Search Results (922)

Parkinson’s disease (PD), with its rising global prevalence, poses severe risks from falls and motor impairments. Current fall risk assessments rely heavily on subjective clinical evaluations, underscoring the need for quantitative methods. In this exploratory study, wearable inertial and photoelectric sensors attached to the limbs and trunk were used to objectively collect biomechanical movement data during standardized MDS-UPDRS motor assessments. Leveraging the clinically validated correlation between Hoehn-Yahr (H-Y) staging and fall risk, we propose a data-driven framework to quantify risk. Mutual information (MI) analysis links biomechanical features to H-Y stages, generating a weighted Fall FRS (FRS). Machine learning validation was further performed to preliminarily evaluate the discriminative capability of the proposed FRS in stratifying patients by risk severity. Based on a cohort of 92 PD patients, experimental results on the independent test set showed that incorporation of the FRS improved classification accuracy from 50.00% to 82.14%, while the macro-average AUC increased from 0.698 to 0.907. These findings suggest that wearable sensor–based biomechanical assessment may provide useful quantitative information for exploratory fall-risk stratification in PD patients. Full article

Background/Objectives: To describe the progression of axial and segmental ocular biometric lengths and refractive status in adolescents and study independent associations between these changes and baseline ocular biomechanics. Methods: Prospective cohort of 126 eyes from 63 individuals followed for 2.5 years. Data from general health and lifestyle were collected through a validated questionnaire. Data from ocular biometry (IOL MASTER 700^®), objective refraction, and ocular biomechanics (Corvis ST^®) were collected at baseline and the end of follow-up timepoints. Biomechanical parameters were correlated with the variation in axial length (d_AL), vitreous cavity length (d_VCL), and spherical equivalent (d_SE). Multivariable linear regression models (one eye randomly assigned) adjusted for age, SE, and AL were developed to identify independent associations between baseline biomechanics and d_AL, d_VCL, and d_SE. Results: The cohort of the present work had a mean age of 14.1 ± 2.6 years at baseline. Variations of 0.122 ± 0.17 mm, 0.092 ± 0.17 mm, and −0.32 ± 0.9 D were found in AL, VCL, and SE at follow-up, respectively. Within the multivariable regression models, the biomechanical parameters found to be independently associated with d_AL (model 1), d_VCL (model 2), and d_SE (model 3) were as follows: Model 1—Biomechanically corrected IOP (bIOP), Integrated Radius (IR), and A2 Deflection Area (A2DArea); Model 2—bIOP, IR, and A2DArea; and Model 3—IR and WholeEyeMovementMAxTime (MaxWEMT). Conclusions: The study of ocular biomechanical behavior may play a pivotal role in the risk assessment of ocular elongation and myopic progression. This work found independent associations between ocular biomechanical behavior at baseline and axial and segmental ocular elongation and refractive myopization, mainly including bIOP, IR, and MaxWEMT. Full article

Recent research highlights the importance of effective teaching methodologies to enhance scientific learning from the earliest years of schooling. The present study investigates the effects of the Spaced Learning (SL) methodology in science education in Italian primary schools, with particular attention to scientific knowledge and students’ scientific reasoning skills. The study involved 401 third- and fourth-grade pupils (aged 8–11) from three primary schools in Palermo, Italy, during the 2024/2025 school year. A quasi-experimental design was adopted, with classes assigned to an experimental group that adopted SL or to a control group that followed traditional teaching. The intervention lasted seven months and was supported by continuous teacher training and collaboration with university researchers. The data were collected through a pre-test/post-test questionnaire developed and validated by experts in physics education. The tool assessed the students’ general scientific reasoning skills through multiple-choice items inserted in everyday life contexts. Descriptive statistics were calculated and between-group comparisons were made by Student’s t-test or Welch’s t-test when the assumption of homogeneity of variances was not met. The results indicate that students exposed to the SL methodology achieved higher post-test scores than those who received traditional education, suggesting a positive effect of time-distributed, movement-integrated learning on science learning outcomes. Such results support the effectiveness of SL as a promising teaching approach to promote meaningful and lasting scientific learning in primary school. Full article

