Search Results (4,383)

This study aimed to develop a monocular vision-based tennis serve analysis system and evaluate its effectiveness in beginner training. The system uses MediaPipe Pose to extract 33 body landmarks from monocular video, calculates joint angles using three-dimensional vector operations, identifies serve phases through threshold-based rules, constructs an approximate 3D pose representation using anthropometric constraints, and generates corrective feedback through a rule-based expert system. In a quasi-experimental study, 90 beginner tennis players (final n = 82) completed an 8-week intervention and were allocated to a high-frequency feedback group, a moderate-frequency feedback group, or a conventional training group. All groups showed significant improvements in Serve Quality Mastery (SQM) scores (p < 0.001). The high-frequency feedback group showed the greatest SQM improvement (SQM: +30.5 points), followed by the moderate-frequency feedback group (+24.2 points) and the conventional training group (+15.5 points). Between-group differences were significant F(2, 79) = 74.30, p < 0.001, η² = 0.65. These findings indicate a graded pattern across the feedback-frequency groups, with more frequent system-generated feedback being associated with greater improvements in training performance. The findings support the potential use of the monocular pose-based, rule-driven feedback system as a supplementary tool for beginner tennis serve instruction. Full article

This paper introduces a graphical framework for smooth Lie group actions based on tangent orbit interactions. In contrast with classical intersection graphs, where vertices usually represent algebraic subobjects and edges record set-theoretic intersections, the present construction uses non-trivial orbits as vertices and creates edges from common nonzero tangent directions inside the fixed ambient embedding. Starting from infinitesimal tangent spaces generated by the action, we construct Lie orbit tangent graphs and analyze their adjacency structure, connectedness, completeness, degrees and diameter estimates. To describe local and global interactions, tangent fibers, local tangent orbit cliques, tangent orbit hypergraphs and incidence structures are introduced. We further develop separating tangent paths and use them to construct neighborhood systems and tangent-separating topologies. The framework gives a unified way to encode orbit-level tangent interactions and may be useful in geometric analysis, symmetry-based dynamical systems, differential topology and mathematical physics, where orbits and infinitesimal directions describe invariant motions, constraints or symmetry-reduced configurations. Several examples are included to illustrate how Lie group actions, graph structures, hypergraphs and tangent geometry interact within the proposed scheme. Full article

Bilateral deficit (BLD) is traditionally defined as the reduced capacity to produce force during simultaneous bilateral contractions compared with the summed output of unilateral actions. However, in applied sport settings, BLD is frequently estimated from countermovement jump (CMJ) performance, representing a functional rather than a direct mechanical measure of force production. Therefore, the aim of this study was to examine the association between a CMJ-derived functional BLD index and multidirectional performance in soccer players. Forty male university soccer players (age: 23 ± 1 years) performed unilateral and bilateral CMJ. The BLD index was calculated from jump height values obtained during these assessments. Participants subsequently completed the 505 change-of-direction (CoD) test, which was analyzed using two-dimensional video-based motion analysis. Participants were classified according to BLD magnitude into low, moderate, and high BLD groups. Group differences were assessed using Kruskal–Wallis tests with Bonferroni-adjusted post hoc comparisons. Additionally, Spearman correlation analyses were performed using BLD as a continuous variable. Significant between-group differences were observed across all temporal phases of the 505 test (p < 0.001), with players exhibiting lower BLD values demonstrating superior acceleration, deceleration, reacceleration, and overall CoD performance. Significant negative correlations were also observed between BLD and reaction time, acceleration, deceleration, reacceleration, CoD time, and CoD deficit (r_s = −0.42 to −0.69; p < 0.001). No significant associations were found for stride length, acceleration ability, or inter-limb asymmetry. These findings suggest that lower magnitudes of a CMJ-derived functional BLD index are associated with superior multidirectional performance in soccer players. However, given that BLD was estimated from jump performance, the results should be interpreted as associations with a functional neuromuscular performance index rather than as direct evidence of bilateral force production capacity. Full article

