Search Results (3,730)

Understanding human movement in industrial environments requires more than simple step counts—it demands contextual information to interpret activities and enhance workflows. Key factors such as location and process context are essential. However, research on context-sensitive human activity recognition is limited by the lack of publicly available datasets that include both human movement and contextual labels. Our work introduces the DaRA dataset to address this research gap. DaRA comprises over 109 h of video footage, including 32 h from wearable first-person cameras and 77 h from fixed third-person cameras. In a laboratory environment replicating a realistic warehouse, scenarios such as order picking, packaging, unpacking, and storage were captured. The movements of 18 subjects were captured using inertial measurement units, Bluetooth devices for indoor localization, wearable first-person cameras, and fixed third-person cameras. DaRA offers detailed annotations with 12 class categories and 207 class labels covering human movements and contextual information such as process steps and locations. A total of 15 annotators and 8 revisers contributed over 1572 h in annotation and 361 h in revision. High label quality is reflected in Light’s Kappa values ranging from $78.27$% to $99.88$%. Therefore, DaRA provides a robust, multimodal foundation for human activity and context recognition in industrial settings. Full article

Background: Feigned restriction of shoulder joint movement for secondary gain is clinically relevant and may misdirect care, distort disability determinations, and inflate system costs. Distinguishing feigning from structural pathology and from functional or psychosocial presentations is difficult because pain is subjective, performance varies, and no single sign or test is definitive. This comprehensive review hypothesizes that the systematic integration of clinical examination, objective biomechanical and neurophysiological testing, and emerging technologies can substantially improve detection accuracy and provide defensible medicolegal documentation. Methods: PubMed and reference lists were searched within a prespecified time frame (primarily 2015–2025, with foundational earlier works included when conceptually essential) using terms related to shoulder movement restriction, malingering/feigning, symptom validity, effort testing, functional assessment, and secondary gain. Evidence was synthesized narratively, emphasizing objective or semi-objective quantification of motion and effort (goniometry, dynamometry, electrodiagnostics, kinematic sensing, and imaging). Results: Detection is best approached as a stepwise, multidimensional evaluation. First-line clinical assessment focuses on reproducible incongruence: non-anatomic patterns, internal inconsistencies, distraction-related improvement, and mismatch between claimed disability and observed function. Repeated examinations and documentation strengthen inference. Instrumented strength testing improves quantification beyond manual testing but remains effort-dependent; repeat-trial variability and atypical agonist–antagonist co-activation can indicate submaximal performance without proving intent. Imaging primarily tests plausibility by confirming lesions or highlighting discordance between claimed limitation and minimal pathology, while recognizing that normal imaging does not exclude pain. Diagnostic anesthetic injections and electrodiagnostics can clarify pain-mediated restriction or exclude neuropathic weakness but require cautious interpretation. Motion capture and inertial sensors can document compensatory strategies and context-dependent normalization, yet validated standalone thresholds are limited. Conclusions: Feigned shoulder impairment cannot be confirmed by any single test. The desirable strategy combines structured assessment of inconsistencies with objective biomechanical and neurophysiologic measurements, interpreted within the whole clinical context and rigorously documented; however, prospective validation is still needed before routine implementation. Full article

This paper presents a virtual reference-based preview semi-active suspension system using a Magneto-Rheological (MR) damper to improve ride comfort when traversing bumps. The algorithm is designed to track the virtual reference profile of the vehicle’s corner by introducing a Model Predictive Control (MPC) method while considering the passivity of the MR damper. The proposed MPC is formulated to rely solely on estimable variables from an Inertial Measurement Unit (IMU) and vertical accelerometer. To support implementation on an Electronic Control Unit (ECU), the suspension state estimator employs a simple band-limited filtering structure. The proposed method is evaluated in simulation and achieves performance comparable to a controller that has accurate prior knowledge of the road profile. In addition, simulation results demonstrate that the proposed approach exhibits low sensitivity to sensor noise and bump perception uncertainty, making it well suited for real-world vehicle applications. Full article

