Search Results (99)

Driver drowsiness detection has become an important application of sensor-based monitoring systems aimed at improving road safety. This review focuses on sensing technologies and physiological parameters used for real-time drowsiness detection in drivers. The surveyed approaches are categorized into physiological sensing methods, including electroencephalography (EEG), electrocardiography (ECG), galvanic skin response (GSR), and photoplethysmography (PPG), and mechanical sensing methods, including respiration rate, eye blinking, head movement, yawning, and steering wheel gripping force. Each method is analyzed from a sensor system perspective, considering signal acquisition principles, measurement location, and practical deployment constraints. In addition, the reviewed techniques are evaluated based on real-time capability, level of sensor attachment, cost, restriction of user movement, and suitability for standalone operation. The comparison highlights that mechanical sensing approaches provide non-invasive and cost-effective solutions; however, they are sensitive to environmental noise and behavioral variability. In contrast, physiological sensing methods offer more direct and earlier indicators of fatigue-related changes in biosignals, although they typically require wearable or contact-based sensors and more complex acquisition systems. The review further indicates that multimodal sensor fusion is increasingly being adopted to improve robustness and reliability in real-world driving conditions. Overall, this work provides a structured overview of sensing modalities and highlights key considerations for designing efficient, real-time driver monitoring systems. Full article

Background/Objectives: The aim of this study was to investigate the effects of intraocular pressure (IOP)-lowering drops on the sublayers of the human tear film as assessed by a novel nanometer-resolution Tear Film Imager (TFI, AdOM, Israel). Methods: In a prospective, cross-sectional study, 98 eyes from 56 adult human subjects were imaged using the TFI. The dataset included data from 18 eyes from 12 subjects treated with preserved IOP-lowering drops and 80 eyes from 44 control subjects not under ocular hypotensive therapy. Subjects in the IOP treatment group used a variety of IOP-lowering medications, including prostaglandin analogs, beta-blockers, carbonic anhydrase inhibitors, alpha agonists, and combination drops. A linear mixed effects model was used to assess the association between IOP-lowering therapy and tear film (TF) metrics, controlling for age and intra-individual correlation. The following parameters were measured: muco-aqueous layer thickness (MALT), muco-aqueous layer thinning rate (MALTR), lipid layer thickness (LLT), lipid map uniformity (LMU), inter-blink intervals (IBI), and lipid break-up time (LBUT). Results: Average ages significantly differed (p = 0.013) between the treatment group (66.5 years) and control group (average age 51.5 years), and thus results were adjusted for age accordingly. IOP was 17.1 mmHg in the treatment group and 16.1 mmHg in the control group. When analyzing the sublayers of the TF, MALTR had a significant association with IOP-lowering therapy after adjusting for age, with a difference of −52.68 nm/s; 95% confidence interval [−96.87, −8.48]; p-value = 0.020. Additionally, IBI was significantly associated with IOP-lowering therapy after log transformation (p = 0.049), with shorter IBI in the treatment group. All other metrics (MALT, LLT, LMU, and LBUT) were statistically insignificant (p > 0.05). Conclusions: These pilot results suggest that IOP-lowering drops may accelerate thinning of the TF, specifically the muco-aqueous layer. Longitudinal studies with significantly larger samples are needed to specify the differential impact of various ocular hypotensive therapies on the human TF and the clinical implications of these findings. Full article

Locked-in Syndrome (LIS) is a neurological condition in which individuals remain conscious but experience complete paralysis of voluntary muscles, except for eye movements—highlighting the need for reliable assistive communication technologies. This study presents the design and evaluation of an Arabic electrooculogram (EOG)-based communication system with adaptive classification capabilities for LIS applications. A custom-designed EOG acquisition circuit incorporating filtering and amplification stages was implemented and compared with the OpenBCI Cyton board. The system employed a hybrid classification approach combining amplitude, temporal, and statistical features to distinguish between blinks and voluntary vertical eye movements. Testing with ten healthy subjects yielded a mean classification accuracy of 83.96% ± 4.59% and an information transfer rate of 10.43 letters per minute, corresponding to a 30.38% improvement over conventional approaches. The custom-designed circuit achieved a signal-to-noise ratio of 25.21 dB, outperforming the OpenBCI Cyton board by 8% while reducing system cost by 62%. The integration with a Morse code-based interface enabled Arabic letter composition, while the system incorporated auto-completion and text-to-speech functionalities to further enhance communication efficiency. This cost-effective solution addresses a critical gap in assistive technologies for Arabic-speaking individuals with LIS and shows strong potential for enhancing their communication abilities and overall quality of life. Full article

