Search Results (1,945)

Reading is a complex skill that requires accurate word recognition, fluent decoding, and effective comprehension. Children with dyslexia often face challenges in these areas, resulting in ongoing reading difficulties. This study systematically reviews the use of eye-tracking technology to assess dyslexia, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines. The review identifies the specific types of eye-tracking technologies used, examines the cognitive and behavioral abilities assessed (such as reading fluency and attention), and evaluates the primary purposes of these evaluations—screening, assessment, and diagnosis. This study explores key questions, including how eye-tracking outcomes guide intervention strategies and influence educational practices, and assesses the practicality and time efficiency of these evaluations in real-world settings. Furthermore, it considers whether eye-tracking provides a holistic developmental profile or a targeted analysis of specific skills and evaluates the generalizability of eye-tracking results across diverse populations. Gaps in the literature are highlighted, with recommendations proposed to improve eye-tracking’s precision and applicability for early dyslexia intervention. The findings underscore the potential of eye-tracking to enhance diagnostic accuracy through metrics such as fixation counts, saccadic patterns, and processing speed, key indicators that distinguish dyslexic from typical reading behaviors. Additionally, studies show that integrating machine learning with eye-tracking data can enhance classification accuracy, suggesting promising applications for scalable, early dyslexia screening in educational settings. This review provides new insights into the value of eye-tracking technology in identifying dyslexia, emphasizing the need for further research to refine these methods and support their adoption in classrooms and clinics. Full article

Visual object tracking is a fundamental task in computer vision, with applications ranging from video surveillance to autonomous driving. Despite recent advances in transformer-based one-stream trackers, unrestricted feature interactions between the template and the search region often introduce background noise into the template, degrading the tracking performance. To address this issue, we propose FETrack, a feature-enhanced transformer-based network for visual object tracking. Specifically, we incorporate an independent template stream in the encoder of the one-stream tracker to acquire the high-quality template features while suppressing the harmful background noise effectively. Then, we employ a sequence-learning-based causal transformer in the decoder to generate the bounding box autoregressively, simplifying the prediction head network. Further, we present a dynamic threshold-based online template-updating strategy and a template-filtering approach to boost tracking robustness and reduce redundant computations. Extensive experiments demonstrate that our FETrack achieves a superior performance over state-of-the-art trackers. Specifically, the proposed FETrack achieves a 75.1% AO on GOT-10k, 81.2% AUC on LaSOT, and 89.3% $P_{norm}$ on TrackingNet. Full article

This paper introduces an enhanced APF method to address challenges in automatic lane changing and collision avoidance for autonomous vehicles, targeting issues of infeasible target points, local optimization, inadequate safety margins, and instability when using DLQR. By integrating a distance adjustment factor, this research aims to rectify traditional APF limitations. A safety distance model and a sub-target virtual potential field are established to facilitate collision-free path generation for autonomous vehicles. A path tracking system is designed, combining feed-forward control with DLQR. Linearization and discretization of the vehicle’s dynamic state space model, with constraint variables set to minimize control-command costs, aligns with DLQR objectives. The aim is precise steering angle determination for path tracking, negating lateral errors due to external disturbances. A Simulink–CarSim co-simulation platform is utilized for obstacle and speed scenarios, validating the autonomous vehicle’s dynamic hazard avoidance, lane changing, and overtaking capabilities. The refined APF method enhances path safety, smoothness, and stability. Experimental data across three speeds reveal reasonable steering angle and lateral deflection angle variations. The controller ensures stable reference path tracking at 40, 50, and 60 km/h around various obstacles, verifying the controller’s effectiveness and driving stability. Comparative analysis of visual trajectories pre-optimization and post-optimization highlights improvements. Vehicle roll and sideslip angle peaks, roll-angle fluctuation, and front/rear wheel steering vertical support forces are compared with traditional LQR, validating the optimized controller’s enhancement of vehicle performance. Simulation results using MATLAB/Simulink and CarSim demonstrate that the optimized controller reduces steering angles by 5 to 10°, decreases sideslip angles by 3 to 5°, and increases vertical support forces from 1000 to 1450 N, showcasing our algorithm’s superior obstacle avoidance and lane-changing capabilities under dynamic conditions. Full article