To address the urgent need for high-fidelity motion data for validating navigation algorithms for Unmanned Underwater Vehicles (UUVs), this paper proposes a data generation method based on a parametric motion model. First, based on the principles of rigid body dynamics and fluid mechanics, a decoupled six-degrees-of-freedom (6-DOF) Linear and Angular Acceleration Vector (LAAV) model is constructed, establishing a dynamic mapping relationship between the rudder angle and speed setting commands and motion acceleration. Second, a segmentation–identification framework is proposed for three-dimensional trajectory segmentation, integrating Gaussian Process Regression and Ordering Points To Identify the Clustering Structure (GPR-OPTICS), along with a Dynamic Immune Genetic Algorithm (DIGA). This framework utilizes real vessel data to achieve motion segment clustering and parameter identification, completing the construction of the LAAV model. On this basis, by introducing sensor error models, highly credible Inertial Measurement Unit (IMU) data are generated, and a complete attitude, velocity, and position (AVP) motion sequence is obtained through an inertial navigation solution. Experiments demonstrate that the AVP data generated by our method achieve over 88% reliability compared with the real vessel dataset. Furthermore, the proposed method outperforms the PSINS toolbox in both the reliability and accuracy of all motion parameters. These results validate the effectiveness and superiority of our proposed method, which provides a high-fidelity data benchmark for research on underwater navigation algorithms. Full article

The historical and conceptual foundations of General Relativity are revisited, putting the main focus on the physical meaning of the invariant $d{s}^2$, the Equivalence Principle, and the precise interpretation of spacetime geometry. It is argued that Albert Einstein initially sought a dynamical formulation in which $d{s}^2$ encoded the gravitational effects, without invoking curvature as a physical entity. The now more familiar geometrical interpretation—identifying gravitation with spacetime curvature—gradually emerged through his collaboration with Marcel Grossmann and the adoption of the Ricci tensor in 1915. Anyhow, in his 1920 Leiden lecture, Einstein explicitly reinterpreted spacetime geometry as the state of a physical medium—an “ether” endowed with metrical properties but devoid of mechanical substance—thereby actually rejecting geometry as an independent ontological reality. Building upon this mature view, gravitation is reconstructed from the Weak Equivalence Principle, understood as the exact compensation between inertial and gravitational forces acting on a body under a uniform gravitational field. From this fundamental principle, together with an extension of Fermat’s Principle to massive objects, the invariant $d{s}^2$ is obtained, first in the static case, where the gravitational potential modifies the flow of proper time. Then, by applying the Lorentz transformation to this static invariant, its general form is derived for the case of matter in motion. The resulting invariant reproduces the relativistic form of Newton’s second law in proper time and coincides with the weak-field limit of General Relativity in the harmonic gauge. This approach restores the operational meaning of Einstein’s theory: spacetime geometry represents dynamical relations between physical measurements, rather than the substance of spacetime itself. By deriving the gravitational modification of the invariant directly from the Weak Equivalence Principle, Fermat Principle and Lorentz invariance, this formulation clarifies the physical origin of the metric structure and resolves long-standing conceptual issues—such as the recurrent hole argument—while recovering all the empirical successes of General Relativity within a coherent and sound Machian framework. Full article

This study presents a standardized method for multimodal monitoring of exercise execution in isolated, confined, and extreme (ICE) environments, addressing the need for reproducible assessment of neuromuscular and cardiovascular responses under space- and equipment-limited conditions. The method integrates wearable surface electromyography (sEMG), inertial measurement units (IMU), and electrocardiography (ECG) to capture muscle activation, movement, and cardiac dynamics during space-efficient exercise. Ten exercises suitable for confined habitats were implemented during analog missions conducted in the DeepLabH03 facility, with feasibility evaluated in a seven-day campaign involving three adult participants. Signals were synchronized using video-verified repetition boundaries, sEMG was normalized to maximum voluntary contraction, and sEMG amplitude- and frequency-domain features were extracted alongside heart rate variability indices. The protocol enabled stable real-time data acquisition, reliable repetition-level segmentation, and consistent detection of muscle-specific activation patterns across exercises. While amplitude-based sEMG indices showed no uniform main effect of exercise, robust exercise-by-muscle interactions were observed, and sEMG mean frequency demonstrated sensitivity to differences in movement strategy. Cardiac measures showed limited condition-specific modulation, consistent with short exercise bouts and small sample size. As a proof-of-concept feasibility study, the proposed protocol provides a practical and reproducible framework for multimodal physiological monitoring of exercise in ICE analogs and other constrained environments, supporting future studies on exercise quality, training load, and adaptive feedback systems. The protocol is designed to support near-real-time monitoring and forms a technical basis for future exercise-quality feedback in confined habitats. Full article

