Search Results (6,378)

This study aimed to establish and assess the reliability of spino-pelvic and sagittal balance parameters measured during walking in patients with back pain, some of whom had radiological signs of sagittal imbalance, reflecting real-world clinical conditions. Dynamic assessment offers an alternative to conventional static measurements, potentially improving the evaluation of sagittal balance. Ten patients aged 56–73 years completed a six-minute walking assessment while being monitored by the optoelectric Qualisys Motion Capture System. Forty-nine reflective markers were placed to measure the spino-pelvic and sagittal balance parameters across five gait phases: pre-walk, initial-walk, mid-walk, end-walk, and post-walk. Test–retest reliability was evaluated using the intraclass correlation coefficient (ICC). The results showed excellent reliability for thoracic kyphosis angle (ICC = 0.97), C7-L5 sagittal trunk shift (ICC = 0.91), and global tilt angle (ICC = 0.99); good reliability for auditory meatus-hip axis sagittal trunk shift (ICC = 0.85); and moderate reliability for pelvic angle (ICC = 0.57), lumbar lordosis angle (ICC = 0.72), and sagittal trunk angle (ICC = 0.73). Despite minor marker placement inconsistencies and variations in body movement across trials, the findings support the use of this dynamic assessment method in research settings. Its clinical application could also enhance diagnostic accuracy and treatment planning for patients with sagittal balance disorders, allowing for better-tailored therapeutic interventions. Full article

Emotion recognition in video aims to estimate human emotions using acoustic, visual, and linguistic information. This problem is considered multimodal and requires learning different modalities, such as visual, verbal, and vocal cues. Although previous studies have focused on developing sophisticated deep learning models, this work proposes a different approach using dynamic restrained adaptive loss inspired by multitask learning to understand multimodal inputs jointly. This training strategy allows predictions from one modality to enhance the accuracy of predictions from other modalities, mirroring the concept of multitask learning, where the results of one task can improve the performance of related tasks. Furthermore, this work introduces the extended multimodal bottleneck transformer, an efficient and effective mid-fusion method designed for problems involving more than two modalities to enhance the performance of emotion recognition systems. The proposed method significantly improves results compared to other end-to-end multimodal fusion techniques on three multimodal benchmarks—Interactive Emotional Dyadic Motion Capture (IEMOCAP), Carnegie Mellon University Multimodal Opinion Sentiment and Emotion Intensity (CMU-MOSEI), and the Chinese Multimodal Sentiment Analysis dataset with independent unimodal annotations (CH-SIMS). Full article

Surface electromyography (sEMG) is increasingly important for prevention, diagnosis, and rehabilitation in healthcare. The continuous monitoring of muscle electrical activity enables the detection of abnormal events, but existing sEMG systems often rely on disposable pre-gelled electrodes that can cause skin irritation and require precise placement by trained personnel. Wearable sEMG systems integrating textile electrodes have been proposed to improve usability; however, they often suffer from poor skin–electrode coupling, leading to higher impedance, motion artifacts, and reduced signal quality. To address these limitations, we propose a preliminary model of smart socks, integrating biocompatible hybrid polymer electrodes positioned over the target muscles. Compared with commercial Ag/AgCl electrodes, these hybrid electrodes ensure lower the skin–electrode impedance, enhancing signal acquisition (19.2 ± 3.1 kΩ vs. 27.8 ± 4.5 kΩ for Ag/AgCl electrodes). Moreover, to the best of our knowledge, this is the first wearable system incorporating hydrogel-based electrodes in a sock specifically designed for the analysis of lower limb muscles, which are crucial for evaluating conditions such as sarcopenia, fall risk, and gait anomalies. The system incorporates a lightweight, wireless commercial module for data pre-processing and transmission. sEMG signals from the Gastrocnemius and Tibialis muscles were analyzed, demonstrating a strong correlation (R = 0.87) between signals acquired with the smart socks and those obtained using commercial Ag/AgCl electrodes. Future studies will further validate its long-term performance under real-world conditions and with a larger dataset. Full article