Background: The vestibular system has traditionally been interpreted within a reflex-based framework, mainly centered on gaze stabilization, vestibulo-ocular reflex pathways, and peripheral vestibular deficits. This model remains essential, but it is insufficient to explain the full spectrum of postural, perceptual, visual-motion, and self-motion complaints observed in contemporary clinical otoneurology. Objective: This conceptual narrative review examines selected representative works by Kathleen E. Cullen as landmarks in a broader transition from reflex physiology to predictive, multimodal, context-dependent, body-centered self-motion control. Methods: This is not a systematic or bibliometric review. Papers were selected because they mark distinct conceptual steps in Cullen’s work: neural encoding of self-motion, peripheral and central coding strategies, multimodal integration, active versus passive self-motion, reafference suppression, body-centered encoding, proprioceptive prediction, vestibular cerebellar internal models, sensory reweighting, and clinical translation. Synthesis: Angelaki and Cullen’s 2008 synthesis and Cullen’s subsequent work demonstrate that vestibular processing is inherently multimodal from the earliest central stages and that neural representations of self-motion depend on behavioral context. Vestibular nuclei, visual-vestibular networks, and vestibular cerebellar circuits integrate labyrinthine signals with optic flow, proprioceptive, oculomotor, motor, cerebellar, cortical, and contextual information. This architecture enables the brain to distinguish expected from unexpected motion, suppress predictable vestibular reafference during voluntary action, compute internal estimates of body motion, adapt to altered sensory reliability, and reweight sensory inputs according to task demands. Conclusions: The clinical relevance of this trajectory is substantial. Patients may show preserved high-acceleration vestibulo-ocular reflex responses while experiencing persistent instability, visually induced dizziness, defective self-motion perception, or abnormal sustained vestibular processing. Such dissociations are not paradoxical when the vestibular system is understood as a predictive, distributed, body-centered control system. Cullen’s long lesson offers a neurophysiological foundation for a modern vestibular grammar in which clinical findings are interpreted across the reflexive, perceptual, postural, visual-vestibular, sustained, and predictive domains. Full article

Accurate attitude measurement of non-cooperative space targets is essential for on-orbit servicing, active debris removal, and autonomous rendezvous missions. To address the challenges associated with unknown geometry, rapid tumbling motion, and the limited observability of single-view systems, this study proposes a cooperative multi-view measurement framework for three-dimensional reconstruction and attitude estimation. Multiple spacecraft are deployed to form a stable observation configuration, and multi-view image sequences are acquired to strengthen geometric constraints. A learning-based multi-view stereo reconstruction module is used to estimate depth information and reconstruct point clouds, which are further processed through iterative closest point (ICP) registration to derive inter-frame attitude variations. An extended Kalman filter (EKF) is then introduced to improve temporal consistency and suppress measurement noise. Validation is conducted in a numerical simulation using a simplified Fengyun-1 (FY-1) satellite model under a three-spacecraft cooperative fly-around scenario. The simulation results demonstrate that the proposed method achieves high-precision attitude estimation, with attitude errors below $0.3^{\circ }$ and positional errors within $0.05\mathrm{m}$. Comparative experiments show that the method maintains stable measurement performance under varying observation distances and viewing configurations. The proposed framework provides a reliable and robust measurement solution for dynamic attitude determination of free-tumbling space targets. Full article

Low-latency visual perception is essential for interactive machine vision on edge AI devices, but conventional frame-based image sensors impose frame period delays and generate dense image data that increase memory bandwidth and processing latency. Although Dynamic Vision Sensors (DVSs) are known to provide low latency, sparse output, and high dynamic range, these sensor-level properties do not automatically translate into practical application-level latency reduction on resource-constrained edge platforms. This paper presents a latency-driven sensing algorithm co-design approach for DVS-based low-latency machine vision. The main objective is to connect DVS sensor-level characteristics, event representations, task-dependent processing flows, and measured response times on mobile application processors. We first analyze latency requirements for three representative edge AI applications (i.e., person detection, gesture recognition, and Simultaneous Localization and Mapping (SLAM)), which correspond to different latency regimes and processing structures. We then describe the DVS operating principle, pixel-level event latency, and readout latency, showing how asynchronous event generation reduces sensing delay and suppresses redundant static background information before algorithmic processing. In contrast to prior event camera studies that mainly optimize a single task or a specific event representation, this work evaluates three task-specific event processing systems on mobile processors. Person detection achieves 92 ms processing latency on Exynos 7570, gesture recognition based on event-driven 4-DoF motion estimation achieves 20 ms latency on Exynos 5422, and SLAM achieves 15.9 ms latency on Snapdragon 845. These results satisfy the practical latency targets of the corresponding applications and demonstrate that DVS-based sensing can provide not only sensor-level speed advantages but also system-level latency benefits for AIoT, mobile, robotics, and AR/VR machine vision systems. Full article

