechanical performance when the laser energy accumulation was optimized three times. They exhibited remarkable attributes, such as high sensitivity (~41.4), a low detection limit (0.05%), a rapid time response (response time of ~150 ms; relaxation time of ~100 ms), and excellent response stability even after 2000 s at a strain of 1.0% or 8.0%. These findings unequivocally show that flexible wearable sensors based on LIG have significant potential for capturing human motion posture, wrist pulse rates, and eye blinking patterns. Moreover, the sensors can capture various physiological signals for pilots to provide real-time capturing.

Eye Blink Detection- A New System for Driver Drowsiness and Distraction Detection

The advancement of technology has led to innovative solutions for ensuring road safety, particularly in the detection of driver drowsiness and distraction. This abstract presents a novel approach using eye blink detection as a fundamental component in a system designed to mitigate the risks associated with drowsy and distracted driving. The proposed system integrates cutting-edge computer vision techniques to accurately detect and analyze eye blinks in real-time. By utilizing high-resolution cameras installed within the vehicle, the system captures and processes images of the driver's face, focusing on the eyes to track blinking patterns and durations. Through sophisticated algorithms and machine learning models, it distinguishes between normal blinks and extended periods of eye closure, indicative of drowsiness or distraction. Furthermore, the system incorporates contextual awareness by considering various factors such as environmental conditions, driving behavior, and facial expressions to enhance the accuracy of detection. Machine learning algorithms trained on diverse datasets enable the system to adapt and improve its performance over time, making it more adept at recognizing subtle variations in blink patterns unique to each driver. The applications of this system are multifaceted. Firstly, it serves as an early warning system, alerting drivers when signs of drowsiness or distraction are detected. This proactive approach aims to prevent accidents by prompting drivers to take necessary breaks or refocus their attention on the road. Moreover, the system can integrate with existing driver assistance technologies, allowing for automated interventions, such as adjusting seat vibrations or emitting audible alerts to re-engage the driver. In conclusion, the proposed eye blink detection system represents a significant advancement in the realm of driver safety technologies. By leveraging sophisticated algorithms and real-time analysis of blink patterns, it offers a promising solution to mitigate the risks associated with driver drowsiness and distraction, ultimately contributing to safer roads and reducing the likelihood of accidents. KEYWORDS: Eye blinking, Detection, Driver, Drowsiness, Distraction, Detection, computer vision, machine learning, Deep learning, image processing.

Attentional modulation of eye blinking is altered by sex, age, and task structure.

Spontaneous eye blinking is gaining popularity as a proxy for higher cognitive functions, as it is readily modulated by both environmental demands and internal processes. Prior studies were impoverished in sample size, sex representation and age distribution, making it difficult to establish a complete picture of the behavior. Here we present eye-tracking data from a large cohort of normative participants (n=604, 393 F, aged 5-93 years) performing two tasks: one with structured, discrete trials (interleaved pro/anti-saccade task; IPAST) and one with a less structured, continuous organization in which participants watch movies (free-viewing; FV). Sex- and age-based analyses revealed that females had higher blink rates between the ages of 22 and 58 years in the IPAST, and 22 and 34 years in FV. We derived a continuous measure of blink probability to reveal behavioral changes driven by stimulus appearance in both paradigms. In the IPAST, blinks were suppressed near stimulus appearance, particularly on correct anti-saccade trials, which we attribute to the stronger inhibitory control required for anti-saccades compared to pro-saccades. In FV, blink suppression occurred immediately after scene changes, and the effect was sustained on scenes where gaze clustered among participants (indicating engagement of attention). Females were more likely than males to blink during appearance of novel stimuli in both tasks, but only within the age bin of 18-44 years. The consistency of blink patterns in each paradigm endorses blinking as a sensitive index for changes in visual processing and attention, while sex and age differences drive interindividual variability.Significance Statement Eye-tracking is becoming useful as a non-invasive tool for detecting preclinical markers of neurological and psychiatric disease. Blinks are understudied despite being an important supplement to saccade and pupil eye-tracking metrics. The present study is a crucial step in developing a healthy baseline for blink behavior to compare to clinical groups. While many prior blink studies suffered from small sample sizes with relatively low age- and sex-diversity (review by Jongkees & Colzato, 2016), our large cohort of healthy participants has permitted a more detailed analysis of sex and age effects in blink behavior. Furthermore, our analysis techniques are robust to temporal changes in blink probability, greatly clarifying the relationship between blinking, visual processing, and inhibitory control mechanisms on visual tasks.

