Search Results (3,401)

Based on fundamental mechanics, movement and force associate head and pelvic movement asymmetry with asymmetry of force production. We investigate, how often racehorses undergoing strenuous training regimens show evidence of switching between “preferred” limbs, i.e. one limb producing increased force, when assessed at monthly intervals? We hypothesize that clinical asymmetry thresholds designed for “detecting lameness” are frequently exceeded and that when applying previously established Thoroughbred-specific repeatability values, horses rarely switch between showing left- and right-sided asymmetry. Monthly gait assessments (inertial sensors) were conducted in 256 Thoroughbred racehorses at least twice per horse (up to 16 times per horse). Descriptive statistics for absolute differences for head and pelvic movement were compared to published Thoroughbred-specific repeatability values. The percentage of left–right switches between repeat assessments was calculated in comparison to three different levels of pre-defined thresholds (perfect symmetry, clinical lameness thresholds, previously established Thoroughbred-specific repeatability values) and switch frequencies compared between the three thresholds. Ranges containing 95% of monthly differences were higher than published daily and weekly values except for pelvic vertical range of motion. Approximately 30% of monthly differences in individual symmetry parameters showed left–right switches around “perfect symmetry”. Utilizing clinical lameness thresholds for categorizing left–right switches, a significantly (p < 0.001) reduced percentage of 4–11% of measurements for head movement and 7–17% for pelvic movement showed switches. Using daily repeatability values for categorization, a further significantly (p < 0.001) reduced percentage of switches was observed: 0.3–3.6% for head movement and 0.6–7.0% for pelvic movement. While racehorses in training regularly switch between small left- or right-sided movement symmetries, they less frequently switch between more pronounced left- and right-sided movement symmetries defined based on daily variations. Further studies should investigate the reasons for these rare switches. Full article

Recordings of wind velocity and associated wind turbine (WT) power possess noise, owing to inaccurate sensor measurements, atmosphere conditions, working stops, and flaws. The measurements still contain noise even after purification, so the fit curve of the wind turbine power might be different from the datasheet. The model of wind turbine power (MWTP) is significant, owing to its utilization for predicting and managing the wind energy. There are two types of MWTP, namely the parametric and the non-parametric types. Parameter identification of the parametric MWTP can be treated as a high nonlinear optimization problem. The fitness function is to minimize the root average squared errors (RASEs) between the calculated and measured wind powers while subject to a set of parameter constraints. The non-parametric MWTP is identified through training through machine learning. In this article, machine learning, namely the support vector regression (SVR), is innovatively applied for the identification of the non-parametric MWTP. Additionally, the dynamic force and the eigen parameters of WTs at different wind velocities are studied theoretically. The theoretical model for analyzing the natural frequencies of WT is validated using two techniques, namely the finite element method and the Euler–Bernoulli beam theory. The simulations are executed using MATLAB. The SVR is assessed via the comparison of its results with those of three parametric MWTP, viz. the 5-, 6-parameter logistic functions, and the modified hyperbolic tangent. It can be affirmed that the SVR execution is excellent and can produce the non-parametric MWTP with a RASE less than other algorithms by 0.4% to 93.8%, with a small computation cost. Full article

The advancement of sensor technologies enables the measurement of high-quality continuous blood pressure signals, which has become an important area in healthcare. The development of such application-specific sensors can be time-consuming, expensive, and difficult to test or validate with known and consistent waveforms. In this manuscript, an open-source blood pressure waveform simulator with a Python validation package is described. The core part, a 3D-printed cam, can be generated based on real blood pressure waveforms. The validation software framework compares in detail the waveform used to design the cam with the time series from the sensor being validated. The simulator was validated using a 3D force sensor. The RMSE of accuracy was 1.94 (44)–2.74 (63)%, and the Pearson correlation with the nominal signal was 99.84 (13)–99.39 (18)%. As for precision, the RMSE of the repeatability of cam rotations was 1.53 (71)–2.13 (116)% and the Pearson correlation was 99.85 (16)–99.59 (57)%. The presented simulator proved to be robust and accurate in short- and long-term use, as it produced the signal waveform reliably and with high fidelity. It reduces development costs for early-stage sensor development and research, offering a solution that is easy to manufacture yet capable of continuously outputting human arterial blood pressure waveforms spanning multiple consecutive cardiac cycles. Full article