Background/Objectives: Various surgical methods have been proposed for the treatment of comminuted Mason III/IV radial head fractures. In particular, the advantages and disadvantages between prosthesis implantation (RHA) or radial head resection (RHR) are not sufficiently quantified in the current literature. Methods: A systematic literature search was conducted using PubMed Web of Science, Cochrane Library, and Embase in February 2024. Studies conducted on patients with Mason type III or IV radial head fractures and studies relating to surgical methods, including radial head resection or Radial head prosthesis implantation, were included. The two methods were evaluated in terms of clinical and functional results through the DASH score (Disability of the arm, shoulder, and hand), Mayo Elbow Performance Index (MEPI), and flexion-extension range of motion. The onset of osteoarthritis and complications were also assessed. Risk of bias and quality of evidence were assessed using Cochrane guidelines. Results: A total of 345 articles were evaluated and, of these, 21 were included in the study for a total of 552 patients. The results of the meta-analysis showed no significant differences in favor of RHA or RHR in terms of Mayo Elbow Performance (p = 0.58), degrees of flexion (p = 0.689), degrees of extension deficit (p = 0.697), and overall incidence of complications (p = 0.389), while it highlighted a statistically significant difference in terms of DASH score (19.2 vs. 16.2, respectively; p = 0.008) and subjects who developed osteoarthritis (13.4% vs. 47.3%, respectively; p = 0.046). Conclusions: The results of this meta-analysis confirm that both surgical methods provide good functional outcomes, with no significant differences in MEPI, DASH, and range of motion. However, a higher incidence of post-traumatic osteoarthritis was observed in patients undergoing RHR. Additionally, RHR patients exhibited slightly worse functional outcomes in the DASH score; however, this difference is not substantial enough to be considered clinically significant. These findings suggest that while both techniques are viable, RHA may be preferable in patients at higher risk of joint degeneration and instability, and the choice of treatment should be tailored to individual patient characteristics. Full article

Accurate and reliable human pose estimation (HPE) is essential in interactive systems, particularly for applications requiring personalized adaptation, such as controlling cooperative robots and wearable exoskeletons, especially for healthcare monitoring equipment. However, continuously maintaining diverse datasets and frequently updating models for individual adaptation are both resource intensive and time-consuming. To address these challenges, we propose a meta-transfer learning framework that integrates multimodal inputs, including high-frequency surface electromyography (sEMG), visual-inertial odometry (VIO), and high-precision image data. This framework improves both accuracy and stability through a knowledge fusion strategy, resolving the data alignment issue, ensuring seamless integration of different modalities. To further enhance adaptability, we introduce a training and adaptation framework with few-shot learning, facilitating efficient updating of encoders and decoders for dynamic feature adjustment in real-time applications. Experimental results demonstrate that our framework provides accurate, high-frequency pose estimations, particularly for intra-subject adaptation. Our approach enables efficient adaptation to new individuals with only a few new samples, providing an effective solution for personalized motion analysis with minimal data. Full article

