Search Results (4,739)

Accurate measurement of the 6-DOF motion responses of marine floating structures is essential for structural safety assessment and operational decision-making. To address the critical issues of integration drift in inertial navigation systems, susceptibility of GNSS to sea-surface multipath effects, and deployment complexity of binocular vision systems, this paper proposed a 6-DOF motion measurement method for floating structures based on monocular vision and natural feature point tracking. This method eliminates the reliance on artificial cooperative targets and auxiliary sensors, instead utilizing the inherent surface textures of the floating structures as feature sources. Stable feature point tracking is achieved through multi-strategy cascaded detection and the pyramidal KLT optical flow algorithm. RANSAC geometric consistency verification is introduced to eliminate outlier matches, retaining only identical physical points between two consecutive frames for motion estimation. In-plane translations and $RZ$ angle are extracted from the similarity transformation, while $RX$ and $RY$ angles are estimated using principal component analysis of the covariance matrix of the feature point set. The depth-direction displacement is linearly mapped from variations in the scale factor. Subsequently, two series of physical model tests under different conditions were conducted to validate the measurement accuracy and robustness of the proposed method on different floating structures. The results demonstrate that the proposed method can accurately capture the motion attitudes of floating structures, maintaining a consistently high inlier ratio exceeding 80% in regular waves and averaging 85.2% in irregular waves, with a reprojection error of less than 0.05 pixels. The NRMSE for the primary motion directions are all below 10%, and the dominant frequency errors are essentially zero. It offers advantages such as low cost, easy deployment, and strong robustness, thereby providing valuable technical support for field monitoring of marine floating structures. Full article

Indoor mobile robots equipped with low-cost and sparse sensors often suffer from limited vertical perception and dynamic residual artifacts in the final map. This paper presents a lightweight 2.5D simultaneous localization and mapping (SLAM) framework using a single-line laser distance sensor (LDS), time-of-flight (ToF) sensing, wheel odometry, and an inertial measurement unit (IMU). In this work, 2.5D refers to a 2D grid map with discretized vertical occupancy bins for each grid cell, rather than a full continuous 3D reconstruction. The system integrates multi-sensor synchronization, motion correction, error-state Kalman filter (ESKF)-based state estimation, normal distributions transform (NDT) registration, and pose graph optimization to reconstruct a pose-consistent global map. Based on this map, an offline dynamic refinement module estimates temporal voxel support across keyframes, extracts low-support candidate regions, and applies geometric clustering and isolated-point filtering to suppress transient residual artifacts while preserving stable structures. A 24-bit RGB occupancy encoding is further proposed to store the discretized vertical occupancy state in a compact three-channel image format. The proposed framework emphasizes system-level deployment value by combining sparse multi-sensor mapping, conservative offline refinement, and compact height-aware map export on a low-cost indoor robot platform. Experiments on public datasets, embedded hardware, and self-collected indoor sequences evaluate odometry reference performance, resource usage, platform-specific 2.5D mapping, dynamic refinement, and height-aware encoding. Full article

In Internet-of-Things (IoT)-based rehabilitation, patient data from wearable sensors (IMU, EMG, heart rate), clinical assessments, and surveys differ in format and granularity, complicating unified patient-state construction. Existing AI-based systems often omit fatigue level or rehabilitation stage, or they place large language models (LLMs) in the clinical decision-making role, exposing patients to hallucination risk. We propose an IoT-ready framework comprising three components: an input-source-independent context abstraction pipeline, a digital twin that simulates clinical-score trajectories, and an LLM Agent that interprets the outputs of a deterministic algorithm and a digital twin as (subject, predicate, object) Triplets to produce a retrieval-augmented clinical report. The algorithm—not the LLM—selects the 13-exercise sequence from Shoulder Pain and Disability Index (SPADI) item-level responses. Explicit per-layer schemas let IoT-sensor branches be added without changing the downstream interface. Here we evaluate the framework on the clinical-score path (three patient-reported outcome measures and six range-of-motion measures); the multi-modal IoT branches maintain deployment-target functionality. On 48 IRB-approved shoulder rehabilitation patients (144 longitudinal records; augmented to 7200 only to train the trajectory generator), real-only leave-one-subject-out evaluation gave VAS RMSE 0.658 (0–10) and SPADI RMSE 6.499 (0–100). Ablation across four surface forms revealed a fidelity–accuracy trade-off—Narrative highest on template fidelity (BERTScore F1 0.250), raw JSON highest on judge-rated accuracy—and the framework adopts the balanced-midpoint Triplet form. A claim-level audit of the Triplet-form reports left 22–31% of atomic claims unsupported (Claude Sonnet and GPT-4o judges) regardless of guideline retrieval. A raw-LLM control never reproduced the algorithm-defined sequence exactly (0 of 48), supporting deterministic, auditable sequence selection and the need for clinician review before clinical use. Full article