Role of corneal sensory nerves in tearing and blinking

The cornea is innervated trigeminal neurons classified in three different functional types: polymodal nociceptor, mechano‐nociceptor and cold thermoreceptor neurons. Corneal sensory nerves are responsible of the conscious sensations evoked by corneal stimulation, and are also involved in the trophic maintenance of the tissue. Besides, corneal nerve activity is also involved in the regulation of the protective functions of the ocular surface such as tearing and blinking.Regulation of reflex and basal tearing is integrated at the brainstem and, in both cases, under the influence of the sensory inflow provided by several types of corneal sensory nerves. While reflex tearing is induced selectively by the stimulation of corneal polymodal nociceptors, basal tearing rate depends on the spontaneous activity of cold thermoreceptors. Thus, when cold thermoreceptors are silenced by environmental warming, basal tearing is compromised.Voluntary, reflex and spontaneous (basal) blinking present differences in their characteristics and regulation mechanisms. Voluntary blinking depends on the direct instructions generated at the brain cortex, acting over the motor neurons located at the facial nucleus. Reflex blinking is integrated at the brainstem, being induced by the stimulation of the cornea and periocular skin, and even by loud sounds. Spontaneous blinking is claimed to be controlled by a CNS centre that established a blinking pattern. This pattern is modified under different conditions such as cognitive activity, being for example reduced during visual attentional tasks in order not to lose relevant visual information. When the activity of corneal cold thermoreceptors is abolished or reduced by ocular instillation of local anaesthetics, the frequency and characteristics of the spontaneous blinking is not modified. However, when the activity of cold thermoreceptors is increased for prolonged periods (for instance after topical instillation of F6H8 drops, which reduces the ocular surface temperature), blinking pattern become more regular and an increased number of blinks of larger the amplitude and shorter duration are induced. This increased blinking frequency produced when the activity of cold thermoreceptors is increased is sufficient to even counteract the reduction of blinking frequency induced during visual tasks performance. The increase in blink frequency driven by corneal cold thermoreceptors' activation may constitute a protective mechanism to prevent excessive desiccation of the ocular surface.(Supported by PID2020‐115934RB‐I00 from DOI: MCIN/AEI/10.13039/50110001103, and CIPROM/2021/48 from the Generalitat Valenciana, Spain)

Generating Talking Face With Controllable Eye Movements by Disentangled Blinking Feature.

In virtual reality, talking face generation is committed to using voice and face images to generate real face speech videos to improve the communication experience in the case of limited user information exchange. In a real video, blinking is an action often accompanied by speech, and it is also one of the indispensable actions in real face speech videos. However, the current methods either do not pay attention to the generation of eye movements, or cannot control the blinking in the generated results. To this end, this article proposes a novel system which produces vivid talking face with controllable eye blinks driven by the joint features including identity feature, audio feature, and blink feature. In order to disentangle the blinking action, we designed three independent features to individually drive the main components in the generated frame, namely the facial appearance, mouth movements, and eye movements. Through the adversarial training of the identity encoder, we filter out the information of the eye state from the identity feature, thereby strengthening the independence of the blinking feature. We introduced the blink score as the leading information of the blink feature, and through training, the value can be consistent with human perception to form a complete and independent control of the eyes. Experimental results on multiple datasets show that our method can not only reproduce real talking faces, but also ensure that the blinking pattern and time are fully controllable.

