Search Results (432)

Suspended ceiling systems are among the most seismically vulnerable non-structural components in buildings, posing significant life-safety risks and economic losses, yet understanding their full-field kinematic behavior under seismic loading remains a major experimental challenge. Conventional contact sensors offer limited spatial coverage and can alter the dynamic properties of lightweight panels due to mass loading. In contrast, non-contact optical alternatives are rarely feasible in shake-table environments due to restricted viewing angles, extensive areal coverage requirements, and the risk of equipment damage from falling panels. This study proposes an end-to-end three-dimensional displacement measurement framework for large-scale shake-table testing of suspended ceiling systems, employing consumer-grade cameras with purpose-built tools that cover the complete experimental workflow, including motion-based video trimming, semi-automated calibration, a robust multi-stage image-tracking pipeline that maintains trajectory continuity under extreme inter-frame displacements, and a ceiling system motion visualization and analysis tool. The framework was validated through a full-scale shake-table experiment continuously tracking 324 spatial nodes across 81 ceiling panels, achieving an RMSE below 3 mm in all spatial directions and exact peak-frequency agreement in 9 out of 10 test cases. A parallel processing architecture reduced total processing time from over 27 h to under 10 min without GPU acceleration, and six-degree-of-freedom rigid-body analysis resolved the complete panel failure sequence from constrained oscillation through multi-axis rotation to gravitational free fall, a level of kinematic detail unattainable with conventional instrumentation. This framework establishes a practical, scalable foundation for full-field seismic performance assessment of non-structural systems where conventional instrumentation is physically or logistically infeasible. Full article

Public open spaces (POS) in old communities are important settings for daily neighborhood life, yet many renovated POS remain underused after physical upgrading. Existing evaluations often rely on subjective perceptions, providing limited evidence on how facilities are associated with vitality. This study analyzes the associations between facility provision and POS vitality in 63 renovated POS across 11 old communities in Jiulongpo District, Chongqing, China. POS vitality is operationalized through two behavioral dimensions, use frequency and stay duration, derived from video detection and tracking using YOLOv8 and ByteTrack. Facility provision was then classified by facility type and examined in relation to the vitality indicators through descriptive analysis and Generalized Estimating Equations models. Descriptive evidence indicates substantial heterogeneity in both facility provision and POS vitality. Resting amenities and landscape elements are more commonly provided, whereas children’s facilities show the lowest provision and greater spatial selectivity. Higher use frequency and longer stay duration are concentrated in some POS. The Generalized Estimating Equations analysis further indicates that facilities are not associated with vitality in a uniform way. Children’s facilities show the strongest positive associations with both use frequency and stay duration despite their limited provision, supporting their key role in POS vitality. Landscape elements and lighting facilities are more closely associated with stay duration, highlighting the role of environmental support in sustaining longer use. In contrast, the negative associations for fitness facilities, together with the non-significant results for resting and sanitation amenities, suggest that not all facility provision translates into stronger vitality. Taken together, renovation performance should be judged not by the quantity of upgraded facilities alone, but by whether facilities support the behavioral dimensions of vitality that a POS is expected to achieve. Full article

Background: Advances in monitoring systems featuring wearable sensors, computer vision, and artificial intelligence (AI) have been increasingly used in sports science and rehabilitation practices as a means of movement pattern analysis, injury prevention, and training optimization. These technologies are becoming essential components of athlete-performance analysis and rehabilitation-monitoring systems designed to support biomechanical assessment, athlete development, and movement-quality evaluation. Athlete-performance analysis and rehabilitation monitoring increasingly rely on intelligent multimodal sensing systems capable of continuously evaluating movement quality, biomechanical patterns, training execution, and recovery progress. Human activity recognition (HAR) serves as a key enabling technology for these applications by providing automated assessment of human movement using wearable and vision-based sensing modalities. Therefore, the purpose of this study was to develop and evaluate an attention-based multimodal framework that integrates wearable inertial sensing and RGB video analysis for robust athlete-performance assessment and rehabilitation monitoring through accurate recognition of human movement patterns. Methods: Athlete-performance analysis and rehabilitation monitoring combining inertial sensor data and RGB-based visual information was introduced. Inertial signals were segmented with adaptive windowing, whereas silhouette refinement was performed to analyze motion structures from visual inputs in support of athlete-performance analysis and rehabilitation monitoring. Temporal, spatial, and motion features such as trajectory, orientation, and skeleton-based space-time representations were calculated from multimodal inputs. The proposed framework was designed to capture complex movement dynamics associated with rehabilitation exercises and sports-related motion patterns across heterogeneous sensing environments. Extracted features were then combined and optimized with a multimodal feature fusion approach, while the Ranger optimization algorithm was utilized during the process. An attention-based deep learning classifier was implemented to classify movement activities. Results: The results showed that the proposed framework reached accuracy scores of 88.40% and 87.96% on the VIDIMU dataset and the UTD-MHAD dataset respectively. Recognition performance across both inertial and vision-based modalities provided greater robustness than single-modality solutions. The integration of wearable sensing and computer vision modalities further improved the ability of the framework to analyze complex movement behaviors under varying execution conditions and environmental variations. Conclusion: The proposed multimodal framework provides a foundation for intelligent athlete-performance and rehabilitation-monitoring systems by integrating wearable sensing, computer vision, and attention-based artificial intelligence for robust movement analysis. The findings highlight its potential to support biomechanical assessment, movement-quality evaluation, training-performance monitoring, rehabilitation tracking, and injury-risk management in modern sports and healthcare environments. Full article

