Search Results (483)

Study develops and field-validates a SCADA-based real-time monitoring system to reduce unplanned dilution and hanging-wall over-break in underground long-hole stoping at a Zimbabwean gold mine. The objectives were to detect and constrain drilling deviation in real time, quantify the impact on stope stability and dilution, and evaluate operational and economic effects. The system integrates IMU inclinometers (hole angle), rotary encoders (depth), and LiDAR (collar spacing) with a Siemens S7 PLC (RS Americas, Fort Worth, TX, USA) and AVEVA™ InTouch HMI 2023 R2. Field trials across three production stopes (12L, 14L, 15L) compared baseline manual monitoring to SCADA control. Mean angular deviation fell from 0.8–1.6° to 0.2–0.3°, length deviation from 0.8–1.1 m to 0.05–0.08 m, and positional error from 0.25–0.32 m to 0.04–0.06 m; major collapses were eliminated, and ELOS dropped (e.g., 0.20 m to 0.05 m). Dilution decreased from 25% (typical 21–26%) to 16–18%, with mill feed grade rising from 1.90 to 2.25 g/t; production rates were maintained, with brief auto-stops in 5% of holes and rapid operator correction. Real-time drilling control materially reduces unplanned dilution and improves wall stability without productivity penalties, yielding compelling economics. Full article

Current drone navigation systems primarily rely on Global Navigation Satellite Systems (GNSSs), but their signals are susceptible to interference, spoofing, or suppression in complex environments, leading to degraded positioning performance or even failure. To enhance the positioning accuracy and robustness of low-altitude drones in satellite-denied environments, this paper investigates an absolute visual localization solution. This method achieves precise localization by matching real-time images with reference images that have absolute position information. To address the issue of insufficient feature generalization capability due to the complex and variable nature of ground scenes, a visual-based image retrieval algorithm is proposed, which utilizes a fusion of shallow spatial features and deep semantic features, combined with generalized average pooling to enhance feature representation capabilities. To tackle the registration errors caused by differences in perspective and scale between images, an image registration algorithm based on cyclic consistency matching is designed, incorporating a reprojection error loss function, a multi-scale feature fusion mechanism, and a structural reparameterization strategy to improve matching accuracy and inference efficiency. Based on the above methods, a hierarchical absolute visual localization system is constructed, achieving coarse localization through image retrieval and fine localization through image registration, while also integrating IMU prior correction and a sliding window update strategy to mitigate the effects of scale and rotation differences. The system is implemented on the ROS platform and experimentally validated in a real-world environment. The results show that the localization success rates for the h, s, v, and w trajectories are 95.02%, 64.50%, 64.84%, and 91.09%, respectively. Compared to similar algorithms, it demonstrates higher accuracy and better adaptability to complex scenarios. These results indicate that the proposed technology can achieve high-precision and robust absolute visual localization without the need for initial conditions, highlighting its potential for application in GNSS-denied environments. Full article

Digital Mobility outcomes can serve as objective biomarkers of health, but their validation in populations with multiple long-term conditions (MLTCs) based on wrist-worn devices remains unexplored. We refined, improved, and introduced novel algorithms, specifically tailored and adapted for (i) gait sequence detection, (ii) initial contact identification, and (iii) stride length estimation from a single wrist-worn device. Validation was performed using data from 28 participants with co-occurring MLTCs performing a 2.5 h real-world monitoring session. Reference data from an established multi-sensor system were used to assess algorithm performance across diverse gait patterns of co-occurring MLTCs. Twenty-eight participants (mean age 70.4 years, 43% females) had a median of three co-occurring MLTCs. Among six gait sequence detection methods, improved versions of the Kheirkhahan algorithm performed best (accuracy = 0.92, specificity = 0.96). For initial contact detection (nine methods tested), Shin’s algorithm achieved the highest performance index (0.85) followed by McCamley (0.84). Stride length estimation was most accurate using novel approaches based on the Weinberg method (performance index > 0.70). The proposed fine-tuned algorithms, the newly developed adaptive variants, and the foot-length augmented versions demonstrated robust performance, surpassing many existing methods and addressing the complexity of gait patterns in MLTCs. These findings enable scalable, real-time mobility monitoring in complex clinical populations using accessible wearable technology. Full article