Mobile gaming accounts for more than 50% of global online gaming revenue, surpassing console and browser-based gaming. The success of mobile gaming titles depends on optimizing applications for the specific hardware constraints of mobile devices, such as smaller displays and lower computational power, to maximize battery life. Additionally, these applications must dynamically adapt to the variations in network speed inherent in mobile environments. Ultimately, user engagement and satisfaction are critical, necessitating a favorable comparison to browser and console-based gaming experiences. While Quality of Experience (QoE) subjective evaluations through user surveys are the most reliable method for assessing user perception, various factors, termed influence factors (IFs), can affect user ratings of stimulus quality. This study examines human influence factors in mobile gaming, specifically analyzing the impact of user delight towards displayed content and the effect of gaze tracking. Using Pupil Core eye-tracking hardware, we captured user interactions with mobile devices and measured visual attention. Video stimuli from eight popular games were selected, with resolutions of 720p and 1080p and frame rates of 30 and 60 fps. Our results indicate a statistically significant impact of user delight on the MOS for most video stimuli across all games. Additionally, a trend favoring higher frame rates over screen resolution emerged in user ratings. These findings underscore the significance of optimizing mobile gaming experiences by incorporating models that estimate human influence factors to enhance user satisfaction and engagement. Full article

Attentional deficits have tragic consequences on road safety. These deficits are not solely caused by distraction, since they can also arise from other mental impairments such as, most frequently, mental fatigue. Fatigue is among the most prevalent impairing conditions while driving, degrading drivers’ cognitive and physical abilities. This issue is particularly relevant for professional drivers, who spend most of their time behind the wheel. While scientific literature already documented the behavioral effects of driving fatigue, most studies have focused on drivers under sleep deprivation or anyhow at severe fatigue degrees, since it is difficult to recognize the onset of fatigue. The present study employed an EEG-driven approach to detect early signs of fatigue in professional drivers during a simulated task, with the aim of studying visual attention as fatigue begins to set in. Short-range and long-range professional drivers were recruited to take part in a 45-min-long simulated driving experiment. Questionnaires were used to validate the experimental protocol. A previously validated EEG index, the MDrow, was adopted as the benchmark measure for identifying the “fatigued” spans. Results of the eye-tracking analysis showed that, when fatigued, professional drivers tended to focus on non-informative portions of the driving environment. This paper presents evidence that an EEG-driven approach can be used to detect the onset of fatigue while driving and to study the related visual attention patterns. It was found that the onset of fatigue did not differentially impact drivers depending on their professional activity (short- vs. long-range delivery). Full article

In response to escalating concerns about the indoor transmission of respiratory diseases, this study introduces a sophisticated software tool engineered to accurately determine contact rates among individuals in enclosed spaces—essential for public health surveillance and disease transmission mitigation. The tool applies YOLOv8, a cutting-edge deep learning model that enables precise individual detection and real-time tracking from video streams. An innovative feature of this system is its dynamic circular buffer zones, coupled with an advanced 2D projective transformation to accurately overlay video data coordinates onto a digital layout of the physical environment. By analyzing the overlap of these buffer zones and incorporating detailed heatmap visualizations, the software provides an in-depth quantification of contact instances and spatial contact patterns, marking an advancement over traditional contact tracing and contact counting methods. These enhancements not only improve the accuracy and speed of data analysis but also furnish public health officials with a comprehensive framework to develop more effective non-pharmaceutical infection control strategies. This research signifies a crucial evolution in epidemiological tools, transitioning from manual, simulation, and survey-based tracking methods to automated, real time, and precision-driven technologies that integrate advanced visual analytics to better understand and manage disease transmission in indoor settings. Full article

Nowadays, technology plays a fundamental role in data collection and analysis, which are essential for decision-making in various fields. Agile methodologies have transformed project management by focusing on continuous delivery and adaptation to change. In multiple project management, assessing the progress and pace of work in Sprints is particularly important. In this work, a data model was developed to evaluate the progress and pace of work, based on the visual interpretation of numerical data from certain graphs that allow tracking, such as the Burndown chart. Additionally, experiments with machine learning algorithms were carried out to validate the effectiveness and potential improvements facilitated by this dataset development. Full article

One of the most widely researched fields within the automotive industry is the effect vehicles place on the environment. To achieve a sustainable transport system, reducing the pollution of vehicles is an essential issue. The aim of this paper is to examine how weather conditions influence a vehicle’s operation. The study examines potential methods to evaluate the effect of different weather conditions on the aerodynamic parameters of a vehicle. Aerodynamic properties can be measured with the help of computational fluid dynamics (CFD), a wind tunnel and test-track measurements. On-board diagnostics are also examined to collect data on aerodynamics. These methods can monitor several parameters to measure and visualize the effects of weather conditions. The theoretical background to the related aerodynamic parameters is summarized. Full article