This study presents a gesture-based authentication system utilizing triboelectric nanogenerator (TENG) sensors. As self-powered devices capable of generating high-voltage outputs without external power, TENG sensors are well-suited for low-power IoT sensors and smart device applications. The proposed system recognizes single tap, double tap, and holding gestures. The electrical characteristics of the sensor were evaluated under varying pressure conditions, confirming a linear relationship between applied force and output voltage. These results demonstrate the sensor’s high sensitivity and precision. A threshold-based classification algorithm was developed by analyzing signal features enabling accurate gesture recognition in real time. To enhance the practicality and scalability of the system, the algorithm was further configured to automatically segment raw sensor signals into gesture intervals and assign corresponding labels. From these segments, time-domain statistical features were extracted to construct a training dataset. A random forest classifier trained on this dataset achieved a high classification accuracy of 98.15% using five-fold cross-validation. The system reduces security risks commonly associated with traditional keypad input, offering a user-friendly and reliable authentication interface. This work confirms the feasibility of TENG-based gesture recognition for smart locks, IoT authentication devices, and wearable electronics, with future improvements expected through AI-based signal processing and multi-sensor integration. Full article

Background and Objectives: Knee Osteoarthritis affects about 10% of people over 50, causing pain and functional limitation. Hyaluronic acid (HA) is crucial in regulating the osteocartilaginous matrix. Patients are usually assessed using clinical scores to examine symptoms and quality of life, and in this context, gait analysis could provide an objective assessment of walking patterns to identify any deficits. This systematic review investigates the short and long-term effects of intra-articular HA injections on gait kinematics, pain and activities of daily living (ADL), investigating the correlation between outcomes. Materials and Methods: The review followed PRISMA guidelines. The PICO model included patients with radiographic knee osteoarthritis who received intra-articular HA injections, comparing them to healthy controls or those receiving corticosteroids or placebo. Outcomes included gait kinetics and functional scales at baseline and during follow-ups. Results: From 342 identified articles, 13 were included, comprising a total of 321 patients. The gait analysis utilized optoelectronic systems, inertial sensors, and electromyographic sensors pre- and post-HA treatment. Clinical parameters were assessed using the Visual Analogue Scale, WOMAC OA, Knee Society Score, Lequesne Score, and SF-36. The data showed significant improvement in speed (p = 0.001) and step cadence (p < 0.005) 30 days post-treatment and improvements in knee adduction moment (p < 0.001) and sagittal ground reaction force vectors (p < 0.01) up to six months post-treatment. Pain reduction and improvements in VAS (p < 0.001) and Lequesne score (p < 0.001) were observed in short-term follow-ups. Conclusions: Our study suggests an improvement in pain and knee function after hyaluronic acid injection. Moreover, gait analysis is an important tool for objectively assessing deficits and developing personalized rehabilitation programs. Furthermore, combining infiltrative treatment with rehabilitation could extend the effects of hyaluronic acid and improve results. Full article

Purpose: To quantify real-time hand–foot coupling in tennis and test whether the coupling pattern differs by playing standard. Methods: Fifteen nationally certified second-level male athletes and fifteen recreational beginners performed multi-directional swings, alternating forehand–backhand groundstrokes and serve-and-volley sequences while tri-axial ankle acceleration and racket-grip force were synchronously recorded in wearable inertial measurement units (IMUs). Grip metrics (mean force, peak force, force duration) and acceleration magnitudes were analysed with MANOVA and Hedges’ g effect sizes, followed by the Benjamini–Hochberg correction (α = 0.025). Results: Across tasks, athletes showed higher mean ankle acceleration (standardised mean difference, Hedges’ g) but 45% lower mean grip force (Hedges’ g = −1.28; both p < 0.01). The association between acceleration and grip metrics was moderate-to-strong and negative in athletes (r = −0.62 with mean grip force; r = −0.69 with force duration), whereas beginners exhibited moderate-to-strong positive correlations (r = 0.48–0.73). Conclusion: We quantified hand–foot coordination in tennis by synchronising tri-axial ankle acceleration with calibrated racket-grip force across three match-realistic tasks. Relative to beginners, athletes demonstrated an inverse coupling between ankle acceleration and grip-force metrics, whereas beginners showed a direct coupling, consistent with our purpose of quantifying coordination via synchronised wearable sensors. Full article

