Search Results (3,425)

Tibial torsion significantly influences knee biomechanics, yet its interaction with ACL reconstruction history in elite alpine skiers remains under-investigated. In this cross-sectional observational study, we analyzed 20 elite alpine skiers (7 ACL-reconstructed, 13 non-injured) using a markerless motion capture system during dynamic tasks (Squat, Single-Leg Squat, Lunge). Static tibial torsion was assessed via the Transmalleolar Axis and Thigh–Foot Angle. The results revealed a critical divergence in biomechanical strategies based on tibial alignment (p < 0.05). Skiers with rotational deformity adopted a pattern we describe as a “Stiffness Strategy”, characterized by suppressed knee valgus and hip rotation, but relied on excessive ankle dorsiflexion (39.5°)—a compensatory mechanism that may become limited when constrained by rigid ski boots. In contrast, ACL-reconstructed skiers with normal alignment exhibited what we term an “Instability Strategy”, showing dynamic valgus collapse and persistent asymmetry. These findings suggest that “one-size-fits-all” rehabilitation may be insufficient. We propose that injury prevention protocols may benefit from incorporating anatomical screening, focusing on decoupling mobility for skiers with tibial torsion and enhancing dynamic stability for those with normal alignment. Full article

This paper presents robotic manipulation methods for rapid high-arc object transfer using dynamic, non-prehensile interactions. Two complementary techniques are introduced, two-fingered scoop-and-flick and one-fingered topple-and-flick, designed for objects with low and high centers of mass, respectively. Both methods enable a robot to retrieve objects resting on a surface and launch them into controlled projectile trajectories without requiring stable grasp formation. To support these maneuvers, we develop physics-based models of object acquisition and release, and combine them with a data-driven framework. While analytical modeling guides the acquisition phase, the highly nonlinear flicking dynamics are captured using learned predictive models that enable accurate selection of control parameters for desired trajectories. The proposed techniques enable dynamic object transfer, reduced grasp planning complexity, and adaptability to environmental constraints. Experiments conducted on a custom robotic platform demonstrate reliable and accurate high-arc object transfer, in which the majority of object displacement is achieved through projectile motion. Full article

Background: Knee osteoarthritis (KOA) is a major cause of pain, mobility limitation, and increased fall risk among older adults. Gait dysfunction, characterized by spatiotemporal and kinematic alterations, is a key functional consequence of KOA. While sagittal-plane gait deviations are well-established, multiplanar kinematic changes—particularly in the frontal and transverse planes—remain less clearly understood. This study aimed to compare three-dimensional gait characteristics between older adults with and without KOA. Methods: Ninety older adults (45 with KOA and 45 controls) completed gait assessments using a VICON™ motion capture system. Participants walked at a self-selected speed along a straight walkway without turning movements during data collection. Spatiotemporal parameters and lower-limb joint kinematics (hip, knee, and ankle) were recorded during key gait phases: initial contact, mid-stance, toe-off, and mid-swing. Group comparisons were performed using independent t-tests with statistical significance set at p < 0.05. Results: Compared with controls, participants with KOA demonstrated significantly slower gait velocity (p = 0.001), reduced cadence (p = 0.020), shorter stride length (p = 0.011), increased step time (p = 0.006), prolonged double support time (p = 0.009), and reduced single support time (p = 0.012). Kinematic analysis revealed greater knee adduction at initial contact (p = 0.001), reduced hip adduction (p = 0.002) and greater knee adduction (p = 0.003) during mid-stance, and increased ankle plantarflexion at toe-off (p = 0.004) in the KOA group. No significant between-group differences were observed during the mid-swing phase. Conclusions: Older adults with KOA exhibit distinct spatiotemporal and multiplanar kinematic gait alterations, particularly during weight-bearing phases. These changes may reflect adaptive gait patterns associated with joint dysfunction rather than definitive compensatory mechanisms. Three-dimensional gait analysis may provide valuable biomechanical insights to support early identification of mobility impairments and inform targeted rehabilitation planning in individuals with KOA. Full article