Understanding and monitoring driver mental workload is essential for improving road safety. This study proposes a multimodal machine learning framework to classify drivers’ mental workload using eye movement metrics, physiological signals, and driving behavior features. A driving simulator experiment was conducted with 26 participants under two workload levels induced by a secondary auditory task. Seven feature combinations and six classification algorithms were evaluated. The results showed that eye metrics were the most informative modality, and that feature selection had a greater impact on classification performance than algorithm choice. A support vector machine with optimized features was selected as the final model based on performance and stability, achieving an accuracy of 87.8% and an AUC of 0.95. To improve model transparency, SHapley Additive exPlanations (SHAP) was applied, highlighting key predictors such as blink rate and heart rate, and uncovering synergistic effects between visual and physiological variables. The model was further validated in a tunnel entrance scenario, where it identified increased workload associated with steeper longitudinal slopes. These findings emphasize the importance of multimodal data integration—particularly eye movements—for assessing mental workload. Future applications should prioritize feature diversity over algorithm complexity to enhance real-world implementation in workload monitoring systems. Full article

Electrical water-bath stunning remains the predominant method used in commercial poultry slaughter worldwide yet its effectiveness and welfare implications may vary among breeds. Taiwanese red-feathered chickens differ from commercial broilers in growth rate and body composition, which may influence their response to electrical stunning. This study investigated the relationships between electrical stunning conditions, electroencephalographic (EEG) indicators of unconsciousness, behavioural reflexes, and carcass quality in Taiwanese red-feathered chickens. A total of 200 female chickens were subjected to direct-current water-bath stunning at 80, 100, 120, 140, or 160 V for 7 s. EEG activity and physical indicators of consciousness were assessed during the first 40 s after stunning, and carcass defects were evaluated post-mortem. Of the 200 birds initially evaluated, EEG data from 153 birds met predefined signal quality criteria and were included in the final analysis. EEG-defined unconsciousness was more frequent and lasted longer at higher voltages (140–160 V), although intermediate voltage levels (e.g., 120 V) did not follow a strictly linear trend. Corneal reflex and spontaneous eye blinking were strongly associated with EEG-based unconsciousness, supporting their use as practical on-site welfare indicators. At the lowest voltage (80 V), birds with higher abdominal fat percentages were more likely to be effectively stunned. In contrast, no statistically significant associations between abdominal fat percentage and stunning effectiveness were observed at 100–160 V. However, higher voltages were also associated with an increased prevalence and severity of carcass defects. These findings suggest that stunning conditions or commercial broilers may not ensure effective unconsciousness in Taiwanese red-feathered chickens. Corneal reflex and spontaneous eye blinking provide reliable, welfare-relevant indicators of unconsciousness under field conditions. Electrical settings must be carefully balanced to achieve effective stunning while minimising adverse welfare outcomes associated with excessive neuro-muscular responses. Full article

This study investigated how perceptual workload in driving situations is captured by subjective ratings versus eye-tracking metrics. Fifty participants completed low- and high-complexity conditions while fixation behavior, blinks, and pupil diameter were recorded, and workload was assessed using the DALI scale. High-load scenes elicited longer fixations, fewer fixations per minute, reduced blinking, and increased pupil dilation, indicating elevated attentional demand. DALI scores increased with scene complexity and were negatively associated with fixation duration, demonstrating that participants’ subjective ratings were driven primarily by perceptual strain rather than cognitive effort. Eye-tracking patterns supported this interpretation: fixation-based indicators tent to reflect the cognitive component of demand, whereas DALI selectively tracked perceptual overload. Together, these results show that DALI is highly sensitive to visual density, and that eye-movement measures provide converging evidence for its specificity as a perceptual load instrument. Full article