Real-time monitoring of operator fatigue is critical for ensuring operational safety and reliability in dynamic industrial environments, especially under swing conditions such as offshore floating nuclear power platforms. This study proposes a multimodal fatigue monitoring framework based on surface electromyography (sEMG) and heart rate variability (HRV) sensors for real-time fatigue recognition. Experiments were conducted on a six-degree-of-freedom motion platform with three swing levels, involving 23 participants performing simulated emergency operation tasks. Four machine learning models (Naive Bayes, K-Nearest Neighbor, Multilayer Perceptron, and Random Forest) were employed for fatigue state classification. The results show that the Random Forest model achieves the best performance, with an overall accuracy of 98.2%, 100% true positive rate for the normal state and fatigue, and 66.7% true precision for severe fatigue. The proposed multimodal fusion method effectively suppresses motion artifacts and improves recognition robustness under swing interference. Rigorous subject-level stratified cross-validation eliminates sample leakage risks; bootstrap confidence intervals and pairwise significance tests statistically verify model performance differences; class imbalance mitigation strategies are deployed to quantify uncertainty for the scarce severe-fatigue category; literature-supported Borg CR-10 grading thresholds are validated via retrospective cutoff sensitivity analysis to guarantee reliable fatigue labeling. This sensor-based intelligent monitoring system provides a reliable solution for real-time fatigue detection of operators in dynamic digital industrial scenarios, supporting accident prevention and sustainable operation of high-risk industrial systems. Full article

This study focuses on the development of a sensor-informed motion-continuity control method for shared-return electro-hydraulic actuator networks subject to neighboring-branch disturbances. The objective is to reduce the local velocity fluctuations induced by return-line pressure transients while retaining explicit hydraulic and valve constraints. A control-oriented shared-return disturbance model is established to map neighboring-valve action, T-port replenishment, accumulator buffering, common return-line pressure, net driving pressure difference, and local actuator motion. On this basis, sensor-derived motion and pressure states together with neighboring-action prior information are used to reconstruct the objective of a constrained predictive controller according to the disturbance stage. Soft Actor–Critic is restricted to bounded objective-weight inference, whereas the valve command remains generated by locally linearized receding-horizon optimization; bounded mapping, smoothing update, and soft pressure constraints preserve positive weighting matrices and online quadratic programming solvability. Co-simulation, simulation-based ablation and baseline comparisons, timing evaluation, and scaled dual-branch experiments show that the proposed framework improves motion continuity, reduces disturbance-induced pressure-difference excursions, maintains smoother valve execution, and completes each tested online update within the sampling period. These findings support feasibility-preserving sensor-driven objective reconstruction under the investigated shared-return disturbance scenarios. Full article

Foot-worn electromagnetic harvesters are driven by irregular rigid-body motion, while their response is limited by mechanical stroke, coil geometry, mounting direction, and the electrical interface. This paper presents a reproducible numerical design study of a finite-stroke magnetic quasi-zero-stiffness (QZS) moving-magnet harvester. Two public three-axis foot-IMU records are processed with stated gyroscope-bias estimation, six-axis attitude estimation, gravity removal, residual-offset correction, filtering, and angular-acceleration calculation. Three explicit axes are used in the design screen, and the selected candidate is then evaluated over a 62-direction spherical grid. Rigid-body angular-acceleration and centripetal terms are included for specified sensor-to-harvester offsets. Two normalized magnetic force laws are compared. The electrical model uses position-dependent flux linkage, explicit series connection and polarity of coil sections, winding-derived resistance, and a position-dependent electromagnetic reaction force. A fixed-seed random screen evaluates 720 geometry-constrained candidates. The highest-ranked nominal candidate is a 150 mm external foot-worn module with a 40.6 g moving mass, a 30 mm hard half-stroke, 1649 turns in two series sections, and a 25.27 mm coil outer diameter. Across 72 design-screen cases formed from 12 five-second windows, three mounting axes, and two magnetic laws, this candidate remained hard-stroke- and design-stroke-safe. Its conditional ideal load-side power had a 10th percentile of 1.38 mW and a median of 2.03 mW. In the 62-direction check, all 1488 cases remained hard-stroke-safe; two opposite directions each produced one design-stroke exceedance, with a maximum displacement of 24.15 mm. Re-ranking all 30 Stage-2 candidates under coupling and magnetic-stiffness changes retained the long geometry family, although a 30% coupling reduction changed the highest-ranked candidate from 600 to 632. Soft-stop sensitivity, equation-level consistency, and multi-case Runge–Kutta convergence are also reported. The results support finite-stroke design screening, but they do not constitute prototype, finite-element, or delivered-power validation. Full article