Since Visual Simultaneous Localization and Mapping (VSLAM) inherently requires intensive computational operations and consumes many hardware resources, these limitations pose challenges to implementing the entire VSLAM architecture within limited processing power and battery capacity. This paper proposes a novel solution to improve the efficiency and performance of exchanging data between the unmanned aerial vehicle (UAV) and the cloud server. First, an adaptive ORB (oriented FAST and rotated BRIEF) method is proposed for precise tracking, mapping, and re-localization. Second, efficient visual data encoding and decoding methods are proposed for exchanging the data between the edge device and the UAV. The results show an improvement in the trajectory RMSE and accurate tracking using the adaptive ORB-SLAM. Furthermore, the proposed visual data encoding and decoding showed an outstanding performance compared with the most used standard JPEG-based system over high quantization ratios. Full article

Identification of light sensitivities, manifesting either as hyper-sensitive (over-stimulating) or hypo-sensitive (under-stimulating) in children with autism spectrum disorder (ASD), is crucial for the development of personalized sensory environments and therapeutic strategies. Traditional methods for identifying light sensitivities often depend on subjective assessments and manual video coding methods, which are time-consuming, and very keen observations are required to capture the diverse sensory responses of children with ASD. This can lead to challenges for clinical practitioners in addressing individual sensory needs effectively. The primary objective of this work is to develop an automated system using Internet of Things (IoT), computer vision, and data mining techniques for assessing visual sensitivities specifically associated with light (color and illumination). For this purpose, an Internet of Things (IoT)-based light sensitivities assessing system (IoT-LSAS) was designed and developed using a visual stimulating device, a bubble tube (BT). The IoT-LSAS integrates various electronic modules for (i) generating colored visual stimuli with different illumination levels and (ii) capturing images to identify children’s emotional responses during sensory stimulation sessions. The system is designed to operate in two different modes: a child control mode (CCM) and a system control mode (SCM). Each mode uses a distinct approach for assessing light sensitivities, where CCM uses a preference-based approach, and SCM uses an emotional response tracking approach. The system was tested on a sample of 20 children with ASD, and the results showed that the IoT-LSAS effectively identified light sensitivities, with a 95% agreement rate in the CCM and a 90% agreement rate in the SCM when compared to the practitioner’s assessment report. These findings suggest that the IoT-LSAS can be used as an alternative to traditional assessment methods for diagnosing light sensitivities in children with ASD, significantly reducing the practitioner’s time required for diagnosis. Full article

Background/Objectives: The incorporation of biomechanics into stroke neurorehabilitation may serve to strengthen the effectiveness of rehabilitation strategies by increasing our understanding of human movement and recovery processes. The present bibliometric analysis of biomechanics research in stroke neurorehabilitation is conducted with the objectives of identifying influential studies, key trends, and emerging research areas that would inform future research and clinical practice. Methods: A comprehensive bibliometric analysis was performed using documents retrieved from the Scopus database on 6 August 2024. The analysis included performance metrics such as publication counts and citation analysis, as well as science mapping techniques, including co-authorship, bibliographic coupling, co-citation, and keyword co-occurrence analyses. Data visualization tools such as VOSviewer and Power BI were utilized to map the bibliometric networks and trends. Results: An overabundance of recent work has yielded substantial advancements in the application of brain–computer interfaces to electroencephalography and functional neuroimaging during stroke neurorehabilitation., which translate neural activity into control signals for external devices and provide critical insights into the biomechanics of motor recovery by enabling precise tracking and feedback of movement during rehabilitation. A sampling of the most impactful contributors and influential publications identified two leading countries of contribution: the United States and China. Three prominent research topic clusters were also noted: biomechanical evaluation and movement analysis, neurorehabilitation and robotics, and motor recovery and functional rehabilitation. Conclusions: The findings underscore the growing integration of advanced technologies such as robotics, neuroimaging, and virtual reality into neurorehabilitation practices. These innovations are poised to enhance the precision and effectiveness of therapeutic interventions. Future research should focus on the long-term impacts of these technologies and the development of accessible, cost-effective tools for clinical use. The integration of multidisciplinary approaches will be crucial in optimizing patient outcomes and improving the quality of life for stroke survivors. Full article

