Search Results (160)

The header platform of a combine harvester is subjected to severe abrasive and corrosive wear from rice stalks and environmental factors, which significantly limits its service life and operational efficiency. Accurately predicting the complex distribution of this wear over time and across the platform’s surface, however, remains a significant challenge. This paper, for the first time, systematically establishes a quantitative mapping relationship from “material motion trajectory” to “component wear profile” and introduces a novel method for time-sequence wear validation based on corrosion color gradients, providing a complete research paradigm to address this challenge. To this end, an analytical model based on rigid-body dynamics was first developed to predict the motion trajectory of a single rice stalk. Subsequently, a full-scale Discrete Element (DEM) model of the header platform–flexible rice stalk system was constructed. This model simulated the complex flow process of the rice population with high fidelity and was used to analyze the influence of key operating parameters (spiral auger rotational speed, cutting width) on wear distribution. Finally, real-world wear data were obtained through in situ mapping of a header platform after long-term service (1300 h) and multi-period (0–1600 h) image analysis. Through a three-way quantitative comparison among the theoretical trajectory, simulated trajectory, and the actual wear profile, the results indicate that the simulated and theoretical trajectories are in good agreement in terms of their macroscopic trends (Mean Squared Error, MSE, ranging from 0.4 to 6.2); the simulated and actual wear profiles exhibit an extremely high degree of geometric similarity, with the simulated wear area showing a 95.1% match to the actual measured area (Edit Distance: 0.14; Hamming Distance: 1). This research not only confirms that the flow trajectory of rice is the determining factor for the wear distribution on the header platform but, more importantly, the developed analytical and numerical methods offer a robust theoretical basis and effective predictive tools for optimizing the wear resistance and predicting the service life of the header platform, thereby demonstrating significant engineering value. Full article

The standard motion analysis systems are limited to laboratory settings; therefore, an individual’s gait may not be realistic, as it is removed from the day-to-day environment in which people ambulate. The modern world and advanced technologies have driven portable, affordable, and wearable sensors for real-world gait assessment that can be used outside the laboratory and during day-to-day activities. Wearable sensors offer a promising solution; however, despite that, the reliability of many wearable systems, especially under unsupervised and real-world-like conditions, remains insufficiently validated. This study aimed to establish intra- and inter-rater reliability of the inertial sensors as a tool used in gait analysis in a quasi-real-world environment. Ninety-eight healthy participants (52% females) aged 19–33 years took part in this reliability study. The research procedures included two separate measurements of gait analysis at participants’ preferred walking speed, conducted by two raters assessing selected spatiotemporal parameters. The reliability was calculated using intraclass correlation coefficients (ICC), and the bias was assessed using the Bland–Altman method. The analysis of intraclass correlation coefficients (ICC) revealed excellent, or near-excellent, reliability for walking speed, cadence, and stride length between raters (ICC = 0.932–0.941, 0.950–0.957, and 0.916–0.938, respectively) and between measurements (ICC = 0.916–0.928, 0.934–0.938, and 0.888–0.906, respectively). Bland–Altman plots confirmed minimal systematic bias for both inter- and intra-rater assessments, with differences in walking speed below 0.038 km/h, cadence below 0.283 steps/min, and stride length below 0.827 cm. The examined sensors are reliable tools for walking speed, cadence, and stride length in a quasi-real-world environment gait assessment. Future studies should include gait analysis involving random path and direction changes, turns, uneven or slippery surfaces, and natural environments. Additionally, research should consider individuals ambulating with various walking aids, or those with unilateral disorders, such as stroke. Full article

