Search Results (160)

Background: Core stability is a cornerstone of optimum athletic performance, and its reduction is a risk factor for athletic injuries. Evidence has shown that core impairments can alter lower-limb mechanics through the kinetic chains. Additionally, plantar pressure can be influenced by proximal conditions, such as core muscle fatigue. Therefore, this study aimed to investigate the correlation between core endurance and plantar pressure distribution (PPD) during double-leg stance, single-leg stance, and single-leg squat positions in healthy male athletes. Methods: A total of 21 healthy male recreational athletes between 19 and 26 years of age volunteered to participate in this correlational study. The McGill core endurance test was used to measure the endurance of their core flexors, extensors, and lateral flexors. The participants’ PPD was evaluated using the Tekscan Mobile Mat pressure measurement system in three positions (double-leg stance, single-leg stance, and single-leg squat) for both the dominant and non-dominant feet. Results: There was a poor and insignificant correlation (p > 0.05) between the core flexors’, extensors’, and side flexors’ endurance and the peak and total PPD in all the tested positions for both the dominant and non-dominant feet. Conclusions: Core muscle endurance is neither a component that affects nor is affected by the PPD in this study population. Thus, the endurance of core flexors, extensors, and side flexors may not be considered in screening, examination, or intervention for the total and peak pressure during double-leg stance, single-leg stance, and single-leg squat positions for both the dominant and non-dominant feet in the study population. Further similar studies are warranted in various sports and during dynamic tasks to better understand the different dimensions of the studied relationship in athletes. Full article

Concerning building acoustics, the impact of sound propagation in the building structure can be considered one of the most relevant problems. Floating floors are an efficient solution, composed of a rigid walking surface above a resilient material. Functioning as a spring, the resilient layer must have adequate damping properties and compressive strength against permanent and imposed loads to guarantee its performance over time. In this context, this study aims to completely evaluate the impact sound reduction of composite lightweight floating floors formed by ceramic tiles and recycled rubber mats when subjected to prolonged loads, from material characterization to their application in a hypothetical scenario. This study was based on the dynamic stiffness (ISO 9052-1) and compressive creep (ISO 16534) of the resilient layer and the physical characterization of the ceramic tiles, predicting the present and future (15 years) impact sound reductions and their application in a hypothetical room, considering direct and indirect transmissions paths (ISO 12354-2). The results showed that the lightweight floating floor compositions lost their damping capability to a degree that can reduce their weighted reduction in the impact sound pressure level by up to 2 dB over prolonged periods (15 years). Howsoever, the compositions had considerable initial impact sound insulation capability and adequate performance maintenance over time. Full article

Sleep posture detection is a potentially important component of sleep quality assessment and health monitoring. Accurate identification of sleep postures can offer valuable insights into an individual’s sleep patterns, comfort levels, and potential health risks. For example, improper sleep postures may lead to musculoskeletal issues, respiratory disturbances, and even worsen conditions like sleep apnea. Additionally, for long-term bedridden patients, continuous monitoring of sleep postures is essential to prevent pressure ulcers and other complications. Traditional methods for sleep posture detection have several limitations: wearable sensors can disrupt natural sleep and cause discomfort, camera-based systems raise privacy concerns and are sensitive to environmental conditions, and pressure-sensing mats are often complex and costly. To address these issues, we have developed a low-cost non-contact sleeping posture detection system. Our system features eight optimally distributed triaxial accelerometers, providing a comfortable and non-contact front-end data acquisition unit. For sleep posture classification, we employ an improved density peak clustering algorithm that incorporates the K-nearest neighbor mechanism. Additionally, we have constructed a Parallel Convolutional Spatiotemporal Network (PCSN) by integrating Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and Bidirectional Long Short-Term Memory (Bi-LSTM) modules. Experimental results demonstrate that the PCSN can accurately distinguish six sleep postures: prone, supine, left log, left fetus, right log, and right fetus. The average accuracy is 98.42%, outperforming most state-of-the-art deep learning models. The PCSN achieves the highest scores across all metrics: 98.64% precision, 98.18% recall, and 98.10% F1 score. The proposed system shows considerable promise in various applications, including sleep studies and the prevention of diseases like pressure ulcers and sleep apnea. Full article