Background: Promoting physical activity in early childhood is essential for supporting motor, cognitive, and socio-emotional development. Outdoor environments rich in natural stimuli may further enhance these benefits. Recent approaches suggest that integrating movement with narrative contexts may provide additional developmental opportunities by engaging cognitive and affective processes. This study examined the associations between three outdoor motor activity approaches—Storytelling in Motion, Free Play, and Traditional Motor Instruction—and motor competence and inhibitory control in preschool children. Methods: Eighty-seven preschool children (M_age = 5.32 ± 0.60 years) participated in a quasi-experimental pretest–posttest study conducted in outdoor educational settings in Northern Italy, including a natural environment, a structured playground, and a school courtyard. Participants were assigned at the class level to three groups of unequal size (Storytelling in Motion n = 36, Free Play n = 22, Traditional Motor Instruction n = 29). All groups completed ten weekly sessions lasting approximately 60 min. Motor competence was assessed using selected tasks derived from the Test of Motor Competence and the Movement Assessment Battery for Children-2, while inhibitory control was evaluated using the Day/Night Test. Results: Significant Time × Group interactions were observed for several outcomes. The Storytelling in Motion group showed numerically greater improvements at a descriptive level in dynamic balance (Heel-to-Toe Walking: p < 0.001, η²p = 0.229) and fine motor control (Bicycle Trail: p < 0.001, η²p = 0.194) compared to the other groups. The Free Play group showed greater improvements in coordination-related tasks and upper-body strength. No significant differences between groups were observed for inhibitory control. These differences remained significant after adjustment but should be interpreted cautiously due to the non-randomized design. Accordingly, these findings should be considered preliminary and hypothesis-generating (ANCOVA, p < 0.05). Conclusions: Narrative-based outdoor motor activities may represent a potentially relevant approach; however, no firm conclusions can be drawn from the present design. Given the quasi-experimental nature of the study and the contextual differences between intervention settings, the findings should be interpreted with caution. Future research using randomized controlled designs and standardized environments is needed to clarify the independent and combined effects of instructional and environmental factors. Full article

Robust exercise recognition is essential for robot-assisted lower-limb rehabilitation, where misclassifications of sensor-derived movements can degrade therapy execution and supervision. This study proposes a novel hybrid weighted stacking ensemble to increase the efficiency of the intelligent module of the LegUp parallel robotic system for lower limb rehabilitation. The approach combines a Residual Multilayer Perceptron (ResMLP) and an optimized Kernel Extreme Learning Machine (KELM), where model hyperparameters are tuned using Optuna and the base-model probability outputs are fused through optimized weighting and a meta-learner. Experiments were conducted on a five-class dataset built from nine IMU orientation features acquired from three sensors placed on the healthy limb. Four meta-learners were evaluated (Logistic Regression, Random Forest, Gradient Boosting, and AdaBoost), with AdaBoost providing the best overall performance. To further assess the robustness and generalization capability of the proposed approach, a 5-fold cross-validation procedure was performed for the ResMLP, KELM, and the hybrid ensemble models. The proposed stacking hybrid ensemble consistently surpassed the performance of the strongest individual classifiers as well as the original LegUp Multilayer Perceptron model. These results indicate that combining residual learning with kernel-based classification in a weighted stacking framework yields a stable and high-performing solution for multi-class rehabilitation exercise recognition. Full article