Background. Clinical assessment of Parkinson’s disease (PD) is limited by high subjectivity and inter-rater variability. Markerless video analysis, namely Computer Vision (CV), offers objective and scalable characterization of motor signs. We systematically reviewed CV technologies suited for PD diagnosis, symptom monitoring, and treatment management. Methods. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, we searched PubMed for articles published between 1 January 1984 and 9 May 2025. We used the following search strategy: (“Parkinson Disease” [MeSH Terms] OR “parkinson’s disease” OR “parkinson disease”) AND (“computer vision” OR “video analysis” OR “pose estimation” OR “OpenPose” OR “DeepLabCut” OR “OpenFace” OR “YOLO” OR “MediaPipe” OR “markerless motion capture” OR “skeleton tracking”). Results. Out of 154 identified studies, 45 met eligibility criteria and were synthesized. Gait was assessed in 42% of studies, followed by bradykinesia items (17.7%). OpenPose and custom CV solutions were each used in 36% of studies, followed by MediaPipe (16%), DeepLabCut (9%), YOLO (4%). Across aims, CV pipelines consistently showed diagnostic discrimination and severity tracking aligned with expert ratings. Conclusions. CV non-invasively quantifies PD motor impairment, holding potential for objective diagnosis, longitudinal monitoring, and therapy response. Guidelines for standardized video-recording protocols and software usage are needed for real-world applications. Full article

Accidental opioid overdose and Sudden Unexpected Death in Epilepsy (SUDEP) represent major forms of preventable mortality, often involving sudden-onset catastrophic events that could be survivable with rapid detection and intervention. The current physiological monitoring technologies are potentially applicable, but face challenges, including complex setups, poor patient compliance, high costs, and uncertainty about community-based use. Paradoxically, simple clinical observation in supervised injection facilities has proven highly effective, suggesting observable changes in central body motion may be sufficient to detect life-threatening events. We describe a novel wearable biosensor for continuous central body motion monitoring, offering a potential early warning system for life-threatening events. The biosensor incorporates a low-power, triaxial MEMS accelerometer within a discreet, chest-worn device, enabling long-term monitoring with minimal user burden. Two system architectures are described: stored data for retrospective analysis/research, and an in-development system for real-time overdose detection and response. Early user research highlights the importance of accuracy, discretion, and trust for adoption among people who use opioids. The initial clinical data collection, including the OD-SEEN study, demonstrates feasibility for capturing motion data during real-world opioid use. This technology represents a promising advancement in non-invasive monitoring, with potential to improve the outcomes for at-risk populations with multiple health conditions. Full article

This paper presents the third-generation design of Bimu, a compact wearable inertial measurement unit (IMU) tailored for advanced human motion tracking. Building on prior iterations, Bimu R2 focuses on enhancing thermal stability, data integrity, and energy efficiency by integrating onboard memory, redesigning the power management system, and optimizing the communication interfaces. A detailed performance evaluation—including noise, bias, scale factor, power consumption, and drift—demonstrates the device’s reliability and readiness for deployment in real-world applications ranging from clinical gait analysis to high-speed motion capture. The improvements introduced offer valuable insights for researchers and engineers developing robust wearable sensing solutions. 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 Maxwell–Boltzmann (MB) distribution is important because it provides the statistical foundation for connecting microscopic particle motion to macroscopic gas properties by statistically describing molecular speeds and energies, making it essential for understanding and predicting the behavior of classical ideal gases. This study advances the statistical modeling of lifetime distributions by developing a comprehensive reliability analysis of the MB distribution under an improved adaptive progressive censoring framework. The proposed scheme strategically enhances experimental flexibility by dynamically adjusting censoring protocols, thereby preserving more information from test samples compared to conventional designs. Maximum likelihood estimation, interval estimation, and Bayesian inference are rigorously derived for the MB parameters, with asymptotic properties established to ensure methodological soundness. To address computational challenges, Markov chain Monte Carlo algorithms are employed for efficient Bayesian implementation. A detailed exploration of reliability measures—including hazard rate, mean residual life, and stress–strength models—demonstrates the MB distribution’s suitability for complex reliability settings. Extensive Monte Carlo simulations validate the efficiency and precision of the proposed inferential procedures, highlighting significant gains over traditional censoring approaches. Finally, the utility of the methodology is showcased through real-world applications to physics and engineering datasets, where the MB distribution coupled with such censoring yields superior predictive performance. This genuine examination is conducted through two datasets (including the failure times of aircraft windshields, capturing degradation under extreme environmental and operational stress, and mechanical component failure times) that represent recurrent challenges in industrial systems. This work contributes a unified statistical framework that broadens the applicability of the Maxwell–Boltzmann model in reliability contexts and provides practitioners with a powerful tool for decision making under censored data environments. Full article