Background: Pressure injuries (PIs) and shoulder pain (SP) are frequent problems in individuals with spinal cord injury (SCI), affecting both quality of life and healthcare use. Although pressure relief (PR) is recommended to prevent PIs, it is often not performed regularly, and its long-term benefits remain unclear. Furthermore, some PR methods may contribute to SP, resulting in conflicting clinical guidelines. This study aims to objectively measure PR performance and investigate its long-term relationship with PI and SP. Methods: This study is a longitudinal observational study involving 70 manual wheelchair users with complete SCI. Over one year, participants attend five study visits to assess confounding factors such as comorbidities and shoulder range of motion. PR performance (technique, frequency, duration) is continuously monitored for three weeks after each of the first four visits using textile measurement mats, while SP is assessed weekly with a questionnaire. Causal associations with PI and SP will be examined using directed acyclic graphs and multivariable regression modelling. Results: The study is ongoing. Long-term objective data on PR performance will provide insights into its relationship with PI and SP. Conclusions: Findings will inform clinical practice and contribute to improved evidence-based PR guidelines for individuals with SCI. Full article

The quality of medicinal plants is closely related to the ecological factors of their growing environment, as their efficacy is reflected in the content of key medicinal components, which in turn indicates the quality of the plants. This study measured the daily variations in major constituents, including lobetyolin, polysaccharides, and total flavonoids, in Codonopsis pilosula (Franch.) Nannf., which in the Changzhi and Jincheng regions of Shanxi Province, China is known as Lu Tangshen. Throughout its growth cycle. Additionally, the study explored the effects of 11 ecological factors (both climatic and soil variables) on the primary medicinal components of C. pilosula. Through block experiments and comparisons between future data predictions and actual measurements, the reliability of the model and the consistency of block experimental data were ultimately confirmed. Principal component analysis (PCA), stepwise multiple linear regression analysis, and nonlinear polynomial modeling were employed to investigate the relationships between ecological factors and quality-related constituents (polysaccharides, total flavonoids, and lobetyolin). The results showed that linear models effectively explained daily temperature (DT) with an adjusted R² exceeding 0.8, but due to the inherently nonlinear nature of the data, it is evident that linear models are fundamentally inadequate for accurately capturing the underlying relationships. Therefore, their fit for total flavonoids and lobetyolin was suboptimal. The introduction of nonlinear polynomial models (second-, fourth-, and fifth-order) significantly improved the model fit, indicating the existence of complex nonlinear relationships between ecological factors and medicinal components. For polysaccharides, the fourth-order model demonstrated the best performance, while fifth-order models were required to adequately describe the relationships for total flavonoids and lobetyolin. Based on the best models, the optimal ranges for key ecological factors were identified: polysaccharides were best influenced by atmospheric pressure (AP) between 9.1 and 9.3 kPa, air relative humidity (ARH) between 30% and 60%, 40 cm soil mean annual temperature (40cmMAT) between 27.5 °C and 28.5 °C, soil pH between 9.68 and 9.72, and soil nitrogen (N) content between 7 and 9 mg/kg. For total flavonoids, narrow optimal ranges were observed for temperature, humidity, and pH (MAT between 10 °C and 15 °C, 40cmMAT between 27.5 °C and 28.5 °C, and pH between 9.68 and 9.72). Lobetyolin showed optimal conditions at AP of 9.1 to 9.3 kPa, 40cmMAT of 28.0 °C to 28.5 °C, ARH of 65% to 75%, pH near 9.70, and days after planting (DAP) between 10 and 50. The adoption of higher-order polynomial models clarified critical nonlinear inflection points and optimal ecological ranges, providing a refined reference for enhancing the content of medicinal components. These findings offer valuable insights for precision cultivation strategies aimed at improving the quality of C. pilosula. Full article

Information describing forces applied to the horse are needed to inform regulatory decisions regarding equine health and wellbeing. This study compares forces exerted beneath the noseband and headpiece of a snaffle bridle (SB) and a double bridle (DB). Horses were fitted with the same type of SB and DB. Forces were measured by pressure mats under the noseband (nasal/mandibular) and headpiece (occipital) of the bridle and by force sensors inserted bilaterally between the bit(s) and reins. The noseband was adjusted to 2 finger-equivalents using a tightness gauge. Data were recorded for eleven high-level dressage horses ridden in SB and DB in random order at collected walk, trot (sitting), and canter. The noseband pressures were similar between bridle types. Minimal, maximal, and mean occipital force and pressure were significantly higher for DB at walk, trot, and canter (all p ≤ 0.01), except minimal force for collected canter (p = 0.04). The rein tension for the bridoon bit alone and for the combined bridoon and curb bits was significantly lower than for the snaffle bit. Similar forces occur when ridden in SB and DB except that occipital force and pressure are higher due to the greater weight of the DB, and rein tension is lower for the DB. Full article

