Search Results (3,055)

The sleep monitoring terminal display (SMTD) designated for sleep cabins provides data visualizations of sleep profiles, yet poses challenges concerning visually informed interfaces. Given the scarcity of research on the SMTD interface, this study aims to evaluate the influence of SMTD-related factors, including stimulus areas of interest (AOIs), user experiences and tasks, on user visual perceptions while interacting with the SMTD system. Eye-tracking experimental contexts drawn from authentic settings are used to examine how the SMTD interfaces affect visual perceptions under varying tasks. Forty valid samples were collected, and pupil sizes (PSs), task performances, satisfaction, and usability were statistically compared and evaluated. The findings indicated that tasks had no significant effects, but user experiences and stimuli AOIs had significant main effects on PSs. In addition, task completion time ratio and tracking ratio between the two tasks varied; physical demand exceeded mental demand in task 1, whereas it was the opposite in task 2. The effectiveness of post-optimized interfaces was additionally substantiated through combining subjective ratings and objective metrics. The SMTD study provides novel insights for digital interface development and helps enhance users’ integrated visual perception. Full article

Addressing the issues of insufficient adaptability and limited energy efficiency optimization capabilities in traditional tunnel lighting control methods under complex traffic conditions, this paper proposes a dynamic dimming strategy for tunnel lighting based on the Proximal Policy Optimization (PPO) algorithm. First, the tunnel lighting system is modeled as a reinforcement learning environment. A state space integrating multidimensional information—including traffic flow, vehicle speed, external luminance, and tunnel section location—is constructed, and a continuous action space is designed to enable precise dimming control for each functional section. Based on this, a multi-objective reward function is established that integrates luminance tracking error, energy consumption optimization, control stability, and environmental adaptability to guide the agent in learning the optimal dimming strategy. Subsequently, model training and experimental validation were conducted using actual tunnel operation data. Experimental results show that, compared with the conventional $L_{20}$ strategy, the proposed method achieves significant energy savings during the 10:00–17:00 period, with the energy-saving rate remaining above 20% for most of the time from 11:00 to 16:00 and peaking at nearly 24%, while ensuring driving safety and visual comfort. In summary, the PPO-based dynamic dimming strategy demonstrates promising application prospects and engineering value in intelligent tunnel lighting systems. Full article

The assembly quality of scaffolding systems directly governs the safety of personnel on construction sites. According to construction safety statistics, scaffolding-related accidents account for approximately 30–40% of construction fatalities globally, with geometric assembly deviations being a contributing factor in over 60% of scaffold collapse incidents. Traditional scaffolding inspections rely heavily on manual measurements, which are inherently inefficient, hazardous, and difficult to scale comprehensively. This study presents an automated evaluation framework that integrates computer vision with structural mechanics simulations. First, an object detection model based on the SegFormer encoder architecture is developed to precisely identify scaffolding standards, ledgers, and couplers against complex site backgrounds. Its hierarchical Transformer encoder and global self-attention mechanism enable the model to capture long-range topological relationships, achieving a mean Average Precision (mAP@0.5) of 95.2% on a custom dataset with an inference speed of 45 FPS per 640 × 640 image patch. For complete high-resolution frame processing including tiling and geometric extraction, the end-to-end pipeline requires approximately 8–12 s per frame. Second, a simplified Hough transform with a restricted parameter domain is introduced. Integrated with a dual-track image processing workflow, this algorithm performs sub-pixel centerline fitting to automatically extract critical geometric parameters, including lift height and bay width, maintaining a relative measurement error within 3.5% compared to manual ground truth. Finally, a parameterized finite element model is established. An automated mapping middleware dynamically injects the extracted as-built parameters into the simulation environment. Comparative simulation analysis indicates that a 14.7% deviation in standard lift height, coupled with an initial tilt defect of 1/150, precipitates a 22.4% reduction in the predicted structural stability factor, illustrating the framework’s capability for assessing relative capacity degradation between design intent and as-built conditions. This framework establishes a robust, closed-loop pipeline spanning visual perception and structural safety assessment, indicating potential for automated construction site safety management. Full article