Sleep monitoring is critical for diagnosing and treating sleep disorders. Although polysomnography (PSG) remains the clinical gold standard, its complexity, discomfort, and lack of portability limit its applicability for long-term and home-based monitoring. To overcome these challenges, this study introduces a novel flexible headband system designed for multi-modal physiological signal acquisition, incorporating dry electrodes, a six-axis inertial measurement unit (IMU), and a temperature sensor. The device supports eight EEG channels and enables wireless data transmission via Bluetooth, ensuring user convenience and reliable long-term monitoring in home environments. To rigorously evaluate the system’s performance, we conducted comprehensive assessments involving 13 subjects over two consecutive nights, comparing its outputs with conventional PSG. Experimental results demonstrate the system’s low power consumption, ultra-low input noise, and robust signal fidelity, confirming its viability for overnight sleep tracking. Further validation was performed using the self-collected HBSleep dataset (over 184 h recordings of the 13 subjects), where state-of-the-art sleep staging models (DeepSleepNet, TinySleepNet, and AttnSleepNet) were applied. The system achieved an overall accuracy exceeding 75%, with AttnSleepNet emerging as the top-performing model, highlighting its compatibility with advanced machine learning frameworks. These results underscore the system’s potential as a reliable, comfortable, and practical solution for accurate sleep monitoring in non-clinical settings. Full article

Functional hallux limitus (FHL) is a biomechanical condition defined by restricted motion of the first metatarsophalangeal joint during walking, which may impair stability and increase fall risk in older adults. This study compared fall risk between patients with asymptomatic FHL and healthy controls using validated assessments. The case–control design included 40 participants over 65 years, divided into 20 with FHL and 20 controls. Mobility was evaluated with the Timed Up and Go Test, postural stability with the Berg Balance Scale, and fear of falling with the Falls Efficacy Scale—International (FES-I). Spatiotemporal gait parameters were measured using an inertial measurement unit (IMU). No significant differences were found between groups in the Timed Up and Go Test (p = 0.694), Berg Balance Scale (p = 0.903), Falls Efficacy Scale—International (p = 0.913), or spatiotemporal parameters. These results suggest that asymptomatic FHL does not significantly affect mobility, stability, or fear of falling in older adults, indicating that it is not a determining factor for fall risk under controlled conditions. Further research is needed in less controlled settings or in patients with painful FHL. Full article

Accurate vehicle state estimation in a lane coordinate system is essential for safe and reliable operation of Advanced Driver Assistance Systems (ADASs) and autonomous driving. However, achieving robust lane-based state estimation using only low-cost sensors, such as a camera, an IMU, and a steering angle sensor, remains challenging due to the complexity of vehicle dynamics and the inherent signal delays in vision systems. This paper presents a lane-coordinate-based vehicle state estimator that addresses these challenges by combining a vehicle dynamics-based bicycle model with an Extended Kalman Filter (EKF) and a signal delay compensation algorithm. The estimator performs real-time estimation of lateral position, lateral velocity, and heading angle, including the unmeasurable lateral velocity about the lane, by predicting the vehicle’s state evolution during camera processing delays. A computationally efficient camera processing pipeline, incorporating lane segmentation via a pre-trained network and lane-based state extraction, is implemented to support practical applications. Validation using real vehicle driving data on straight and curved roads demonstrates that the proposed estimator provides continuous, high-accuracy, and delay-compensated lane-coordinate-based vehicle states. Compared to conventional camera-only methods and estimators without delay compensation, the proposed approach significantly reduces estimation errors and phase lag, enabling the reliable and real-time acquisition of vehicle-state information critical for ADAS and autonomous driving applications. Full article

The advancement of onshore and offshore wind turbines depends on the experimental validation of new technologies, novel component designs, and innovative concepts. However, full-scale models are typically very expensive, have limited functionality, and are difficult to adapt to diverse research needs. To address this shortcoming, this article presents the design of a low-cost, modular 3D-printed small prototype of a wind turbine. It includes a multi-hollow platform for marine environments configuration and stabilization, the turbine tower, and three blades with active pitch control, not always included in wind turbine prototypes. The modular tower design allows for easy height extensions, while the rotor incorporates custom blades optimized for the prototype geometry and experimental setup. Tests were conducted to evaluate the system’s operational response and verify the proper functioning of the assembled components at various wind speeds and blade pitch angles. The results confirm that the rotor speed with the prototype’s onshore configuration is highly pitch-dependent, reaching a maximum efficiency of approximately 5°. The tower displacement, measured with an IMU, remained within a narrow range, oscillating around 2° and reaching up to 4° at higher wind speeds due to elastic deflections of the PLA structure. These results, consistent with the prototype scale, validate its usefulness in capturing essential aerodynamic and structural behaviors of the wind turbine. They also demonstrate its relevance as a new tool for experimental studies of wind turbines and open up new research, validation, and control possibilities not considered in previous developments by incorporating blade pitch control. Full article