Population aging is an inevitable trend in contemporary society, and the application of technologies such as human–machine interaction, assistive healthcare, and robotics in daily service sectors continues to increase. The lower limb exoskeleton rehabilitation robot has great potential in areas such as enhancing human physical functions, rehabilitation training, and assisting the elderly and disabled. This paper integrates the structural characteristics of the human lower limb, motion mechanics, and gait features to design a biomimetic exoskeleton structure and proposes a human–machine integrated lower limb exoskeleton rehabilitation robot. Human gait data are collected using the Optitrack optical 3D motion capture system. SolidWorks 3D modeling software Version 2021 is used to create a virtual prototype of the exoskeleton, and kinematic analysis is performed using the standard Denavit–Hartenberg (D-H) parameter method. Kinematic simulations are carried out using the Matlab Robotic Toolbox Version R2018a with the derived D-H parameters. A physical prototype was fabricated and tested to verify the validity of the structural design and gait parameters. A controller based on BP fuzzy neural network PID control is designed to ensure the stability of human walking. By comparing two sets of simulation results, it is shown that the BP fuzzy neural network PID control outperforms the other two control methods in terms of overshoot and settling time. The specific conclusions are as follows: after multiple walking gait tests, the robot’s walking process proved to be relatively safe and stable; when using BP fuzzy neural network PID control, there is no significant oscillation, with an overshoot of 5.5% and a settling time of 0.49 s, but the speed was slow, with a walking speed of approximately 0.18 m/s, a stride length of about 32 cm, and a gait cycle duration of approximately 1.8 s. The model proposed in this paper can effectively assist patients in recovering their ability to walk. However, the lower limb exoskeleton rehabilitation robot still faces challenges, such as a slow speed, large size, and heavy weight, which need to be optimized and improved in future research. Full article

We investigate the molecular dynamics of glycolide/lactide/caprolactone (Gly/Lac/Cap) copolymers using differential scanning calorimetry (DSC), Fourier transform infrared spectroscopy (FTIR), ¹H second-moment, ¹H spin-lattice relaxation time (T₁) analysis, and ¹³C solid-state NMR over a temperature range of 100–413 K. Activation energies and correlation times of the biopolymer chains were determined. At low temperatures, relaxation is governed by the anisotropic threefold reorientation of methyl (-CH₃) groups in lactide. A notable change in T₁ at ~270 K and 294 K suggests a transition in amorphous phase mobility due to translational diffusion, while a second relaxation minimum (222–312 K) is linked to CH₂ group dynamics influenced by caprolactone. The activation energy increases from 5.9 kJ/mol (methyl motion) to 22–33 kJ/mol (segmental motion) as the caprolactone content rises, enhancing the molecular mobility. Conversely, lactide restricts motion by limiting rotational freedom, thereby slowing global dynamics. DSC confirms that increasing ε-caprolactone lowers the glass transition temperature, whereas higher glycolide and lactide content raises it. The onset temperature of main-chain molecular motion varies with the composition, with greater ε-caprolactone content enhancing flexibility. These findings highlight the role of composition in tuning relaxation behavior and molecular mobility in copolymers. Full article

Electric Road Systems (ERS) are emerging technologies that enable electricity transfer to electric vehicles in motion. However, their implementation presents challenges due to high energy demands and infrastructure requirements. This technology offers a significant opportunity for decarbonizing road freight transport, one of the most carbon-intensive sectors, contributing to the European Union’s climate goals. This study hypothesizes that implementing an inductive ERS for freight transport along the AP-7 corridor in Spain will generate environmental benefits—primarily through greenhouse gas (GHG) emission reductions—that outweigh the associated socioeconomic costs, making it a viable decarbonization strategy. To test this hypothesis, an impact assessment framework based on Cost–Benefit Analysis (CBA) is conducted, incorporating climate change and other environmental benefits. The framework is applied to a section of the Mediterranean Highway Corridor AP-7 in Spain. The results indicate that the most significant benefits are derived from positive environmental impacts and lower vehicle operation costs. Through a sensitivity analysis, our research identifies key variables affecting the system’s socioeconomic profitability, including payload capacity, volatility of energy prices and shadow prices of GHG emissions. The study provides insights for policymakers to optimize ERS deployment strategies, ensuring maximum social benefits while addressing economic and environmental challenges. Full article

