Search Results (1,998)

Background: The alternative Overlap Syndrome (aOVS), the coexistence of bronchial asthma and Obstructive Sleep Apnea (OSA), represents a distinct clinical phenotype associated with worse clinical outcomes, but little is yet known about its characteristics. We aimed to investigate differences in sleep stability and clinical burden between OSA and aOVS patients matched for age, gender, BMI and Apnea-Hypopnea Index (AHI). Methods: 45 aOVS and 45 OSA patients were compared using demographic, clinical and polysomnographic data. Results: Patients with aOVS exhibited significantly higher odds ratio product (ORP) values for total sleep time (ORPmed: 0.8 ± 0.2 vs. 0.5 ± 0.1, p < 0.001) and Non-Rapid Eye Movement (ORPnr: 0.7 ± 0.3 vs. 0.4 ± 0.1, p < 0.001) sleep compared with OSA patients. Furthermore, patients with aOVS showed a significantly higher risk of developing clinically significant anxiety and fatigue, showing a significantly higher General Anxiety Disorder-7 (GAD-7: 8.7 ± 5.6 vs. 5.7 ± 4.7, p = 0.02) and significantly higher prevalence of fatigue (71% vs. 41%, p = 0.01). These associations remained significant after multivariable adjustment and were independent of OSA severity (AHI). Conclusions: Our findings support the concept that aOVS is characterised by significantly more unstable sleep and a greater psychological burden, even after matching with OSA patients for age, gender, BMI and AHI. Our study also highlights the need to integrate traditional sleep measures with more recent ones, such as ORP, in order to better capture the multidimensional burden of aOVS. Full article

Background: This review discusses the application of eye-tracking technology in the detection and monitoring of binocular vision anomalies among children. Methods: A scoping review using PRISMA guidelines was conducted through Scopus, ScienceDirect, and PubMed using the keywords “eye-tracking,” “binocular,” “vision,” “anomalies,” “paediatrics,” and “children” from 2015 to 2025. Studies excluded were not written in English, did not apply the eye tracker as a research tool, involved an ineligible population, or involved non-human subjects. Results: The search strategy identified 77 citations, yet only 14 studies met the inclusion criteria. This review revealed a variety of binocular vision anomalies detectable through eye-tracking systems, along with the specific models and parameters employed in these assessments. Application of eye-tracking technology in diagnosing conditions such as strabismus and amblyopia demonstrated potential for improved accuracy and early detection. Discussion: Eye-tracking technology demonstrates considerable potential for the detection and monitoring of binocular vision anomalies in children, particularly as a non-invasive method for early screening, thereby strengthening its clinical applicability. By assessing fixation stability, saccadic movements, and vergence responses, eye-tracking allows for the early detection of subtle visual anomalies, especially in the paediatric population. Conclusions: Eye-tracking technology represents a valuable advancement in paediatric vision care, enabling the more objective and earlier detection of binocular vision anomalies in the paediatric population. 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

Collecting a large number of dangerous omen scenarios from drivers’ first-person perspective is of great significance for training and improving end-to-end autonomous driving models. In this study, we aim at capturing driver-perspective scenarios when recognizing dangerous omens. Firstly, through the design and implementation of vehicle and virtual driving experiments, the electroencephalogram, electrocardiogram and eye movement data of the subjects are collected. Statistical tests are conducted to analyze the characteristic differences among drivers across three distinct states. It also reveals that the driver can perceive and distinguish the dangerous omen clearly. Secondly, the evolution law of drivers’ perception state is analyzed to accurately judge the time period of drivers’ dangerous omen perception. Thirdly, the Hidden Markov Model is used to build the driver perception state transition model, and then the model is calibrated and verified. Finally, the model is utilized to identify drivers’ dangerous omen perception states and extract the corresponding perspective objective scenarios, which can provide sufficient samples for training end-to-end autonomous driving models. This study is of great significance to enable the capability of vehicles to recognize dangerous omens, advancing end-to-end and other high-level autonomous driving technologies and further securing vehicle safety. Full article