Mercury (Hg) is a globally recognized toxic element, and the Minamata Convention on Mercury entered into force in 2017 to address its associated risks. Under the United Nations Environment Programme, international efforts to reduce Hg emissions and monitor its environmental presence are ongoing. In support of these initiatives, we developed a simple and rapid mercury detection device based on a quartz crystal microbalance (QCM-Hg sensor), which utilizes the direct amalgamation reaction between Hg and a gold (Au) electrode. The experimental results demonstrated a proportional relationship between Hg concentration and the resulting oscillation frequency shift. Increased flow rates and prolonged measurement durations enhanced detection sensitivity. The system achieved a detection limit of approximately 1 µg/m³, comparable to that of commercially available analyzers. Furthermore, a measurement configuration integrating the reduction-vaporization method with the QCM-Hg sensor enabled the detection of mercury in aqueous samples. Based on the experimental results and the gas-phase detection sensitivity achieved to date, concentrations as low as approximately 0.05 µg/L appear to be detectable. These findings highlight the potential of the QCM-Hg system for on-site mercury monitoring. This review aims to provide a comprehensive yet concise overview of QCM-Hg sensor development and its potential as a next-generation tool for environmental and occupational mercury monitoring. Full article

Nomex honeycomb composites (NHCs) are highly sensitive to the abnormal wear state of disc tools during cutting, leading to poor product quality. This paper proposes a real-time anomaly detection method combining a novel CNN–GRU–Attention (CGA) deep learning model with an Exponentially Weighted Moving Average (EWMA) control chart to monitor sensor data from the disc tool. The CGA model integrates an improved CNN layer to extract multidimensional local features, a GRU layer to capture long-term temporal dependencies, and a multi-head attention mechanism to highlight key information and reduce error accumulation. Trained solely on normal operation data to address the scarcity of abnormal samples, the model predicts cutting force time series with an RMSE of 0.5012, MAE of 0.3942, and R² of 0.9128, outperforming mainstream time series data prediction models. The EWMA control chart applied to the prediction residuals detects abnormal tool wear trends promptly and accurately. Experiments on real NHC cutting datasets demonstrate that the proposed method effectively identifies abnormal machining conditions, enabling timely tool replacement and significantly enhancing product quality assurance. Full article

Non-nutritive sucking (NNS) is an instinctive behavior in newborns, consisting of two stages: sucking and expression. It plays a critical role in preparing the infant for oral feeding. In neonatal and pediatric units, NNS assessment is routinely performed to determine feeding readiness. However, these evaluations are often subjective and rely heavily on the clinician’s experience. While other medical devices that support the development of NNS skills exist, they are not specifically designed for the comprehensive assessment of NNS, and their high cost limits accessibility for many hospitals and tertiary care units globally. This paper presents the development and pilot testing of a low-cost, portable device and accompanying software for assessing NNS in newborns hospitalized in neonatal care units. Methods: The device uses force-sensitive resistors to capture expression pressure and a differential pressure sensor to measure NNS. Data were acquired through the analog–digital converter of a microcontroller and transmitted via Bluetooth for real-time graphical analysis. Pilot testing was conducted with six hospitalized preterm newborns, measuring intensity, number of bursts, and sucks per burst. Results demonstrated that the system reliably captures both stages of NNS. Significance: This device provides an affordable, portable solution to support clinical decision-making in clinical units, facilitating accurate, objective monitoring of feeding readiness and developmental progression. Full article

