Search Results (807)

Anterior cruciate ligament (ACL) injury risk is elevated in female youth football, yet knee joint loading has mainly been studied under controlled laboratory conditions. This limits understanding of how injury risk emerges during realistic match situations. This study provided a field-based kinetic characterization of football-specific movements by estimating knee abduction moments (KAMs) using wearable sensors and machine learning. Fifty-two highly talented female youth players performed agility tasks during training, including structured exercises (F-EX) and game-based play (F-GAME). Full-body kinematics were collected with inertial measurement units, and a validated support vector machine model, trained on synchronized motion capture and force plate data, classified trials as high or low KAM. Across 662 change-in-direction trials, 9–12% were classified as high KAM in both conditions, indicating that potentially high-risk loading regularly occurs during routine actions. High KAM trials showed reduced knee and pelvis flexion, increased hip flexion, and greater pelvis rotation toward the cutting direction, reflecting upright, stiff movement strategies. Performance analyses revealed smaller cut angles in exercises and greater approach acceleration in game play, without differences in peak velocity. These findings demonstrate the feasibility of field-based kinetic screening and support a complex-systems perspective on ACL injury risk. Full article

Background: Fundamental movement skills (FMS) underpin lifelong physical activity (PA) and health, yet many children are failing to meet age-appropriate standards. Caregivers hold a critical influence over children’s motor development, but little is known about what helps or hinders family participation, including messaging. This study explored the determinants of family FMS engagement in the United Kingdom (UK) during early childhood, addressing unexplored gaps in how guidance reaches families and the role of grandparents in supporting children’s motor development. Methods: Twenty-three semi-structured interviews were conducted with 15 caregivers and 8 educators, including 4 grandparents and 2 family hub practitioners who offered original insights. Eleven children aged 3–5 years completed a flexible draw-and-tell task, enabling inclusion of rarely represented 3-year-olds. Thematic analysis was deployed. Results: Families and outdoor spaces were pivotal to children’s movement opportunities. However, awareness and understanding of FMS and UK PA guidance were poor, even among educators, disrupting dissemination of information to families. Greater emphasis on PA and FMS concepts within professional development, alongside clearer signposting to resources, more visible public-facing campaigns, and digital formats, could improve how families receive these messages. Tensions emerged between parents’ concerns about grandparents’ physical capability and grandparents’ belief that they could adapt to support children’s development. Unexpectedly, no children drew technology despite screen time frequently displacing active play, hinting at its normalisation and regulatory role in children’s lives. Conclusions: To enhance family understanding, value, and participation in FMS, UK policy must evolve to become more visible, relatable, and responsive to diverse family needs. Full article

Background: Glutaric acidemia type 1 (GA1) is an autosomal recessive neurometabolic disorder caused by pathogenic variants in glutaryl-CoA dehydrogenase (GCDH), with variable clinical severity despite early biochemical detectability. Population-specific mutational spectra and genotype–phenotype correlations remain insufficiently defined in infantile-onset disease. Therefore, this study aimed to define the GCDH variant spectrum in GA1 patients with mild hepatopathy and assess genotype–phenotype correlations. Methods: We performed integrated clinical, biochemical, and molecular characterization of 15 unrelated patients with infantile-onset GA1. Whole-exome sequencing (WES) was performed for all participants, and the resulting data were compared with the reference sequence of the GCDH gene. Results: All patients presented within the first 6 months of life with macrocephaly, seizures, dystonia, and feeding difficulties. Neurological impairment and mild hepatopathy were variably observed, and one patient developed an acute encephalopathic crisis. Six homozygous GCDH variants were identified, predominantly missense. A common variant, c.541G>C (p.Glu181Gln), accounted for 73.3% of cases and defined a consistent phenotype of early macrocephaly and movement disorder with frequent mild hepatic involvement, suggesting regional enrichment and raising the possibility of a founder effect that warrants confirmation in future haplotype studies. A truncating variant, c.382C>T (p.Arg128Ter), was associated with severe early encephalopathy. Exon 6 represented a mutational hotspot. Biochemically, all patients showed elevated urinary glutaric and 3-hydroxyglutaric acids, increased glutarylcarnitine, and low-to-normal free carnitine, with higher metabolite levels in clinically more severe cases. All variants were pathogenic or likely pathogenic and extremely rare in population databases. Conclusions: This cohort reveals a striking predominance of the GCDH c.541G>C variant and establishes a clear biochemical signature with genotype-associated clinical patterns in infantile-onset GA1. These findings support a population-specific mutational spectrum, refine genotype–phenotype correlations, and underscore the importance of early molecular diagnosis to guide targeted neurological and hepatic monitoring as well as regional screening strategies. Full article

