Search Results (990)

Intelligent video analysis tools have advanced significantly, with numerous cameras installed in various locations to enhance security and monitor unusual events. However, the effective detection and monitoring of violent incidents often depend on manual effort and time-consuming analysis of recorded footage, which can delay timely interventions. Deep learning has emerged as a powerful approach for extracting critical features essential to identifying and classifying violent behavior, enabling the development of accurate and scalable models across diverse domains. This study presents the Int.2D-3D-CNN architecture, which integrates a two-dimensional convolutional neural network (2D-CNN) and 3D-CNNs for video-based violence recognition. Compared to traditional 2D-CNN and 3D-CNN models, the proposed Int.2D-3D-CNN model presents improved performance on the Hockey Fight, Movie, and Violent Flows datasets. The architecture captures both static and dynamic characteristics of violent scenes by integrating spatial and temporal information. Specifically, the 2D-CNN component employs lightweight MobileNetV1 and MobileNetV2 to extract spatial features from individual frames, while a simplified 3D-CNN module with a single 3D convolution layer captures motion and temporal dependencies across sequences. Evaluation results highlight the robustness of the proposed model in accurately distinguishing violent from non-violent videos under diverse conditions. The Int.2D-3D-CNN model achieved accuracies of 98%, 100%, and 98% on the Hockey Fight, Movie, and Violent Flows datasets, respectively, indicating strong potential for violence recognition applications. Full article

With the increasing complexity of human–computer interaction scenarios, conventional digital human facial expression systems show notable limitations in handling multi-emotion co-occurrence, dynamic expression, and semantic responsiveness. This paper proposes a digital human system framework that integrates multimodal emotion recognition and compound facial expression generation. The system establishes a complete pipeline for real-time interaction and compound emotional expression, following a sequence of “speech semantic parsing—multimodal emotion recognition—Action Unit (AU)-level 3D facial expression control.” First, a ResNet18-based model is employed for robust emotion classification using the AffectNet dataset. Then, an AU motion curve driving module is constructed on the Unreal Engine platform, where dynamic synthesis of basic emotions is achieved via a state-machine mechanism. Finally, Generative Pre-trained Transformer (GPT) is utilized for semantic analysis, generating structured emotional weight vectors that are mapped to the AU layer to enable language-driven facial responses. Experimental results demonstrate that the proposed system significantly improves facial animation quality, with naturalness increasing from 3.54 to 3.94 and semantic congruence from 3.44 to 3.80. These results validate the system’s capability to generate realistic and emotionally coherent expressions in real time. This research provides a complete technical framework and practical foundation for high-fidelity digital humans with affective interaction capabilities. Full article

Men have limited options for contraception, despite the widely accepted public health benefits of it, placing the contraceptive burden solely on women. The current study focuses on inhibiting the PP1γ2 enzyme, which plays a role in sperm maturation and motility. The study considered three top compounds based on the findings of molecular docking. The three compounds exhibited a good interaction profile with a binding affinity score of D751-0223 (−8.7 kcal/mol), D751-014 (−8.1 kcal/mol), and N117-0087 (−8 kcal/mol) measured in kcal/mol. Molecular dynamics simulation (MDS) were performed on the PP1γ2–ligand complexes along with the Apo form. The results suggested that all the complexes were stable with no major deviations observed compared to Apo. The average RMSDs for PP1γ2-D751-0223, D751-014, and Apo were 1.27 Å, 1.73 Å, 1.39 Å, and 1.69 Å, respectively. The PP1γ2–ligand complexes were observed with unique salt bridge interactions such as Glu133-Arg137, Asp4-Lys107, Asp188-Arg116, and Glu120-Arg90. The principal component analysis (PCA) findings indicated that every complex had a distinctive motion state. Furthermore, the net MM/PBSA scores for D751-0223, D751-0143, and N117-0087 were −80.01 kcal/mol, −72.18 kcal/mol, and −64.26 kcal/mol, respectively, while the MM/GBSA and MM/PBSA values were −82, −73.07,−67.26 and −80.01, −72.18, −64.26, measured in kcal/mol, respectively. The WaterSwap energy estimation was performed to validate the former technique, and the findings demonstrated that PP1γ2-D751-0223 is a stable complex, with a value of −51.05 kcal/mol. This work provides a baseline to researchers for the identification of novel therapeutic approaches for non-hormonal male contraceptives. Full article