Rapid detection of foodborne pathogenic and spoilage microorganisms is critical for ensuring food safety and quality in liquid matrices. While Raman tweezers spectroscopy (RTS) enables label-free single-cell analysis, its application in high-throughput inline inspection faces a fundamental bottleneck: high flow rates required for efficiency induce severe motion blur and low signal-to-noise ratios (SNR), which blind automated control systems and destabilize optical trapping. To overcome this, we present a Spatiotemporal Video-Enhanced Raman Tweezers (SVERT) system integrating a deceleration-optimized microfluidic chip with a deep learning-based visual feedback loop. We propose a Local–Global Unified Denoising Network (LGU-Net) tailored to recover high-fidelity bacterial structures from low-SNR video streams, achieving a deterministic processing latency of ~0.49 ms. Experimental results demonstrate that SVERT improves the optical trapping success rate from 21.27% ± 2% to 91.47% ± 1.8% compared to raw video input, enabling a four-fold increase in spectral acquisition efficiency. Leveraging the acquired high-quality dataset, we achieved a classification accuracy of 96.74% across four bacterial species of relevance to food safety and quality. Crucially, we validated the system’s practical robustness by successfully isolating and tracking trace E. coli in an unpurified commercial beverage. This capability to effectively mitigate natural background interference demonstrates the system’s promising potential to be expanded for broader applications in liquid food safety screening. Full article

Background/Objectives: Single-case studies represent a sophisticated and rigorous methodological framework, widely established in clinical research for providing high-resolution data on individual functional responses. This study evaluates the clinical utility of integrating immersive Virtual Reality (VR) gaming as a novel “functional ingredient” within the Video Game Therapy (VGT) protocol. Given the exploratory single-case nature of this intervention, clinical state-modulations cannot be rigorously validated using standard aggregated group statistics. Therefore, the core objective of this paper is to investigate the therapeutic potential of the VR session on psychological state-modulation, introducing the Single-Case Normative Analytics (SCNA) framework as the mandatory statistical vehicle required to validate individual longitudinal shifts against normative data. Methods: The study treats individual VR exposures as independent, short-term clinical probes embedded within a real-world clinical journey. The SCNA framework was deployed by integrating Crawford’s modified t-tests with longitudinal percentile tracking against an empirical normative reference group (n = 20). Acute state-anxiety variations (STAI-Y1), psychological well-being (PGWBI), and flow dynamics were tracked across three distinct sessions to monitor the patient’s relative repositioning within the normative distribution. Results: The inferential analysis indicates that the immersive 20-min environment facilitated reliable, statistically significant changes in acute state anxiety and flow dimensions, systematically exceeding standard measurement error boundaries and successfully moving the patient’s psychometric profile toward healthy normative ranges. Conclusions: While these findings focus on individual, idiographic reactivity, they demonstrate the utility of the SCNA framework in providing clinicians with objective, evidence-based feedback on the clinical viability of specific VR-based functional units. This approach allows for a rigorous evaluation of standalone digital tools independently of a full, holistic VGT protocol, offering a structured alternative to traditional designs focused on identifying general patterns across groups. Full article