The object of the present research is a machine-tractor unit based on a tractor consisting of an energy (high-energy tractor—EM) and a technological (additional traction axle—TM) module. This tractor uses three-row crop cultivators, each with an operating width of 4.2 m. One of these cultivators is attached centrally to the technological module, and the other two are connected to the sides of the energy module in its front part. The research aimed to determine the influence degree of such a unit scheme and design parameters on the stability of its movement in the horizontal plane. The theoretical analysis was performed using amplitude (AFC) and phase (PFC) frequency characteristics obtained based on the developed mathematical model of the modular unit movement when it processes a disturbing effect. The difference in traction resistances of the unit’s side cultivators was adopted as the latter. Finally, it was established that blocking the vertical hinge of the TM contributes to a significant increase in the unit motion stability. The practical implementation of this method of joining EM and TM in the horizontal plane allows the unit to work out a disturbing influence in the frequency range 0.65–5.50 s⁻¹ to eliminate the resonant AFC peaks and reduce their value. When the TM hinge is locked at a torque oscillation frequency of 2.5 s⁻¹, the AFC is 2.82 times less than when it is free. Blocking the TM vertical hinge increases the delay in the unit’s response to a disturbance. At an oscillation frequency of the latter of 1.5 s⁻¹, this delay is 0.6 s, and at a frequency of 0.5 s⁻¹, it increases by more than three times, reaching a level of 1.9 s. Full article

This research examines the free vibration characteristics of composite ring-like structures enhanced with carbon nanotubes (CNTs), taking into account the effects of CNT agglomeration. The structural framework comprises two concentric composite rings linked by elastic springs, creating a coupled beam ring (CBR) system. The first-order shear deformation theory (FSDT) is applied to account for transverse shear deformation, while Hamilton’s principle is employed to formulate the governing equations of motion. The effective mechanical properties of the composite material are assessed with regard to CNT agglomeration, which has a significant impact on the elastic modulus and the overall dynamic behavior of the structure. The numerical analysis explores the influence of porosity distribution, boundary conditions (BCs), and the stiffness of the springs on the natural vibration frequencies (NVFs). The results demonstrate that an increase in CNT agglomeration leads to a reduction in the stiffness of the composite, consequently decreasing the NVFs. Furthermore, asymmetric porosity distributions result in nonlinear fluctuations in NVFs due to irregularities in mass and stiffness, whereas uniform porosity distributions display a nearly linear relationship. This study also emphasizes the importance of boundary conditions and elastic coupling in influencing the vibrational response of CBR systems. These findings offer significant insights for the design and optimization of advanced composite ring structures applicable in aerospace, nanotechnology, and high-performance engineering systems. Full article

Recycling polyethylene terephthalate (rPET) from packaging materials consumes a vast amount of energy and incurs significant economic and environmental costs. This study proposes directly recycling rPET into woven fabrics to eliminate reprocessing while still preserving the mechanical performance of the material. The mechanical properties of rPET were tested along two orthogonal directions, and the resulting test data were used to calibrate an elasto-plastic model in order to capture the constitutive behaviour of the material. Additionally, the virtual weaving of rPET fibres into fabrics was modelled using finite element analysis (FEA) to replicate the actual manufacturing process. The results show that rPET that is directly recycled into woven fabrics exhibits superior performance to the same material derived from reprocessing. A strong anisotropy of rPET materials was observed, with distinct elastic and ductile behaviours. The FEA simulation also revealed the critical role of the ductility of rPET fibres when used as warp yarns. The process parameters to achieve a successful weaving operation for different yarn configurations, taking into account the motion and tension of the fibres during manufacture, were also identified. A further sensitivity study highlights the influence of friction between the fibres on the tension force of warp yarns. The virtual manufacture-by-weaving model suggests that utilising rPET with a simplified recycling approach can lead to the sustainable manufacture of fabrics with broad industrial applications. Full article