When two floating bodies are engaged in side-by-side operations, gap resonance is prone to occur. This phenomenon leads to violent, large-amplitude fluid motions inside the gap, posing a serious threat to operational safety. To address this issue, the present study establishes a numerical wave tank based on a two-way coupled potential–viscous flow method. In the vicinity of the floating bodies, viscous flow is solved to capture nonlinear effects; in the far field, a potential flow solver is employed to simulate wave propagation. Information exchange between the two domains is achieved through a two-way coupling strategy involving coupling interfaces and relaxation zones. Then, the numerical method is validated by simulating the gap wave elevation and the sway motion of a floating body under regular waves, with computed results compared against experimental data. Subsequently, to reveal the distinct roles of fixed and moving bodies in modulating gap resonance behavior, the hydrodynamic interactions between two identical floating bodies in regular waves are investigated under two representative configurations, one in which both bodies remain fully fixed, and another in which the upstream body is held fixed while the downstream body is allowed coupled motion in three degrees of freedom. The results demonstrate that the multi-degree-of-freedom (DOF) motion of the downstream floating body has a significant effect on the behavior of the resonance frequency and amplitude of the gap resonance. Full article

Water-surface perception is critical for autonomous surface vehicle navigation, where reliable tracking of task-relevant objects is essential for safe and robust operation. Referring multi-object tracking (RMOT) provides a flexible tracking paradigm by allowing users to specify objects of interest through natural language. However, existing RMOT benchmarks are mainly designed for ground or satellite scenes and fail to capture the distinctive visual and semantic characteristics of water-surface environments, including strong reflections, severe illumination variations, weak motion constraints, and a high proportion of small objects. To address this gap, we introduce Refer-ASV, the first RMOT dataset tailored for ASV navigation in complex water-surface scenes. Refer-ASV is constructed from real-world ASV videos and features diverse navigation scenes and fine-grained vessel categories. To facilitate systematic evaluation on Refer-ASV, we further propose RAMOT, an end-to-end baseline framework that enhances visual–language alignment throughout the tracking pipeline by improving visual–language alignment and robustness in challenging maritime environments. Experimental results show that RAMOT achieves a HOTA score of 39.97 on Refer-ASV, outperforming existing methods. Additional experiments on Refer-KITTI demonstrate its generalization ability across different scenes. Full article

This study employed a gamification-based integrated physical and cognitive program for older adults to examine the applicability of kinematic assessment using a markerless motion capture system (OpenCap version 1.0.1). The program was designed as a step-based dual-task intervention with progressively adjusted difficulty to simultaneously stimulate physical and cognitive functions. Nineteen older adults participated in the study, which evaluated their lower-extremity functional performance (Five Times Sit-to-Stand Test), dynamic balance (Four Square Step Test), curved walking ability (Figure-of-8 Walk Test, F8WT), cognitive function, and program satisfaction. Significant reductions in completion time were observed across all physical performance tests, suggesting within-group improvements in functional performance related to dynamic balance and curved walking ability. Cognitive function also showed significant within-group changes. Kinematic data collected using OpenCap system indicated a significant increase in knee joint angular velocity at the midpoint of the movement, but not in joint range of motion. In addition, high attendance and satisfaction levels were reported. These findings indicate that participation in the gamification-based dual-task program was associated with improvements in several physical performance and cognitive measures in older adults. In addition, the OpenCap system was used as a markerless motion analysis tool to capture movement-related kinematic data. Full article