Drowsiness and Yawning Detection

Abstract: Driving fatigue, drowsiness, and momentary lapses of attention, known as microsleep episodes, contribute significantly to road safety risks. This paper presents an innovative approach to address this issue by proposing a real-time drowsiness and yawning detection system that leverages a mobile camera as a non-intrusive monitoring device. The primary objective of the system is to promptly identify signs of drowsiness and yawning, alerting the driver to mitigate the potential for accidents. The system employs facial landmark tracking techniques to monitor crucial facial features such as the eyes, mouth, and eyebrows. By analyzing variations in these features, the system can detect drowsiness-related changes, including eye closures, blink patterns, and yawning episodes. Upon detecting such indicators, the system initiates timely interventions to notify the driver. These interventions encompass displaying a warning message on the mobile screen and activating an audible alarm. An essential advantage of the proposed drowsiness and yawning detection system lies in its utilization of ubiquitous mobile cameras for monitoring. This ensures a non-intrusive implementation that does not require additional specialized hardware. Moreover, the system's real-time detection capabilities enhance its effectiveness in preventing potential accidents caused by driver fatigue.

Characteristics of the Ocular Surface in Myopic Child Candidates of Orthokeratology Lens Wear.

The purpose of this study was to investigate the characteristics of objective ocular surface parameters using non-invasive objective instruments in children with myopia who are candidates for orthokeratology lens wear. Children with myopia who are candidates for orthokeratology lens wear were retrospectively investigated. The subjects were divided into three age groups. The Keratograph 5M and LipiView interferometry were used to assess non-invasive tear meniscus height (TMH), non-invasive tear film break-up time (NIBUT), conjunctival hyperemia redness score (RS), meibomian gland loss (MGL) score, lipid layer thickness (LLT), and blink pattern analysis, including the number of partial blinks (PB) and total blinks (TB), as well as the partial blink rate (PBR). A total of 1119 children with myopia (2070 eyes) aged 7-18years were selected. The mean TMH, NIBUT, and LLT of the subjects was 0.21mm, 12.45s, and 65.28nm, respectively. The mean RS and upper and lower MGL scores were 0.64, 1.00, and 1.06, respectively. The mean PB, TB, and PBR was 5.13, 6.46, and 0.81, respectively. Age was significantly correlated to all ocular surface parameters (p = 0.00), except for PB. NIBUT and LLT did not differ between male participants and female participants (all p > 0.05). TMH, RS, and upper and lower MGL were significantly higher in male participants than in female participants (all p < 0.01). In addition, NIBUT was positively associated with TMH (r = 0.13, p = 0.00) and LLT (r = 0.28, p = 0.00). Both upper and lower MGL were positively correlated with TMH, PB, and TB (all p = 0.00), whereas upper MGL was negatively correlated with NIBUT and LLT (all p < 0.05). TB was negatively correlated with NIBUT and LLT (all p = 0.00). PB had no relation with TMH, NIBUT, and LLT (all p > 0.05). In addition, PBR was positively correlated with NIBUT and LLT (all p = 0.00) but not with TMH, RS, or MGL (all p > 0.05). Overall, 57.00% had a TMH ≤ 0.2mm, 43.20% had a NIBUT ≤ 10s, 48.10% had an LLT ≤ 60nm, and 88.10% had a PBR > 0.4. Child orthokeratology candidates have enhanced tear secretion and increased meibomian gland deficiency with aging. In addition, the adult dry eye diagnostic criteria may apply to orthokeratology candidates aged 12-18years but should be lower for younger candidates. Given the proportion of abnormal risk, it is necessary to assess tear film status and blink pattern by reliable and feasible objective examination before fitting orthokeratology.