The squat is a critical component of numerous rehabilitation and functional assessment protocols, playing a significant role in enhancing athletic performance and activities of daily living. Although some of the characteristics gathered during the squat need additional confirmation, Kinovea provides a free two-dimensional squat motion analysis tool that is simple to use in clinical practice. This analytical, cross-sectional reliability study aimed to evaluate the intra-rater and test–retest reliability (with a 20 min interval between performances) of loaded squat kinematics in a sample of women using Kinovea. Twenty women performed a loaded back squat; intra-rater reliability was assessed by re-analyzing the same video one week apart, and test–retest reliability was assessed across two performances separated by 20 min. The results showed good to excellent intra-rater reliability (ICC: 0.75–0.99; SEM: 0.16 cm to 5.14°; MDC: 0.44 cm to 14.24°), and moderate to excellent test–retest reliability (ICC: 0.64–0.98; SEM: 0.36 cm to 14.29°; MDC: 0.99 cm to 39.61°). Variables tracked in the sagittal plane showed high precision. Conversely, the head angle and knee angle in the frontal plane exhibited greater variability, reflected by higher SEM and MDC values. In conclusion, Kinovea is a reliable and accessible tool for clinical kinematic assessment of the squat, particularly in the sagittal plane parameters. However, due to the elevated measurement error observed in head angles and frontal-plane knee dynamics, the integration of 3D motion capture is recommended over 2D digital protocols for these variables. Full article

The expanding capabilities of deep learning-based media synthesis have intensified concerns regarding the authenticity of digital content and the reliability of forensic analysis tools. In response to these challenges, this work introduces DeepFakeX, a collection of 800 synthetically generated videos available under controlled access for research purposes. The dataset encompasses four distinct categories of AI-driven synthesis: facial identity replacement, audio track substitution, neural voice cloning, and combined audiovisual alteration. Unlike existing deepfake datasets that predominantly focus on facial synthesis, DeepFakeX covers a broader range of manipulation modalities, reflecting the diversity of synthetic media encountered in real-world settings. All deepfakes were generated using state-of-the-art, publicly available tools. Standardized post-processing procedures were applied to each video to ensure uniformity in terms of quality, duration and encoding format. DeepFakeX also emphasizes diversity in gender, age, ethnicity, and language. Video contexts span speeches, informational videos, movie clips, news broadcasts, and interviews that reflect content scenarios commonly encountered in real-world online environments. The dataset includes videos in both English and Urdu. The dataset’s quality and structural variability were assessed through visual and audio analyses using the Structural Similarity Index Measure (SSIM), Mel-Frequency Cepstral Coefficients (MFCCs), and Principal Component Analysis (PCA). The evaluation results revealed substantial variability within each manipulation category, along with clearly distinguishable patterns specific to each modality. DeepFakeX has been developed to facilitate rigorous and transparent research in deepfake detection, cross-modal forensic analysis, and AI-driven media forensics. It is hosted on Zenodo under controlled access for research use. Full article

This work presents a pilot study to analyze the effect of aging on motor performance of young adults (YAs) and older adults (OAs) through wrist movement assessment, using an upper limb rehabilitation robot (ULRR) in passive mode coupled to a maze-solving task serious video game. The proposed approach considers the use of kinematic metrics, such as ROM, path accuracy, and movement smoothness, as quantitative biomarkers that evidence differences between YAs and OAs. An experimental protocol was conducted with 20 participants: 10 OAs and 10 YAs. Standardized wrist movements corresponding to flexion (F), extension (E), radial deviation (R), and ulnar deviation (U) were assessed at each level of the maze. The kinematic analysis was based on metrics for range of motion (ROM), path accuracy, smoothness, and root-mean-square error (RMSE) in trajectory tracking. The results revealed clear differences between the groups: the YAs achieved a greater ROM and made fewer errors on mean (2.167 errors for YAs compared to 6.000 errors for OAs), and showed a lower RMSE, while the OAs showed greater smoothness in their movements, because the YAs exhibit greater variability and disturbances in movement when correcting and controlling their movements to achieve good performance, reflecting more precise motor control and a greater capacity for error correction during movements with trajectory constraints. Full article

