Search Results (264)

Accurate measurement of sea-surface winds is critical for climate science, physical oceanography, and the rapidly expanding offshore wind energy sector. Buoy-based platforms—moored meteorological buoys, drifters, and floating LiDAR systems (FLS)—provide practical alternatives to fixed offshore structures, especially in deep water where bottom-founded installations are economically prohibitive. Yet these floating platforms are subject to continuous pitch, roll, heave, and yaw motions forced by wind, waves, and currents. Such six-degree-of-freedom dynamics introduce multiple error pathways into the measured wind signal. This paper synthesizes the current understanding of motion-induced measurement errors and the techniques developed to compensate for them. We identify four principal error mechanisms: (1) geometric biases caused by sensor tilt, which can underestimate horizontal wind speed by 0.4–3.4% depending on inclination angle; (2) contamination of the measured signal by platform translational and rotational velocities; (3) artificial inflation of turbulence intensity by 15–50% due to spectral overlap between wave-frequency buoy motions and atmospheric turbulence; and (4) beam misalignment and range-gate distortion specific to scanning LiDAR systems. Compensation strategies have progressed through four recognizable stages: fundamental coordinate-transformation and velocity-subtraction algorithms developed in the 1990s; Kalman-filter-based multi-sensor fusion emerging in the 2000s; Response Amplitude Operator modeling tailored to FLS platforms in the 2010s; and data-driven machine-learning approaches under active development today. Despite this progress, key challenges persist. Sensor reliability degrades under extreme sea states precisely when accurate data are most needed. The coupling between high-frequency platform vibrations and turbulence remains poorly characterized. No unified validation framework or benchmark dataset yet exists to compare methods across platforms and environments. We conclude by outlining research priorities: end-to-end deep-learning architectures for nonlinear error correction, adaptive algorithms capable of all-sea-state operation, standardized evaluation protocols with open datasets, and tighter integration of intelligent software with next-generation low-power sensors and actively stabilized platforms. Full article

Tracking knee joint movement during activities of daily living can have the potential to transform the rehabilitation and functional assessment of patients. The present study evaluated the validity of a low-cost, instrumented knee brace to determine whether it was appropriate for the monitoring and quantification of human knee function during five activity-of-daily-living (ADL) tasks including walking, inclined walking, stepping, sitting, and object manipulation. A sensor platform was designed to acquire sagittal plane knee data from 13 healthy participants across five different tasks and compared to gold-standard motion analysis. The brace showed good-to-excellent validity (RMSE: 4.97–8.65°), with differences in knee joint angles and angular velocities noted during various ADLs, specifically during early and late portions of a given movement. The results for instantaneous knee joint angles and angular velocities were very similar to those of the gold-standard system (mean bias: 0.59–9.52°·s⁻¹), which may be applicable to everyday movement tasks, but may preclude analyses at a clinical level. Although the low-cost sensor platform shows promise an effective monitoring tool, it is not ready yet for a clinical application. Full article

The surface tension of a liquid droplet can be determined by fitting its actual profiles using the Young–Laplace equation, effectively reducing the measurement of surface tension to an accurate determination of the droplet’s profiles. Spectral confocal sensors are high-precision, interference-resistant, non-contact measurement systems for droplet surface profiling, employing a light source together with a dispersive objective lens and a spectrometer to acquire depth-dependent spectral information. The accuracy and stability of surface tension measurements can be effectively enhanced by spectral confocal sensors measuring the droplet surface profile. Although existing spectral confocal sensors have significantly improved measurement range and accuracy, their angular measurement performance remains limited, and deviations may arise at droplet edges with large inclinations or pronounced surface profile variations. This study presents the optical design of a large-angle spectral confocal sensor. By theoretically analyzing the conditions for generating linear axial dispersion in the dispersive objective lens, a front-end dispersive objective lens was designed by combining positive and negative lenses. Based on a Czerny–Turner (C-T) configuration, the back-end spectrometer was designed under the astigmatism-free condition, taking into account both central and edge wavelength effects. Zemax was employed for simulation optimization and tolerance analysis of each optical module. The results show that the designed system achieves an axial dispersion of 1.5 mm over the 430–700 nm wavelength range, with a maximum allowable object angle of ±40° and a theoretical resolution of 3 μm. The proposed spectral confocal sensor maintains high measurement accuracy over a wide angular range, facilitating precise measurement of droplet surface tension at large inclination angles. Full article

