Search Results (1,027)

Motion capture (MoCap) data derived from wearable Inertial Measurement Units is essential to applications in sports science and healthcare robotics. However, a significant amount of the potential of this data is limited due to missing data derived from sensor limitations, network issues, and environmental interference. Such limitations can introduce bias, prevent the fusion of critical data streams, and ultimately compromise the integrity of human activity analysis. Despite the plethora of data imputation techniques available, there have been few systematic performance evaluations of these techniques explicitly for the time series data of IMU-derived MoCap data. We address this by evaluating the imputation performance across three distinct contexts: univariate time series, multivariate across players, and multivariate across kinematic angles. To address this limitation, we propose a systematic comparative analysis of imputation techniques, including statistical, machine learning, and deep learning techniques, in this paper. We also introduce the first publicly available MoCap dataset specifically for the purpose of benchmarking missing value imputation, with three missingness mechanisms: missing completely at random, block missingness, and a simulated value-dependent missingness pattern simulated at the signal transition points. Using data from 53 karate practitioners performing standardized movements, we artificially generated missing values to create controlled experimental conditions. We performed experiments across the 53 subjects with 39 kinematic variables, which showed that discriminating between univariate and multivariate imputation frameworks demonstrates that multivariate imputation frameworks surpassunivariate approaches when working with more complex missingness mechanisms. Specifically, multivariate approaches achieved up to a 50% error reduction (with the MAE improving from 10.8 ± 6.9 to 5.8 ± 5.5) compared to univariate methods for transition point missingness. Specialized time series deep learning models (i.e., SAITS, BRITS, GRU-D) demonstrated a superior performance with MAE values consistently below 8.0 for univariate contexts and below 3.2 for multivariate contexts across all missing data percentages, significantly surpassing traditional machine learning and statistical methods. Notable traditional methods such as Generative Adversarial Imputation Networks and Iterative Imputers exhibited a competitive performance but remained less stable than the specialized temporal models. This work offers an important baseline for future studies, in addition to recommendations for researchers looking to increase the accuracy and robustness of MoCap data analysis, as well as integrity and trustworthiness. Full article

This article presents a principled framework for selecting and tuning classical computer vision algorithms in the context of optical displacement sensing. By isolating key factors that affect algorithm behavior—such as feed window size and motion step size—the study seeks to move beyond intuition-based practices and provide rigorous, repeatable performance evaluations. Computer vision-based optical odometry sensors offer non-contact, high-precision measurement capabilities essential for modern metrology and robotics applications. This paper presents a systematic comparative analysis of three classical tracking algorithms—phase correlation, template matching, and optical flow—for 2D surface displacement measurement using synthetic image sequences with subpixel-accurate ground truth. A virtual camera system generates controlled test conditions using a multi-circle trajectory pattern, enabling systematic evaluation of tracking performance using 400 × 400 and 200 × 200 pixel feed windows. The systematic characterization enables informed algorithm selection based on specific application requirements rather than empirical trial-and-error approaches. Full article

This cross-sectional study analyzed the lower limb (LL) behavior in terms of gait asymmetry and joints’ kinematic parameters, comparing people with multiple sclerosis (pwMS) and unaffected individuals. Methods: Data from 15 patients, EDSS ≤ 4.5, and 15 healthy control volunteers were gathered. The VICON Motion Capture System (14 infrared cameras), NEXUS software, Plug-in–Gait skeleton model and reflective markers were used to collect data for each subject during five gait cycles on a plane surface. Biomechanical analysis included evaluation of LL joints’ range of motion (ROM) bilaterally, as well as movement symmetry. Results: Comparative biomechanical analysis revealed a hierarchy of vulnerability between the groups: the ankle is the most affected joint in pwMS (p = 0.008–0.014), the knee is moderately affected (p = 0.015 in swing phase), and the hip is the least affected (p > 0.05 in all phases). The swing phase showed the most significant left–right asymmetry impairment, as reflected by root mean square error (RMSE) values: swing-phase RMSE = 9.306 ± 4.635 (higher and more variable) versus stance-phase RMSE = 6.363 ± 2.306 (lower and more consistent). Conclusions: MS does not affect the joints structurally; rather, it eliminates the ability to differentiate the fine-tuning control between them. The absence of significant left–right joint asymmetry differences during complete gait cycle indicates dysfunction in the global motor control. Full article