There have been significant breakthroughs in developing highly functional myoelectric prostheses, yet individuals who have experienced upper-limb loss consistently report low levels of satisfaction when performing daily tasks while using myoelectric prostheses. This research aims to evaluate the change in the user’s experience after completing a training program in which tasks are designed to facilitate adaptation to the myoelectric arm in performing the activities of daily living. One participant with a left transradial limb difference was recruited for this project. The user’s experience was evaluated by comparing task completion time, trunk and shoulder angles, object control, movement smoothness, vertical ground reaction forces, the center of pressure location, and selected muscle activation of her affected arm between baseline and post-training. The data collection was performed through a motion capture system, a pressure mat, and wireless EMG modules. While indications of potential improvements in balance, muscle efficiency, and functionality were present, the data were inconclusive as to the effectiveness of the training procedure. One outcome measure that showed improvement across most tasks was the task completion time, which, on average, for the targeted Box and Block Test (tBBT) task was reduced by 24.0 s, box lifting by 1.6 s, bottle pouring by 9.0 s, and the pulley task by 8.4 s. This project serves as part of a larger multi-visit study evaluating the effects of home-based functional training on facilitating users’ adaptation to the myoelectric arm. Full article

Eurasian drylands are vital for the global climate and ecological balance. Quantifying spatiotemporal variations in surface soil moisture (SSM) is essential for monitoring water, energy, and carbon cycles. The suitability of recent global-scale surface soil moisture datasets for Eurasian arid and semi-arid regions has not been comprehensively evaluated. This study investigates spatiotemporal trends of five SSM products—MERRA-2, ESACCI, GLEAM, GLDAS, and ERA5—from 1980 to 2023. The performance of these products was evaluated using in situ station data and the three-cornered hat (TCH) method, followed by partial correlation analysis to assess the influence of environmental factors, including mean annual temperature (MAT), mean annual precipitation (MAP), potential evapotranspiration (PET), vapor pressure deficit (VPD), and leaf area index (LAI), on SSM from 1981 to 2018. The results showed consistent SSM patterns: higher values in India, the North China Plain, and Russia, and lower values in the Arabian Peninsula, the Iranian Plateau, and Central Asia. Regionally, MAT, PET, VPD, and LAI increased significantly (0.04 °C yr⁻¹, 1.66 mm yr⁻¹, 0.004 kPa yr⁻¹, and 0.003 m² m⁻² yr⁻¹, respectively; p < 0.05), while MAP rose non-significantly (0.29 mm yr⁻¹). ERA5 exhibited the strongest correlation with in situ station data (R² = 0.42), followed by GLEAM (0.37), ESACCI (0.28), MERRA2 (0.19), and GLDAS (0.17). Additionally, ERA5 showed the highest correlation (correlation = 0.72), while GLEAM had the lowest bias (0.03 m³ m⁻³) and ESACCI exhibited the lowest ubRMSE (0.03 m³ m⁻³). The three-cornered hat method identified ERA5 and GLDAS as having the lowest uncertainties (<0.03 m³ m⁻³), with ESACCI exceeding 0.05 m³ m⁻³ in northern regions. Across land cover types, cropland had the lowest uncertainty among the five SSM products, while forest had the highest. Partial correlation and dominant factor analysis identified MAP as the primary driver of SSM. This study comprehensively evaluated SSM products, highlighting their strengths and limitations. It underscored MAP’s crucial role in SSM dynamics and provided insights for improving SSM datasets and water resource management in drylands, with broader implications for understanding the hydrological impacts of climate change. Full article

Background and Objectives: Musculoskeletal disorders (MD) affect over 1.7 billion people worldwide, with neck and low back pain being prevalent and debilitating conditions. Current treatments include various interventions, but novel approaches are needed to improve functionality and reduce disability. To evaluate the effects of a postural hammock on pain and functionality in people with chronic neck and low back pain. Materials and Methods: A randomized controlled trial was conducted with participants experiencing chronic neck and/or low back pain. They were assigned to either an experimental group using a postural hammock or a control group lying on a mat. Participants underwent five sessions of 10 min each over five consecutive days. Results: Forty-three subjects completed the study. While both groups showed improvements, the experimental group exhibited significant increases in hamstring flexibility and pain tolerance, measured through the Visual Analog Scale (VAS) and pressure pain thresholds (PPT). Postural hammock use demonstrated potential benefits in pain management and flexibility compared to conventional methods. Conclusions: Using a postural hammock may offer benefits for individuals with chronic back pain. Future research should explore combining hammock therapy with other interventions to enhance outcomes and improve the quality of life for patients with back pain. Full article