Continuous monitoring of sitting posture is crucial for ergonomic assessment and fatigue prevention, yet many existing approaches rely on vision-based systems or single-modality sensing that are limited in capturing spatial and temporal biomechanical dynamics. This paper presents a multimodal smart-skin sensing system for spatial and temporal ergonomic fatigue analysis in sitting postures. The proposed platform integrates 42 distributed pressure, temperature, and vibration sensors arranged in 14 trimodal sensing nodes embedded across anatomical seating and back regions to enable real-time multimodal acquisition of human–chair interaction patterns. The study introduces an analytical framework combining anatomical heatmap visualization, temporal evolution analysis, delta pressure mapping, fatigue intensity estimation, and hotspot detection to characterize dynamic pressure redistribution during prolonged sitting. Experimental evaluations were conducted using a biomechanical mannequin and a single human participant with identical anthropometric characteristics (165 cm height and 62 kg body mass) across nine seated conditions, including neutral sitting, reclining, leaning, periodic shifting, and vibration-induced motion. Each posture condition was recorded as a time-series session and segmented into temporal phases to analyze fatigue evolution during prolonged sitting. Statistical analysis of pressure redistribution dynamics indicates significantly higher pressure drift in human measurements compared with the mechanically stable mannequin baseline (p < 0.001). The proposed framework provides a scalable sensing approach for ergonomic monitoring, intelligent seating systems, and human–machine interface applications. Full article

High-frame-rate (HFR) ultrasound imaging enables the acquisition of up to several thousand frames per second, substantially improving the temporal resolution of echocardiography. This technical advancement allows visualization of rapid mechanical and hemodynamic events that are not captured by conventional systems. In this review, we summarize the methods used to achieve HFR acquisition and examine their application across three principal domains: deformation imaging, mechanical wave imaging, and blood flow imaging. In deformation imaging, clinical studies have demonstrated higher feasibility for myocardial motion tracking and more reliable temporal deformation parameters. Mechanical wave imaging has emerged as a complementary domain, using HFR acquisition to capture transient mechanical events and estimate regional myocardial stiffness under both physiological and pathological conditions. In flow imaging, improved temporal resolution enables detailed visualization of rapid intracardiac flow and the evaluation of complex hemodynamic patterns. This technology expands the scope of functional and quantitative cardiac assessment and is emerging as a valuable modality for noninvasive diagnosis and monitoring in cardiovascular disorders. Full article

Rapid and accurate prediction of seismic response and fragility for bridge pile-group foundations (PGFs) is crucial for assessing seismic resilience. However, the high computational cost of traditional high-fidelity nonlinear analysis limits the application of probabilistic seismic risk analysis. To address this, an integrated deep learning framework is proposed that employs a unidirectional, multi-layer LSTM network for end-to-end prediction of structural responses directly from ground motions. The proposed model features two innovations. First, its multi-output capability enables simultaneous prediction of complete response time histories and peak values for key engineering demand parameters—bending moment, curvature, and pile cap displacement. Second, the network incorporates sliding time windows and residual connections to capture complex nonlinear soil–structure interaction. These predictions are integrated into a probabilistic seismic demand model to generate fragility curves. The framework is validated using a high-fidelity OpenSees model of a real bridge PGF subjected to 1000 ground motions. Results demonstrate the model’s excellent predictive accuracy: for peak bending moment, the mean predicted-to-actual ratio ranges from 0.97 to 1.03, with standard deviation below 0.12; the derived fragility curves show excellent agreement with benchmarks, achieving an average R² of 0.985 across four damage states. More importantly, the framework reduces the time for a complete fragility assessment (200 incremental dynamic analyses) from approximately 12 h to about 1 s—a 40,000× speed-up—making data-driven rapid and large-scale seismic risk assessment a reality. The proposed framework provides engineers with a practical design tool for rapidly evaluating alternative foundation configurations and informing seismic design decisions, thereby integrating advanced data-driven methods directly into the engineering design workflow. Full article