Rosemary (Salvia rosmarinus) has been linked to improvements in psychological wellbeing through cholinergic mechanisms. However, this study investigated whether individual differences in eye blink rate (EBR) and blink variability (EBV), which are proxies of dopaminergic activity and attentional control, influence the cognitive and mood-enhancing properties of a rosemary-containing drink. Forty-eight healthy adults completed a three-stimulus odd-ball cognitive task under rosemary or control conditions, while vertical electrooculograms were recorded. Event-related brain potentials (ERPs) were also measured using the P3a component at the Cz scalp electrode as an additional index of dopaminergic activity. Subjective mood and arousal (alert, contented, calm) were collected pre- and post-task using Bond–Lader visual analogue scales. Reaction times during the task were modelled with ex-Gaussian parameters (μ, σ, τ). Rosemary ingestion led to increased alertness and contentedness following the task. Cognitive effects were moderated by blink metrics, with significant interactions between rosemary and blink metrics for mean reaction time μ and response variability σ. Rosemary also increased P3a amplitudes, indicative of dopaminergic contribution. The effects of rosemary on cognition and mood were moderated by individual blink profiles, indicating that baseline neurocognitive state plays a role. Although cholinergic accounts are well established, this study highlights the use of proxies of dopamine to investigate broader neurotransmitter involvement in rosemary’s enhancing properties. Full article

While multi-view visualization systems are widely used for mining vehicle teleoperation, they often impose high cognitive load and restrict operator attention. To explore a more efficient alternative, this study evaluated a merged-view interface that integrates multiple camera perspectives into a single coherent display. In a controlled experiment, 35 participants navigated a teleoperated robot along a 50 m lab-scale path representative of an underground mine under both multi-view and merged-view conditions. Task performance and eye-tracking data—including completion time, path adherence, and speed-limit violations—were collected for comparison. The merged-view system enabled 6% faster completion times, 21% higher path adherence, and 28% fewer speed-limit violations. Eye-tracking metrics indicated more efficient and distributed attention: blink rate decreased by 29%, fixation duration shortened by 18%, saccade amplitude increased by 11%, and normalized gaze-transition entropy rose by 14%, reflecting broader and more adaptive scanning. NASA-TLX scores further showed a 27% reduction in perceived workload. Regression-based sensitivity analysis revealed that gaze entropy was the strongest predictor of efficiency in the multi-view condition, while fixation duration dominated under merged-view visualization. For path adherence, blink rate was most influential in the multi-view setup, whereas fixation duration became key in merged-view operation. Overall, the results indicated that merged-view visualization improved visual attention distribution and reduced cognitive tunneling indicators in a controlled laboratory teleoperation task, offering early-stage, interface-level insights motivated by mining-relevant teleoperation challenges. Full article

Background/Objectives: The physical properties of topical eyedrop formulations used for the treatment of dry-eye disease play an important role in the lubrication of the ocular surface. In the present study, we investigate the shear-stress-dependent viscous properties of seven different commercially available lubricants using a novel optical rheometer for accurate analysis of the viscosity of liquid samples. In addition, the viscosity of natural tears was studied. Methods: Viscosity measurements were performed using Fluidicam RHEO technology (FORMULACTION, Toulouse, France), an automated optical rheometer combining microfluidic and imaging technologies. Measurements were conducted at a temperature of 34 °C and at shear rates ranging from 3000 s⁻¹ to 30,000 s⁻¹ to mimic conditions during eye blinking. Results: Natural tears showed minimal change in viscosity in response to changes in shear stress, with viscosity values of 0.91 mPa·s at 3000 s⁻¹ and 0.80 mPa·s at 30,000 s⁻¹. Among the artificial tear formulations, Thealoz^® Duo had the lowest viscosity (2.62 ± 0.01 mPa·s at 3000 s⁻¹), followed by Ivizia^® (2.42 ± 0.02 mPa·s), Hylo Comod^® (3.69 ± 0.01 mPa·s), Hylo Parin^® (3.87 ± 0.01 mPa·s), Xailin HA^® (4.73 ± 0.02 mPa·s), Vismed^® (5.42 ± 0.02 mPa·s), and Systane Hydration^® (7.76 ± 0.1 mPa·s). Conclusions: This study demonstrates that commercially available ocular lubricants exhibit varying degrees of shear-thinning behavior, a finding that is clinically relevant for their performance on the ocular surface. Formulations containing low-molecular-weight hyaluronic acid, such as Thealoz^® Duo, exhibited viscosity values closest to those of natural tears at the measured shear rates. Full article