Background: Knee osteoarthritis (KOA) alters the performance of daily activities, such as stair negotiation, by compromising lateral stability and neuromuscular control. This pilot study aimed to evaluate the feasibility of a 10-week Dynamic Neuromuscular Stabilization (DNS) program and to explore its preliminary biomechanical effects during stair ascent and descent in middle-aged women with KOA. Methods: Twenty-six participants were randomly assigned to a DNS group (n = 13) or a control group (n = 13). The DNS group completed a 10-week intervention (twice weekly). Feasibility was assessed via recruitment, retention, and adherence. Primary outcomes were mediolateral (ML) center of pressure (COP) parameters, while secondary outcomes included anteroposterior (AP) COP parameters and lower limb range of motion (ROM). Effect sizes (η²_p) were estimated using 3D motion analysis and force plates. Results: The intervention showed high potential feasibility, with 100% recruitment and retention rates and 98.5% compliance. No adverse events occurred. Large effect sizes were observed for reduced ML COP velocity (ascent: η²_p = 0.79; descent: η²_p = 0.62) and RMS (descent: η²_p =0.16). Secondary outcomes, including AP COP parameters and joint ROM (increased sagittal flexion and decreased coronal instability), also demonstrated large effect sizes. Conclusions: This pilot study suggests that progressive DNS training appears to be feasible and safe for patients with KOA. The preliminary effect sizes observed in COP control and lower kinetic chain mechanics may serve as useful foundational data for designing future adequately powered clinical trials to further examine the efficacy and underlying biomechanical mechanisms of DNS training. Full article

Assessment of upper-limb kinematics is essential in clinical practice for diagnosis, rehabilitation monitoring, and treatment personalization. Markerless Motion Capture (MMC) systems, such as the Microsoft Azure Kinect (AK), offer a low-cost and time-efficient alternative to marker-based systems. However, while AK accuracy has been extensively studied for lower-limb movements, its performance for upper-limb analysis—especially under clinically relevant, suboptimal conditions—remains underexplored. This study aims to validate AK for upper-limb motion tracking against a gold-standard optoelectronic system under optimal and suboptimal conditions. Sixteen healthy adults performed ten upper body motor tasks in three scenarios: optimal setup, seated posture with table occlusion, and increased camera distance. Joint angles were compared using normalized Root Mean Squared Error (nRMSE) and Pearson’s correlation coefficient. Performance Indicators (PIs) including Range of Motion (ROM), smoothness, and Time to Peak Velocity (TTPV) were also evaluated. AK accurately captured movements performed within the camera plane, with median nRMSE below 20% in optimal conditions and no significant degradation in suboptimal setups. In contrast, movements occurring on planes perpendicular to the camera were poorly captured. ROM estimation was acceptable and highly reproducible, while TTPV showed moderate-to-poor reliability and smoothness deviated substantially from the reference system. These findings suggest that careful attention to Kinect positioning is essential to ensure effective acquisitions, even in suboptimal scenarios. Future research should evaluate AK validity in clinical populations and explore the effects of system interference in multi-device setups. Full article

Reliable indoor localization is required for automated guided vehicles (AGVs), robot validation, and industrial digital-twin applications, but ultrasonic positioning can degrade sharply when acoustic visibility changes. This paper evaluates Marvelmind Super-Beacon localization in controlled laboratory experiments involving both AGV tracking and UR10 robot-arm positioning. The non-inverse architecture (NIA) and inverse architecture (IA) configurations are included as parallel validation scenarios to assess the robustness of the proposed mitigation framework across different Marvelmind deployment modes. The baseline analysis identifies the dominant acoustic failure modes, including multipath-induced scatter, crossover-zone handover jumps, update-rate degradation, complete non-line-of-sight (NLoS) outages, and height-dependent 3D jitter. To mitigate these effects, an embedded ultrasonic–inertial pipeline is implemented on an ESP32-S3-WROOM-1 module. The system combines UART packet validation, interrupt-driven ICM-20948 inertial acquisition at 500 Hz, sliding-window kinematic outlier rejection, and a 15-state error-state Kalman filter (ESKF). The embedded estimator logic is designed to maintain motion continuity during intermittent or corrupted acoustic positioning while reintroducing validated ultrasonic absolute corrections. Using recorded AGV and UR10 datasets, mitigation performance was quantitatively assessed through a firmware-consistent replay of the recorded measurements, using the same gating, inertial propagation, and measurement-update logic as the real-time ESP32-S3 implementation. Across ten trials per configuration, the replay-based trial-mean RMSE in the 2D AGV scenarios decreased from 101.2–104.1 mm for raw ultrasonic data to 47.2–48.7 mm after fusion, while peak failure-interval errors were reduced by 64.2–65.7%. In the 3D UR10 scenarios, replay-based trial-mean RMSE decreased from 157.6–158.4 mm to 80.2–80.5 mm, and peak height-sensitive 3D errors were reduced by 58.8–60.0%. The results demonstrate the feasibility of embedded ultrasonic–inertial robustness enhancement for localization in controlled laboratory AGV and robot-arm scenarios. While the proposed approach shows promising performance under the investigated conditions, further validation is required before extending the conclusions to larger-scale and dynamically changing industrial environments. Full closed-loop online robot localization and control based directly on the fused localization output remain subjects for future investigation. Full article