Space debris is an increasingly severe problem in the space industry. According to projections, the number of satellites will increase from the current 10,000 to 100,000 by 2030, specially in LEO orbits. This significant rise in the number of satellites threatens space sustainability, forcing satellites to perform more maneuvers to avoid impacts or leading to the production of more and more space debris due to collisions (Kessler Syndrome). Consequently, substantial efforts have been made to detect and track space debris, leading to the development of the current catalogs. However, with existing technology, detecting and tracking small debris remains challenging. In order to improve the current system, several proposals of Space-Based Situational Awareness (SBSA) have been made. These proposals involve satellites equipped with telescopes to detect space debris and determine their orbits. Unlike prior works, focused primarily on detection rates, this research aims to quantify their accuracy in orbit determination as a function of observation duration, the number of observers, and sensor precision. The Unscented Kalman Filter (UKF) is employed as the core estimation algorithm, leveraging both simulated single-case analyses and Monte Carlo simulations to evaluate system performance under various configurations and uncertainties. The results indicate that a constellation of at least three observers with high-precision instruments and sub-kilometer positioning accuracy can reliably estimate debris orbits within an observation period of 4–7 min, with the mean error in position and velocity obtained being 2.2–3 km and 3–4 m/s, respectively. These findings offer critical insights for designing future SBSA constellations and optimizing their operational parameters to address the growing challenge of orbital debris. Full article

Electrochemical sensors have been developed based on polyethylene terephthalate track-etched membranes (PET TeMs) modified by photograft copolymerization of N-vinylformamide (N-VFA) and trimethylolpropane trimethacrylate (TMPTMA). The modification, structure and properties of the modified PET TeMs were thoroughly characterized using scanning electron microscopy (SEM) and atomic force microscopy (AFM), thermogravimetric analysis (TGA), Fourier-transform infrared (FTIR) spectroscopy, gas permeability measurements and contact angle analysis. Optimal membrane modification was achieved using C = 10% (N-VFA), 60 min of UV irradiation and a UV lamp distance of 10 cm. Furthermore, the modified membranes were implemented in a two-electrode configuration for the determination of Eu³⁺, Gd³⁺, La³⁺ and Ce³⁺ ions via square-wave anodic stripping voltammetry (SW-ASV). The sensors exhibited a linear detection range from 10⁻⁷ M to 10⁻³ M, with limits of detection of 1.0 × 10⁻⁶ M (Eu³⁺), 6.0 × 10⁻⁶ M (Gd³⁺), 2.0 × 10⁻⁴ M (La³⁺) and 2.5 × 10⁻⁵ M (Ce³⁺). The results demonstrated a significant enhancement in electrochemical response due to the grafted PET TeMs-g-N-PVFA-TMPTMA structure, and the sensor showed practical applicability and consistent performance in detecting rare earth ions in tap water. Full article

The half-sitting posture is essential for many functional tasks performed by industrial workers. Thus, passive lower-limb exoskeletons, known as wearable chairs, are increasingly used to relieve lower-limb loading in such scenarios. However, although these devices lighten muscle effort during half-sitting tasks, they can disrupt walking mechanics and balance. Moreover, rigorous biomechanical data on joint moments and contact forces during walking with such a device remain scarce. Therefore, this study conducted a biomechanical evaluation of level walking with a wearable chair to quantify its effects on gait and joint loading. Participants performed walking experiments with and without the wearable chair. An optical motion capture system and force plates collected kinematic and ground reaction data. Six-axis force sensors measured contact forces and moments. These measurements were fed into a Newton–Euler inverse dynamics model to estimate lower-limb joint moments and assess joint loading. The contact measurements showed that nearly all rotational load was absorbed at the thigh attachment, while the ankle attachment served mainly as a positional guide with minimal moment transfer. The inverse dynamics analysis revealed that the wearable chair introduced unintended rotational stresses at lower-limb joints, potentially elevating musculoskeletal risk. This detailed biomechanical evidence underpins targeted design refinements to redistribute loads and better protect lower-limb joints. Full article