Background: The effectiveness of striking techniques in combat sports depends not only on peak force but also on how force is applied over time. The knife-hand strike (sonkal taerigi) in ITF taekwon-do can be executed in inward and outward directions; however, biomechanical differences between these variants and the role of limb laterality remain unclear. This study aimed to evaluate the effects of movement direction and limb side on selected kinetic variables. Methods: Fifteen experienced male taekwon-do practitioners (black belts, ≥10 years of training) performed knife-hand strikes using both hands (right and left) and two movement directions (inward and outward) on a ground reaction force platform. Three trials were recorded for each condition. The analyzed variables included peak resultant force (F), relative force (Fr), contact time (t), and impulse (J). Paired t-tests or Wilcoxon signed-rank tests were applied depending on data distribution, and effect sizes were calculated. Results: Inward strikes produced significantly higher resultant force (F), relative force (Fr), impulse (J), and slightly longer contact time (t) compared to outward strikes (all p ≤ 0.001), with large to very large effect sizes. The effect of limb side was limited and statistically significant only for impulse (p = 0.031), indicating generally high bilateral symmetry. Differences in contact time, although significant, were of negligible practical magnitude. Conclusions: Movement direction is the primary determinant of biomechanical effectiveness in the sonkal taerigi technique. Inward strikes provide more favorable mechanical conditions for force and impulse generation, whereas the influence of limb laterality is minimal. Impulse appears to be a sensitive and functionally relevant indicator of striking performance and may be particularly useful for performance assessment and training monitoring. Full article

Rift Valley fever (RVF) is a viral zoonosis endemic in Madagascar, threatening human and animal health as well as the economy. Trade-related livestock movements are a major factor in the spread of RVF virus. While previous RVF research in Madagascar has focused on farmers or general ecology, this study is the first to specifically target livestock traders, the primary drivers for long-distance viral spread, in the Alaotra Mangoro endemic hotspot. This study aimed to assess the level of knowledge, prevailing attitudes and current practices regarding RVF among people engaged in livestock trade in the Alaotra Mangoro region, as well as the factors associated with these KAPs. A descriptive and analytical cross-sectional survey was conducted among 406 livestock traders in five districts of the Alaotra Mangoro region, using a structured questionnaire. A multi-stage sampling approach was employed, utilising purposive selection of markets followed by snowball sampling to reach informal traders often missed by traditional surveys. Generalised linear mixed models were used to analyse factors associated with KAPs regarding RVF. Awareness of RVF was very low (only 18.5% respondents had heard of it), with significant regional disparities (0% in Anosibe An’Ala versus 51.6% in Moramanga). Veterinarians (15.5%), family (12.8%), radio (9.6%) and neighbours (9.6%) were the main sources of information. Understanding of symptoms and modes of transmission (particularly mosquito bites) was limited. Higher levels of education (OR = 181.6; 95% CI: 29.9–1123.7; p < 0.001) and older age (50–60 years) were associated with better knowledge. Proactive attitudes were scarce (21.4%), although more than half (53.4%) believed that RVF is a real disease. Perception of personal risk and the contribution of livestock trade to the spread of the disease was low. However, confidence in animal vaccination was relatively high (60.3%). Preventive practices were highly inadequate. The majority did not wear protective equipment when handling sick animals (94.6%) and rarely avoided touching aborted foetuses (12.6%). Less than half (48.3%) expressed a willingness to report sick or dead animals, and nearly half admitted to having sold or purchased sick livestock (49.5%). Cooking meat (95.1%) and using mosquito nets (74.1%) were the only well-established practices. More than half of respondents (57.9%) lived more than 5 km from veterinary services, and cost was the most frequently cited barrier to consultation. Participation in awareness campaigns was virtually non-existent (5.4%). Results revealed critical gaps in KAP that may contribute to the persistence of RVF. A “One Health” approach is imperative, integrating human, animal and environmental health. Full article