Anterior cruciate ligament (ACL) injuries frequently lead to persistent gait asymmetries, posing challenges for early rehabilitation and functional status. Comprehensive monitoring of pelvic gait symmetry during rehabilitation remains underexplored. This study evaluated post-operative functional status using an integrated monitoring approach combining pelvic-mounted inertial measurement unit (IMU) sensors with standardized clinical assessments in 32 individuals (9 women, 23 men; aged 19–64) following ACL reconstruction with patellar tendon autografts. IMU recordings captured pelvic oscillations in the sagittal, frontal, and transverse planes during standardized 10 m walking tests, providing objective digital biomarkers of gait symmetry. Clinical assessments included knee range of motion, thigh circumference, swelling, and pain using a modified 0–20 visual analogue scale (VAS). Across the early rehabilitation period, VAS scores decreased from 13.6 to 3.0, knee swelling from 2.88 cm to 1.09 cm, knee extension deficit from −9.38° to −2.03°, and knee flexion improved from 61.56° to 98.75°. Thigh hypotrophy increased from 1.13 cm to 2.53 cm. Pelvic oscillations improved in all planes (sagittal: 36.2 to 49.2; frontal: 71.9 to 92.2; transverse: 73.4 to 90.9), reflecting progressive restoration of gait control as patients transitioned from crutch-assisted to independent walking. The integration of wearable sensor data with clinical metrics enabled sensitive tracking of pelvic gait symmetry and functional status, demonstrating the utility of technology-supported monitoring to support individualized clinical assessment and early-phase monitoring following ACL reconstruction. Full article

To address the challenge of detecting weak targets with small radar cross-sections (RCS), this work explores an integrated framework that leverages cross-level multimodal fusion of radar echoes. This method considers the target’s motion properties via Doppler spectrum and phase sequences (direct physical level), and introduces the Gramian Angular Field (GAF) to map the echo amplitude sequence into two-dimensional (2D) structured images, thereby revealing the dynamic evolution characteristics of echo energy (abstract representation level). This approach integrates direct physical attributes and abstract system evolution features within a unified representation. To accommodate the structural differences among modalities, a heterogeneous branch processing network is designed: the Transformer is employed to capture long-range dependencies in one-dimensional (1D) sequences, while ResNet18 is used to extract spatial texture features from two-dimensional images. A self-attention mechanism is further introduced to achieve adaptive fusion of the multimodal data. Experimental results based on the IPIX dataset suggest that this cross-level strategy provides improved detection performance across various scenarios, as observed in complex marine environments. Full article

This study investigates the hydrodynamic responses and energy harvesting performance of a hemispherical point-absorber wave energy converter (WEC) in uniform current. A frequency-domain Rankine source method (RSM) is developed to rigorously account for current-modified free-surface conditions, and an approximate free-surface Green-function method (AFSGM) is implemented to assess practical applicability under weak-current assumptions. The numerical settings for body, free-surface, and radiation-boundary discretizations are determined through convergence tests. Model validation is performed by comparing motion responses against published benchmark results under both zero-current and current conditions. The effects of current and motion constraints are examined for surge–heave free and heave-only cases. Results show that current can amplify the heave response and that surge freedom enhances heave motion through coupling effects, leading to increasing discrepancies between RSM and AFSGM as current strengthens. For heave-only motion, AFSGM provides practically acceptable predictions within $\mid F_r\mid \le 0.045$, while noticeable differences appear near resonance beyond this range, for which RSM is recommended. Energy harvesting is evaluated using a linear PTO damping model, revealing that current alters the capture width ratio (CWR) and shifts the optimal PTO damping and frequency, indicating the necessity of considering current in performance assessment and PTO design. Full article

To meet the real-time computational requirements of active suspension control systems, this study shifts from complex microscopic physical equations to a direct nonlinear functional mapping between the relative motion states (displacement and velocity) and the output force of air springs. This approach aims to preserve critical nonlinear hysteresis characteristics while significantly reducing the computational overhead. A progressive modeling strategy is implemented to characterize these complex behaviors. Initially, polynomial fitting is employed to identify key input features; however, its limited capacity to capture intricate nonlinearities necessitates more advanced methods. Subsequently, standard Feedforward Neural Networks (FNNs) are explored for their nonlinear mapping capabilities, yet their inherent “black-box” nature often leads to convergence difficulties and restricted generalization. To address these issues, a Physics-Informed Neural Network (PINN) architecture is introduced, embedding physical governing equations as regularization constraints within the loss function to integrate data-driven flexibility with mathematical rigor. Recognizing that conventional PINNs often encounter convergence challenges due to conflicts between PDE constraints and data-driven loss terms, this research develops a Physics-Embedded Hierarchical Network (PEHN). By deriving specialized PDE constraints tailored to air spring dynamics and designing a hierarchical architecture aligned with these physical requirements, the PEHN effectively balances physical priors with experimental data. Experimental results demonstrate that, compared to the baseline models, the proposed PEHN exhibits stronger stability and superior accuracy in capturing the complex nonlinearities of air spring dynamics. Full article