Background/Objectives: Anxiety and depression are prevalent global health concerns, especially prominent in vulnerable groups such as older adults, individuals with chronic health conditions (e.g., neurodegeneration and cancer), and those from low socioeconomic backgrounds. Digital interventions, including computerized cognitive training (CCT), show promise in addressing emotional dysfunctions in a more accessible and cost-effective manner. The CREATE platform aims to enhance Emotion Regulation (ER) through targeted Working Memory (WM) training, aesthetic engagement, and creativity, while accounting for dopamine activity via spontaneous Eye Blink Rate (sEBR). The purpose of the present study is to evaluate the platform’s feasibility and validity through a single pilot trial. Methods: The study enrolled twenty-seven healthy adults (aged 21–44) who completed standardized self-report questionnaires on sleep quality and ER. They were also enrolled in sEBR recordings and performed a CCT-adapted Corsi block-tapping task and an aesthetic art evaluation. Affective textual narratives and valence/arousal ratings were also collected. Participants were divided into “Good Sleepers” and “Poor Sleepers”. The platform evaluation enrolled a multi-modal pipeline including correlations and regression analysis of intervention metrics, sentiment analysis, and group comparisons. Results: WM task performance correlated positively with global ER and Cognitive Reappraisal scores. Post-training sEBR was significantly associated with ER, and lower sleep efficiency negatively impacted changes in dopamine activity (sEBR Diff). Dopamine activity of “Good Sleepers”, as indicated by sEBR, reached the high levels of the “Poor Sleepers” group after the training, suggesting a dopamine boost caused by the CREATE platform for those with quality sleep. Creativity and emotional expression, as indicated by sentiment analysis, were related to sleep quality. Conclusions: The CREATE platform shows promise in enhancing ER through multi-modal digital engagement, integrating cognitive training, art, and creativity. The findings support the inclusion of sleep and dopamine markers in intervention evaluation. Further studies with larger samples and clinical cohorts are warranted to establish efficacy and generalizability, as the present one was not powered to test the effectiveness of our training platform but was designed to assess its feasibility and validity instead. Full article

Neurological disorders significantly affect ocular surface homeostasis, influencing parameters such as blink rate (BR), tear production, corneal nerve density, and sensitivity. This review summarizes recent findings on ocular surface alterations associated with neurological diseases, including Alzheimer’s disease (AD), Parkinson’s disease (PD), Guillain-Barré syndrome (GBS), trigeminal neuralgia (TN), multiple sclerosis (MS), and Charcot–Marie–Tooth disease (CMT). Notably, ocular manifestations such as reduced BR, decreased tear break-up time (TBUT), impaired tear secretion, and corneal nerve fiber loss are consistently reported. In AD, elevated tear amyloid-beta and tau proteins emerge as promising biomarkers for early disease detection. PD patients frequently experience dry eye symptoms attributed to reduced BR and tear film instability. GBS is linked to lagophthalmos and corneal nerve impairment, potentially leading to severe ocular surface damage. TN demonstrates bilateral ocular surface dysfunction despite unilateral neuropathic symptoms. MS is associated with significant ocular surface alterations, reflecting broader neuroinflammatory and autonomic disturbances. Similarly, CMT patients show reduced corneal sensitivity and tear production, underscoring the systemic nature of neurological impacts. Awareness of these ocular manifestations is essential for improving patient care and guiding future research into ocular biomarkers and targeted therapies. Full article

Road traffic accidents remain a major global public health concern, where complex urban driving environments significantly elevate drivers’ visual load and accident risks. Unlike existing research that adopts a macro perspective by considering multiple factors such as the driver, vehicle, and road, this study focuses on the driver’s visual load, a key safety factor, and its direct source—the driver’s visual environment. We have developed an interpretable framework combining computer vision and machine learning to quantify how road scene features influence oculomotor behavior and scene-induced visual load, establishing a complete and interpretable link between scene features, eye movement behavior, and visual load. Using the DR(eye)VE dataset, visual attention demand is established through occlusion experiments and confirmed to correlate with eye-tracking metrics. K-means clustering is applied to classify visual load levels based on discriminative oculomotor features, while semantic segmentation extracts quantifiable road scene features such as the Green Visibility Index, Sky Visibility Index and Street Canyon Enclosure. Among multiple machine learning models (Random Forest, Ada-Boost, XGBoost, and SVM), XGBoost demonstrates optimal performance in visual load detection. SHAP analysis reveals critical thresholds: the probability of high visual load increases when pole density exceeds 0.08%, signage surpasses 0.55%, or buildings account for more than 14%; while blink duration/rate decrease when street enclosure exceeds 38% or road congestion goes beyond 25%, indicating elevated visual load. The proposed framework provides actionable insights for urban design and driver assistance systems, advancing traffic safety through data-driven optimization of road environments. Full article