Objectives: The purpose of the present study was to investigate the acute effects of cold-water immersion (CWI), cryotherapy (CRT), and intermittent pneumatic compression (IPC) on lower-body neuromuscular performance in female basketball players. Methods: Eighteen athletes volunteered to participate (body mass = 63.0 ± 7.2 kg; height = 171.4 ± 6.5 cm; age = 16.4 ± 1.2 years), completing testing at three time points: (i) pre-practice, (ii) post-practice, and (iii) 45–60 min following a randomly assigned recovery intervention. At each time point, athletes performed three countermovement vertical jumps on a dual force plate system sampling at 1000 Hz (VALD Performance). To standardize external load across groups, all players wore inertial measurement units (Kinexon). Results: The two-way repeated measures ANOVA showed no statistically significant interaction (p > 0.05) between the three testing time points and recovery modalities for any of the analyzed variables. However, a significant main effect of time was observed, with 13 of 20 force-time metrics (65%), including jump height, reactive strength index-modified, contraction time, and concentric peak and mean force, declining post-recovery compared with pre-practice values, regardless of the recovery intervention applied. External load measures (e.g., total distance, number of jumps) remained consistent across groups. Conclusions: Overall, these findings suggest that CWI, CRT, and IPC were no more effective than passive recovery (i.e., control group) in mitigating post-practice declines in lower-body force and power-producing capacities. Full article

Aiming to address the safety and stability issues caused by typical faults of Unmanned Aerial Vehicle (UAV) navigation sensors, a novel fault-tolerant method is proposed, which can capture the temporal dependencies of fault feature evolution, and complete the classification, prediction, and data reconstruction of fault data. In this fault-tolerant method, the feature extraction module adopts the FNKPCA method—integrating the Frobenius Norm (F-norm) with Kernel Principal Component Analysis (KPCA)—to optimize the kernel function’s ability to capture signal features, and enhance the system reliability. By combining FNKPCA with Support Vector Machine (SVM) and Bidirectional Long Short-Term Memory (BiLSTM), an active fault-tolerant processing method, namely FNKPCA–SVM–BiLSTM, is obtained. This study conducts comparative experiments on public datasets, and verifies the effectiveness of the proposed method under different fault states. The proposed approach has the following advantages: (1) It achieves a detection accuracy of 98.64% for sensor faults, with an average false alarm rate of only 0.15% and an average missed detection rate of 1.16%, demonstrating excellent detection performance. (2) Compared with the Long Short-Term Memory (LSTM)-based method, the proposed fault-tolerant method can reduce the RMSE metrics of Global Positioning System (GPS), Inertial Measurement Unit (IMU), and Ultra-Wide-Band (UWB) sensors by 77.80%, 14.30%, and 75.00%, respectively, exhibiting a significant fault-tolerant effect. Full article

Indoor bouldering is a popular and rapidly growing sport in which climbers fall repeatedly from walls up to 4–5 m high, making lower-limb injuries common. It is therefore essential to understand fall kinematics and impact conditions, yet fall kinematics remain poorly documented because laboratory motion capture is impractical in gyms. This study aimed to validate a markerless multi-camera pipeline (Pose2Sim) against a 2D video annotation tool (Kinovea) for displacement and velocity measurement, and against IMUs for peak acceleration. Ten teenage athletes (3 males, 7 females; 14–17 years) performed 40 falls recorded with five cameras (GoPro HERO12, USA, 2.7 K, 240 fps) and three IMUs (Blue Trident, Vicon, UK; ±200 g, 1600 Hz). Cut-off frequencies were set using Yu’s method (13 Hz for video, 39 Hz for IMUs). Pose2Sim’s results closely matched those of Kinovea for fall height and peak velocity with non-significant differences but underestimated peak acceleration. At the forehead, no significant difference was found, likely due to smaller accelerations at the head. Markerless video analysis is appropriate for studying fall kinematics and typology in indoor bouldering. IMUs remain necessary to quantify impact intensity, and future work should explore the combination of both IMUs and video to overcome this limitation. Full article