This work investigates the intricate dynamics of the Carreau hybrid nanofluid’s inclined magnetohydrodynamic (MHD) flow, exploring both active and passive control modes. The study incorporates critical factors, including Arrhenius activation energy across a stretched sheet, chemical interactions, and nonlinear thermal radiation. The formulation of the boundary conditions and governing equations is inherently influenced by symmetric considerations in the physical geometry and flow assumptions. Such symmetry-inspired modeling facilitates dimensional reduction and numerical tractability. The analysis employs realistic boundary conditions, including convective heat transfer and control of nanoparticle concentration, which are solved numerically using MATLAB’s bvp5c solver. Findings indicate that an increase in activation energy results in a steeper concentration boundary layer under active control, while it flattens in passive scenarios. An increase in the Biot number ($Bi$) and relaxation parameter ($\mathsf{\Gamma }$) enhances heat transfer and thermal response, leading to a rise in temperature distribution in both cases. Additionally, the 3D surface plot illustrates elevation variations from the surface at low inclination angles, narrowing as the angle increases. The Nusselt number demonstrates a contrasting trend, with thermal boundary layer thickness increasing with higher radiation parameters. A graphical illustration of the average values of skin friction, Nusselt number, and Sherwood number for both active and passive scenarios highlights the impact of each case. Under active control, the Brownian motion’s effect diminishes, whereas it intensifies in passive control. Passive techniques, such as zero-flux conditions, offer effective and low-maintenance solutions for systems without external regulation, while active controls, like wall heating and setting a nanoparticle concentration, maximize heat and mass transfer in shear-thinning Carreau fluids. Full article

Background/Objectives: This study aimed to evaluate the diagnostic performance of simplified intravoxel incoherent motion (IVIM) diffusion-weighted imaging (DWI) parameters in distinguishing malignant from benign breast lesions, and to explore their association with clinicopathological features. Methods: This retrospective study included 108 women who underwent breast MRI with multi-b-value DWI (0, 20, 200, 500, 800 s/mm²). Of those 108 women, 73 had pathologically confirmed malignant lesions. IVIM maps (ADC_map, D, D*, and perfusion fraction f) were generated using IB-Diffusion™ software version 21.12. Lesions were manually segmented by radiologists, and clinicopathological data including receptor status, Ki-67 index, cancer type, histologic grade, and molecular subtype were extracted from medical records. Nonparametric tests and ROC analysis were used to assess group differences and diagnostic performance. Additionally, a binary logistic regression model combining D, D*, and f was developed to evaluate their joint diagnostic utility, with ROC analysis applied to the model’s predicted probabilities. Results: Malignant lesions demonstrated significantly lower diffusion parameters compared to benign lesions, including ADC_map (p = 0.004), D (p = 0.009), and D* (p = 0.016), indicating restricted diffusion in cancerous tissue. In contrast, the perfusion fraction (f) did not show a significant difference (p = 0.202). ROC analysis revealed moderate diagnostic accuracy for ADC_map (AUC = 0.671), D (AUC = 0.657), and D* (AUC = 0.644), while f showed poor discrimination (AUC = 0.576, p = 0.186). A combined logistic regression model using D, D*, and f significantly improved diagnostic performance, achieving an AUC of 0.725 (p < 0.001), with 67.1% sensitivity and 74.3% specificity. ADC_map achieved the highest sensitivity (100%) but had low specificity (11.4%). Among clinicopathological features, only histologic grade was significantly associated with IVIM metrics, with higher-grade tumors showing lower ADC_map and D* values (p = 0.042 and p = 0.046, respectively). No significant associations were found between IVIM parameters and ER, PR, HER2 status, Ki-67 index, cancer type, or molecular subtype. Conclusions: Simplified IVIM DWI offers moderate accuracy in distinguishing malignant from benign breast lesions, with diffusion-related parameters (ADC_map, D, D*) showing the strongest diagnostic value. Incorporating D, D*, and f into a combined model enhanced diagnostic performance compared to individual IVIM metrics, supporting the potential of multivariate IVIM analysis in breast lesion characterization. Tumor grade was the only clinicopathological feature consistently associated with diffusion metrics, suggesting that IVIM may reflect underlying tumor differentiation but has limited utility for molecular subtype classification. Full article