This paper presents research on a Predictive and Uncertainty Assessment Framework (PUAF), providing a comparative analysis of two prominent methods, Monte Carlo (MC) Dropout and Bootstrap-based models, used in uncertainty estimation techniques of Neural Network predictions of human activity recognition using accelerometer data. Unlike traditional studies that optimize classification accuracy, this work emphasizes uncertainty quantification to enhance model reliability, particularly for critical health-related activities. Among the five activity classes of Sit, Sleep, Walk, Cough and Fall, this work concentrates on the Cough and Fall cases. The study exploits acceleration data from a wearable device positioned on the user’s chest, with features derived from three-axis motion measurements. Synthetic datasets are generated by systematically introducing noise variations, added to the original dataset across all axes, to assess robustness under real-world conditions. Each uncertainty estimation method estimates the probabilities for the five different classes along with the corresponding 95% confidence intervals to quantify the prediction uncertainty. A detailed evaluation is conducted by analyzing the average width of these confidence intervals across different noise levels, identifying the most reliable feature and model combination. Both the MC Dropout and Bootstrap enhance model robustness and uncertainty awareness under noisy sensor conditions. The MC Dropout provides sharper and more sensitive uncertainty estimates, while the Bootstrap yields more stable and better-calibrated predictions. The evaluation using the proposed PUAF demonstrates that each method offers distinct advantages, highlighting the importance of uncertainty quantification for reliable wearable-based HAR systems. Full article

Predicting a ship’s motion trend in waves is crucial for safe navigation and operation. Existing prediction models are mostly based on the assumption of local linear dynamics, which can achieve great performance in idealized ocean environments. However, ships typically sail in real marine environments with regular or irregular waves, which makes the robustness and real-time performance of ship motion estimation models particularly important. To address this limitation, this paper proposes a global linear predictor (GLP) based on the Koopman operator, which can effectively represent the nonlinear rolling dynamics of ships. Furthermore, the GLP model is used to predict and control the rolling motion of a ship in real time. The proposed method is validated in both regular and irregular wave environments. The simulation experiment results show that the accuracy of the proposed method is about 14% higher than that of other classical methods on ships’ rolling dynamics. And it achieves a more than 91% rolling reduction efficiency in all wave conditions, significantly decreasing the amplitude of a ship’s rolling. Full article

To address the problem of high-frequency roll disturbances generated during dynamic balancing in non-coaxial two-wheeled robots, this paper proposes a Rotation-Optimized Inertial–Visual SLAM system (ROIV-SLAM) for robust state estimation. The proposed approach adopts a decoupled architecture for translation and rotation estimation. In the front-end, an Extended Kalman Filter (EKF) is employed to fuse LiDAR, an inertial measurement unit (IMU), and wheel odometry to obtain an initial translation estimate. Meanwhile, a physical manifold constraint is constructed using the gravity vector and surface normals extracted from RGB-D point clouds, supporting stable rotation estimation under high-frequency disturbances through Lie-group-based optimization. In the back-end, a factor graph is established, and loop closure robustness is enhanced through vision–LiDAR scan matching. Experimental results indicate that ROIV-SLAM achieves improved trajectory consistency with respect to the optimized reference trajectory and more robust mapping performance compared with the evaluated baseline approaches in the tested scenarios. The results further suggest that introducing task-specific physical dynamic constraints and a decoupled estimation mechanism helps suppress high-frequency motion noise inherent to balancing robots, thereby improving the robustness of state estimation in complex environments. Full article