Interpersonal synchrony is a time-dependent coordination pattern in which interacting partners’ body movements become temporally aligned. This study frames interpersonal synchrony as a human motion analysis problem and presents Synchrony Vision, an RGB-D sensor-based system for real-time monitoring and event-level analysis of interpersonal motion synchrony in free dialog. The system transforms Kinect-derived skeletal positions into joint acceleration signals, applies sensor-specific conditioning, detects movement peaks, and estimates event-level phase differences between two participants within a ±1.0 s window. The operator-facing interface supports live RGB-D monitoring, acceleration visualization, joint selection, millisecond-scale phase-difference histograms, four synchrony metrics (Frequency, Lead–lag, Width, and Strength), and exportable acceleration, timestamp, peak-pairing, and summary artifacts. We evaluated the deployed pipeline on 25 Kinect-tracked dyads engaged in unconstrained conversation. Across 200.6 min comprising 245,835 frames, the system detected 2681 synchrony events. The observed event rate exceeded circular-surrogate baselines, and dyad rankings remained stable across reasonable parameter settings. Motion Energy Analysis-style cross-correlation on the same acceleration signals also confirmed above-chance synchrony but produced different dyad rankings. These findings show that RGB-D skeletal sensing can extend human motion analysis from individual movement capture to transparent, event-level quantification of interpersonal coordination. Full article

The dynamic evolution of an optically induced vortex flow in nematic microliter droplets caused by exposure to a focused laser beam has been studied both experimentally using polarized optical microscopy and numerically within the framework of a corresponding nonlinear extension of the Ericksen–Leslie theory supplemented by thermomechanical correction of the stress tensor and the entropy balance equation. The vortex flow in nematic droplets consisting of 4-pentyl-4′-cyanobiphenyl molecules spreading over the functionalized surface was visualized in microliter droplets doped with monodisperse polystyrene tracers, under exposure to a laser beam with an optical power equal to 8.0 mW. Using the computer vision detection algorithm, we have identified radial symmetry in the tracers motion, where comet-like tracks are aligned along the rays emanating from the center of the resulting structure. At the same time, some of the “comets” are flying towards the center, while others are moving away from it. This allowed us to estimated the average value of tracer flows, which is of 17 µm/s. All these observations indicate that vortex currents are excited in the droplet under the action of the focused laser beam. The nature of thermally excited vortex flows in the microliter hybrid aligned nematic droplet with a free upper LC/air interface and spreading over the solid surface under the influence of the heat flux directed through the lower bounding surface is also numerically investigated. It was shown that due to the interaction between $\nabla T$ and the gradient of the director field $\nabla \widehat{\mathbf{n}}$, the thermally driven bi-vortical flow is maintained in nematic microvolume. Full article

Background/Objectives: Children with autism spectrum disorder (ASD) often exhibit differences in attention regulation and visual behavior. However, many ASD eye-tracking datasets lack reliable moment-to-moment behavioral or emotional annotations, limiting the direct application of supervised learning approaches. To address this challenge, this study proposes an interpretable gaze-based unsupervised framework for discovering behavioral pseudo-zones from unlabeled ASD eye-tracking data. Methods: Raw gaze recordings from ASD participants were segmented into fixed temporal windows and represented using interpretable gaze features, including gaze dispersion, fixation duration, tracking quality, motion ratio, pupil size, and gaze velocity measures. Multiple clustering models and alternative temporal window sizes were systematically compared, including K-means, Gaussian Mixture Modeling (GMM), Agglomerative Clustering, and HDBSCAN. Results: Among the evaluated configurations, the combination of 1000 ms windows with K-means clustering (k = 4) was retained as the final exploratory configuration. Although alternative solutions achieved slightly stronger internal validation metrics, the selected configuration provided a more interpretable four-zone structure while maintaining acceptable clustering quality. The final retained solution produced four interpretable behavioral pseudo-zones with statistically significant differences across all extracted gaze features according to the Kruskal–Wallis test (p < 0.05). A PCA projection further supported the exploratory structure of the discovered pseudo-zones, with the first two principal components explaining 72.3% of the total variance. Conclusions: The findings demonstrate that unlabeled ASD gaze data can be organized into interpretable behavioral pseudo-zones using an unsupervised and transparent feature-based framework. This work contributes a data-driven and interpretable framework for future gaze-based behavioral analysis and autism-related AI research. Full article