Photogrammetry applied to sports provides precise data on athlete positions and time instants, especially with digital motion capture systems. However, detecting and identifying specific events in athletic movements such as discus throwing can be challenging when using only images. For example, with high-speed video, it is difficult to pinpoint the exact frame when events like foot touchdown or takeoff occur, as contact between shoe and ground may span several frames. Inertial measurement units (IMUs) can detect maxima and minima in linear accelerations and angular velocities, helping to accurately determine these specific events in throwing movements. As a result, comparing photogrammetry data with IMU data becomes challenging because of the differences in the methods used to detect events. Even if comparisons can be made with IMU data from other sports researchers, variations in methodologies can invalidate the comparison. To address this, the paper proposes a simple methodology for detecting the five phases of a discus throw using three IMUs located on the thrower’s wrist and on the instep or ankle of the feet. Experiments with three elite male discus throwers are conducted and the results are compared with existing data in the literature. The findings demonstrate that the proposed methodology is effective (100% of phases detected in the experiments without false positives) and reliable (results validated with professional coaches), offering a practical and time- and cost-effective solution for accurately detecting key moments in athletic movements. Full article

The timely detection of falls among the elderly remains challenging. Single modality sensing approaches using inertial measurement units (IMUs) or vision-based monitoring systems frequently exhibit high false positives and compromised accuracy under suboptimal operating conditions. We propose a novel bimodal deep learning-based bimodal sensing framework to address the problem, by leveraging a memory-based autoencoder neural network for inertial abnormality detection and an attention-based neural network for visual pose assessment, with late fusion at the decision level. Our experimental evaluation with a custom dataset of simulated falls and routine activities, captured with waist-mounted IMUs and RGB cameras under dim lighting, shows significant performance improvement by the described bimodal late-fusion system, with an F1-score of 97.3% and, most notably, a false-positive rate of 3.6% significantly lower than the 11.3% and 8.9% with IMU-only and vision-only baselines, respectively. These results confirm the robustness of the described fall detection approach and validate its applicability to real-time fall detection under different light settings, including nighttime conditions. Full article

Hand rehabilitation exoskeletons play a critical role in restoring motor function in patients with stroke or hand injuries. However, most existing designs rely on fixed-axis assumptions, neglecting the rolling–sliding coupling of finger joints that causes instantaneous center of rotation (ICOR) drift, leading to kinematic misalignment and localized pressure concentrations. This study proposes the Instant Radius Method (IRM) based on medical imaging to continuously model ICOR trajectories of the MCP, PIP, and DIP joints, followed by the construction of an equivalent ICOR through curve fitting. Crossing-type biomimetic kinematic pairs were designed according to the equivalent ICOR and integrated into a three-loop ten-linkage exoskeleton capable of dual DOFs per finger (flexion–extension and abduction–adduction, 10 DOFs in total). Kinematic validation was performed using IMU sensors (Delsys) to capture joint angles, and interface pressure distribution at MCP and PIP was measured using thin-film pressure sensors. Experimental results demonstrated that with biomimetic kinematic pairs, the exoskeleton’s fingertip trajectories matched physiological trajectories more closely, with significantly reduced RMSE. Pressure measurements showed a reduction of approximately 15–25% in mean pressure and 20–30% in peak pressure at MCP and PIP, with more uniform distributions. The integrated framework of IRM-based modeling–equivalent ICOR–biomimetic kinematic pairs–multi-DOF exoskeleton design effectively enhanced kinematic alignment and human–machine compatibility. This work highlights the importance and feasibility of ICOR alignment in rehabilitation robotics and provides a promising pathway toward personalized rehabilitation and clinical translation. Full article

Traditional road asset management relies on periodic, often inefficient, inspections. Digital Twins offer a paradigm shift towards proactive, data-driven maintenance by creating a real-time virtual replica of physical infrastructure. This paper proposes a comprehensive, formalized framework for a highway Digital Twin, structured into three integrated components: a Physical Space, which defines key performance indicators through mathematical state vectors; a Data Interconnection layer for real-time data processing; and a Virtual Space equipped with hybrid models. We provide a formal definition of these state vectors and a dynamic synchronization mechanism between the physical and virtual spaces. In this study, we focused on pavement condition assessment by using a data-driven component using accessible technology. This study show the synergy between the Digital Twin and deep learning, specifically by integrating advanced analytical models within the Virtual Space for intelligent pavement condition assessment. To validate this approach, a case study was conducted to classify road surface anomalies using low-cost Inertial Measurement Unit (IMU) data. We evaluated several machine learning classifiers and introduced a novel parallel Gated Recurrent Unit network. The results demonstrate that our proposed architecture achieved superior performance, with an accuracy of 89.5% and an F1-score of 0.875, significantly outperforming traditional methods. The findings validate the viability of the proposed Digital Twin framework and highlight its potential to achieve high-precision pavement monitoring using low-cost sensor data, a critical step towards intelligent road infrastructure management. Full article