Inertial Measurement Units (IMUs) offer promising alternatives to traditional motion capture systems, especially in real-world sports scenarios. Accurate foot contact detection (FCD) is crucial for biomechanical analysis, and since on-the-field force plates are unsuitable, IMU-based FCD algorithms have been extensively investigated. However, sports activities leading to musculoskeletal injuries are multidirectional and high-dynamics in nature and FCD algorithms, which have mostly been studied in gait analysis, might sensibly worsen performance. This systematic review (PROSPERO, ID: CRD420251010584) aimed to evaluate IMU-based FCD algorithms applied to high-dynamics sports tasks, identifying strengths, limitations, and areas for improvement. A multi-database search was conducted until May 2025. Studies were included if they applied IMU-based FCD algorithms in high-dynamic movements. In total, 37 studies evaluating 71 FCD algorithms were included. Most papers focused on running, with only 3 on cut manoeuvres. Almost all studies involved healthy individuals only, and foot linear acceleration was the most inspected FCD metric. FCD algorithms demonstrated high accuracy, though speed variation impacted performance in 23/37 studies. This review highlights the lack of validated IMU-based FCD algorithms for high-dynamic sports movements and emphasizes the need for improved methods to advance sports biomechanics testing in injury prevention. Full article

This article presents a unique study on the demolition process of a high-rise reinforced concrete building simulated using a methodology based on the Applied Element Method (AEM). Prior to the parametric analyses, the progressive collapse-based solid model was visually validated against real-world controlled demolition footage captured by both Unmanned Aerial Vehicles (UAVs) and fixed cameras, showing close agreement in building motion and debris dispersion patterns. In contrast to the Finite Element Method (FEM) model, the simulation is not blast-induced; it is instead developed on a column removal approach, which is widely adopted in progressive collapse assessments. Discussions related to the FEM model are provided as well. The parametric analysis is conducted in two stages. First, a constant removal sequence (removal of 4, 3, and 2 floors, respectively, in the first, second, and third axes) is applied to both 20- and 15-storey buildings under three time delays: 100 ms, 300 ms, and 500 ms. Based on these results, a 300 ms delay is identified as a suitable compromise for controlling debris dispersion, and this value is adopted for the subsequent analyses. In the second stage, three distinct removal sequences are examined on the 20-storey structure using the fixed 300 ms delay: Scenario 1 (4–3–2), Scenario 2 (12–8–6), and Scenario 3 (16–12–6). Among these, Scenario 3 yields the most compact horizontal debris spread. The findings indicate a strong correlation between the actual demolition behavior and the proposed model, demonstrating its capability to realistically capture complex structural failure mechanisms and provide practical guidance for optimizing controlled demolition strategies. 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