Hydrogen-bond-like M···H–C interactions in square-planar d⁸ metal complexes have recently gained attention as structure-directing elements and design motifs in asymmetric catalysis. In this study, we explore these weak interactions not as static features, but as key modulators of molecular motion. We synthesized four cis-[N,N′-pentamethylenebis(iminomethylazolato)]M(II) (M = Pt, Pd), including iminomethyl-2-imidazole, iminomethyl-5-imidazole, and iminomethylpyrrolato Pt(II) complexes and an iminomethylpyrrolato Pd(II) analog. All complexes display reversible flipping of the alkylene bridge across the coordination plane, with the M···H–C interaction alternately engaging from above or below. This dynamic motion was characterized by variable-temperature ¹H NMR spectroscopy, revealing activation parameters for the flipping process. X-ray crystallography confirmed geometries consistent with hydrogen-bond-like interactions, while NBO analysis based on DFT calculations provided insight into their electronic nature. Interestingly, although Pt and Pd display comparable M···H–C distances, solvent effects dominate the flipping kinetics over metal identity. These findings highlight the role of hydrogen-bond-like M···H–C interactions not only in structural stabilization, but also in regulating conformational dynamics. Full article

This paper addresses the issue of rotor swings in a high-power synchronous generator during stable operation with a stiff power grid. The analysis of electromechanical swings was conducted using a circuit model incorporating fractional-order derivatives. Assuming that variations in the load angle under small disturbances from a stable equilibrium are minor, a linearized differential equation describing the electrodynamic state of the synchronous machine was derived. Based on this linearized equation of motion and the identified parameters of the equivalent circuit, calculations were performed for a 200 MW turbogenerator. The results indicate that the electromechanical swings are characterized by a constant pulsation and a low damping factor. Calculations were also carried out using a lumped-parameter equivalent circuit model. Based on the obtained results, it can be stated that the fractional-order model provides a more accurate fit of the frequency characteristics compared with the classical model with the same number of rotor equivalent circuits. The relative approximation errors for the fractional-order model are, for the d-axis (one rotor equivalent circuit), relative magnitude error δ_m = 1.53% and relative phase error δ_φ = 6.32%, and for the q-axis (two rotor equivalent circuits), δ_m = 3.2% and δ_φ = 8.3%. To achieve comparable approximation accuracy for the classical model, the rotor electrical circuit must be replaced with two equivalent circuits in the d-axis and four equivalent circuits in the q-axis, yielding relative errors of δ_m = 2.85% and δ_φ = 6.51% for the d-axis, and δ_m = 1.86% and δ_φ = 5.49% for the q-axis. Full article

For Linear Position Transducers (LPTs) to represent an ideal tool for velocity-based training, it needs to be both valid and reliable. Multiple studies assessed the reliability of LPT yet wrongfully incorporated biological variability. Moreover, all studies investigating validity conclude a negative impact of horizontal displacement, therefore constraining LPT use to solely multi-joint movement. The objectives were to assess the validity and the reliability of (1) the Tendo Sport LPT in a linear setting presenting almost no biological variability, and (2) an equation allowing the analysis of angular movement. (1) A weight of 10 kg was dropped vertically 100 times and both time and position measurement from the LPT were compared to motion equation. (2) Angular movements were performed first with a bike wheel and then by a human shoulder. The angles estimated with the equation, from LPT output, were compared to the angle measured from 3D motion capture. In the linear settings, bias, ULOA and LLOA are, respectively, equal to −0.008 s, +0.012 s and −0.016 s if errors come solely from the time measurement and 0.011 m, 0.029 m and −0.025 m if errors come solely from the distance. It is likely that error could come from both the time and the distance measurements. In the angular settings, the bike wheel condition yields excellent reliability (ICC = 0.9999) and good validity (RMSD = 9.12°), while the shoulder condition yields high validity (RMSD = 2.49°). LPT can be used to investigate angular kinematics in certain conditions described in this article and yield valid, reliable results for angles below 120°. Full article