Background/Objectives: Patients with poor sleep are at high risk of developing type II diabetes mellitus (T2DM). Since T2DM is linked to increased risk of obstructive sleep apnea (OSA), and Metformin is commonly used to treat T2DM, we examined how Metformin affects sleep stages in patients with concurrent T2DM and OSA-related symptoms of snoring and fatigue. Patients with T2DM on Metformin progressively develop increased insulin resistance associated with sleep disturbances and poor glycemic control. We therefore explored sleep pattern changes in patients with OSA symptoms and T2DM on Metformin, with a special focus on whether Metformin affects sleep architecture. Methods: Polysomnogram (PSG) data from patients with T2DM on Metformin was evaluated along with data on age, body-mass index (BMI), and biological sex. Data analysis included mean ± standard deviation, t-test with p < 0.05 taken as significant, and linear regression. Results: Patients with a BMI of less than 30 (non-obese) and taking Metformin exhibited a significantly shorter rapid eye movement sleep stage (REM) duration than patients on alternative therapies (p = 0.036). No such difference in REM was found for patients with a BMI of 30 or greater (obese) taking Metformin. While there was also no significant difference in slow-wave sleep stage (N3) duration with Metformin use, linear regression identified a moderate negative correlation between N3 and age in patients taking non-Metformin therapies (R² = 0.4555). No significant correlations between sleep stage duration and patient sex, smoking status, or BMI greater than 30 were identified. Conclusions: Overall, patients with OSA and T2DM on Metformin had lower mean quantities of N3, and REM sleep compared to those not on Metformin. Non-obese patients with T2DM and OSA being treated with Metformin were observed to have less REM sleep, regardless of sex or smoking history. N3 and REM sleep are needed for the timely secretion of growth hormone and memory consolidation. Since Metformin is correlated with differences in N3 and REM sleep, it may contribute to the development of insulin resistance. Future studies are needed to explore potential causes for this relationship and how it may affect the treatment of T2DM. Full article

Eye movements are difficult to observe and replicate, making them a promising yet understudied modality for behavioral biometrics. This study is the first to examine the feasibility of using eye movement patterns during manga reading as a biometric identifier, leveraging the medium’s rich behavioral data from diverse reading behaviors. Eye movement data from 59 participants were recorded while they read two manga works on a screen. A comprehensive set of gaze features was extracted and evaluated using five machine learning classifiers, among which Random Forest (RF) consistently achieved the best performance. Under constrained experimental conditions, the RF classifier achieved a Rank-1 identification rate of 95.0% and an equal error rate (EER) of 1.9%. Furthermore, this study systematically investigated two critical challenges for practical deployment: stimulus dependency and template aging. Cross-stimulus evaluation revealed substantial performance degradation when training and testing used different manga works, and template aging analysis over an approximately 90-day interval demonstrated notable declines in identification accuracy. These results provide preliminary evidence supporting the potential of natural reading behaviors for biometric continuous authentication systems while highlighting the need for further research into cross-stimulus generalization and temporal stability. 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

Peripheral facial palsy (PFP) causes pronounced facial asymmetry and functional impairment, highlighting the need for reliable, objective assessment. This study presents a novel, fully automated, reference-free method for quantifying facial symmetry using artificial intelligence (AI)-based facial landmark detection. A total of 405 datasets from 198 PFP patients were analyzed, each including nine standardized facial expressions covering both resting and dynamic movements. AI detected 478 landmarks per image, from which 225 paired landmarks were used to compute local asymmetry angles. Systematic evaluation identified 91 highly informative landmark pairs, primarily around the eyes, nose and mouth, which simplified the analysis and enhanced discriminatory power, while also enabling region-specific assessment of asymmetry. Statistical evaluation included Kruskal–Wallis H-tests across clinical scores and Spearman correlations, showing moderate to strong associations (0.32–0.73, p < 0.001). The fully automated pipeline produced reproducible results and demonstrated robustness to head rotation. Intuitive full-face angle maps allowed direct assessment of asymmetry without a reference image. This AI-driven approach provides a robust, objective, and visually interpretable framework for clinical monitoring, severity classification, and treatment evaluation in PFP, combining quantitative precision with practical applicability. Full article

Orbital floor fractures may cause long-term functional and esthetic impairments. Diplopia due to impaired function of the inferior rectus muscle is frequently an indication for surgical repair, but some cases, such as those where the diagnosis has been delayed or a previous attempt at repair has been made, may not always be amenable to surgical correction. It is advantageous for the surgeon to know whether the proper function of the inferior rectus muscle can be restored for the purposes of surgical planning and prognostication. The authors hypothesized that real-time MRI could be used to characterize the appearance of the inferior rectus muscle in a way that would facilitate future analysis of inferior rectus function in patients with diplopia due to orbital floor fractures. Real-time MRI was performed on 10 volunteer participants with normal ophthalmic function and orbital anatomy to assess inferior rectus appearance during vertical duction testing. ImageJ software was used to measure and record characteristics of the inferior rectus muscle, viewed in a quasi-sagittal plane. The ratios evaluated included inferior rectus muscle length in upgaze versus downgaze (UDR, mean 1.58) as well as inferior rectus muscle length versus distance from inferior rectus origin to inferior rectus inflection point in upgaze (LIR, mean 1.30) and downgaze (mean 1.20). These values were found to be conserved between orbits and individuals. This data offers quantitative insight regarding inferior rectus muscle appearance across the full arc of vertical gaze in healthy individuals. We plan to use this normative baseline dataset as a comparison for future phases of this project, using real-time MRI to evaluate traumatized orbits with diplopia and derangement of the inferior rectus muscle. Full article