Background: This review discusses the application of eye-tracking technology in the detection and monitoring of binocular vision anomalies among children. Methods: A scoping review using PRISMA guidelines was conducted through Scopus, ScienceDirect, and PubMed using the keywords “eye-tracking,” “binocular,” “vision,” “anomalies,” “paediatrics,” and “children” from 2015 to 2025. Studies excluded were not written in English, did not apply the eye tracker as a research tool, involved an ineligible population, or involved non-human subjects. Results: The search strategy identified 77 citations, yet only 14 studies met the inclusion criteria. This review revealed a variety of binocular vision anomalies detectable through eye-tracking systems, along with the specific models and parameters employed in these assessments. Application of eye-tracking technology in diagnosing conditions such as strabismus and amblyopia demonstrated potential for improved accuracy and early detection. Discussion: Eye-tracking technology demonstrates considerable potential for the detection and monitoring of binocular vision anomalies in children, particularly as a non-invasive method for early screening, thereby strengthening its clinical applicability. By assessing fixation stability, saccadic movements, and vergence responses, eye-tracking allows for the early detection of subtle visual anomalies, especially in the paediatric population. Conclusions: Eye-tracking technology represents a valuable advancement in paediatric vision care, enabling the more objective and earlier detection of binocular vision anomalies in the paediatric population. Full article

Background/Objectives: Providing an appropriate diet to older adults with dysphagia can prevent aspiration, choking, and nutritional deficiencies and help preserve their quality of life. Therefore, assessments for determining the appropriateness of food types are required. This multicenter study aimed to determine the reliability and validity of the Meal Rounds Observation Form (MROF), which was developed to identify food forms that can be safely consumed by older adults with dysphagia. Methods: We analyzed 532 food–texture observations obtained from 155 participants (114 men and 41 women). The reliability and validity of the MROF were compared with those of videofluoroscopic (VF) or videoendoscopic (VE) examinations of swallowing. Results: The food-form categories were water (108 pairs), 0j (54 pairs), 0t (118 pairs), 1j (20 pairs), 2-1 (28 pairs), 2-2 (37 pairs), 3 (68 pairs), 4 (67 pairs), and normal food (32 pairs) based on JDD 2021 codes. The AUC was lowest for the water (0.568) category and highest for food forms requiring chewing, such as those of the 4 and normal food (0.678) categories. The sensitivity and specificity of the Gugging Swallowing Screen were 60.1% and 69.1%, respectively (p < 0.001). The agreement between the Gugging Swallowing Screen and the MROF evaluation for food types requiring mastication was 73.2%. Logistic regression analysis revealed asymmetric movement of the corners of the mouth and coughing as important indicators when evaluating food types requiring mastication. Conclusions: The MROF is useful for determining food intake safety when VF or VE tests cannot be performed in medical and nursing care settings and can guide clinical decision-making. However, caution is required in applying it clinically because of its relatively low specificity. Full article

Sleep quality is closely associated with cardiovascular, metabolic, and mental health outcomes, yet the clinical gold standard, polysomnography (PSG), is costly and intrusive for long-term home monitoring. Ballistocardiography (BCG) enables unobtrusive in-bed sensing and is therefore attractive for low-burden sleep assessment in natural environments. However, most existing BCG studies are PSG-referenced and mainly focus on sleep staging, while movement and out-of-bed episodes are often treated as artifacts rather than modeled jointly. In this study, we propose an interpretable unsupervised proxy-state modeling framework for three-state in-bed monitoring from BCG signals under an unlabeled setting. BCG recordings were segmented into 30 s windows with 50% overlap, and multi-domain features were extracted from waveform morphology, spectral power, heart rate-related dynamics, and wavelet energy distribution. K-means clustering (K = 3) was used to construct cluster-derived proxy labels, TreeSHAP-based feature ranking together with inner-CV-guided Top-N subset selection was used for training-only feature screening, and multiple classifiers were compared under a strict leave-one-subject-out protocol, with an ROA-optimized RBF-SVM achieving the best overall performance. Using data from 32 volunteers, the framework achieved an accuracy of 0.9932 ± 0.0047 (mean ± SD), together with consistently strong Macro-F1 and MCC scores. Overall, it outperformed the alternative methods compared in this study. Full article