Amaranthus, a genus in Amaranthaceae, is divided into three subgenera—Amaranthus, Acnida, and Albersia—and contains approximately 70 to 80 species. Understanding its phylogenetic relationships is essential for species classification, genetic diversity assessment, and evolutionary studies. This knowledge is vital for improving Amaranthus utilization in crop improvement and managing the ecological impacts of invasive weeds. In this study, we analyzed the chloroplast genomes of 27 Amaranthus species across all three subgenera to characterize their genomic features and construct a comprehensive phylogenetic tree. Our aim was to elucidate the phylogenetic relationships within the genus and evaluate interspecific affinities among the subgenera. We also addressed the taxonomic ambiguity surrounding A. bouchonii and A. powellii to determine their distinct species within the genus. Chloroplast genome sizes ranged from 149,949 to 150,818 bp, with GC content varying between 36.52% and 36.63%. Comparative structural analyses confirmed highly conserved quadripartite structures, gene content, and organization, comprising 87 protein-coding genes, 37 tRNAs, and 8 rRNAs. Repeat and codon usage analyses revealed conserved repeat patterns and a preference for codons ending in A or U. Selection pressure analysis indicated a predominantly purifying selection, with matK showing signs of positive selection, particularly in A. spinosus. Phylogenetic analysis of 80 protein-coding genes confirmed the monophyly of subgenus Amaranthus but found Alberisa and Acnida to be paraphyletic. Despite their morphological similarity, A. bouchonii and A. powellii were placed in separate clades within subgenus Amaranthus, with A. bouchonii clustering with A. retroflexus, and A. powellii aligning with the A. hybridus complex. Additionally, we identified 16 variable regions as potential molecular markers for species identification. Our study provides the most comprehensive Amaranthus chloroplast genome dataset to date, offering new insights into its evolutionary relationships and valuable genomic resources for taxonomy, germplasm management, and invasive risk assessment. Full article

The accurate measurement of human balance is required in numerous analysis and training applications. Force plates are frequently used but are too costly to be suitable for home-based systems such as balance training. A growing body of research and commercial products use Pressure-Sensitive Mats (PSMs) for balance measurement. Low-cost PSMs are constructed with a piezoresistive material and use copper tracks as conductors. However, these lack accuracy, as they often have a low resolution and suffer from noise, non-repeatable effects, and crosstalk. This paper proposes novel algorithms that enable the Centre of Pressure (CoP) to be computed using low-cost PSM designs with significantly higher accuracy than is currently achievable. A mathematical model of a general low-cost PSM was developed and used to select the design of the PSM (track width and placement) that maximises CoP accuracy. These yield new optimal PSM geometries that decrease the mean absolute CoP error from 17.37% to 5.47% for an 8 × 8 sensor layout. Then, knowledge of the footprint was used to further optimise accuracy, showing a decrease in absolute error from 17.37% to 3.93% for an 8 × 8 sensor layout. A third algorithm was derived using models of human movement to further reduce measurement error. Full article

As a novel seismic reinforcement measure for earth–rock dams, the PFA-reinforced rockfill materials technology lacks comprehensive investigation into both its reinforcement efficacy and the underlying mechanisms. In this study, we establish a fluid–solid coupling model of PFA-reinforced rockfill materials utilizing the matrix discrete element software MatDEM3.24, developed independently by Nanjing University. The model simulates the dynamic process of polymer grouting within the rockfill body and analyzes the impact of slurry diffusion patterns and various grouting pressures on polymer grouting. Our findings reveal that the diffusion of polymer foam in rockfill occurs in three distinct stages, which are characteristic of penetration grouting. Moreover, we observed that grouting pressure had a significant effect on the diffusion range of the slurry, with greater sensitivity noted in the Z direction. Additionally, we observed a decrease in porosity with increasing grouting pressure, and stress augmentation exhibited an approximately linear relationship with grouting pressure, but the stress augmentation in different directions was different. These research outcomes offer valuable insights into the practical implementation and optimization of PFA-reinforced rockfill materials technology, bearing considerable engineering implications. Full article