Intelligent-assisted assessment of pilot flight training ability is a method of automating the evaluation of pilots’ flight skills using artificial intelligence. Currently, using AI to assist or replace human instructors in flight skill assessment has become a mainstream research direction in the field of intelligent aviation. Existing flight skill assessment methods suffer from limitations in data types and insufficient assessment accuracy. To address these issues, we evaluate and predict pilot performance in simulated flight missions based on physiological signals. Following the “OODA loop” theory, we established a multimodal dataset including pilot eye movement, electroencephalogram (EEG), electrocardiogram (ECG), electrodermal signaling (EDS), heart rate, respiration, and flight attitude data. This dataset records changes in physiological rhythms and flight behaviors during pilots’ flight training at different difficulty levels. To enhance the signal-to-noise ratio, we propose an enhanced wavelet fuzzy thresholding denoising algorithm utilizing LSTM optimization. We address the problem of isolated features across different time frames in multimodal data modeling by introducing a multi-feature fusion algorithm based on STFT. Furthermore, by combining a high-efficiency sub-attention mechanism with a Transformer network, we construct a multi-classification network for intelligent-assisted assessment of pilot flight training ability, further improving the output accuracy of each category. Experiments show that our designed algorithm can achieve a classification accuracy of up to 85% on the dataset (5-fold cross-validation), which meets the requirements for auxiliary assessment of flight capabilities. Full article

Non-organic (muscle) weakness (NOw) is proposed as a distinct pathological entity characterized by maladaptive neuroplasticity (learning) affecting motor control. Functional deficits are most directly revealed through the manual muscle testing (MMT) break test, which uniquely exposes a muscle’s ability to adapt to increasing external load, potentially serving as an index of motor control integrity. We advance the “muscle-motor-movement PTSD” (mPTSD) model in which learning during pain or stress (trauma) yields chronic avoidance (inhibition) of the associated muscles. In a second stage, compensatory synergies develop, overriding attempts at hypertrophy-oriented training. This non-systematic, integrative review synthesizes clinical reports, learning theories, motor control and pain literature, and objective tests of force and movement over time during MMT. Predictive processing and reinforcement learning offer complementary accounts of how hyper-precise priors and passive avoidance may maintain NOw beyond functional recovery. Unexplained muscle weakness is found in non-specific musculoskeletal disorders and functional motor disorder (functional weakness), but may also contribute to other conditions, such as kinesiophobia. Effective alternative treatments for NOw may act by updating or erasing maladaptive motor learning by disrupting memory reconsolidation, allowing immediate restoration of function. Analogous to psychoneuroimmunology’s role in immune function, we propose “psychoneurokinesiology”, the study of how maladaptive learning affects movement. Full article

Street vegetation is an important component of urban green space which plays a crucial role in promoting human well-being. To examine the impact of different types of street vegetation on individuals’ mental health, we presented two types of four street vegetation scenes in the real environment, concerning the form and species. One type consisted of random shrubs and regular shrubs. The other type consists of trees with single species and trees with diverse species. Forty participants took part in an experimental design to evaluate psychological and physiological changes before and after exposure to the street vegetation using the measures of EEG, HRV and eye movement. Our results identified that exposure to street vegetation enhanced alpha brain activity and reduced the HRV. In addition, eye movement was used to enhance restorative effects. The effect of different types of street vegetation varied significantly. It indicated that regular shrubs had a more positive effect on measures of relaxation compared with the random shrubs. The type of street vegetation of trees with diverse species had a more positive effect on measures of relaxation than the type of single species. The POMS scores of the regular shrubs decreased compared to the random shrubs and the diverse species decreased compared to the single species. The ROS scores of the regular and diverse types are higher than the random and single. The study suggests that the type manual-pruned street vegetation and the type of trees combined with plant diversity are generally more favorable in enhancing subjective comfort in the street vegetation. These findings underscore the importance of form and species in landscape planning and design to promote relaxation and comfort in the urban street environment. Full article