Eye movements are difficult to observe and replicate, making them a promising yet understudied modality for behavioral biometrics. This study is the first to examine the feasibility of using eye movement patterns during manga reading as a biometric identifier, leveraging the medium’s rich behavioral data from diverse reading behaviors. Eye movement data from 59 participants were recorded while they read two manga works on a screen. A comprehensive set of gaze features was extracted and evaluated using five machine learning classifiers, among which Random Forest (RF) consistently achieved the best performance. Under constrained experimental conditions, the RF classifier achieved a Rank-1 identification rate of 95.0% and an equal error rate (EER) of 1.9%. Furthermore, this study systematically investigated two critical challenges for practical deployment: stimulus dependency and template aging. Cross-stimulus evaluation revealed substantial performance degradation when training and testing used different manga works, and template aging analysis over an approximately 90-day interval demonstrated notable declines in identification accuracy. These results provide preliminary evidence supporting the potential of natural reading behaviors for biometric continuous authentication systems while highlighting the need for further research into cross-stimulus generalization and temporal stability. Full article

Neurodegenerative diseases such as Alzheimer’s (AD) and Huntington’s (HD) remain major therapeutic challenges due to limited treatment efficacy. Imidazoline I2 receptor (I2-IR) ligands have recently emerged as promising neuroprotective agents, with reported roles in modulating oxidative stress, neuroinflammation, and protein aggregation. This study evaluates the therapeutic potential of several I2-IR ligands, including Idazoxan, CR4056, and novel compounds, using Caenorhabditis elegans (C. elegans) models of AD and HD. Transgenic strains CL2006 (expressing human Aβ1-42) and EAK103 (expressing Ht513) were employed to assess locomotor activity, oxidative stress tolerance, Aβ and Ht aggregation, and sod-1 gene expression. Several ligands significantly improved movement, reduced Aβ and Ht aggregates, and enhanced antioxidant gene expression, particularly Idazoxan, LSL42, and PIP01. Notably, some compounds exhibited prooxidant effects, highlighting the utility of C. elegans for early in vivo toxicity screening. Importantly, this study provides the first in vivo evidence of the efficacy of I2-IR ligands in HD models and reinforces their potential as therapeutic candidates for HD. Overall, these findings suggest a potential role for modulation of I2-IR-related pathways in neurodegeneration and support the utility of C. elegans as a rapid, cost-effective platform for preclinical drug evaluation. Full article

Windsurfing (WS) and kitesurfing (KS) share the same environment but differ in biomechanics and equipment demands. This cross-sectional study compared physical performance between WS and KS athletes. Twenty-five male recreational athletes participated (WS n = 13, age 27.7 ± 7.0 years; KS n = 12, age 29.0 ± 7.5 years). Body composition, isometric strength (handgrip and back-and-leg dynamometer), dynamic balance (Y-Balance Test: YBT), functional movement quality (FMS), and drop-jump performance (ground contact time, reactive strength index, jump height, take-off time) were assessed. Groups were compared using the Mann–Whitney U test (p < 0.05), and Cliff’s δ was calculated for significant outcomes. Participant characteristics were similar, although surfing experience was greater in WS. KS showed higher leg strength (p = 0.041; δ = 0.481) with no difference in handgrip strength. KS also demonstrated higher FMS shoulder mobility (p = 0.022; δ = 0.532) and total FMS score (p = 0.014; δ = 0.577). No between-group differences were found for YBT metrics or drop-jump variables (p > 0.05). These findings indicate that KS athletes exhibit greater isometric pulling strength and movement proficiency, whereas balance and reactive jump performance are comparable, supporting discipline-specific conditioning priorities. Full article