Wearable inertial measurement units (IMUs) offer an objective, low-cost basis for Parkinson’s disease (PD) assessment, but multi-task clinical protocols yield heterogeneous recordings across body locations and small cohorts, and it is unclear whether such data can support reliable PD detection without training deep models from scratch. We therefore ask whether a motion-pretrained representation transfers to this setting, and quantify how much of the discriminative signal it supplies. Each subject is represented by five task-level motion embeddings, one per clinical task, produced by a frozen pretrained spatio-temporal graph convolutional network (ST-GCN) that fuses the thirteen body-worn sensors into a whole-body embedding; a three-layer Transformer with validity-mask weighting aggregates these tokens for binary PD-versus-control classification on the WearGait-PD cohort (181 subjects: 100 PD, 81 controls). Under a leakage-free nested protocol with repeated subject-disjoint stratified 5-fold cross-validation (5 seeds; 25 estimates per model) and paired significance testing, the model attains a balanced accuracy of 0.834 ± 0.087, macro-F1 of 0.842 ± 0.094, and AUC of 0.842 ± 0.103. It leads six classical baselines and a spectrogram-CNN on accuracy-based metrics, though random forest, gradient boosting, and the spectrogram-CNN edge ahead on AUC; after correction for fold correlation, none of these between-model differences is significant. The one robust finding is a transfer effect: replacing the pretrained encoder with a random one of identical architecture lowers balanced accuracy by 15.5 points when frozen (p = 0.043) and 20.4 when trained end-to-end (p = 0.014). Discrimination is preserved under 1:1 age matching (0.846) and across both genders, so it is not explained by age imbalance. Motion-pretrained skeletal encoders thus supply the majority of the discriminative signal, while the aggregator contributes gains inseparable from noise at this cohort size. Full article

Despite rapid advances in laser display technology, existing evaluation frameworks remain confined to isolated physical metrics, decoupled from human visual perception. This study presents the laser display comprehensive testing system (LD-CTS003), a unified platform integrating physical characterization and visual perceptual assessment. Grounded in opponent-process theory, the system implements a complete color conversion pipeline from display RGB through CIE XYZ and LMS to the Derrington–Krauskopf–Lennie space, linking spectral output to retinal cone responses. The hardware architecture features five-axis precision motion and multi-sensor synchronous acquisition, while the software supports both conventional optical measurements and psychophysical experiments. Static image resolution was evaluated via stripe-pattern modulation analysis across viewing distances, and visual contrast sensitivity was measured using Gabor stimuli under varying luminance and eccentricity. The results demonstrate that reduced viewing distances enhance effective resolution, with text display imposing stricter requirements than image display. Contrast sensitivity functions exhibit band-pass profiles, with luminance and eccentricity strongly modulating achromatic and red–green channels, whereas yellow–violet responses remain relatively robust peripherally. By unifying objective metrology and subjective evaluation, this work establishes a perception-oriented framework for laser display quality assessment, providing a physiologically grounded foundation for display optimization and standard development. Full article