Visual body-tracking techniques have seen widespread adoption in applications such as motion analysis, human–machine interaction, tele-robotics and extended reality (XR). These systems typically provide 2D landmark coordinates corresponding to key limb positions. However, to construct a meaningful 3D kinematic model for body joint reconstruction, a mapping must be established between these visual landmarks and the underlying joint parameters of individual body parts. This paper presents a method for constructing a 3D kinematic model of the human hand using calibrated 2D landmark-tracking data augmented with depth information. The proposed approach builds a hierarchical model in which the palm serves as the root coordinate frame, and finger landmarks are used to compute both forward and inverse kinematic solutions. Through step-by-step examples, we demonstrate how measured hand landmark coordinates are used to define the palm reference frame and solve for joint angles for each finger. These solutions are then used in a visualization framework to qualitatively assess the accuracy of the reconstructed hand motion. As a future work, the proposed model offers a foundation for model-based hand kinematic estimation and has utility in scenarios involving occlusion or missing data. In such cases, the hierarchical structure and kinematic solutions can be used as generative priors in an optimization framework to estimate unobserved landmark positions and joint configurations. The novelty of this work lies in its model-based approach using real sensor data, without relying on wearable devices or synthetic assumptions. Although current validation is qualitative, the framework provides a foundation for future robust estimation under occlusion or sensor noise. It may also serve as a generative prior for optimization-based methods and be quantitatively compared with joint measurements from wearable motion-capture systems. Full article

Utilizing tactile sensors embedded in intelligent mats is an attractive non-intrusive approach for human motion analysis. Interpreting tactile pressure 2D maps for accurate posture estimation poses significant challenges, such as dealing with data sparsity, noise interference, and the complexity of mapping pressure signals. Our approach introduces a novel dual-diffusion signal enhancement (DDSE) architecture that leverages tactile pressure measurements from an intelligent pressure mat for precise prediction of 3D body joint positions, using a diffusion model to enhance pressure data quality and a convolutional-transformer neural network architecture for accurate pose estimation. Additionally, we collected the pressure-to-posture inference technology (PPIT) dataset that relates pressure signals organized as a 2D array to Motion Capture data, and our proposed method has been rigorously evaluated on it, demonstrating superior accuracy in comparison to state-of-the-art methods. Full article

(1) Background: Image acquisition systems based on videogrammetry principles are widely used across various research fields, particularly in mechanics, with applications ranging from civil engineering to biomechanics and kinematic analysis. This study presents the design, development, and validation of a low-cost, two-camera 3D videogrammetry system for the kinematic analysis of human motion. (2) Materials and Methods: Built using commercially available components and custom MATLAB^® (version 2019b) software, the system captures synchronized video streams and extracts precise 3D coordinates of markers. Its performance was validated against the Vicon^® (Vicon Nexus 1.7.1) system, a gold standard in musculoskeletal motion analysis. Comparative tests were conducted under static and dynamic conditions at varying working distances and velocities. (3) Results: Results demonstrate that the proposed system achieves high accuracy, with maximum measurement errors below 0.3% relative to Vicon^®, and similar repeatability (SD of approximately 0.02 mm in static conditions). Compared to manual caliper measurements, both vision systems yielded similar results, with errors ranging between 0.01% and 0.82%. (4) Conclusions: A low-cost, two-camera videogrametric system was validated, offering full transparency, flexibility, and affordability, making it a practical alternative for both clinical and research settings in biomechanics and human movement analysis, with potential to be extended to general kinematic analysis. Full article

The purpose of this study was to identify differences between patellofemoral joint stress (PFJS), patellofemoral joint reaction force (PFJRF), quadriceps force, trunk and knee flexion angles, and horizontal position of applied load relative to the knee and heel between the front squat (FS) and back squat (BS) exercises at two depths (60 and 80% of leg length, where 60% represents a lower squat depth). Twenty-two healthy college-aged females (age: 22.23 ± 1.86 years, mass: 67.65 ± 9.60 kg, height: 171.34 ± 6.38 cm) participated in this study. Mechanical variables were measured or estimated using a 15-camera 3D motion analysis (180 Hz) system and force platforms (1800 Hz). Five repetitions of each squatting technique at each depth were performed. Multivariate testing showed a difference in patellofemoral loading variables, trunk and knee kinematics, and bar position relative to the heel and knee (p = 0.00) between squat depths. There was no difference between techniques, no interaction between depth and techniques (p > 0.05). Follow-up univariate analyses showed differences in PFJS, PFJRF, quadriceps force, horizontal bar position relative to the heel and knee, and knee and trunk flexion between squat depths. The similar joint stress observed between FS and BS may be explained by compensatory trunk mechanics or the use of a light external load. Full article