Urban mobility is a central challenge for sustainable and inclusive cities, as climate change, congestion, and spatial inequality increasingly reveal mobility patterns as expressions of deeper social and spatial structures. Inclusive urban mobility examines whether transport systems equitably support the everyday movements and accessibility needs of historically marginalized and underserved populations. The integration of artificial intelligence with geographic information science, combined with multimodal geospatial data fusion, provides powerful tools to diagnose and address these disparities by integrating heterogeneous data sources such as satellite imagery, GPS trajectories, transit records, volunteered geographic information, and social sensing data into scalable, high-resolution urban mobility analytics. This paper presents a systematic survey of recent GeoAI studies that fuse multiple geospatial data modalities for key urban mobility tasks, including accessibility mapping, demand forecasting, and origin–destination flow prediction, with particular emphasis on inclusive and equity-oriented applications. The review examines 18 multimodal GeoAI studies identified through a PRISMA-ScR screening process from 57 candidate publications between 2019 and 2025. The survey synthesizes methodological trends across data-, feature-, and decision-level fusion strategies, highlights the growing use of deep learning architectures, and examines emerging techniques such as knowledge graphs, federated learning, and explainable AI that support equity-relevant insights across diverse urban contexts. Building on this synthesis, the review identifies persistent gaps in population coverage, multimodal integration, equity optimization, explainability, validation, and governance, which currently constrain the inclusiveness and robustness of GeoAI applications in urban mobility research. To address these challenges, the paper proposes a structured research roadmap linking these gaps to concrete methodological and governance directions including equity-aware loss functions, adaptive multimodal fusion pipelines, participatory and human-in-the-loop workflows, and urban data trusts to better align multimodal GeoAI with the goals of inclusive, just, and sustainable urban mobility systems. Full article

Global Navigation Satellite Systems (GNSS) offer precise movement analyses based on distance and speed in open-water sports. Despite the influence of swimming in triathlon, its performance analysis remains underdeveloped due to methodological limitations in capturing continuous data in aquatic environments. This review aimed to: (1) systematically analyse and compare the sensor-based technologies applied to open-water movement analysis, and (2) propose a framework for continuous GNSS-based assessment of triathlon swim performance. A systematic search was conducted prior to the 14 August 2025 across four databases (Web of Science, SPORTDiscus, PubMed, and SPONET). Studies were eligible if they analysed open-water sports using GNSS-based technologies for continuous movement or performance analysis. Studies limited to indoor swimming, inertial sensors, or non-sporting applications were excluded. Methodological quality and potential sources of bias were evaluated using a custom scheme based on GNSS reporting guidelines, as methodological heterogeneity precluded the application of standardised tools. Following screening and eligibility assessment, articles were analysed qualitatively. In total, 20 articles were included and focused on surfing, sailing, water skiing, windsurfing, kitesurfing, stand-up paddling (SUP), and swimming. Most studies focused on board- and sail-based sports, employed sampling frequencies between 1 and 15 Hz, and demonstrated substantial variability in device specifications and reporting quality. Different sensors and GNSS-derived variables were central to discipline-specific performance analysis. The strength of evidence is limited by the heterogeneous methodologies, and variable reporting quality. The proposed framework provides methodological guidance for implementing high-resolution GNSS-based monitoring in triathlon swimming to improve pacing analysis and race strategy development. Full article

Human–robot interaction (HRI) takes place in dynamic environments where both humans and robots act as active agents, making the system inherently unpredictable. Abrupt movements can originate from either side and include human reflexes, fatigue, or unexpected reactions, as well as robot malfunctions, control errors, or task changes. These unpredictable events generate significant risks for both interaction fluency and safety, affecting not only the physical domain (e.g., collisions, excessive forces) but also cognitive aspects such as trust and predictability. Although different application areas present domain-specific challenges, a comprehensive overview of abrupt movements in HRI is still lacking, especially in the industrial scenario. This review aims to consolidate current knowledge regarding how abrupt phenomena are analyzed, prevented, and mitigated across various contexts and to offer new insights for researchers. In detail, after describing the literature search and the screening process, the review categorizes abrupt events, highlights key methodological approaches, and identifies gaps and future directions. By providing a structured synthesis of existing strategies, this work guides researchers in developing safer and more adaptive HRI frameworks capable of handling unpredictability. Full article

Background: Traditional functional mobility assessments often fail to detect subclinical postural decline in active aging populations. This study introduces the Head–Sternum Dissociation Index as a novel digital biomarker to identify latent sensorimotor deficits before macroscopic balance failure occurs. Methods: Ninety-four participants (Young, Middle-Aged Civil, Middle-Aged Dancers, and Older Adults) performed instrumented limits of stability tasks, specifically functional and lateral reach tests, utilizing a three-sensor inertial measurement unit configuration. Postural strategies were quantified via the Head–Sternum Dissociation Index and the peak ratio of corrective micro-movements, validating the sensor output against a gold-standard force platform. Results: A significant kinematic breakpoint in postural control was identified at age 55 (p < 0.001). However, Middle-Aged Civilians exhibited early kinematic divergence despite maintaining normal Timed Up and Go test performance. Receiver operating characteristic analysis revealed distinct, sex-specific physiological limits: aging males predominantly adopted a rigid “Stiffness” strategy (peak ratio ≤ 1.15, head–sternum dissociation threshold > 0.63°), while females utilized a broader, more permissive “Continuous” strategy (head–sternum dissociation threshold > 0.31°). Notably, recreational rhythmic training (dance) completely neutralized this age-related decay, with middle-aged dancers maintaining highly efficient, youthful stabilization profiles (Cohen’s d = 2.20). Conclusions: The Head–Sternum Dissociation Index, combined with relative corrective frequency, successfully phenotypes early sensorimotor erosion. These findings advocate for the integration of sex-specific kinematic screening into primary care, allowing clinicians to prescribe targeted interventions well before clinical fall risk manifests. Full article