The development of digital twins for railway freight vehicles represents a key step toward more efficient, data-driven maintenance and safety assessment. This study focuses on the creation of a digital twin of the T3000 articulated freight wagon, one of the most widespread intermodal transport solutions in Europe. Despite its relevance, the dynamic behavior of this vehicle type has been scarcely investigated so far in scientific literature. A dedicated onboard measurement layout was defined to enable comprehensive monitoring of vehicle dynamics and the interactions between adjacent wagons within the train. The experimental setup integrates inertial sensors and a 3D vision system, allowing for detailed analysis of both rigid-body and vibrational responses under real operating conditions. A high-fidelity multibody model of the articulated wagon was developed and tuned using the acquired data, achieving optimal agreement with experimental measurements in both straight and curved track segments. The resulting model constitutes a reliable and scalable digital twin of the T3000 wagon, suitable for predictive simulations and virtual testing. Future developments will focus on a deeper investigation of the buffer interaction through an additional experimental campaign, further extending the digital twin’s capability to represent the full dynamic behavior of articulated freight trains. Full article

In the realm of wearable technology, achieving robust continuous authentication requires balancing high security with the strict resource constraints of embedded platforms. Conventional machine learning approaches and deep learning-based biometrics often incur high computational costs, making them unsuitable for low-power edge devices. To address this challenge, we propose H-PPG, a lightweight authentication system that integrates photoplethysmography (PPG) and inertial measurement unit (IMU) signals for continuous user verification. Using Hyperdimensional Computing (HDC), a lightweight classification framework inspired by brain-like computing, H-PPG encodes user physiological and motion data into high-dimensional hypervectors that comprehensively represent individual identity, enabling robust, efficient and lightweight authentication. An adaptive learning process is employed to iteratively refine the user’s hypervector, allowing it to progressively capture discriminative information from physiological and behavioral samples. To further enhance identity representation, a dimension regeneration mechanism is introduced to maximize the information capacity of each dimension within the hypervector, ensuring that authentication accuracy is maintained under lightweight conditions. In addition, a user-defined security level scheme and an adaptive update strategy are proposed to ensure sustained authentication performance over prolonged usage. A wrist-worn prototype was developed to evaluate the effectiveness of the proposed approach and extensive experiments involving 15 participants were conducted under real-world conditions. The experimental results demonstrate that H-PPG achieves an average authentication accuracy of 93.5%. Compared to existing methods, H-PPG offers a lightweight and hardware-efficient solution suitable for resource-constrained wearable devices, highlighting its strong potential for integration into future smart wearable ecosystems. Full article

Background/Objectives: Falls represent a major health concern for stroke survivors, necessitating effective risk assessment tools. This study proposes the Instrumented Fall Risk Assessment (IFRA) scale, a novel screening tool derived from Instrumented Timed Up and Go (ITUG) test data, designed to capture mobility measures often missed by traditional scales. Methods: We employed a two-step machine learning approach to develop the IFRA scale: first, identifying predictive mobility features from ITUG data and, second, creating a stratification strategy to classify patients into low-, medium-, or high-fall-risk categories. This study included 142 participants, who were divided into training (including synthetic cases), validation, and testing sets (comprising 22 non-fallers and 10 fallers). IFRA’s performance was compared against traditional clinical scales (e.g., standard TUG and Mini-BESTest) using Fisher’s Exact test. Results: Machine learning analysis identified specific features as key predictors, namely vertical and medio-lateral acceleration, and angular velocity during walking and sit-to-walk transitions. IFRA demonstrated a statistically significant association with fall status (Fisher’s Exact test p = 0.004) and was the only scale to assign more than half of the actual fallers to the high-risk category, outperforming the comparative clinical scales in this dataset. Conclusions: This proof-of-concept study demonstrates IFRA’s potential as an automated, complementary approach for fall risk stratification in post-stroke patients. While IFRA shows promising discriminative capability, particularly for identifying high-risk individuals, these preliminary findings require validation in larger cohorts before clinical implementation. Full article