The use of lightweight drones has surged in recent years across both personal and commercial applications, necessitating the ability to conduct live video analytics on drones with limited computational resources. While edge computing offers a solution to the throughput bottleneck, it also opens the door to potential privacy invasions by exposing sensitive visual data to risks. In this work, we present a lightweight, privacy-preserving framework designed for real-time video analytics. By integrating a novel split-model architecture tailored for distributed deep learning through edge computing, our approach strikes a balance between operational efficiency and privacy. We provide comprehensive evaluations on privacy, object detection, latency, bandwidth usage, and object-tracking performance for our proposed privacy-preserving model. Full article

This study presents new results on the practical adhesion behavior of a boride layer formed on Monel 400 alloy, developed using the powder-pack boriding (PPBP) at 1223 K for 2, 4, and 6 h of exposure times, obtaining layer thicknesses from approximately 7.9 to 23.8 µm. The nickel boride layers were characterized using optical microscopy, Berkovich nanoindentation, X-ray diffraction (XRD), and scanning electron microscopy (SEM) to determine microstructure, hardness distribution, and failure mechanisms over the worn tracks. Scratch tests were conducted on the borided Monel 400 alloy according to the ASTM C-1624 standard, applying a progressively increasing normal load from 1 to 85 N using a Rockwell-C diamond indenter, revealing that critical loads (L_C1, L_C2, and L_C3) increased with layer thickness. The tests monitored the coefficient of friction and residual stress in real time. Critical loads were determined based on the correlation between the normal force and visual inspection of the worn surface, identifying cracks (cohesive failure) or detachment (adhesive failure). The results exposed those cohesive failures that appeared as Hertzian cracks, while adhesive failures were chipping and delamination, with critical loads reaching up to 49.0 N for the 6 h borided samples. Also, the results indicated that critical loads increased with greater layer thickness. The boride layer hardness was approximately 12 ± 0.3 GPa, ~4.0 times greater than the substrate, and Young’s modulus reached 268 ± 15 GPa. These findings underscore that PPBP significantly enhances surface mechanical properties, demonstrating the potential for applications demanding high wear resistance and strong layer adhesion. Full article

Because soundscapes affect human health and comfort, methodologies for evaluating them through physiological responses have attracted considerable attention. In this study, we proposed a novel method for evaluating visual attention by using eye-tracking technology to objectively assess soundscape perception. The study incorporated questionnaire surveys and physiological measurements focusing on visual attention responses. Results from the questionnaire indicated that perceptions of vehicles and the sky were 6% and 26% more sensitive, respectively, whereas perceptions of vegetation, based on physiological responses, were approximately 3% to 50% more sensitive. The soundscape quality prediction model indicates that the proposed methodology can complement conventional questionnaire-based models and provide a nuanced interpretation of eventfulness relationships. Additionally, the visual attention quantification technique enhanced the restoration responses of questionnaire-based methods by 1–2%. The results of this study are significant because the study introduces a novel methodology for quantifying visual attention, which can be used as a supplementary tool for physiological responses in soundscape research. The proposed method can uncover novel mechanisms of human perception of soundscapes that may not be captured by questionnaires, providing insights for future research in soundscape evaluation through physiological measurements. Full article

This study aims to bridge the gap between traditional consumer behavior analysis and modern techniques by integrating big data analysis, eye-tracking technology, and survey methods. The researchers considered that understanding consumer behavior is crucial for creating effective advertisements in the digital age. Initially, a big data analysis was performed to identify significant clusters of consumer sentiment from online reviews generated during a recent seasonal promotional campaign. The key factors were identified and grouped into the “Product”, “Model”, “Promo”, and “Effect” categories. Using these clusters as a foundation, an eye-tracking analysis measured visual attention metrics such as the fixation duration and count to understand how the participants engaged with the different advertisement content. Subsequently, a survey assessed the same participants’ purchase intentions and preferences related to the identified clusters. The results showed that the sentiment clusters related to products, promotions, and effects positively impacted the customer satisfaction. The eye-tracking data revealed that advertisements featuring products and models garnered the most visual attention, while the survey results indicated that promotional content significantly influenced the purchase intentions. This multi-step approach delivers an in-depth understanding of the factors that affect customer satisfaction and decision-making, providing valuable information for optimizing marketing strategies in the Korean skincare market. The findings emphasize the importance of integrating consumer sentiment analysis with visual engagement metrics to develop more effective and compelling marketing campaigns. Full article