Accurate identification of oil–water two-phase flow patterns is essential for ensuring the safety and operational efficiency of oil and gas extraction systems. While traditional methods using empirical models and sensor technologies have provided basic insights, they often struggle to capture the nonlinear features of complex operational conditions. To address the challenge of data scarcity commonly found in experimental settings, this study employs a data augmentation strategy that combines the Synthetic Minority Over-sampling Technique (SMOTE) with Gaussian noise injection, effectively expanding the feature space from 60 original experimental nodes. Next, a physics-constrained attention mechanism model was developed that incorporates a physical constraint matrix to effectively mask irrelevant feature interactions. Experimental results show that while the standard attention model (83.88%) and the baseline BP neural network (84.25%) have limitations in generalizing to complex regimes, the proposed physics-constrained model achieves a peak test accuracy of 96.62%. Importantly, the model demonstrates exceptional robustness in identifying complex transition regions—specifically Dispersed Oil-in-Water (DO/W) flows—where it improved recall rates by about 24.6% compared to baselines. Additionally, visualization of attention scores confirms that the distribution of attention weights aligns closely with fluid-dynamic mechanisms—favoring inclination for stratified flows and flow rate for turbulence-dominated dispersions—thus validating the model’s interpretability. This research offers a novel, interpretable approach for modeling dynamic feature interactions in multiphase flows and provides valuable insights for intelligent oilfield development. Full article

Para cross-country (XC) skiing has become a prominent sport since its debut at the Örnsköldsvik Winter Olympic Games in 1976. Nevertheless, the lack of studies focusing on standing para XC skiing highlights the need to provide a comprehensive description of this sport, investigating how different prosthetic devices may influence the athletic outcome. In this exploratory case study, the biomechanics of an elite standing para-athlete, with a right-sided transfemoral amputation, was investigated. Tests were performed during diagonal XC skiing on a treadmill, at different speeds and inclinations. Specifically, two different prosthetic feet were compared: the athlete used an Ottobock Genium X3 prosthetic knee with either the Ottobock Taleo or the Ottobock Evanto prosthetic foot. Inertial Measurement Units (IMUs) were employed to estimate joint angles and detect pole hits and lifts. Additionally, data were collected using embedded sensors in the knee prosthesis. Diagonal stride spatiotemporal parameters were further calculated. Results revealed that the Evanto foot significantly increased swing phase duration and hip range of motion, while generating higher knee torque, ankle torque, and axial loading compared to the Taleo foot. This research represents the first application of the employed testing methodology to para standing XC skiing, and it therefore provides a framework for future studies on this discipline. Full article

Robot grinding requires a constant interaction force between the tool and the workpiece, even under inclination changes. This paper proposes a compact single-axis pneumatic constant-force floating compensator (CFFC) to achieve constant force output. The proportional pressure valve and pressure sensor are used to regulate the cylinder’s pressure. Pneumatic components and sensors are integrated into the narrow space between the cylinder and the slide rail. Embedded controller, power, and communication modules are developed and integrated into a control box and interact with the operator by a touch screen. The mathematical models of the compensator are established and the stability and response dynamics are analyzed through transfer functions. A dual-loop force controller based on active disturbance rejection control (ADRC) is designed to address bias load, inclination change, friction, and the sealing cover spring effect. The outer loop is compensated by displacement, tilt, and pressure sensors, and the unmodeled dynamics are estimated by an extended state observer (ESO) and a recursive least square (RLS). Finally, the CFFC is installed on a testing platform to simulate grinding conditions. The experimental results show that even under large floating stroke, inclination changes, and biased load, the CFFC can still quickly and stably output the desired grinding force. Full article

Background: The relationship between varus thrust (VT) during gait and static limb alignment on radiography in knee osteoarthritis (OA) remains unclear. Therefore, the present study investigated the association between the tibial inclination angle (TA), which was noninvasively measured from the body surface, and radiographic parameters. In Addition, this study analyzed how TA changes under different loading conditions (ΔTA) relate to VT acceleration (VTA) during early stance using an inertial measurement unit (IMU) sensor. Methods: Nineteen female patients (mean age: 63.5 ± 8.6 years) with knee OA or medial meniscus injury were included. The TA was defined as the angle between the tibial mechanical axis and a vertical line from the floor, which was measured in standardized standing and supine positions. The ΔTA was calculated as the difference between these positions. To assess lower limb alignment, the femorotibial angle (FTA) and joint line convergence angle (JLCA) were measured. The VTA was measured using IMU sensors on the thigh and tibia, and the differences between lateral and medial VTA were defined as femoral and tibial ΔVTA, respectively. Spearman’s correlation coefficient and linear regression were used for analysis. Results: The standing TA was significantly correlated with the FTA (ρ = 0.47, p = 0.04) and JLCA (ρ = 0.80, p < 0.01). The ΔTA was significantly associated with femoral ΔVTA (β = 0.70, p < 0.01) and tibial ΔVTA (β = 0.67, p < 0.01). Conclusions: Surface-measured TA reflects radiographic alignment. The ΔTA also captures dynamic instability not explained by static measures, suggesting its potential utility as an assessment indicator, although further validation is warranted. Full article