Clear-cutting often causes negative public reactions due to its visual impact, especially in recreational forests. Understanding how people perceive early regeneration stages may support more socially acceptable management practices. This pilot study investigated how young adults visually perceive early forest regeneration after clear-cutting, with a particular focus on gender differences and attention-attracting elements. Field research was conducted in October in central Poland on a 0.97 ha site with Scots pine regeneration. Participants were studied using Tobii Pro Glasses 2. Over 950,000 visual events were recorded, including approx. 9000 fixations and 900 saccades. Analyses included Areas of Interest, heat maps, and fixation and saccade metrics. Women tended to show longer fixation durations than men in this pilot study (602 ms vs. 458 ms; p < 0.001), particularly in clear-cut regeneration areas, where median values were more than 35% higher. They also had shorter and more variable saccades. Visual attention was concentrated on the boundaries between mature forest and regeneration areas, and on elements that reduced the perceived “nakedness” of clear cuts, such as undergrowth and young trees. These patterns suggest that structural and compositional characteristics of regenerating forest landscapes affect visual attention. The results highlight the potential of eye-tracking methods for studying responses to forest management and landscape perception. Full article

Outdoor public spaces are important built-environment settings that support health, social interaction, psychological restoration, and everyday urban life. In cold-region cities, however, winter conditions such as low temperature, snow and ice, short daylight, reduced vegetation, low solar altitude, and declining outdoor activity substantially weaken their usability, attractiveness, and vitality. Existing studies have mainly addressed these problems through thermal comfort, microclimate adaptation, snow safety, and physical environmental optimization, but they provide limited explanation of how users visually perceive winter spaces, allocate attention, and form subsequent spatial interpretations, perceptual evaluations, and behavioral intentions. To address this gap, this conceptual article develops a built-environment framework for explaining winter visual perception mechanisms and proposes an agenda for future eye-tracking-based validation. Through conceptual synthesis across cold-region public-space research, outdoor thermal comfort, environmental psychology, landscape visual perception, eye-tracking studies, public-space behavior, and architectural and built-environment design, the study conceptualizes winter public spaces as seasonal perceptual environments. It identifies five categories of winter visual stimuli: surface-related, vegetation-related, building interface-related, lighting-related, and activity-related stimuli. The framework clarifies how visual attention may serve as an observable mediating process between winter visual stimuli and inferred spatial interpretation, perceptual evaluation, and behavioral intention. Rather than empirically confirming these relationships, the article formulates a testable conceptual model and future validation agenda that should be examined through eye-tracking, behavioral observation, subjective evaluation, and environmental measurements. For architectural and built-environment research, the framework provides a theoretical basis for evaluating and optimizing façades, ground-floor interfaces, entrances, canopies, semi-outdoor spaces, path boundaries, lighting systems, vegetation configuration, and winter activity nodes. Full article

Real-time pupil detection is a critical prerequisite for eye-tracking applications such as vestibular videonystagmography (VNG) and gaze-based interaction: deep learning methods offer high accuracy but incur substantial computational cost, while traditional thresholding methods are fast but lack robustness under complex illumination. This paper proposes the Confidence-Guided Fallback (CGF) framework, which employs four geometric factors—circularity, aspect ratio, area consistency, and convex-hull ratio—to evaluate the reliability of traditional detection results, and it invokes RITNet (a deep segmentation network finetuned on randomly cropped s-OpenEDS data) only when the confidence score falls below a threshold, thereby adaptively balancing accuracy and efficiency on a per-frame basis. RITNet is not trained on LPW; thus, LPW evaluation simultaneously constitutes cross-dataset validation. Hyperparameters (confidence weights and threshold values) are tuned on a validation set (15% of LPW subjects) and final performance is reported on an independent test set (18% of subjects), avoiding data leakage. On the validation set, the combined confidence factors achieve an AUROC of 0.961 for distinguishing high-quality frames, and the confidence distribution exhibits a bimodal structure with a valley near 0.7, providing a data-driven basis for threshold selection. On the independent test set (23,999 frames), CGF ($\tau =0.8$) detects 79.0% of frames; among detected frames, 84.0% achieve sub-5 px accuracy (median error 1.25 px) at 298 FPS on NVIDIA RTX 5090. At $\tau =0.7$, detection rate reaches 89.6% with 72.0% sub-5 px accuracy at 391 FPS. Failure-mode analysis reveals that approximately 64.3% of triggered deep-model frames represent “dilemma frames” where both traditional and deep methods fail, marking the practical ceiling of single-frame visual methods and pointing toward temporal modeling. The framework demonstrates robustness to confidence-weight perturbations (±20% weight changes yield < 1% performance variation) and exhibits strong discriminative power on high-confidence frames (93.4% accuracy within 5 px on the test set). Full article