Comparison of Blink Rate and Blink Pattern between Hard Copy Soft Copy Readers

Comparison of Blink Rate and Blink Pattern between Hard Copy Soft Copy Readers

Development of Action-Intention Indicator for Sidewalk Vehicles

Many small ground vehicles that travel on sidewalks, such as electric wheelchairs, are equipped with a differential-wheeled mechanism to turn precisely on the spot and accurately control their trajectory. Conventional ground vehicles operating on public roads have action indicator devices such as blinkers and brake lights, which are effective in preventing collisions during operations. However, similar blinkers may not be suitable for sidewalk navigation because the differential-wheeled mechanism allows a neutral turn without considering the surroundings. In this study, we developed a new action-intention indicator for future differential-wheeled mobile robots that employs RGB light-emitting diode strip lights to express various colors and complex blinking patterns. Sensitivity evaluation experiments were conducted with approximately 50 subjects to determine the optimal colors and blinking patterns for a differential-wheeled mobile robot. Additionally, reaction-time acquisition experiments were conducted to confirm the effectiveness of the proposed intention-display device for mobile robots. Finally, we evaluated whether the determined blinking patterns exhibited appropriate power consumption.

Applying computer vision techniques to depression symptomatology through eye blink patterns in university students

ABSTRACT Objective The increasing rate of depression among university students is a cause of great concern worldwide. With the recent growth in computer vision technology, eye movement features are proving beneficial in the assessment of depression owing to their non-invasiveness. Our objective was to determine the presence of depression through emotional elicitation by studying blink patterns in a student population. Method The blink data of 50 university students (26 males, 24 females) from different regions of the country within the age group of 21–26 years were collected using an experimental setup. Results Statistical tests on blink data revealed that blink rate changes with changes in emotion from joy to sadness or vice versa, irrespective of one’s depression status. Another explanation is that the blink rates of the healthy and depressed groups differed significantly during sad emotional states. The tests also indicated a half-closed eye state as a possible symptom of depression. Conclusions These findings suggest that the prevalence of mental health problems can be detected at an early stage by designing simple non-invasive procedures, such as blink pattern analysis driven by technology and expert advice, which will help prevent the further development of such illnesses.

Short-term application of diquafosol ophthalmic solution benefits children with dry eye wearing orthokeratology lens.

This aim of this study was to evaluate the effect of 3% Diquafosol Ophthalmic Solution (DQS) on children with dry eye from wearing overnight orthokeratology (OrthoK) lenses. Myopic children aged 8-18 years with dry eye syndrome were enrolled in this prospective observational study, and they were grouped according to their OrthoK treatment history for at least 1 year. All participants received DQS 4 times per day for 1 month. The following indicators were measured at baseline 1 month after treatment: the Dry Eye Questionnaire-5 (DEQ-5), non-invasive tear meniscus height (TMH), non-invasive tear film break-up time (first and average, NIBUT-F and NIBUT-A), meibomian gland score (MG score), conjunctival hyperemia redness score (R-scan), and blink pattern analysis. A total of 104 participants (189 eyes) including 40 OrthoK wearers (72 eyes) and 64 Orthok candidates (117 eyes) completed the study. Of all, after DQS treatment for 1 month, DEQ-5 scores reduced from 5.54 ± 3.25 to 3.85 ± 2.98 (t = -3.36, p = 0.00). TMH increased from 0.20 ± 0.05 mm to 0.21 ± 0.05 mm (t = 2.59, p = 0.01), NIBUT-F and NIBUT-A were prolonged from 6.67 ± 4.71 s to 10.32 ± 6.19 s and from 8.86 ± 5.25 s to 13.30 ± 6.03 s (all p = 0.00), respectively. R-scan decreased from 0.69 ± 0.28 to 0.50 ± 0.25 (t = -9.01, p = 0.00). Upper MG scores decreased from 1.04 ± 0.32 to 0.97 ± 0.36 (t = -2.14, p = 0.03). Lower MG scores, partial blink rate, partial blinks, and total blinks did not change significantly. Both break-up time (BUT) and R-scan improved significantly after DQS treatment for 1 month (all p = 0.00) in OrthoK candidates and OrthoK wearers. Among the OrthoK wearers, TMH and dry eye symptoms increased significantly (all p = 0.00) but did not increase in OrthoK candidates (p > 0.05). There were no adverse events related to DQS. Diquafosol Ophthalmic Solution was effective for children wearing overnight orthokeratology in relieving dry eye symptoms and improving ocular surface parameters, which may help improve children's OrthoK wearing tolerance and compliance.