Couples generally manage chronic diseases together and the management takes an emotional toll on both patients and their romantic partners. Consequently, recognizing the emotions of each partner in daily life could provide insight into their emotional well-being in chronic disease management. Currently, the process of assessing each partner’s emotions is manual, time-intensive, and costly. Despite the existence of works on emotion recognition among couples, none of these works have used data collected from couples’ interactions in daily life. In this work, we collected 85 h (1021 5-min samples) of real-world multimodal smartwatch sensor data (speech, heart rate, accelerometer, and gyroscope) and self-reported emotion data (n = 612) from 26 partners (13 couples) managing diabetes mellitus type 2 in daily life. We extracted physiological, movement, acoustic, and linguistic features, and trained machine learning models (support vector machine and random forest) to recognize each partner’s self-reported emotions (valence and arousal). Our results from the best models—balanced accuracies of 63.8% and 78.1% for arousal and valence respectively—are better than the results from (1) chance, (2) prior work that also used data from German-speaking, Swiss-based couples, and (3) partners’ perceptions of each other’s emotions. This work contributes toward building automated emotion recognition systems that would eventually enable partners to monitor their emotions in daily life and enable the delivery of interventions to improve their emotional well-being. Full article

Background: Visio-spatial skills (VSS) are essential perceptual-cognitive skills that enable athletes to process visual information, interpret spatial relationships, and execute appropriate motor responses in dynamic sporting environments. In combat sports, athletes must rapidly anticipate and react to an opponent’s actions, making well-developed VSS crucial for optimal performance. Although boxing, karate, and taekwondo share similar competitive characteristics, each discipline presents distinct technical and perceptual demands that may influence the development of specific VSS profiles. This study aimed to investigate whether significant differences exist in VSS profiles among boxing, karate, and taekwondo athletes. Methods: A comparative cross-sectional design was used involving 150 amateur combat sport athletes, 50 boxers, 50 karate athletes, and 50 taekwondo athletes. Participants were assessed using a VSS test battery measuring six variables: accommodation facility (AF), saccadic eye movement (SEM), speed of recognition (SR), (HEC), peripheral awareness (PA), and visual memory (VM). Data was analyzed using one-way ANOVA with η², ω², and Cohen’s f effect sizes, and principal component analysis (PCA). Results: One-way ANOVA revealed statistically significant differences in five of six VSS (all p < 0.001). PA produced the largest sport-specific differentiation (η² = 0.457, Cohen’s f = 0.918), followed by HEC (η² = 0.273, f = 0.612), SR (η² = 0.224, f = 0.537), and SEM (η² = 0.180, f = 0.468). AF yielded a significant moderate effect (η² = 0.108, f = 0.347). VM was the sole non-significant variable (F (2.147) = 0.74, p = 0.479, ω² = 0.000), suggesting domain-general encoding processes insensitive to discipline-specific training at this developmental level. Boxing athletes achieved the highest scores in SEM, SR, and PA, while karate athletes led in AF and HEC. PCA revealed a single dominant component (PC1 = 93.91% of variance), confirming that VSS function as a highly integrated perceptual-motor construct rather than independent sub-skills. Conclusions: Visio-spatial skills in combat sports are governed by a dominant integrated factor, with discipline-specific variations reflecting unique performance requirements. Visio-spatial skills in combat sport athletes are highly interdependent and largely governed by a single perceptual-motor construct, with discipline-specific profiles observed across boxing, karate, and taekwondo. The findings support the integration of sport-specific, ecologically valid visual training programs targeting key perceptual-cognitive skills, alongside routine assessment to inform athlete development and performance optimization. Full article

Pre-elite female field hockey players have a high incidence of lower extremity injury, highlighting the need for practical and reliable screening approaches. A dual assessment combining Functional Performance Tests (FPTs) with movement quality scoring (QASLS) may provide a more comprehensive evaluation; however, its reliability in this population is unclear. Fifteen pre-elite female field hockey players (16.7 ± 0.7 years) completed an FPT battery (anterior reach (AR), single leg drop vertical jump–land (DVJL), single hop for distance (SHFD), side hop (SH)) on two occasions, 28 days apart. Movement quality was assessed by three raters using QASLS. Reliability was evaluated using ICC with 95% confidence intervals (CI), alongside standard error of measurement (SEM), smallest detectable difference (SDD), and percentage exact agreement (PEA). Test–retest reliability varied across tasks (ICC_2,1 0.33–0.90), with wide confidence intervals indicating uncertainty in several estimates. AR demonstrated the most consistent reliability, supporting its use for monitoring over time. In contrast, the DVJL and SH showed the greatest variability, likely reflecting higher task complexity, while the SHFD required relatively large performance changes to exceed measurement error. Intra-rater reliability for QASLS was consistent across the FPT battery (ICC_2,k 0.79–0.90), whereas inter-rater reliability was more variable (0.38–0.82), indicating rater-dependent differences. PEA demonstrated generally high agreement (60–100%), although lower agreement was observed for pelvic alignment components. These findings support the use of a dual assessment approach as a practicable profiling approach in pre-elite female field hockey, enabling practitioners to identify movement deficits not captured by performance metrics alone. However, variability in complex tasks and between raters highlights the need to consider measurement error and implement standardised rater training when profiling or monitoring performance. Full article