Objective: The objective of this study was to explore the motion characteristics and movement strategies of the hip and knee joints in overweight individuals during sit-to-stand (STS) transfers using statistical parametric mapping (SPM). Methods: Twenty subjects were divided into an overweight group (n = 10) and a normal-weight group (n = 10) based on body mass index (BMI). The Qualisys infrared motion capture system and Kistler three-dimensional force platform were used for motion data collection, and Visual 3D and Matlab were used to calculate the angles and torque indicators of the lower limb hip and knee joints. Results: During the STS process, the maximum hip flexion angle of the overweight group was smaller than that of the control group (Z = −1.83, p = 0.043, r = 0.39), while the maximum abduction and external rotation angles were greater than those of the control group (Z = −2.15, p = 0.022, r = 0.46; Z = −2.02, p = 0.028, r = 0.48). SPM analysis showed that during the 0–52% phase of the hip joint in the frontal plane, the abduction amplitude of the overweight population was greater than that of the normal population (p < 0.05). The minimum external rotation angle of the knee joint was less than that of the control group (F(1,18) = 9.135, p = 0.043). The peak internal adduction and abduction torque of the hip joint in the overweight group was greater than that of the control group (Z = 2.37, p = 0.017, r = 0.54). Conclusions: Compared with the normal-weight population, the overweight population exhibited distinct motion characteristics of the hip and knee joints during the STS, with particularly pronounced differences in the hip joint. To maintain stability during STS, the overweight population adopts a compensatory movement strategy featuring a wider base of support via hip abduction and increased muscular torque to control frontal plane stability, which imposes greater functional loads on the hip joint. BMI-related movement characteristics should be studied in young adults under controlled experimental conditions, and future studies are needed to verify whether similar patterns exist in older adults. Full article

Capturing kinematic data in outdoor sports is challenging, as motions span large capture volumes and occur under difficult environmental conditions. Video-based approaches, particularly with pan–tilt–zoom cameras, offer a practical solution, but the extensive manual post-processing required limits their use to short sequences and few athletes. This study presents a motion capture pipeline that automates the detection of both reference points and sport-specific keypoints to overcome this limitation. The field test employed eight cameras covering a $250\times 80\times 30 \mathrm{m}$ capture volume with nearly 300 reference points. Ten state-certified ski instructors performed eight standardized maneuvers. Reference points were localized through a hybrid approach combining YOLO object detection and ArUco marker identification. AlphaPose was fine-tuned on a new manually annotated dataset to detect skier-specific keypoints (e.g., skis, poles) alongside anatomical landmarks. Continuous frame-wise calibration and 3D reconstruction were performed using Direct Linear Transformation. Evaluation compared automated detections with manual annotations. Automated reference point detection achieved a mean localization error of $4.1$ pixels ($0.1\%$ of 4K width) and reduced 3D segment-length variation by $23\%$. The skier-specific keypoint model reached $98\%$ PCK, mAP of $0.97$, and an MPJPE of $10.3$ pixels while lowering 3D segment-length variation by $0.5 \mathrm{cm}$ compared to manual digitization and $0.6 \mathrm{cm}$ relative to a pretrained model. Replacing manual digitization with automated detection improves accuracy and facilitates kinematic data collection in large outdoor fields with many athletes and trials. The approach also enables the creation of sport-specific datasets valuable for biomechanical research and training next-generation 3D pose estimation models. Full article