Background/Objectives: Vestibular disorders can have functional consequences for children, including balance and gross motor delays, academic difficulties and behavioral manifestations; however, they are frequently undiagnosed in children. The purposes of this study were to evaluate the feasibility and clinical utility of performing a non-instrumented dynamic visual acuity (DVA) test as a primary screening tool for children and to examine typical performance on this non-instrumented DVA test in a large sample of children ages 2 through 13 years. Methods: A clinical DVA test was administered to a convenience sample of 208 children aged 2–13 years. Static visual acuity was assessed using a standard Snellen or LEA eye chart, depending on the child’s ability to read letters. Dynamic visual acuity was then measured while the examiner manually rotated the child’s head at 2 Hz (240 bpm). DVA was calculated as the number of lines of visual acuity lost with head movement. Results: All children aged 4 years and older and 67% of 3-year-olds successfully completed DVA testing. Most 2-year-olds and 33% of 3-year-olds were unable to complete DVA testing. Although the number of visual acuity lines lost with rotational head oscillations at 2 Hz varied between age groups, Kruskal–Wallis test indicated no significant difference in DVA scores between age groups (K = 12.721, DF = 9, P = 0.176). Ninety percent of children who were able to perform DVA testing lost two or fewer lines of visual acuity with head rotations consistent with adult norms. Conclusions: This method of DVA testing is an easily accessible and promising clinically feasible screening tool for identifying children with vestibular dysfunction. The authors recommend widespread vestibular screening of children to facilitate rapid referral for diagnosis and treatment of children with vestibular dysfunction. Full article

Background: The early detection of autism spectrum disorder (ASD) is imperative for enhancing long-term developmental outcomes. Nevertheless, conventional screening methods depend on time-consuming, expert-driven behavioral assessments and are characterized by limited scalability. Automated video-based analysis provides a noninvasive and objective approach for the extraction of behavioral biomarkers from naturalistic recordings. Methods: A modular multimodal framework was developed that integrates motion-based video analysis and facial feature extraction for the purpose of ASD versus typically developing (TD) classification. The system is capable of processing RGB videos, skeleton/stickman representations, and motion trajectory streams. A comprehensive set of kinematic features was extracted, encompassing joint trajectories, velocity and acceleration profiles, posture variability, movement smoothness, and bilateral asymmetry. The repetitive stereotypical behaviors exhibited by the subjects were characterized using frequency-domain analysis via FFT within the 0.3–7.0 Hz band. Facial expression features derived from normalized face crops and landmark-based morphological descriptors were integrated as complementary modalities. The feature-level fusion process was executed subsequent to z-score normalization, and the classification procedure was conducted using a Random Forest model with stratified 5-fold cross validation. The implementation of GPU acceleration was instrumental in facilitating near real-time inference. Results: The motion-based ComplexVideos pipeline demonstrated a cross-validated accuracy of 94.2 ± 2.1% with an area under the ROC curve (AUC) of 0.93. Skeleton-based KinectStickman inputs demonstrated moderate performance, with an accuracy range of 60–80%. In contrast, facial-only models exhibited an accuracy of approximately 60%. The integration of multiple modalities through feature fusion has been demonstrated to enhance the robustness of classification algorithms and mitigate the occurrence of false negative outcomes, thereby surpassing the performance of single-modality models. The mean inference time remained below one second per video frame under standard operating conditions. Conclusions: The experimental results demonstrate that the integration of multimodal cues, including motion and facial features, facilitates the development of effective and efficient video-based screening methods for autism spectrum disorder (ASD). The proposed framework is designed to offer a scalable, extensible, and computationally efficient solution that can support early screening in clinical and remote assessment settings. Full article