Ultra-wideband (UWB) and inertial measurement unit (IMU) fusion is an effective scheme for train and rail-guided target localization because UWB provides absolute ranging results and the IMU provides high-rate motion prediction. In practical rail transportation systems, UWB anchors are usually deployed along the track with large longitudinal intervals and limited lateral separation to reduce installation and maintenance costs. This anchor deployment leads to a large condition number of the observation matrix, so slight ranging errors caused by non-line-of-sight (NLoS) propagation may be amplified into large localization errors. To mitigate LoS/NLoS interference, this article proposes a propagation reliability estimation method for track-constrained UWB/IMU fusion localization. First, rail transportation localization along a narrow path is formulated as a one-dimensional track-constrained problem, and each UWB ranging result is converted into a candidate longitudinal coordinate on the known track centerline. Second, the reliability of each anchor–target propagation is estimated in a sliding window by comparing the motion increments solved by UWB observations with the motion prediction by the IMU. Third, the estimated reliability is incorporated into a reliability-weighted track-domain update before a closed-loop position–velocity Kalman correction. The simulation results show that, under the mixed LoS/NLoS scenario, the proposed method achieves an NLoS-interval RMSE of 0.0090 m. Compared with Track-EKF, Track-Gauss-AUKF, Track-Adaptive KF, and Track-SW-FGO, the proposed method reduces the NLoS-interval RMSE by 92.9%, 62.1%, 92.8%, and 92.6%. A supplementary real-data stress test on the public STAR-loc dataset demonstrates an average longitudinal RMSE of 0.0555 m under a strict online calibrated-range protocol, supporting the algorithm’s practical applicability against real-world lateral sway and asynchronous sensor noise. Full article

Distributed Acoustic Sensing (DAS) provides dense spatial measurements of the dynamic strain along fiber optic cables, offering high-resolution wave sensing for ground motion monitoring and subsurface imaging applications. However, DAS records the axial strain or strain rate, whereas traditional seismic and engineering ground motion equipment and derived metrics are based on particle displacement, velocity, or acceleration, necessitating reliable strain-to-velocity conversion methods. This study evaluates three widely used conversion approaches: the fk-rescaling, curvelet-based conversion, and slant-stack methods. These approaches are applied to a unique high-energy, near-field, active-source dataset collected at the Birds Landing Site in Sherman Island, California. The dataset includes wavefields generated by a large transmission tower collapse and sledgehammer impacts used for subsurface imaging. The wavefields were recorded simultaneously by a 1.4 km DAS array and 71 collocated nodal stations (NSs). Using 63 DAS–NS pairs, we quantify the strain-to-velocity conversion method performance using amplitude and phase transfer functions (TFs) between DAS-derived and NS particle velocity records, with the root-mean-square error (RMSE) evaluated across three frequency bands: 0.5–100 Hz, 1–10 Hz, and 10–100 Hz. The results show that fk-rescaling provides the most stable amplitude response across both source types, while both the fk-rescaling and slant-stack methods generally yield the best phase agreement. Curvelet-based conversion shows a greater variability and larger RMSE values. All methods yield a poorer amplitude reconstruction at higher frequencies, while the phase content is generally preserved more reliably than amplitudes. Differences between the tower collapse and sledgehammer sources demonstrate the influence of the source characteristics and spatial processing window length on the conversion performance. The findings provide practical guidance for selecting suitable strain-to-velocity conversion methods for active-source DAS applications, particularly where collocated reference sensors are unavailable. Full article

In constrained environments with no-fly zones (NFZs), bearing-only UAV target localization requires improving UAV-target relative geometry while avoiding NFZs. Inspired by the avoidance behavior observed in flying insects driven by perceptual stimuli, this paper proposes a bio-inspired anisotropic on-manifold guidance method for bearing-only UAV target localization under NFZ constraints. First, to quantify the measurement information with respect to the UAV-target relative geometry under unknown sensor bias, a projected uncertainty field is proposed. Its spatial gradient drives a perception-guided control law, and its components are further used to characterize local information sensitivity as a perceptual stimulus. Second, this sensitivity is incorporated into NFZ margin adaptation to construct an anisotropic dual-layer manifold field, enabling the UAV to adapt the avoidance margin according to the local information distribution. An on-manifold modulation mechanism is then applied to generate feasible motion under NFZ constraints. Simulations in NFZ-free, nonconvex NFZ, and dense NFZ scenarios show that the proposed method generates UAV trajectories that avoid NFZs while maintaining accurate and convergent target localization. Full article