The integration of robotics and mobile networks (5G/6G) through the Internet of Robotic Things (IoRT) is revolutionizing telemedicine, enabling remote physician participation in scenarios where specialists are scarce, where there is a high risk to them, such as in conflicts or natural disasters, or where access to a medical facility is not possible. Nevertheless, touching a human safely with a robotic arm in non-engineered or even out-of-hospital environments presents substantial challenges. This article presents a novel IoRT approach for healthcare in or from remote areas, enabling interaction between a specialist’s hand and a robotic hand. We introduce the IoRT-in-hand: a smart, lightweight end-effector that extends the specialist’s hand, integrating a medical instrument, an RGB camera with servos, a force/torque sensor, and a mini-PC with Internet connectivity. Additionally, we propose an open-source Android app combining MQTT and ROS for real-time remote manipulation, alongside an Edge–Cloud architecture that links the physical robot with its Digital Twin (DT), enabling precise control and 3D visual feedback of the robot’s environment. A proof of concept is presented for the proposed tele-robotic system, using a 6-DOF manipulator with the IoRT-in-hand to perform an ultrasound scan. Teleoperation was conducted over 2300 km via a 5G NSA network on the operator side and a wired network in a laboratory on the robot side. Performance was assessed through human subject feedback, sensory data, and latency measurements, demonstrating the system’s potential for remote healthcare and emergency applications. The source code and CAD models of the IoRT-in-hand prototype are publicly available in an open-access repository to encourage reproducibility and facilitate further developments in robotic telemedicine. Full article

Vibrating feeders are used to empty bulk materials from storage bins, to feed and dispense materials into weighing bins or dispensers, or to feed materials evenly and smoothly into downstream equipment. The harmonic oscillation of the trough can be provided by an electromagnetic oscillator, which consists of an electromagnet consisting of a core and a coil with a given number of coil turns and armature. The aim of this paper has been to verify whether the working condition of the vibrating feeder, i.e., its fault-free operation and the ability to transport the required mass amount of material, can be described on a basis of the measured vibration values using acceleration sensors. This paper describes three experimental methods that allow us with the use of force sensors to measure the adhesion force of the electromagnet and the deformation force of the bent leaf springs, and the use of acceleration sensors to measure the vibration on the trough and on the steel frame of the vibrating feeder. The highest average value of the effective vibration velocity (56.7 mm·s⁻¹) in the horizontal plane was measured on a steel frame of a vibrating feeder using FR4 Epoxy leaf springs with a stiffness of 47.8 N·mm⁻¹ and a weight of 2.57 kg of conveyed material per trough. The lowest average value of the effective vibration velocity (24.6 mm·s⁻¹) has been measured at a weight of 5.099 kg of material conveyed on the trough. We can state that from the analysis of the measured vibration velocities transmitted to the steel frame of the vibrating feeders, it is possible to monitor the partial phases of their operation and diagnose any faults that may occur. It is also possible to determine whether the optimal amount of bulk material is being loaded onto the trough. Full article

(1) Background: Several tools have been proposed to measure elastic band tension and time under tension (TUT) during elastic band exercise performance. However, current methods are often indirect, non-objective, or expensive. The Elastic Force Evaluation Bracelet (EFEB) is a simple, wearable system designed to estimate both variables. Therefore, the aim of this study was to evaluate the concurrent validity and test–retest reliability of the EFEB as a portable measurement device for application in a therapeutic exercise context. (2) Methods: Thirty-five healthy volunteers were recruited. Exercises with elastic bands were performed on the dominant upper and lower limbs in two sessions with a one-week interval between them, and peak elastic force values were obtained. Validity was assessed in the first session by comparing the force values obtained simultaneously using a force gauge, and the TUT compared to a linear encoder. Test–retest reliability was examined by comparing the measurements obtained between the two sessions. (3) Results: EFEB showed excellent correlation with the force gauge for elastic force (r = 0.883 for shoulder abduction and r = 0.981 for knee extension) and with the linear encoder for TUTs (r = 0.873 and r = 0.883, respectively). EFEB showed good levels of reliability for all four of the following parameters measured: elastic force for shoulder abduction and knee extension (ICC = 0.880 and 0.855, respectively), and TUT in both movements (ICC = 0.768 and 0.765, respectively). (4) Conclusions: In conclusion, EFEB is a valid and reliable device for the measurement of TUT during shoulder abduction and knee extension exercises performed with elastic bands. Full article