Reliable navigation for mobile robots in dynamic, human-populated environments remains a significant challenge, as moving objects often cause localization drift and map corruption. While Simultaneous Localization and Mapping (SLAM) techniques excel in static settings, issues like keyframe redundancy and optimization inefficiencies further hinder their practical deployment on robotic platforms. To address these challenges, we propose DKB-SLAM, a real-time RGB-D visual SLAM system specifically designed to enhance robotic autonomy in complex dynamic scenes. DKB-SLAM integrates optical flow with Gaussian-based depth distribution analysis within YOLO detection frames to efficiently filter dynamic points, crucial for maintaining accurate pose estimates for the robot. An adaptive keyframe selection strategy balances map density and information integrity using a sliding window, considering the robot’s motion dynamics through parallax, visibility, and matching quality. Furthermore, a heterogeneously weighted local bundle adjustment (BA) method leverages map point geometry, assigning higher weights to stable edge points to refine the robot’s trajectory. Evaluations on the TUM RGB-D benchmark and, crucially, on a mobile robot platform in real-world dynamic scenarios, demonstrate that DKB-SLAM outperforms state-of-the-art methods, providing a robust and efficient solution for high-precision robot localization and mapping in dynamic environments. Full article

The fast and accurate calculation of the internal temperature rise in the oil-immersed transformer is the premise to realize the thermal health management and load energy evaluation of the in-service transformer. In view of the influence of nanofluids on the heat transfer process of transformer, a numerical simulation algorithm based on lattice Boltzmann method (LBM) and finite difference method (FDM) is proposed to study the heat and mass transfer process inside nano-modified oil-immersed transformer. Firstly, the D2Q9 lattice model is used to solve the fluid and thermal lattice Boltzmann equations inside the oil-immersed transformer at the mesoscopic scale, and the temperature field and velocity field are obtained by macroscopic transformation. Secondly, the electric field distribution inside the oil-immersed transformer is calculated by FDM. The viscous resistance in LBM analysis and the electric field force in FDM analysis, as well as the gravity and buoyancy of particles, are used to explore the motion characteristics of nanoparticles and metal particles. Finally, compared with the thermal ring method and the finite volume method (FVM), the relative error is less than 5%, which verifies the effectiveness of the numerical model and provides a method for studying the internal electrothermal convection of nano-modified oil-immersed transformers. Full article

Due to the complexity of impurity removal from the residual film, there is currently no better impurity removal equipment. To improve the screening performance of the residual film mixture, the vibrating screen was designed. In this paper, the key factors A, B, C, and D were identified through mechanical analysis of the mixture (where they represented the screen aperture diameter, vibration amplitude, vibration frequency, and screen mesh inclination angle, respectively). The soil screen rate (Y₁) and screening loss rate (Y₂) were evaluated. And the optimal ranges for these factors were determined by single-factor experiments. Based on the EDEM, the discrete element model was established to simulate the interaction between residual film and soil. And the motion characteristics of the residual film mixture were analyzed within the screen body through a combination of simulation and bench tests. The vibrating screen’s structural parameters were optimized using Box-Behnken experiments. The most suitable combination of settings was as shown below: A = 6.5 mm, B = 25 mm, C = 3.8 Hz, and D = 4°. Following the optimization of these parameters, the screening performance was optimized. Results of bench tests showed that the soil screening rate was 80.33% and the screening loss rate was 19.31%. This study was expected to offer theoretical and simulation-based methods for optimizing the parameters of residual film-soil vibrating screening devices. Full article