Background: This study used inertial measurement unit (IMU)-based posture angle estimates to define proxy risk labels and investigated whether these labels can be predicted from demographic, anthropometric, and lifestyle variables through machine learning analysis. Methods: Thirty healthy individuals aged 18–25 years were included. Demographic and anthropometric data and information on daily living activities were collected. The IMU sensors were placed at vertebral levels C1, C7, T5, T12, and L5. Participants were instructed to stand in an upright posture, followed by a relaxed daily posture. Anatomic postural changes between these positions were analyzed. Cervical lordosis, thoracic kyphosis, lumbar lordosis, and scoliosis risks were predicted using machine learning algorithms, including Random Forest (RF) and Artificial Neural Networks (ANN). Results: Incorrect postures during desk work and phone use were associated with an increased likelihood of posture-related deviations, such as cervical lordosis, thoracic kyphosis, and lumbar lordosis. Conversely, daily physical activity reduced these deviations. Using LOSO and stratified cross-validation with imbalance handling, balanced accuracies ranged between 0.55 and 0.82 across targets, with majority-class baselines between 0.53 and 0.87. For cervical lordosis risk, RF achieved a 0.82 balanced accuracy (95% CI: 0.74–0.97), while other categories showed a moderate but consistent performance. AUPRC values exceeded baseline levels across all models. Conclusions: IMU-based posture angle estimates can be used to identify posture-related risk categories. In this study, ML models have demonstrated predictive relationships with demographic, anthropometric, and lifestyle variables. These findings provide exploratory evidence based on IMU-derived proxy labels in a small cohort of healthy young adults. They represent exploratory indicators of postural deviation rather than clinical outcomes and may motivate future studies on preventive strategies. Importantly, the results remain underpowered relative to the a priori power targets and should be interpreted qualitatively. 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

Anterior cruciate ligament (ACL) injury and reconstruction (ACLR) are associated with biomechanical deficits and reinjury risk. Wearable devices offer promising tools for objective assessment of knee joint function. This scoping review aimed to map the use of wearable devices in quantifying knee outcomes following ACL injury or reconstruction, and to evaluate their clinical readiness and methodological quality. Eligible studies were human, English-language studies in ACL/ACLR populations or healthy cohorts assessing ACL-relevant knee outcomes with wearable devices. MEDLINE (Ovid), Embase (Ovid), APA PsycInfo (Ovid), PubMed, and Scopus were searched up to 27 August 2025. Data on devices, tasks, participants, outcomes, and validation were extracted, and an adapted technology readiness level (TRL) mapping was applied. Thirty-two studies met the inclusion criteria. Inertial measurement units (IMUs) were used most often for kinematics. Standalone accelerometers quantified pivot-shift features, while force-sensing insoles captured bilateral loading. Electromagnetic trackers and electrogoniometers served as higher-precision comparators but were workflow-limited. Reporting of calibration and criterion validation was inconsistent. TRL bands clustered at 3–6, and none reached clinical integration. We propose task-matched sampling, transparent calibration, criterion validation, pairing with patient-reported outcome measures (PROMs), and multi-site workflow trials to progress towards routine care. Full article

Background: A single lumbar-mounted inertial sensor offers a practical alternative to optoelectronic systems for gait analysis, simplifying measurements and improving usability in the clinical field. However, its validity can be influenced by sensor placement and signal choice. This study aimed to develop and validate a novel algorithm for estimating spatiotemporal parameters using anteroposterior linear acceleration and angular velocity around the sagittal axis using a single inertial measurement unit (IMU) placed on the lumbar region. The proposed algorithm was validated comparing the parameters computed by the algorithm with the ones computed using a commercial wearable system based on a two-foot-mounted IMU configuration. Thirty healthy subjects underwent a 2 min walk test, and five spatiotemporal parameters were computed using the two methodologies. Study results showed that cadence and gait cycle time exhibited very high agreement, with only a small, statistically significant bias in cadence negligible for practical purposes. In contrast, swing, stance, and double-support parameters showed disagreement due to the presence of systematic proportional errors. This work introduces a novel algorithm for gait event detection and spatiotemporal parameter estimation, addressing uncertainties related to sensor placement, metric models, processing techniques, and signal selection, while avoiding synchronization issues associated with using multiple sensors. Full article