(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

Assessing the driver’s state in real time is a critical challenge in modern vehicle safety systems, as human factors account for the vast majority of traffic accidents. Heart rate (HR) is a key physiological indicator of the driver’s condition, yet contactless measurements in dynamic in-vehicle environments remain difficult due to motion artifacts, vibrations, and varying operational conditions. This paper presents a novel two-stage method for HR estimation using a commercial 60 GHz frequency-modulated continuous wave (FMCW) radar sensor, specifically designed and validated for in-vehicle applications. In the first stage, coarse HR estimation is performed using the discrete wavelet transform (DWT) and autoregressive (AR) spectral analysis. The second stage refines the estimate using an inverse application of the relevance vector machine (RVM) approach, leveraging a narrowed frequency window derived from Stage 1. Final HR estimates are stabilized through sequential Kalman filtering (SKF) across time segments. The system was implemented using an Infineon BGT60TR13C radar module installed in the sun visor of a passenger vehicle. Extensive data collection was conducted during real-world driving across diverse traffic scenarios. The results demonstrate robust HR estimations with an accuracy comparable to that of commercial wearable devices, validated against a Polar H10 chest strap. This method offers several advantages over prior work, including short measurement windows (5 s), operation under varying lighting and clothing conditions, and validation in realistic driving environments. In this sense, the method contributes to the field of biomimetics by transferring the biological principles of continuous vital sign perception to technical sensorics in the automotive domain. Future work will explore the fusion of sensors with visual methods and potential extension to heart rate variability (HRV) estimations to enhance driver monitoring systems (DMSs) further. 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

Accurate modeling of car-following behavior is essential for understanding traffic dynamics and enabling predictive control in intelligent transportation systems. This study presents a novel data-driven framework that combines information-theoretic input selection via conditional transfer entropy (CTE) with dynamic mode decomposition with control (DMDc) for identifying and forecasting car-following dynamics. In the first step, CTE is employed to identify the specific vehicles that exert directional influence on a given subject vehicle, thereby systematically determining the relevant control inputs for modeling its behavior. In the second step, DMDc is applied to estimate and predict the dynamics by reconstructing the closed-form expression of the dynamical system governing the subject vehicle’s motion. Unlike conventional machine learning models that typically seek a single generalized representation across all drivers, our framework develops individualized models that explicitly preserve driver heterogeneity. Using both synthetic data from multiple traffic models and real-world naturalistic driving datasets, we demonstrate that DMDc accurately captures nonlinear vehicle interactions and achieves high-fidelity short-term predictions. Analysis of the estimated system matrices reveals that DMDc naturally approximates kinematic relationships, further reinforcing its interpretability. Importantly, this is the first study to apply DMDc to model and predict car-following behavior using real-world driving data. The proposed framework offers a computationally efficient and interpretable tool for traffic behavior analysis, with potential applications in adaptive traffic control, autonomous vehicle planning, and human-driver modeling. Full article

Background/Objectives: Multiple sclerosis (MS) often leads to gait impairments, even in early stages, and can affect autonomy and quality of life. Traditional assessment methods, while widely used, have been criticized because they lack sensitivity to subtle gait changes. This scoping review aims to map the landscape of advanced gait analysis technologies—both wearable and non-wearable—and evaluate their application in detecting, characterizing, and monitoring possible gait dysfunction in individuals with MS. Methods: A systematic search was conducted across PubMed and Scopus databases for peer-reviewed studies published in the last decade. Inclusion criteria focused on original human research using technological tools for gait assessment in individuals with MS. Data from 113 eligible studies were extracted and categorized based on gait parameters, technologies used, study design, and clinical relevance. Results: Findings highlight a growing integration of advanced technologies such as inertial measurement units, 3D motion capture, pressure insoles, and smartphone-based tools. Studies primarily focused on spatiotemporal parameters, joint kinematics, gait variability, and coordination, with many reporting strong correlations to MS subtype, disability level, fatigue, fall risk, and cognitive load. Real-world and dual-task assessments emerged as key methodologies for detecting subtle motor and cognitive-motor impairments. Digital gait biomarkers, such as stride regularity, asymmetry, and dynamic stability demonstrated high potential for early detection and monitoring. Conclusions: Advanced gait analysis technologies can provide a multidimensional, sensitive, and ecologically valid approach to evaluating and detecting motor function in MS. Their clinical integration supports personalized rehabilitation, early diagnosis, and long-term disease monitoring. Future research should focus on standardizing metrics, validating digital biomarkers, and leveraging AI-driven analytics for real-time, patient-centered care. Full article