Purpose: To investigate the relationship between oculomotor skills, phorias, and visual symptoms in pediatric population aged 5 to 8 years. Methods: A cross-sectional study was conducted with 120 children, divided into three age groups. Each participant underwent a full optometric examination, including the Maddox test for dissociated phoria, and the Northeastern State University College of Optometry (NSUCO) and Developmental Eye Movement (DEM) tests for oculomotor function. In addition, the Convergence Insufficiency Symptom Survey (CISS V-15) questionnaire was administered to assess visual symptoms. Results: The prevalence of binocular and oculomotor dysfunctions varied by age and sex. Differences in saccadic and pursuit eye movement performance were observed between groups. Older children showed patterns of association between phoria measurements, oculomotor performance, and possible visual symptoms, particularly in girls over 6 years of age. Conclusions: This study provides additional descriptive data for the pediatric population and highlights that oculomotor dysfunction and phoria frequently coexist. Symptom scores measured by the CISS V-15 tended to increase with age. The results should be considered preliminary and potentially hypothesis-generating, pending the future availability of a validated questionnaire to assess phoria-related symptoms in children from 5 years of age. Overall, this study underscores the importance of comprehensive binocular vision assessments in school-aged children. Full article

Background: Electromyography (EMG), video-polysomnography (vPSG), and wrist actigraphy are each used to develop diagnostic algorithms for rapid-eye-movement sleep behavior disorder (RBD). However, the extent to which they capture overlapping versus distinct motor phenomena remains unknown. We evaluated the respective contributions of actigraphy, EMG and vPSG to the measurement of REM sleep motor activity. Methods: Seventeen adults with RBD (Mount Sinai n = 9; Stanford n = 8) and eight control participants from an open Newcastle dataset underwent vPSG and concomitant wrist actigraphy. Flexor digitorum superficialis EMG activity and video-detected movements were manually scored in 3 s mini epochs. Actigraphy was quantified using an acceleration-magnitude-based activity count model. Statistical and agreement analyses were performed to assess the motor events captured by all three, any two, or by each modality independently during REM sleep. Results: In participants with RBD, actigraphy-derived movement load was significantly higher during REM sleep than during non-REM stages, a pattern not observed in control participants. REM movement load was also higher in RBD participants compared to controls, although this difference did not remain significant after correction for multiple comparisons. Across 12,941 3 s mini epochs, EMG, actigraphy, and video detected 1703, 1613, and 811 motor events, of which 413 were detected concurrently by all three modalities. Pairwise agreement was moderate and increased from EMG–actigraphy (κ = 0.27 ± 0.10) to actigraphy–video (κ = 0.41 ± 0.12) and EMG–video (κ = 0.45 ± 0.15). Of EMG-detected events, 49.0% were also detected by actigraphy; of actigraphy-detected events, 37.2% were detected by EMG and 34.9% by video. Actigraphy activity counts were highest for events detected by all three modalities and lowest for actigraphy-only events. Conclusions: Actigraphy-measured REM-related motor activity was elevated in RBD but not in controls. EMG, actigraphy, and video captured partially overlapping motor events in RBD patients, with actigraphy showing the highest sensitivity and manually scored video the lowest. Full article

This paper aims to uncover the generation patterns of pilot intentions during complex flight missions and to identify pilot intention states, thereby enabling airborne warning systems to understand and predict pilot intentions for early warning strategies. To achieve this, we designed a simulated flight experiment incorporating various risk scenarios to induce pilot intentions, collected eye-tracking data reflecting pilot intention states, and proposed a method for identifying the persistence of pilot intentions based on eye-tracking data. We then constructed a pilot intention dataset and analyzed the time–frequency characteristics of eye-tracking data in the intention persistence state, revealing key behavioral features following the generation of pilot intentions. Furthermore, we examined eye-tracking features that enhance the performance of pilot intention recognition models. Finally, we developed a deep learning model integrating recurrent neural networks (RNN) and bidirectional long short-term memory (BiLSTM) networks to recognize pilot intentions. The results demonstrate that the model achieved a recognition accuracy of 97.8% on the test set, and its performance in identifying pilot intention states was validated through comparison with a baseline model. This study confirms that eye-tracking data can effectively identify pilot intention states and offers new insights into aircraft safety early warning and intelligent control systems. Full article