Soybean–corn intercropping in the hilly–mountainous regions of Southwest China poses unique challenges to mechanized harvesting because of complex topography and agronomic constraints. Addressing the soybean-harvesting bottleneck in these fields requires advanced sensing and perception rather than purely mechanical redesigns. Prior reviews emphasized flat-terrain machinery or single-crop systems, leaving a gap in sensor-centric solutions for intercropping on steep, irregular plots. This review analyzes how sensors enable the next generation of intelligent harvesters by linking field constraints to perception and control. We frame the core failures of conventional machines—instability, inconsistent cutting, and low efficiency—as perception problems driven by low pod height, severe slope effects, and header–row mismatches. From this perspective, we highlight five fronts: (1) terrain-profiling sensors integrated with adaptive headers; (2) IMUs and inclination sensors for chassis stability and traction on slopes; (3) multi-sensor fusion of LiDAR and machine vision with AI for crop identification, navigation, and obstacle avoidance; (4) vision and spectral sensing for selective harvesting and impurity pre-sorting; and (5) acoustic/vibration sensing for low-damage, high-efficiency threshing and cleaning. We conclude that compact, intelligent machinery powered by sensing, data fusion, and real-time control is essential, while acknowledging technological and socio-economic barriers to deployment. This review outlines a sensor-driven roadmap for sustainable, efficient soybean harvesting in challenging terrains. Full article

Compaction quality is a critical factor in ensuring the long-term performance of subgrade structures; however, traditional testing methods are limited by their destructive nature and delayed feedback. To address these shortcomings, this study proposes a dynamic identification method for subgrade compaction based on embedded attitude sensors. A customized sensor unit integrated with an inertial measurement module was embedded in soil samples to record triaxial acceleration and attitude angles during the compaction process. Signal processing techniques, including an improved wavelet-based denoising strategy, were employed to separate long-term compaction trends from transient impact disturbances. Attitude features such as cumulative angular change, angular velocity, root mean square values, and a comprehensive inclination index were extracted as predictive variables. Ridge regression, random forest, and XGBoost models were constructed to establish the mapping relationship between attitude features and compaction degree. Experimental results on clay, loam, and sand samples indicate that the yaw angle is most sensitive to vertical settlement, while pitch and roll angles provide complementary information on lateral and rotational behaviors. Comparative analysis of filtering methods shows that the transient masking interpolation (TMI) approach outperforms the traditional asymmetric wavelet thresholding (AWT) method in effectively preserving baseline trends. Among the regression models, XGBoost demonstrated the best predictive performance, achieving an R² exceeding 0.995 at high compaction levels. The proposed method has been experimentally demonstrated as a laboratory-scale proof of concept, showing strong potential for future real-time field application, offering a novel technological pathway for intelligent quality control in road construction. Full article

People with limited mobility experience disadvantages when participating in outdoor activities such as cycling, which can lead to negative consequences. This study proposes an indoor physical cycling activity, with the help of technological solutions, for people with limited mobility. The aim is to use enhanced reality (ER) technology, based on virtual reality, to exercise in the person’s own indoor environment. In this system, real track and speed information is received by a 360-degree camera, GPS, and gyroscope sensors and presented to the mechanical system in the electromechanical bike structure with real-time interaction. The pedal force system of the exercise bike is driven using information of the incline, and data from the bike’s speed sensor and head movements are transferred in real time to the track image on the user’s head-up display, creating a realistic experience. With this system, it is possible to maintain an experience close to real cycling through human–computer interaction with hardware and software integration. Thus, using this system, people with limited mobility can improve their quality of life by performing indoor physical activities with an experience close to reality. Full article

Azimuth sensing plays a vital role in numerous industrial applications where tilt angle serves as a key parameter. To address the demand for accurate and reliable measurements, we propose an all-fiber two-dimensional inclinometer based on the Vernier effect in a multi-core fiber Fabry–Perot interferometer. The sensor is capable of simultaneously measuring both inclination and azimuth angles with high accuracy. A cascaded Fabry–Perot interferometer was inscribed in a seven-core fiber using a femtosecond laser plane-by-plane direct writing technique. By monitoring the wavelength shifts in two peripheral cores, we demonstrated the feasibility and performance of the proposed sensor. The experimental results showed that the inclinometer exhibited high sensitivity, with maximum values of −0.5272 nm/° for azimuth measurement (maximum measurement error: 7.33°) and −0.5557 nm/° for inclination measurement (maximum measurement error: 5.97°). The measurement ranges extended from 0° to 360° for azimuth and from –90° to 90° for inclination. Owing to its wide measurement range, compact structure, and high sensitivity, the proposed all-fiber two-dimensional inclinometer holds significant potential for practical applications. Full article