Photogrammetry-based 3D reconstruction of the shape of fast-moving objects from image sequences presents a complex yet increasingly important challenge. The 3D reconstruction of a large number of fast-moving objects may, for instance, be of high importance in the study of dynamic phenomena such as impact experiments and explosions. In this context, analyzing the 3D shape, size, and motion trajectory of the resulting fragments provides valuable insights into the underlying physical processes, including energy dissipation and material failure. High-speed cameras are typically employed to capture the motion of the resulting fragments. The high cost, the complexity of synchronizing multiple units, and lab conditions often limit the number of high-speed cameras that can be practically deployed in experimental setups. In some cases, only a single high-speed camera will be available or can be used. Challenges such as overlapping fragments, shadows, and dust often complicate tracking and degrade reconstruction quality. These challenges highlight the need for advanced 3D reconstruction techniques capable of handling incomplete, noisy, and occluded data to enable accurate analysis under such extreme conditions. In this paper, we use a combination of photogrammetry, computer vision, and artificial intelligence techniques in order to improve feature detection of moving objects and to enable more robust trajectory and 3D shape reconstruction in complex, real-world scenarios. The focus of this paper is on achieving accurate 3D shape estimation and motion tracking of dynamic objects generated by impact loading using stereo- or monoscopic high-speed cameras. Depending on the object’s rotational behavior and the number of available cameras, two methods are presented, both enabling the successful 3D reconstruction of fragment shapes and motion. Full article

This research aims to develop a novel technique to acquire a large amount of high-density, high-precision 3D point cloud data for plant phenotyping using photogrammetry technology. The complexity of plant structures, characterized by overlapping thin parts such as leaves and stems, makes it difficult to reconstruct accurate 3D point clouds. One challenge in this regard is occlusion, where points in the 3D point cloud cannot be obtained due to overlapping parts, preventing accurate point capture. Another is the generation of erroneous points in non-existent locations due to image-matching errors along object outlines. To overcome these challenges, we propose a 3D point cloud reconstruction method named closed-loop coarse-to-fine method with multi-masked matching (CLCFM3). This method repeatedly executes a process that generates point clouds locally to suppress occlusion (multi-matching) and a process that removes noise points using a mask image (masked matching). Furthermore, we propose the closed-loop coarse-to-fine method (CLCFM) to improve the accuracy of structure from motion, which is essential for implementing the proposed point cloud reconstruction method. CLCFM solves loop closure by performing coarse-to-fine camera position estimation. By facilitating the acquisition of high-density, high-precision 3D data on a large number of plant bodies, as is necessary for research activities, this approach is expected to enable comparative analysis of visible phenotypes in the growth process of a wide range of plant species based on 3D information. Full article

(1) Background: Soccer action recognition (SAR) is essential in modern sports analytics, supporting automated performance evaluation, tactical strategy analysis, and detailed player behavior modeling. Although recent advances in deep learning and computer vision have enhanced SAR capabilities, many existing methods remain limited to coarse-grained classifications, grouping actions into broad categories such as attacking, defending, or goalkeeping. These models often fall short in capturing fine-grained distinctions, contextual nuances, and long-range temporal dependencies. Transformer-based approaches offer potential improvements but are typically constrained by the need for large-scale datasets and high computational demands, limiting their practical applicability. Moreover, current SAR systems frequently encounter difficulties in handling occlusions, background clutter, and variable camera angles, which contribute to misclassifications and reduced accuracy. (2) Methods: To overcome these challenges, we propose TAT-SARNet, a structured framework designed for accurate and fine-grained SAR. The model begins by applying Sparse Dilated Attention (SDA) to emphasize relevant spatial dependencies while mitigating background noise. Refined spatial features are then processed through the Split-Stream Feature Processing Module (SSFPM), which separately extracts appearance-based (RGB) and motion-based (optical flow) features using ResNet and 3D CNNs. These features are temporally refined by the Multi-Granular Temporal Processing (MGTP) module, which integrates ResIncept Patch Consolidation (RIPC) and Progressive Scale Construction Module (PSCM) to capture both short- and long-range temporal patterns. The output is then fused via the Context-Guided Dual Transformer (CGDT), which models spatiotemporal interactions through a Bi-Transformer Connector (BTC) and Channel–Spatial Attention Block (CSAB); (3) Results: Finally, the Cascaded Temporal Classification (CTC) module maps these features to fine-grained action categories, enabling robust recognition even under challenging conditions such as occlusions and rapid movements. (4) Conclusions: This end-to-end architecture ensures high precision in complex real-world soccer scenarios. Full article