Vaccination stands as a preeminent public health measure in the fight against infectious diseases, with a proven track record of significantly reducing morbidity and mortality rates. However, the presence of vaccine hesitancy and misinformation, particularly evident during the course of the pandemic, has emerged as a significant challenge. The present study analyzes public perceptions of vaccination by examining YouTube comments on 215 vaccine-related videos, which total over 94,000 comments. Employing advanced topic modeling techniques, such as Hierarchical Dirichlet Process (hLDA), Latent Semantic Analysis (LSA), and Non-Negative Matrix Factorization (NMF), the study identifies key themes, including vaccine safety, side effects, pharmaceutical ethics, and public trust in healthcare authorities. The findings indicate that debates frequently center on political, social, and scientific concepts. Vaccine hesitancy has emerged as a pervasive global phenomenon that transcends cultural boundaries. The dissemination of misinformation regarding the efficacy of vaccines and the safety of treatments, such as ivermectin, is a prevalent phenomenon on social media platforms. This poses significant challenges to public health efforts. The subjects of child vaccination and parental standpoints are also recurring topics of concern. This study underscores the pivotal function of digital platforms such as YouTube in influencing public attitudes regarding vaccination. This underscores the necessity for targeted communication strategies, advanced digital literacy, and proactive policies by social media platforms to address misinformation and promote evidence-based information. Such precautions are imperative to sustaining elevated vaccination rates and safeguarding public health in the digital age. Full article

Visual object tracking finds extensive application in real-time video analysis on edge devices, yet faces dual challenges: decreased speed due to limited computational resources and weak anti-disturbance capability in complex scenarios. This paper proposes the Hybrid Closed-Loop Tracker (HCLT) to enhance both speed and robustness of embedded visual tracking. HCLT integrates high-precision and high-speed trackers to make real-time performance controllable, while a Kalman filter is employed for state observation and feedback. Within this closed-loop framework, we introduce motion and feature point information as supplementary states and further design mechanisms for adaptive search region adjustment and tracking recovery. Our methods effectively mitigate the impact of external disturbances. Experimental results demonstrate that HCLT further improves both speed and robustness on the basis of high-performance trackers, achieving high tracking accuracy across multiple public benchmark datasets. It demonstrates excellent anti-disturbance performance, particularly in challenging scenarios such as blur and occlusions, while maintaining frame rates exceeding 35 frames per second (FPS) at 720p resolution when deployed on an RK3588 embedded device, thus representing a significant improvement over deep neural network trackers. Full article

The clinical reliability of swabs is affected by their ability to collect and elute biological samples for further detection. Since elution is particularly critical for swab functionality, the goal of this work was to develop a nasopharyngeal swab prototype that could potentially facilitate the release of biological specimens through controlled elastic deformation. To this end, a helical swab-head geometry was designed and 3D-printed by means of stereolithography (SLA). A dual post-curing process combining UV and thermal treatment was employed to maximize the mechanical stiffness of the resin—up to about 750 MPa. Microtomography of the 3D-printed prototypes demonstrated the accuracy of SLA printing, with only 0.12% closed porosity due to printing defects. The mechanical deformation of the prototype under compression was then investigated through numerical modeling and experimental analysis. The results of Finite Element (FE) simulations revealed stress localization in the upper coils, with global mechanical integrity. Experimental compression tests validated the predicted deformation behavior, as supported by video tracking and displacement analysis at multiple nodes, showing good agreement between numerical and experimental displacement. Furthermore, preliminary functional tests with P. aeruginosa and S. aureus, both in saline solution and artificial mucus, demonstrated that the swab-tip prototype per se, without any coating or any applied compression, could perform comparably to commercial cotton and flocked swabs. About a 2-log reduction in bacterial load was detected for all swabs compared to the inoculum when used in saline solution, while a bacterial load roughly matching the inoculum was found when the swabs were used in artificial mucus. Overall, these findings demonstrate the feasibility and the potential of the designed swab prototypes. Full article

This study investigates how different rural landscape types shape visual attention and physiological responses, with the aim of informing more targeted rural landscape renewal. Four typical rural landscape types in the suburbs of Hefei, China, were examined: Flat Farmland (FF), Hilly Forest (HF), Developed Plain (DP), and Water-network Lowland (WNL). All four study villages are project villages in the suburban area of Hefei where rural revitalization is currently being advanced. This study therefore treats them as empirical cases within the context of rural revitalization in China, using them to examine perceptual differences among rural landscape types and their implications for rural landscape renewal. A two-stage research design was adopted to balance field realism and laboratory control. In the first stage, 40 representative scene images were selected by combining field video records with fluctuations in on-site skin conductance response (SCR). In the second stage, laboratory experiments were conducted while participants viewed the selected images, during which eye-tracking, skin conductance, and heart rate data were recorded simultaneously. These measures were used to characterize visual attention allocation and autonomic physiological responses across different rural landscape types, rather than to directly measure landscape preference. For Area of Interest (AOI) analysis, each image was coded into six landscape element categories: vegetation, buildings, roads, sky, vernacular buildings, and water bodies. The results revealed significant typological differences in overall visual search patterns and autonomic responses. Gaze hotspots were concentrated on identifiable targets and boundary regions in the foreground and midground, whereas the sky attracted relatively limited attention. FF primarily emphasized vernacular buildings and farmland boundaries, HF emphasized settlement interfaces and spatial transition nodes, DP emphasized road junctions and facilities along routes, and WNL emphasized water bodies and water–land interface zones. These findings suggest that a two-stage multimodal design can provide supporting evidence for understanding type-specific perceptual responses and can support more targeted strategies for rural landscape renewal. Full article