Background and Objectives: Asymmetries in body composition and movement patterns are common in professional basketball due to the sport’s repetitive and unilateral demands. While both structural and functional asymmetries have been independently studied, little is known about their interaction under real training conditions. The aim of this study was to compare structural asymmetries, obtained from bioelectrical impedance analysis, with functional asymmetries, measured through inertial devices in professional basketball players. Methods: Twenty-five male professional basketball players from two Spanish teams were monitored over a two-month period. Structural asymmetries were assessed via the TANITA MC-780MA multi-frequency analyzer, while functional asymmetries were quantified using WIMU Pro™ inertial units during 43 training sessions. Descriptive, correlational, and cluster analyses were performed, followed by linear mixed-effects models adjusted for individual random effects, with statistical significance set at p < 0.05. Results: Descriptive results revealed low overall fat mass and no relevant group-level asymmetries in muscle mass or functional variables, although fat mass asymmetry showed greater variability across players. Correlation analyses indicated weak and non-significant relationships between structural and functional asymmetries. Cluster analysis grouped muscle mass and functional asymmetries together, while fat mass asymmetry formed a distinct cluster. Linear mixed-effects models confirmed significant differences for muscle mass asymmetry and demonstrated high inter-individual variability. Conclusions: Structural and functional asymmetries behave independently, with muscle mass asymmetry showing greater variability and functional relevance. These findings highlight the need for individualized monitoring approaches integrating morphological and functional assessments to optimize performance and reduce injury risk in elite basketball players. Full article

Accurate heading estimation is crucial for the autonomous navigation of small-to-medium tractors. While dual-antenna GNSS systems offer precision, they face installation and safety challenges. Single-antenna GNSS integrated with a low-cost Strapdown Inertial Navigation System (SINS) presents a more adaptable solution but suffers from slow convergence and low accuracy of heading estimation in low-speed farmland operations. This study proposes an adaptive trajectory-constrained heading estimation method. A sliding-window adaptive extended Kalman filter (SWAEKF) was developed, incorporating a heading constraint model that utilizes the GNSS-derived trajectory angle. An enhanced Sage–Husa algorithm was employed for the adaptive estimation of the trajectory angle measurement variance. Furthermore, a covariance initialization strategy based on the variance of trajectory angle increments was implemented to accelerate convergence. Field tests demonstrated that the proposed method achieved rapid heading convergence (less than 10 s for straight lines and 14 s for curves) and high accuracy (RMS heading error below 0.15° for straight-line tracking and 0.25° for curved paths). Compared to a conventional adaptive EKF, the SWAEKF improved accuracy by 23% and reduced convergence time by 62%. The proposed algorithm effectively enhances the performance of GNSS/SINS integrated navigation for tractors in low-dynamic environments, meeting the requirements for autonomous navigation systems. Full article

High-precision calibration of inertial measurement units for automotive safety systems combines fixed automated chamber cycles with semi-manual loading, alignment, and transfer. Motion waste and ergonomic constraints can therefore dominate throughput and cycle time stability. This study redesigns a production calibration workstation using time-and-motion analysis, operator observation, and structured root-cause analysis based on the Ishikawa diagram and the five whys. Three interventions were implemented and validated with pre- and post-measurements: bundled handling that consolidates full-set transfers and reduces non-value-adding motions; a fixture and material handling redesign with a manual lifting aid to reduce physical load and enable reliable single-operator operation; and a modular workstation layout that supports the phased addition of chambers. Total cycle time decreased from 4475 s to 1230 s, a 72 percent reduction, and weekly output rose from 800 to 4500 units without additional staffing or significant automation investment. Overall equipment efficiency improved from 75.3 percent to 85.2 percent, while the quality rate remained at 98.8 percent. Full article