The health and safety of agricultural workers are critical concerns due to their exposure to extreme environmental conditions, physically demanding tasks, and limited access to immediate medical assistance. This study presents the design and development of a novel smartphone application that integrates multiple wearable physiological sensors—a fingertip pulse oximeter, a skin patch thermometer, and an inertial measurement unit (IMU)—via Bluetooth Low Energy (BLE) technology for real-time health monitoring and alert notifications. Unlike many existing platforms, the proposed system offers direct access to raw sensor data, modular multi-sensor integration, and a scalable software framework based on the Model–View–ViewModel (MVVM) architecture with Jetpack Compose for a responsive user interface. Experimental results demonstrated stable BLE connections, accurate extraction of oxygen saturation, heart rate, body temperature, and trunk inclination data, as well as reliable real-time alerts when the system detects anomalies based on predetermined thresholds. The system also incorporates automatic reconnection mechanisms to maintain continuous monitoring. Beyond agriculture, the proposed framework can be adapted to broader occupational safety domains, with future improvements focusing on additional sensors, redundant sensing, cloud-based data storage, and large-scale field validation. Full article

This study investigates the risk of developing musculoskeletal disorders (MSDs) in individuals performing standing tasks, with a focus on real-time posture assessment using motion capture technology. Improper body posture and repetitive movements during daily work activities can impose strain on the musculoskeletal system, increasing the likelihood of discomfort and long-term injury. Data were collected from five male and female participants using the Perception Neuron motion capture system, with body-mounted sensors tracking posture and movement. Joint angles were calculated to distinguish between correct and incorrect postures based on ISO 11226:2000 ergonomic guidelines. Key physical risk factors identified included prolonged forward trunk inclination, elevated arm positions, and repetitive actions. The analysis revealed that participants frequently adopted moderate- to high-risk postures, especially when working at non-ergonomic desk heights, suggesting a heightened risk of MSDs such as back and upper limb pain. These findings underscore the importance of real-time ergonomic monitoring and adaptive workstation design to reduce musculoskeletal risks in standing work environments. Full article

Classifying human locomotor activities from wearable sensor data is an important high-level component of control schemes for many wearable robotic exoskeletons. In this study, we evaluated three machine learning models for classifying activity type (walking, running, jumping), speed, and surface incline using input data from body-worn inertial measurement units (IMUs) and e-textile insole pressure sensors. The IMUs were positioned on segments of the lower limb and pelvis during lab-based data collection from 16 healthy participants (11 men, 5 women), who walked and ran on a treadmill at a range of preset speeds and inclines. Logistic Regression (LR), Random Forest (RF), and Light Gradient-Boosting Machine (LGBM) models were trained, tuned, and scored on a validation data set (n = 14), and then evaluated on a test set (n = 2). The LGBM model consistently outperformed the other two, predicting activity and speed well, but not incline. Further analysis showed that LGBM performed equally well with data from a limited number of IMUs, and that speed prediction was challenged by inclusion of abnormally fast walking and slow running trials. Gyroscope data was most important to model performance. Overall, LGBM models show promise for implementing locomotor activity prediction from lower-limb-mounted IMU data recorded at different anatomical locations. Full article

The pipe-jacking inertial guidance method is a key technology to solve the guidance problems of complex pipe-jacking projects, such as long distances and curves. However, since its guidance information is obtained by gyroscope integration. System errors will accumulate over time and affect the guidance accuracy. To address the above issues, this study proposes an intelligent online system error correction scheme based on single-axis rotation and data backtracking. The method enhances system observability by actively exciting the sensor states and introducing data reuse technology. Then, a Bayesian optimization algorithm is incorporated to construct a multi-objective function. The algorithm autonomously searches for the optimal values of three key control parameters, thereby constructing an optimal correction strategy. The results show that the inclination accuracy improving by 99.36%. The tool face accuracy improving by 94.05%. The azimuth accuracy improving by 94.42% improvement. By comparing different correction schemes, the proposed method shows better performance in estimating gyro bias. In summary, the proposed method uses single-axis rotation and data backtracking, and can correct system errors in inertial navigation effectively. It has better value for engineering and provides a technical foundation for high-accuracy navigation in tunnel, pipe-jacking, and other complex tasks with low-cost inertial systems. Full article