Persistent traffic congestion and the need for efficient traffic monitoring have increased the demand for automated vehicle-analysis systems based on CCTV footage. This study presents a CCTV-based vehicle monitoring system that integrates vehicle detection, tracking, counting, public/private vehicle class prediction, seven-category vehicle-type prediction, vehicle-color recognition, and traffic-state estimation using YOLOv12 and DeepSORT. To reduce manual annotation effort during the initial training stage, a semi-automated method for generating synthetic composite road scenes was developed by combining cropped vehicle images and road-background images. The detector was first trained on 10,000 synthetic images and then sequentially fine-tuned on real CCTV data. Four real-world traffic video clips from Metro Manila were used in the study. Three 5 min clips were used within the staged refinement workflow: the first two for iterative refinement and the third for final post-refinement evaluation of the adapted model. A separate fourth CCTV clip was reserved exclusively for blind evaluation without on-the-fly retraining. The final system achieved average accuracies of 97% for public/private vehicle class prediction, 90% for seven-category vehicle-type prediction, 82% for vehicle-color recognition, and 96.67% for vehicle counting on the final evaluation video. The results show that synthetic pretraining combined with limited real-world fine-tuning can improve performance in CCTV-based vehicle monitoring while reducing the amount of manually labeled real-world data required. The study also discusses the limitations of the current evaluation protocol and the need for broader multi-location testing. Full article

Monitoring both captive animals and wild populations is necessary to ensure adequate animal welfare and wildlife conservation. Existing monitoring tools, e.g., camera traps, enable surveillance, yet analysis can prove time-consuming and labor-intensive if handled manually. The automated nature of machine learning (ML) reduces observer bias and manual workload and improves assessment capacity of behavioral monitoring tools that are often used by staff at zoological institutions. This study investigated the activity and space use of three captive jaguars (Panthera onca) through automated individual recognition, activity tracking, and heatmap visualization using an ML model trained on video footage. In total, 123.8 h of video footage was recorded of the jaguar enclosure in Randers Regnskov, Tropical Zoo. The ML model analyzed all videos containing jaguars from one day. The model achieved satisfactory performance based on its evaluation metrics (mean average precision, recall, precision, and F1-score). The ML model showed repeated movement tracks within specific enclosure areas. The jaguars exhibited significantly more inactive than active behavior and did not seem to exhibit natural bimodal nocturnal or crepuscular hunter activity patterns. It should be stated that, due to the small sample size of only three jaguars and 24 analyzed hours, this study is a proof-of-concept to demonstrate the potential of ML methods as valuable tools for individual recognition, activity tracking, and monitoring of space use to aid in future animal welfare monitoring. Full article

The knowledge base for many small vertebrate species remains limited, largely because traditional manual data collection methods often overlook less charismatic species, such as reptiles. To address this, our pilot study harnesses open-source deep learning and markerless pose estimation technologies to evaluate the technical feasibility of tracking the spatial use and activity profiles of captive ectotherms. Specifically, we tracked these patterns over two months in a dynamically modified environment for Australian barking geckos (Underwoodisaurus milii). Our findings reveal descriptive changes in spatial occupancy and proximity across varying structural layouts. The system achieved a high raw detection accuracy (96.4%) and spatial categorization accuracy (91.7%) when validated against manual ground-truth data, confirming its robust technical performance and precision. Additionally, we automatically evaluated spatial proxies such as activity time budget, velocity, acceleration, and height usage, standardizing the analysis of extensive video recordings for nocturnal species. This pilot test introduces a simple, cost-effective method for rapid data extraction, offering a reliable, scalable monitoring solution for the management of understudied species. Full article