Background: Artificial intelligence (AI) is rapidly transforming healthcare, with increasing applications in sports medicine. Advances in machine learning, deep learning, and computer vision enable the analysis of large, heterogeneous datasets derived from imaging, wearable sensors, performance-monitoring systems, and electronic health records. While these technologies offer opportunities to enhance injury prevention, diagnostic accuracy, rehabilitation monitoring, and clinical decision-making, their integration into athlete care remains complex and context-dependent. Methods: A structured narrative review of the PubMed/MEDLINE database was conducted to identify clinically relevant AI applications in sports medicine. The search focused on key domains including injury risk prediction, musculoskeletal imaging, rehabilitation monitoring, return-to-play assessment, performance management, and clinical workflow support. Evidence from original studies, reviews, methodological reports, and regulatory documents was qualitatively synthesized to provide an overview of current applications, methodological limitations, and decision-level implications. Results: AI demonstrates growing utility across multiple domains of sports medicine. Machine learning models can identify complex, non-linear relationships among training load, physiological responses, and injury risk, though their predictive performance varies widely and is often limited by dataset heterogeneity and a lack of external validation. In musculoskeletal imaging, AI-based algorithms support automated detection and quantification of abnormalities, with performance in selected tasks approaching that of expert readers, yet remaining task-specific and context-dependent. Emerging applications include movement analysis and rehabilitation monitoring through wearable sensors and computer vision systems, as well as data-driven support for return-to-play decisions and clinical workflow optimization. However, current evidence highlights important limitations, including algorithmic bias, limited generalizability, poor interpretability, and the risk of misapplication in complex clinical decision-making contexts. Conclusions: AI is likely to become an important decision-support layer in sports medicine by enabling data integration and longitudinal monitoring. However, model performance does not necessarily translate into improved clinical outcomes, and AI-generated predictions remain probabilistic and context-sensitive. Consequently, clinical decisions—particularly high-stakes processes such as return-to-play—require structured integration of AI outputs within a broader clinical framework. The sports physician remains central as a human-in-the-loop integrator, responsible for contextualizing AI-derived information, mitigating potential errors, and ensuring safe, individualized athlete management. Full article

Background/Objectives: Industry 5.0 emphasizes the protection and empowerment of human workers. Passive wearables reduce physical strain, but the evaluation of their efficacy remains incomplete when based solely on kinematics or electromyographic (EMG) envelope amplitude, failing to capture the underlying neural “cost” or the compensatory strategies. This paper proposes a computational framework centered on muscle synergy analysis to bridge the gap between laboratory-grade neural assessment and real-world industrial applications. The goal is to move beyond simple biomechanical metrics toward a deeper understanding of neural coordination during device interaction. Methods: Given the practical limitations of high-density EMG in industrial settings, we propose a “streamlining” approach: laboratory-derived synergy models guide the understanding of neural processes and the selection of a minimal set of sensors capable of detecting maladaptive motor compensations and early signs of fatigue. Results: This approach allows for long-term monitoring without compromising natural movement. By decoupling neural strategies from kinematic output, “silent” risk situations can be identified even when movement appears correct but the neural coordination is altered by the passive device. This supports personalized ergonomic indices and predictive prevention protocols, transforming wearables from simple mechanical aids into intelligent, human-centric systems. Conclusions: This framework provides a roadmap for translating complex motor control theories into practical tools for the next generation of safe and sustainable manufacturing. Full article