Objective: This study aimed to evaluate the effect of simultaneously combining therapeutic scapular retraction exercise with and without Neuromuscular Electrical Stimulation (NMES) in amateur female handball players with scapular dyskinesis. Methods: In a randomized, single-blind, controlled trial, the sample (n = 34) was randomized into two groups (Group 1 (n = 17) and Group 2 (n = 17)). The intervention consisted of applying a supervised scapular retraction exercise (SRE) program alone or combined with NMES for 4 weeks (2 ss/week). Scapular Static Positioning Assessment parameters (upper and lower horizontal distance of the scapula from the spine (mm)), internal rotation range of motion (degrees), and external rotation strength (newtons and BW%) were measured. Results: A significant interaction was found to favor the group that received the supervised SRE program + NMES (Group 1) in upper horizontal distance (F_1,30 = 30.93 [p < 0.000]; d = 0.65); lower horizontal distance (F_1,30 = 12.79 [p = 0.001]; d = 0.72); ER Strength (N) (F_1,30 = 19.58 [p < 0.000] d = 0.71); and ER Strength (BW%) (F_1,30 = 16.84 [p < 0.000]) d = 0.69), which was statistically significant (p ≤ 0.001 for p < 0.05). In the analysis for treatment benefit, the number needed to treat (NNT) was 2 for upper scapular positioning and 4 for external rotation strength. Conclusions: NMES improves the Scapular Static Positioning and ER Strength when combined with an SRE program in amateur female handball players diagnosed with scapular dyskinesis, with clinically relevant effects. These findings, while promising, are based on a small sample and should be confirmed in larger studies. Full article

The ability to quantify equine kinematics is essential for clinical evaluation, research, and performance feedback. However, current methods are challenging to implement. This study presents a motion capture methodology for horses, where three-dimensional, full-body kinematics are calculated without instrumentation on the animal, offering a more scalable and labor-efficient approach when compared with traditional techniques. Kinematic trajectories are calculated from multi-camera video data. First, a neural network identifies skeletal landmarks (markers) in each camera view and the 3D location of each marker is triangulated. An equine biomechanics model is scaled to match the subject’s shape, using segment lengths defined by markers. Finally, inverse kinematics (IK) produces full kinematic trajectories. We test this methodology on a horse at three gaits. Multiple neural networks (NNs), trained on different equine datasets, were evaluated. All networks predicted over 78% of the markers within 25% of the length of the radius bone on test data. Root-mean-square-error (RMSE) between joint angles predicted via IK using ground truth marker-based motion capture data and network-predicted data was less than 10 degrees for 25 to 32 of 35 degrees of freedom, depending on the gait and data used for network training. NNs trained over a larger variety of data improved joint angle RMSE and curve similarity. Marker prediction error, the average distance between ground truth and predicted marker locations, and IK marker error, the distance between experimental and model markers, were used to assess network, scaling, and registration errors. The results demonstrate the potential of markerless motion capture for full-body equine kinematic analysis. Full article

This study applies Computational Fluid Dynamics (CFD) and mathematical modeling to examine uterine and umbilical arterial blood flow during pregnancy, providing a more detailed understanding of hemodynamic changes across gestation. Statistical analysis of Doppler ultrasound data from a large cohort of more than 200 pregnant women (in the second and third trimesters) reveals significant increases in the umbilical arterial peak systolic velocity ($PSV$) between the 22nd and 30th weeks, while uterine artery velocities remain relatively stable, suggesting adaptations in vascular resistance during pregnancy. By combining the Navier–Stokes equations with Doppler ultrasound-derived inlet velocity profiles, we quantify several key fluid dynamics parameters, including time-averaged wall shear stress ($TAWSS$), oscillatory shear index ($OSI$), relative residence time ($RRT$), Reynolds number ($Re$), and Dean number ($De$), evaluating laminar flow stability in the uterine artery and secondary flow patterns in the umbilical artery. Since blood exhibits shear-dependent viscosity and complex rheological behavior, modeling it as a non-Newtonian fluid is essential to accurately capture pulsatile flow dynamics and wall shear stresses in these vessels. Unlike conventional imaging techniques, CFD offers enhanced visualization of blood flow characteristics such as streamlines, velocity distributions, and instantaneous particle motion, providing insights that are not easily captured by Doppler ultrasound alone. Specifically, CFD reveals secondary flow patterns in the umbilical artery, which interact with the primary flow, a phenomenon that is challenging to observe with ultrasound. These findings refine existing hemodynamic models, provide population-specific reference values for clinical assessments, and improve our understanding of the relationship between umbilical arterial flow dynamics and fetal growth restriction, with important implications for maternal and fetal health monitoring. Full article