Accurate ocean wave sensing in polar and other low-temperature marine environments is of great significance for marine environmental observation, climate research, and navigation safety. However, conventional wave sensors rely on external power supplies and suffer from poor stability under low-temperature and high-salinity conditions, making long-term self-powered waves sensing a significant challenge. Herein, a highly stable composite eutectogel electrode is developed by integrating sodium lignosulfonate, Fe³⁺ crosslinking, Zn²⁺-carboxylate coordination interactions, and a choline chloride/urea deep eutectic solvent (DES). The DES effectively suppresses solvent crystallization and endows the gel with excellent low-temperature tolerance, while the synergistic effect of metal coordination and multiple non-covalent interactions constructs a robust ion-conducting network with enhanced structural stability. Furthermore, eutectogel-based composite electrode architecture is designed to improve electrical conductivity and charge collection efficiency, thereby enabling stable electrical output under harsh marine conditions. Based on the as-prepared eutectogel electrode, a self-powered solid–liquid triboelectric nanogenerator is fabricated for ocean wave-motion sensing. The device can detect the wave amplitude, with an accuracy of 0.2 cm, and sense the frequency of waves ranging from 0.2 Hz to 1.6 Hz. More importantly, the SL-TENG exhibits excellent environmental adaptability, operating reliably in 3.5 wt% simulated seawater and at 0 °C. The current retention ratio reaches approximately 91% at 0 °C, which is significantly higher than that of the hydrogel-based device (≈6%). The remarkably low-temperature and salt-tolerant performance originates from the stable ion-transport network and anti-freezing characteristics of the eutectogel electrode. This work provides an effective strategy for constructing environmentally resilient eutectogel-based triboelectric devices and offers a promising route toward self-powered wave sensing systems for long-term deployment in harsh marine environments. Full article

Wearable biosensing is moving beyond single-signal activity tracking toward multimodal, AI-assisted health monitoring that combines biophysical streams with biochemical information from sweat, interstitial fluid, tears, and other accessible biofluids. Recent work has accelerated progress in flexible optical materials, programmable DNA-based sensing architectures, biosafety-aware sweat patches, and edge AI pipelines capable of denoising, calibration, personalization, and low-latency inference. This review synthesizes current advances across general biosensor platforms, vital-sign monitoring, biochemical sweat sensing, motion and biomechanics sensing, and edge AI/data analytics. Particular attention is given to the translational bottlenecks that now dominate the field, including motion artifacts, sensor drift, biofouling, subject-to-subject variability, limited sweat-to-blood equivalence, insufficient external validation, and uneven regulatory readiness. The central argument of this updated review is that the next phase of progress will not be driven by sensitivity alone but by robust multimodal fusion, clinically anchored validation, interoperable data pipelines, and energy-efficient on-device intelligence. By linking materials, electronics, algorithms, and deployment constraints, the review identifies the wearable biosensing strategies most likely to progress from promising laboratory demonstrations to reliable personalized-health tools. Full article

Vibration energy technology holds promise for self-powered miniature wireless sensor devices in underground coal mines. This study proposes a magnetically coupled bistable composite-beam wind energy harvester (BCBWEH) to enhance the response threshold for harvesting weak ambient wind energy. A nonlinear magnetic model based on magnetic dipoles is established, and the system’s dynamic equations are formulated using the lumped parameter method. Numerical simulations analyze the effect of magnetic spacing on static bifurcation, and discuss the influences of initial static position, wind excitation, and magnetic moment on the system’s dynamic behavior; experimental results validate the accuracy of the numerical predictions. By adjusting the magnetic spacing under the same level of excitation, the system’s motion can transition from single-well oscillation to efficient inter-well vibration. When the initial position lies closer to the shallower potential well, relatively small wind excitation can trigger large-amplitude inter-well vibrations, thereby increasing output power. This study offers guidance for optimizing structural configurations and tuning design parameters of piezoelectric energy harvesters based on composite-beam architectures. Full article

This paper presents a compute-efficient human-following robotic mobility system that integrates multimodal target recognition, LiDAR-based obstacle perception, hierarchical local planning, and low-level motion control into a unified perception–planning–control architecture. The system enables real-time target tracking and obstacle avoidance on resource-constrained embedded hardware. RGB-D vision and Tether Follow Sensors (TFS) are used for target recognition, while LiDAR provides local obstacle information. To reduce the computational burden of local planning, a Hierarchical Dynamic Window Approach (HDWA) is proposed. Unlike conventional DWA, which uniformly evaluates sampled motion candidates, HDWA applies Movement, Direction, and Detail dynamic windows according to obstacle conditions and target direction. This hierarchical structure reduces redundant trajectory evaluations by activating additional computation only when avoidance is required. Experimental validation on a low-cost embedded platform demonstrates that HDWA reduces the candidate-evaluation workload by 30.8–92.3% while maintaining stable target tracking and safe obstacle avoidance. In the left- and right-side avoidance scenarios, HDWA also reduced the path length by 6.5–7.8%, increased the average driving speed by 22.0–30.8%, and reduced the elapsed motion time by 24.4–28.5%. These results demonstrate the feasibility of the proposed compute-efficient system for human-following robots operating under embedded hardware constraints. Full article