The assessment of seismic performance of existing buildings has traditionally focused on structural safety and damage limitation, often neglecting the explicit quantification of the associated economic consequences. In recent years, performance-based earthquake engineering (PBEE) frameworks have enabled a direct link between structural response and probabilistic loss estimation, allowing economic metrics to be integrated into seismic risk evaluation. Among these, the Expected Annual Loss (EAL) represents a comprehensive indicator that accounts for seismic hazard, structural vulnerability, and exposure over the building’s lifetime. This study presents a performance-based seismic loss assessment of an existing reinforced concrete building, adopting EAL as a global metric for seismic performance evaluation. The case study concerns an existing hospital building designed primarily for gravity loads and representative of a large portion of the Italian building stock. A detailed nonlinear numerical model is developed using OpenSees ver. 3.8.0, incorporating shear-critical behavior through nonlinear link elements. Structural performance is evaluated through modal analysis, pushover analysis, and nonlinear time-history analyses using a set of ground motions selected and scaled according to intensity-based criteria. Seismic losses are estimated following the FEMA P-58 methodology, implemented through the PACT software ver. 3.1.2, integrating structural response demands, component fragility functions, collapse probability, and seismic hazard curves. Probabilistic loss curves are derived, and the EAL is computed as a synthetic indicator of economic seismic performance. The results highlight the effectiveness of EAL in capturing the combined effects of seismic hazard and structural vulnerability, demonstrating its potential as a robust decision-support metric for seismic risk mitigation, retrofit prioritization, and insurance-related applications for existing buildings. Full article

Anti-UAV multi-object tracking in remote sensing videos is challenging because UAV targets are small, weakly textured, and often affected by cluttered backgrounds, abrupt motion, occlusion, and intermittent visibility. To address these challenges, we formulate anti-UAV multi-object tracking as a context-aware identity prediction task, in which target identities and locations are inferred from historical trajectory priors instead of current-frame observations alone. Under this formulation, we propose a dual-track parallel tracking framework. The adaptive identity disambiguation (AID) module combines motion cues with appearance features according to their estimated reliability, improving short-term association when visual evidence is weak. In parallel, the motion-evolution temporal memory (METM) module models trajectory dynamics using motion anomaly detection and time-decayed memory, enabling spatiotemporal recovery after occlusion, temporary disappearance, or abrupt motion. The outputs of the two branches are integrated by a unified identity decision layer to produce stable tracking results. Experiments are conducted on the public 4th Anti-UAV Benchmark Track-3 and our newly constructed Anti-UAV Multi-Object Tracking dataset, AU-MOT. On the 4th Anti-UAV Benchmark Track-3, our method achieves 63.6% HOTA and 64.1% IDF1, outperforming the strongest competing method by 3.5% and 3.9%, respectively, while reducing identity switches and track fragments by 20.8% and 23.8%. On AU-MOT, it achieves 67.2% HOTA and 67.8% IDF1, with 20.2% fewer identity switches and 22.3% fewer track fragments. These results demonstrate its effectiveness under long-range observation, weak target appearance, cluttered backgrounds, abrupt motion, and intermittent target visibility. Full article

We present a novel method for deriving radially resolved dynamical chronometers from galaxy rotation curves, allowing galaxy assembly histories to be reconstructed directly from kinematic data. In the Nexus Paradigm, the baryonic Tully–Fisher relation is used to estimate the dynamical mass profile. We compare this profile with independently derived intrinsic baryonic mass distributions obtained from stellar Sérsic fits and gas surface-density measurement yields. This yields a radial ratio that maps to formation redshift with radial resolution. Inverting this ratio within a standard cosmological framework produces a radial lookback-time profile, representing the time since each radial shell last experienced dynamical reconfiguration. Applying the method to a pilot sample of seven SPARC galaxies, including both high- and low-surface-brightness systems as well as the Milky Way, reveals diverse age structures: stratified profiles associated with inside-out growth and flatter profiles consistent with coherent disk assembly. The method requires no dark-matter halo fitting and offers a kinematic chronometer that complements stellar population and chemical evolution approaches. The NP rotation-curve parameters were determined by minimizing the chi-squared statistic between the observed and predicted velocities using a two-stage optimization consisting of a global differential-evolution search followed by nonlinear least-squares refinement. Observational uncertainties were taken from the published rotation-curve data, supplemented by a 5 km s⁻¹ systematic error floor added in quadrature to account for non-circular motions and other unresolved systematics. We also show that the governing dynamical equation admits a gravitoelectromagnetic interpretation, in which a velocity-dependent term generates disk-wide torques that regulate angular momentum transport. This leads to a unified stability framework in which galaxy morphology emerges from a single parameter regime: balanced conditions favor a coherent spiral structure, whereas dynamically hot regimes naturally produce diffuse and ultra-faint systems. The cosmological scaling of the effective gravitomagnetic field further suggests that the spiral structure is partly regulated by cosmic time. Although the inferred ages depend on the accuracy of the baryonic mass reconstruction and on the local validity of the evolving baryonic Tully–Fisher relation, our results show that rotation curves encode time-resolved dynamical information. This establishes the radial dynamical chronometer as a new observable for studying galaxy evolution and testing gravitational frameworks. Full article