Rail infrastructure plays an important role in freight and passenger mobility, and the assessment of rail track structure depends critically on understanding how the rail interacts with the supporting foundation. When rail support degrades (e.g., due to ballast fouling, settlement, etc.), the rail exhibits greater localized deformation that can lead to serious deleterious conditions. Track modulus represents a fundamental diagnostic measure of rail support, encompassing the vertical stiffness characteristics of the foundation and its resistance against downward rail movement. Existing track modulus characterization methodologies typically comprise deflection measurements of railway track (e.g., tie deflections) under known loads. Track modulus estimations result from analyzing deflection and load under assumptions of a traditional Winkler foundation, which can oversimplify mechanic relationships. Specifically, in the context of rail–ballast–subgrade interaction, a tensionless foundation permits gap development which can occur as track structure separates from the supporting ballast; additionally, track modulus may vary along the track length as conditions vary spatially. This paper presents a general analytical solution of ballasted track support characterization based on an iterative algorithm for the static response of a finite beam resting on a tensionless Winkler foundation. The method relates to multiple loads (e.g., concentrated axle loads and distributed self-weight), deflection along the track, and track condition through singularity functions, superposition of discrete support springs, and moment–curvature relationships. The model estimates rail deflections, lift-off points and shear and moment diagrams along the track. The technique permits: (1) validations against benchmark solutions and previously published results, (2) estimations of track modulus from known loads and measured deflections, and ultimately, (3) a framework for designing and processing sensor data streams for use in analyses and evaluations of railway track structure. Full article

Remote exercise supervision and functional movement assessment require sensing pipelines that can capture body motion, interpret protocol progression, and provide meaningful feedback within the same runtime environment. This paper presents ORAMA, an integrated computer vision platform for the execution and remote monitoring of digital exercises and clinically oriented assessment protocols related to physical fitness, mobility, balance, and health. The system combines ZED 2i stereo capture and depth-aware body tracking with a protocol-driven software architecture that includes a computer-vision pipeline, an exercise and assessment engine, a real-time feedback layer, persistent session handling, structured output generation, and a chatbot-assisted interaction path. Unlike solutions that focus only on movement recognition, ORAMA organizes each task as an explicit executable protocol with calibration stages, state transitions, task-specific metrics, and live visual guidance. The paper analyzes the system architecture, reviews the surrounding literature on virtual coaching and rehabilitation-oriented computer vision, and demonstrates representative user-interface and runtime views for both assessment and exercise scenarios. The present work reports a prototype architecture and representative operational demonstrations, rather than a completed clinical validation or participant-based efficacy study. The resulting platform shows how markerless 3D body tracking can be embedded within a unified and interpretable environment for guided exercise, functional testing, and remote follow-up without requiring wearable sensors. Full article

Background: Ancient depictions of Pankration techniques have traditionally been interpreted through qualitative comparison with modern combat sports, without systematic biomechanical evaluation. The present study examines whether postural configurations derived from archeological artifacts are geometrically compatible with a continuous sagittal-plane trajectory under constrained inverse kinematics. Methods: A reduced planar humanoid model with three active rotational degrees of freedom was implemented in MATLAB Simulink(2024b), and artifact-derived initial and terminal postures were treated as boundary conditions. An analytical inverse kinematics solution was used to generate a continuous end-effector trajectory, from which joint kinematics and center-of-gravity displacement were computed. Motion capture data from ten participants were used solely to assess whether the generated trajectory is physically executable within human joint limits. Results: The results demonstrated strong agreement in selected local horizontal joint trajectories, while larger discrepancies were observed in vertical motion and global center-of-gravity behavior, reflecting the limitations of the reduced model. Conclusions: The study provides a reproducible framework for evaluating the kinematic feasibility of artifact-derived movements under explicitly defined constraints, limited to the assessment of geometric compatibility and physical executability. Full article