Reliable obstacle detection and classification are essential capabilities for the safe and efficient navigation of Marine Autonomous Surface Ships. This paper introduces a decision-level multi-sensor fusion framework to enhance situational awareness for autonomous vessels by integrating RGB camera and LiDAR data. Visual information is processed using a pre-trained, open-source object detection model. At the same time, LiDAR measurements are analysed with a clustering-based algorithm, followed by a lightweight Random Forest classifier for semantic labelling. To support practical deployment in real maritime environments, the proposed approach relies on readily available perception modules, avoiding the need for training on proprietary datasets and limiting dependence on extensive task-specific tuning. The fusion of these complementary sources is employed to confirm and characterise dynamic obstacles, whose positions derived from LiDAR are continuously tracked using a Global Nearest Neighbour algorithm supported by a Kalman filter. Each stage of the proposed processing chain is thoroughly described and experimentally validated using real-world data collected in a representative marine environment, demonstrating the approach’s effectiveness in improving perception performance by reducing false positives from noisy measurements and achieving 92% track number accuracy in a complex scenario. Full article

To accurately characterize the warm deformation behavior and workability of the 5A06 aluminum alloy, this study presents an innovative workflow that develops and systematically validates machine learning-assisted Johnson–Cook (ML-JC) frameworks based on artificial neural network (ANN) surrogate models. Two predictive frameworks—the parallel-decoupled PD-ANN-JC and the multi-objective integrated MOI-ANN-JC—were constructed. Quantitatively, both developed ML-JC frameworks achieve significantly higher stress prediction accuracy and superior generalization capability compared with the conventional JC model. Specifically, on the testing set, the MOI-ANN-JC framework yields an average absolute relative error (AARE) of 1.424% and an R² of 0.997, outperforming the PD-ANN-JC framework (AARE of 3.246%, R² of 0.988). On the validation set, the MOI-ANN-JC framework also demonstrates exceptional generalization, with an AARE of 3.302% and an R² of 0.987. Scientifically, the superior performance of the MOI-ANN-JC framework stems from its ANN-mnδ surrogate model, which simultaneously predicts the strain hardening exponent n, thermal softening exponent m, and relative error δ directly from deformation parameters. This mutual coupling establishes an intrinsic correlation between m and n, successfully aligning with the physical reality wherein strain hardening and thermal softening are inherently linked during deformation. Qualitatively and practically, by integrating the MOI-ANN-JC framework into finite element (FE) simulation software, dynamic tracking and visualization of the thermal softening exponent m during warm deformation were achieved. Combined with FE simulations, Vickers hardness testing and EBSD observations, this study successfully establishes a direct qualitative spatial correspondence between low-m regions and macroscopic defects, which was further verified through the warm forging of a thin-walled dual-cavity component. Crucially, this approach for evaluating deformation stability bridges the gap caused by the inapplicability of conventional processing maps within this temperature regime, offering a robust and broadly applicable workflow for complex forming optimization. Full article

Recent advances in wearable, vision-based, trajectory, physiological, and multimodal sensing technologies, together with deep learning, have enabled continuous, objective, and individualized assessment of sport performance and athlete health. Unlike prior reviews that primarily focus on a single sensing modality, sport, or algorithmic series, this review integrates wearable, vision-based, trajectory, physiological, and multimodal sensing streams with deep learning models across both performance analysis and athlete health monitoring, thereby clarifying modality-task-model relationships and translational limitations. This review synthesizes recent progress in sensor-based sports intelligence, focusing on how heterogeneous data streams are transformed into performance- and health-related decision support. The reviewed applications include athlete and ball perception, multi-object tracking, pose estimation, action recognition, trajectory and tactical analysis, training-load and fatigue monitoring, injury-risk prediction, rehabilitation monitoring, and return-to-play support. Deep learning architectures, including CNNs, LSTMs, GRUs, TCNs, Transformers, attention mechanisms, graph neural networks, and multimodal fusion models, are discussed in relation to their suitability for visual, temporal, spatial, physiological, and multisource data. This review further identifies key challenges, including data heterogeneity, annotation scarcity, limited cross-sport and cross-device generalization, real-time deployment constraints, model interpretability, privacy protection, and ethical governance. Moving forward, research efforts should focus on the development of standardized datasets, reliable multimodal data fusion strategies, self-supervised and transfer learning approaches, and deployment on edge or cloud computing platforms. Additionally, enhancing interpretability through explainable AI and implementing closed-loop, individualized monitoring systems are critical. By synthesizing advances in sensing technologies, deep learning methodologies, and real-world applications, this review aims to provide a practical reference for optimizing athletic performance, preventing injuries, guiding rehabilitation, and supporting long-term health management of athletes. Full article