Convolutional neural networks (CNNs) have been widely and successfully demonstrated for closed set recognition in gait identification, but they still lack robustness in open set recognition for unknown classes. To improve the disadvantage, we proposed a convolutional neural network autoencoder (CNN-AE) architecture for user classification based on plantar pressure gait recognition. The model extracted gait features using pressure-sensitive mats, focusing on foot pressure distribution and foot size during walking. Preprocessing techniques, including region of interest (ROI) selection, feature image extraction, and data horizontal flipping, were utilized to establish a CNN model that assessed gait recognition accuracy under two conditions: without carried items and carrying a 500 g object. To extend the application of the CNN to open set recognition for unauthorized personnel, the proposed convolutional neural network-autoencoder (CNN-AE) architecture compressed the average foot pressure map into a 64-dimensional feature vector and facilitated identity determination based on the distances between these vectors. Among 60 participants, 48 were classified as authorized individuals and 12 as unauthorized. Under the condition of not carrying an object, an accuracy of 91.218%, precision of 93.676%, recall of 90.369%, and an F1-Score of 91.993% were achieved, indicating that the model successfully identified most actual positives. However, when carrying a 500 g object, the accuracy was 85.648%, precision was 94.459%, recall was 84.423%, and the F1-Score was 89.603%. Full article

This study compared the effects of Mat Pilates training on cardiovascular risk markers in postmenopausal women with single or multiple cardiometabolic conditions. Forty-four women were divided into single-condition (SINGLE; n = 20) and multiple-condition (MULTI; n = 24) groups. Both groups completed Mat Pilates three times per week for 12 weeks. Measurements of resting blood pressure, body composition, dietary intake, and blood markers were taken before and after the intervention. A Generalized Estimating Equation was used for hypothesis testing. MULTI presented higher body mass, BMI, fat mass, and waist circumference. Systolic blood pressure decreased more in SINGLE (−13 ± 15 mmHg) than in MULTI (−3 ± 16 mmHg, p interaction = 0.016 with diastolic reductions in both groups (SINGLE: −9 ± 12 mmHg; MULTI: −2 ± 11 mmHg, p interaction = 0.053). Triglycerides decreased only in SINGLE (−40 ± 98 mg/dL vs. +31 ± 70 mg/dL in MULTI, p interaction = 0.006), while no significant changes were observed in cholesterol levels. Adiponectin levels decreased in both groups (SINGLE: −1.5 ± 16.3; MULTI: −9.3 ± 12.4 vs. µg/dL, p time = 0.015). Glycated hemoglobin levels decreased over time in both groups (−0.3 ± 0.5% in SINGLE, −0.5 ± 0.6% in MULTI, p time < 0.001), with no significant changes in blood glucose. These findings suggest that Mat Pilates may be more effective in reducing cardiometabolic risk factors in women with a single condition compared to those with multiple conditions. Full article

Deep learning, a subfield of artificial intelligence that uses neural networks with multiple layers, is rapidly changing healthcare. Its ability to analyze large datasets and extract relevant information makes it a powerful tool for improving diagnosis, treatment, and disease management. The integration of DL with pressure mats—which are devices that use pressure sensors to continuously and non-invasively monitor the interaction between patients and the contact surface—is a promising application. These pressure platforms generate data that can be very useful for detecting postural anomalies. In this paper we will discuss the application of deep learning algorithms in the analysis of pressure data for the detection of postural asymmetries in 139 patients aged 3 to 20 years. We investigated several main tasks: patient classification, hemibody segmentation, recognition of specific body parts, and generation of automated clinical reports. For this purpose, convolutional neural networks in their classification and regression modalities, the object detection algorithm YOLOv8, and the open language model LLaMa3 were used. Our results demonstrated high accuracy in all tasks: classification achieved 100% accuracy; hemibody division obtained an MAE of approximately 7; and object detection had an average accuracy of 70%. These results demonstrate the potential of this approach for monitoring postural and motor disabilities. By enabling personalized patient care, our methodology contributes to improved clinical outcomes and healthcare delivery. To our best knowledge, this is the first study that combines pressure images with multiple deep learning algorithms for the detection and assessment of postural disorders and motor disabilities in this group of patients. Full article