The detection of coils in reservoir areas is part of the environmental perception technology of unmanned cranes. In order to improve the perception ability of unmanned cranes to include environmental information in reservoir areas, a method of automatic detection of coils based on two-dimensional LiDAR dynamic scanning is proposed, which realizes the detection of the position and attitude of coils in reservoir areas. This algorithm realizes map reconstruction of 3D point cloud by fusing LiDAR point cloud data and the motion position information of intelligent cranes. Additionally, a processing method based on histogram statistical analysis and 3D normal curvature estimation is proposed to solve the problem of over-segmentation and under-segmentation in 3D point cloud segmentation. Finally, for segmented point cloud clusters, coil models are fitted by the RANSAC method to identify their position and attitude. The accuracy, recall, and F1 score of the detection model are all higher than 0.91, indicating that the model has a good recognition effect. Full article

This study proposed an online early lameness detection method for dairy cow health management to overcome the inability of wearable sensor-based methods for online detection and low sensitivity to early lameness. Wearable IMU sensors collected acceleration data in stationary and moving states; a threshold discrimination module using variance of motion-direction acceleration was designed to distinguish states within 2 s, enabling rapid data screening. For moving-state windowed data, the InceptionTime network was modified with YOLOConv1D and SeparableConv1D modules plus Dropout, which significantly reduced model parameters and helped mitigate overfitting risk, enhancing generalization on the test set. Typical gait features were fused with deep features automatically learned by the network, enabling accurate discrimination among healthy, mild (early) lameness, and severe lameness. Results showed that the online detection model achieved 80.6% dairy cow health status detection accuracy with 0.8 ms single-decision latency. The recall and F1 score for lameness, including early and severe cases, reached 89.11% and 88.93%, demonstrating potential for early and progressive lameness detection. This study improves lameness detection efficiency and validates the feasibility and practical value of wearable sensor-based gait analysis for dairy cow health management, providing new approaches and technical support for monitoring and early intervention on large-scale farms. Full article

Total knee arthroplasty is an extensive orthopedic surgery for patients with severe cases of osteoarthritis. This surgery restores the range of motion in the knee joint and allows for pain-free movement. Advancements in medical techniques used in the surgical zone and implant technology, as well as the management of operations and administration for around two decades prior, have hugely improved surgical outcomes for patients. In this study, advancements in TKA were examined through exploring aspects such as robotic surgery, new implants and materials, minimally invasive surgery, and post-surgery rehabilitation. This paper entails a review of the peer-reviewed literature published between 2005 and 2025 in the PubMed and Google Scholar databases. For predictors, we incorporated clinical relevance together with methodological soundness and relation to review questions to select relevant research articles. We used the PRISMA flowchart to illustrate the article selection system in its entirety. Since robotic surgical and navigation systems have been implemented, surgical accuracy has improved, there is an increased possibility of ensuring alignment, and the use of cementless and 3D-printed implants has increased, offering durable long-term fixation features. The trend in the current literature is that minimally invasive knee surgery (MIS) techniques reduce permanent pain after surgery and length of hospital stays for patients, though the long-term impact still needs to be established. There is various evidence outlining that the enhanced recovery after surgery (ERAS) protocols show positive results in terms of functional recovery and patient satisfaction. The integration of these new advancements enhances TKA surgeries and translates them into ‘need of patient’ procedures, ensuring improved results and increases in patient satisfaction. The aim of this study was to perform a comprehensive analysis of the existing literature regarding TKA advancement studies to identify current gaps and problems. Full article