Playground safety for preschool and early elementary school children has become an increasingly important issue for families and local communities. In semi-open playground environments, direct supervision by caregivers is often difficult, while conventional positioning approaches may be costly, infrastructure-dependent, or unreliable due to frequent signal obstructions. This study presents an integrated low-cost monitoring system that combines commercial BLE beacons, a single smart device installed in the playground, a push-notification server, and a caregiver smartphone application to detect children’s entrance and exit events and visualize their playground usage logs. Rather than proposing a new localization algorithm, the main contribution of this work is the practical system integration and field validation of BLE-based entrance/exit detection in semi-open playground settings. The smart device continuously scans beacon RSSI values, applies Kalman-filter-based smoothing and threshold-based state transitions, and transmits detected events to the server, where daily, weekly, and monthly statistics are generated and visualized for caregivers. Field experiments conducted at five real playgrounds showed that the proposed system achieved over 99% entrance/exit detection accuracy with an average response time of less than 7 s. These results demonstrate that reliable playground entry/exit monitoring can be implemented at low cost, with simple infrastructure and practical deployment in residential environments. Full article

Camouflage design for jungle environments has conventionally relied on the static optimization of color, texture, and edge features, presuming that the background remains visually stable. This presumption diverges from real conditions, in which wind continuously alters leaf orientation and vegetation texture, leaving a gap between static optimization and dynamic visual reality. To address this limitation, this study developed a systematic camouflage design process that integrates the Beaufort scale into a mimetic system for simulating vegetation sway. Dominant colors were extracted using the CIE L*a*b* color space and K-means clustering, and background maps were generated via Gaussian blur. Leaf textures from five plant species were arranged through seamless tiling and overlaid onto the backgrounds to form 15 camouflage samples. Validation employed a fuzzy logic questionnaire and eye-tracking measurements. Under the present experimental conditions, which used screen presentation under visible light, pattern A-13 performed best. Derived from the Terminalia mantaly leaf texture in the dark green variant, it achieved the most favorable balance between distinctiveness from the regional reference pattern and disruption of target–background segmentation, whereas C-15, the light green variant, consistently ranked last. The proposed process is reproducible and applicable to civilian equipment such as tents and backpacks. Full article

This study investigated the relationship between fixation-frequency-based Shannon entropy and dwell-time-based entropy across two different visual task domains: emotional evaluation of automotive exterior designs and safety-critical monitoring of nuclear power plant emergency scenarios. Although gaze entropy has been widely used to explain emotional responses, task performance, and situation awareness, the relationship between entropy measures derived from fixation counts and fixation durations remains insufficiently examined. Eye-tracking data were analyzed from two experiments with different attentional characteristics. In the emotional visual task, 10 participants evaluated three automotive design images. In the safety-critical task, 20 participants performed four nuclear power plant emergency monitoring scenarios. Shannon entropy and dwell-time entropy were calculated using fixation count and fixation duration distributions across Areas of Interest, respectively. Pearson correlation and simple regression analyses were conducted within each task domain. The results showed strong positive associations between Shannon entropy and dwell-time entropy in both domains. The emotional task showed a correlation of r = 0.844, while the safety-critical task showed a correlation of r = 0.890. These findings suggest that fixation-frequency-based and dwell-time-based entropy measures exhibit substantial overlap across different visual task contexts. However, the observed associations may partly reflect mathematical dependency between fixation frequency and cumulative dwell-time, and the findings should be interpreted as exploratory evidence rather than proof of metric interchangeability. The study highlights that gaze entropy metrics should be interpreted in relation to task-dependent attentional contexts. Higher entropy may be associated with exploratory visual attention in emotional evaluation, whereas lower entropy may be associated with focused monitoring in safety-critical tasks. Full article