The peregrine falcon, known as the fastest bird in the world, has been studied for its ability to stabilize during high-speed dives, a capability attributed to the configuration of its dorsal feathers. These feathers have inspired the design of vortex generators devices that promote controlled turbulence to delay boundary layer separation on aircraft wings and turbine blades. This study presents an experimental wind tunnel investigation of a bio-inspired peregrine falcon prototype, equipped with movable artificial feathers, a hot-wire anemometer, and a 3D accelerometer. Wake velocity profiles measured behind the prototype revealed fluctuations associated with feather motion. Spectral analysis of the velocity signals, recorded with oscillating feathers at a wind tunnel speed of 10 m/s, showed attenuation of specific frequency components, suggesting that feather dynamics may help mitigate wake fluctuations induced by structural vibrations. Three-dimensional acceleration measurements indicated that prototype vibrations remained below 1 g, with peak differences along the X and Z axes ranging from −0.06 g to 0.06 g, demonstrating the sensitivity of the vibration sensing system. Root Mean Square (RMS) values of velocity signals increased with wind tunnel speed but decreased as the feather inclination angle rose. When the mean value was subtracted from the signal, higher RMS variability was observed, reflecting increased flow disturbance from feather movement. Fast Fourier Transform (FFT) analysis revealed that, for fixed feather angles, spectral magnitudes increased uniformly with wind speed. In contrast, dynamic feather oscillation produced distinctive frequency peaks, highlighting the feather’s influence on the wake structure in the frequency domain. To complement the experimental findings, 3D CFD simulations were conducted on two HAWT-type wind turbines—one with bio-inspired vortex generators and one without. The simulations showed a significant reduction in turbulent kinetic energy contours in the wake of the modified turbine, particularly in the Y-Z plane, compared to the baseline configuration. Full article

In this paper, we consider the parameter estimation for the fractional Black–Scholes model of the form $S_t^H=S_0^H+\mu {\int }_0^t{S}_s^H\mathrm{d}s+\sigma {\int }_0^t{S}_s^H\mathrm{d}{B}_s^H,$ where $\sigma >0$ and $\mu \in \mathbb{R}$ are the parameters to be estimated. Here, $B^H=\{B_t^H,t\ge 0\}$ denotes a fractional Brownian motion with Hurst index $0<H<1$. Using the quasi-likelihood method, we estimate the parameters $\mu$ and $\sigma$ based on observations taken at discrete time points $\{t_i=ih,i=0,1,2,\dots ,n\}$. Under the conditions $h=h\left(n\right)\to 0$, $nh\to \infty$, and $h^{1+\gamma }n\to 1$ for some $\gamma >0$, as $n\to \infty$, the asymptotic properties of the quasi-likelihood estimators are established. The analysis further reveals how the convergence rate of $n{h}^{1+\gamma }-1$ approaching zero affects the accuracy of estimation. To validate the effectiveness of our method, we conduct numerical simulations using real-world stock market data, demonstrating the practical applicability of the proposed estimation framework. Full article

Additive manufacturing (AM) is gaining prominence in architecture, engineering, and construction (AEC). Within this context, robotic additive manufacturing (RAM) has emerged as a promising solution, offering enhanced flexibility and motion control for fabricating complex geometries and performing on-site production. However, it also introduces new, complex manufacturing processes that impact the design, making the control of manufacturing variables important for achieving accurate and feasible architectural results. In this sense, this study presents a systematic review of the state of the art in RAM for AEC, with a focus on extrusion-based 3D printing using flexible robotic arms and materials such as thermoplastics and paste-based mixtures (cementitious and earth-based compositions). This review includes 142 peer-reviewed journal and conference papers published between 2014 and 2025. It maps key research subfields, geographic trends, and RAM technology evolution, complemented by a bibliometric analysis of co-authorship and keyword networks. This review identifies four key areas of research: process, design, materials, and equipment. Most studies come from North America, Europe, and Asia, with clay emerging as a material receiving growing attention in construction within the RAM field. However, challenges like scalability, programming complexity, and AI integration still limit broader implementation. Full article