This paper presents a novel EEG-based learning system designed to enhance the efficiency and effectiveness of studying by dynamically adjusting the difficulty level of learning materials based on real-time attention levels. In the training phase, EEG signals corresponding to high and low concentration levels are recorded while participants engage in quizzes to learn and memorize Chinese characters. The attention levels are determined based on performance metrics derived from the quiz results. Following extensive preprocessing, the EEG data undergoes several feature extraction steps: removal of artifacts due to eye blinks and facial movements, segregation of waves based on their frequencies, similarity indexing with respect to delay, binary thresholding, and (PCA). These extracted features are then fed into a k-NN classifier, which accurately distinguishes between high and low attention brain wave patterns, with the labels derived from the quiz performance indicating high or low attention. During the implementation phase, the system continuously monitors the user’s EEG signals while studying. When low attention levels are detected, the system increases the repetition frequency and reduces the difficulty of the flashcards to refocus the user’s attention. Conversely, when high concentration levels are identified, the system escalates the difficulty level of the flashcards to maximize the learning challenge. This adaptive approach ensures a more effective learning experience by maintaining optimal cognitive engagement, resulting in improved learning rates, reduced stress, and increased overall learning efficiency. Our results indicate that this EEG-based adaptive learning system holds significant potential for personalized education, fostering better retention and understanding of Chinese characters. Full article

Smartphones have revolutionized our daily lives, becoming portable pocket computers with easy internet access. India, the second-highest smartphone and internet user, experienced a significant rise in smartphone usage between 2013 and 2024. Prolonged smartphone use, exceeding 20 min at a time, can lead to physical and mental health issues, including psychophysiological disorders. Digital devices and their extended exposure to blue light cause digital eyestrain, sleep disorders and visual-related problems. This research examines the impact of 1 h smartphone usage on visual fatigue among young Indian adults. A portable, low-cost system has been developed to measure visual activity to address this. The developed visual activity measurement system measures blink rate, inter-blink interval, and pupil diameter. Measured eye activity was recorded during 1 h smartphone usage of e-book reading, video watching, and social-media reels (short videos). Social media reels show increased screen variations, affecting pupil dilation and reducing blink rate due to continuous screen brightness and intensity changes. This reduction in blink rate and increase in inter-blink interval or pupil dilation could lead to visual fatigue. Full article

Topical drug administration is a common method of delivering medications to the eye to treat various ocular conditions, including glaucoma, dry eye, and inflammation. Drug efficacy following topical administration, including the drug’s distribution within the eye, absorption and elimination rates, and physiological responses can be predicted using physiologically based pharmacokinetic (PBPK) modeling. High-resolution computational models of the eye are desirable to improve simulations of drug delivery; however, these approaches can have long run times. In this study, a fast-running computational quasi-3D (Q3D) model of the human tear film was developed to account for absorption, blinking, drainage, and evaporation. Visualization of blinking mechanics and flow distributions throughout the tear film were enabled using this Q3D approach. Average drug absorption throughout the tear film subregions was quantified using a high-resolution compartment model based on a system of ordinary differential equations (ODEs). Simulations were validated by comparing them with experimental data from topical administration of 0.1% dexamethasone suspension in the tear film (R² = 0.76, RMSE = 8.7, AARD = 28.8%). Overall, the Q3D tear film model accounts for critical mechanistic factors (e.g., blinking and drainage) not previously included in fast-running models. Further, this work demonstrated methods toward improved computational efficiency, where central processing unit (CPU) time was decreased while maintaining accuracy. Building upon this work, this Q3D approach applied to the tear film will allow for more seamless integration into full-body models, which will be an extremely valuable tool in the development of treatments for ocular conditions. Full article