In complex marine environments, accurate prediction of maneuvering motion is crucial for the precise control of intelligent ships. This study aims to enhance the predictive capabilities of maneuvering motion for intelligent ships in such environments. We propose a novel maneuvering motion prediction method based on Long Short-Term Memory (LSTM) and Multi-Head Attention Mechanisms (MHAM). To construct a foundational dataset, we integrate Computational Fluid Dynamics (CFD) numerical simulation technology to develop a mathematical model of actual ship maneuvering motions influenced by wind, waves, and currents. We simulate typical operating conditions to acquire relevant data. To emulate real marine environmental noise and data loss phenomena, we introduce Ornstein–Uhlenbeck (OU) noise and random occlusion noise into the data and apply the MaxAbsScaler method for dataset normalization. Subsequently, we develop a black-box model for intelligent ship maneuvering motion prediction based on LSTM networks and Multi-Head Attention Mechanisms. We conduct a comprehensive analysis and discussion of the model structure and hyperparameters, iteratively optimize the model, and compare the optimized model with standalone LSTM and MHAM approaches. Finally, we perform generalization testing on the optimized motion prediction model using test sets for zigzag and turning conditions. The results demonstrate that our proposed model significantly improves the accuracy of ship maneuvering predictions compared to standalone LSTM and MHAM algorithms and exhibits superior generalization performance. Full article

Contemporary fluid motion modelling techniques, including the phenomenon of liquid sloshing in tanks, are increasingly associated with the use of artificial intelligence methods. In addition to the still frequently used traditional analysis methods and techniques, such as FEM, CFD, VOF and FSI, there is an increasing number of publications that use elements of artificial intelligence. Among others, artificial neural networks and deep learning techniques are used here in the field of prediction and approximation, as well as genetic and other multi-agent algorithms for optimization. This article analyses of the current state of research using the above techniques and the possibilities and main directions of their further development. Full article

Measuring breathing changes during exercise is crucial for healthcare applications. This study used wearable capacitive sensors to capture abdominal motion and extract breathing patterns. Data preprocessing methods included filtering and normalization, followed by feature extraction for classification. Despite the growing interest in respiratory monitoring, research on a deep learning-based analysis of breathing data remains limited. To address this research gap, we optimized CNN and ResNet through systematic hyperparameter tuning, enhancing classification accuracy and robustness. The optimized ResNet outperformed the CNN in accuracy (0.96 vs. 0.87) and precision for Class 4 (0.8 vs. 0.6), demonstrating its capability to capture complex breathing patterns. These findings highlight the importance of hyperparameter optimization in respiratory monitoring and suggest ResNet as a promising tool for real-time assessment in medical applications. Full article

Background: The choice of software package (SP) for image processing affects the reproducibility of myocardial blood flow (MBF) values in [¹³N]NH₃ PET/CT scans. However, the impact of motion correction (MC) tools—integrated software motion correction (ISMC) or data-driven motion correction (DDMC)—on the inter-software reproducibility of MBF has not been studied. This research aims to evaluate reproducibility among three commonly used SPs and the role of MC. Methods: Thirty-six PET/CT studies from patients without myocardial ischemia or infarction were processed using QPET, Corridor-4DM (4DM), and syngo.MBF (syngo). MBF and coronary flow reserve (CFR) values were obtained without motion correction (NMC) and with ISMC and DDMC. Intraclass correlation coefficients (ICC) and Bland-Altman (BA) plots were used to analyze agreement. Results: Good or excellent reproducibility (ICC ≥ 0.77) was found for rest-MBF values, regardless of the SPs or use of MC. In contrast, stress-MBF and CFR values presented mostly a moderate agreement when NMC was used. The RCA territory consistently had the lowest agreement in stress-MBF and CFR in the comparisons involving QPET. The use of MC, particularly DDMC, enhanced the reproducibility of most of the stress-MBF and CFR values by improving ICCs and reducing bias and limits of agreement (LoA) in BA analysis. Conclusions: MBF quantification agreement between SPs is strong for rest-MBF values but suboptimal for stress-MBF and CFR values. MC tools, especially DDMC, are recommended for improving reproducibility in stress-MBF assessments, although differences in SP reproducibility up to 0.77 mL/g/min in global stress-MBF and up to 0.88 in global CFR remain despite the use of MC. Full article