Search Results (1,954)

Parkinson’s disease (PD) is a progressive neurodegenerative disorder characterized by motor control, balance, and gait impairments that significantly elevate fall risk. Traditional gait analysis focuses on spatiotemporal parameters, while gait variability, asymmetry, and balance measures offer more sensitive indicators of PD-related motor deficits. Machine learning studies using wearable gait data frequently report high classification accuracy but lack biomechanical interpretability and methodological rigor. Using the PhysioNet Gait in Parkinson’s Disease database, 93 individuals with PD and 72 healthy controls were analyzed during level-ground walking. Key biomechanical differences were identified: stride time coefficient of variation was significantly higher in PD bilaterally (left p = 0.001; right p = 0.003); swing-phase time was significantly reduced in both limbs (left p = 0.003; right p = 0.001); anterior–posterior center of pressure (COP) variability was significantly lower in PD for both limbs (p < 0.001); and COP path symmetry index was the most prominent asymmetry marker, significantly elevated in PD relative to controls (p = 0.003). A machine-learning analysis pipeline identified HistGradientBoosting as the best-performing classifier (AUC = 0.992; accuracy = 97.6%), but leave-one-study-out evaluation exposed substantial cross-protocol heterogeneity (AUC: 0.500–1.000), indicating that the model relied partly on dataset-specific patterns and may not generalize to independent acquisition protocols. Shapley Additive Explanations (SHAP) analysis showed classification was driven by a multimodal combination of clinical severity measures and biomechanical gait features rather than wearable metrics alone. A pre-specified gait-only sensitivity analysis that excluded clinical severity variables (UPDRS, UPDRSM, Hoehn and Yahr) confirmed that biomechanical features alone retained moderate, but substantially reduced, discriminative ability (gait-only holdout AUC = 0.844), supporting the interpretation that the headline performance reflects multimodal clinical separation rather than a stand-alone wearable-gait biomarker. These findings indicate that Parkinsonian gait impairment is characterized by timing instability and constrained forward COP progression. The combination of biomechanical analysis with interpretable predictive modeling represents a structured analysis pipeline for gait-based PD assessment; however, external validation in independent cohorts and prospective testing across acquisition protocols are required before such a pipeline can be deployed as a clinically generalizable digital biomarker. Full article

Background/Objectives: Pes planus and pes cavus are common foot deformities, which may lead to pain, functional limitations, and impairment of foot biomechanics. While calcaneal pitch, talar declination, and Meary angles, commonly used in diagnosis, provide objective information, their lack of a gold standard and the observer’s dependence on manual measurements limit their reliability. Therefore, in this study, these angles obtained from weight-bearing lateral foot radiographs were evaluated according to literature references, and the aim was to determine the model that provides the most accurate prediction in the diagnosis of pes planus using machine learning algorithms. It should be emphasized that, because the diagnostic labels were derived from literature-based thresholds of these same angles, the machine-learning task addressed here is the automated reproduction and standardization of expert, angle-threshold-based classification, rather than an independent clinical diagnosis from raw images. Methods: This retrospective study was conducted using weight-bearing lateral foot radiographs of 697 male patients obtained from the archives of public hospitals in Gaziantep. Calcaneal pitch, Meary angle, and talar declination angles were evaluated in both feet, and the data were labeled as normal, pes planus, and pes cavus. The dataset, consisting of a total of 1394 feet, was divided into training and test groups and analyzed using Random Forest, XGBoost, Logistic Regression, Support Vector Machine (SVM), and K-Nearest Neighbors (KNN) algorithms; the diagnostic performance of the models was compared using measures such as accuracy, F1 score, sensitivity, and specificity. Results: A total of 1394 feet from 697 male patients (mean age 24.8 ± 5.57 years) were analyzed using five machine learning algorithms with calcaneal pitch angle (CPA), Meary angle (MA), and talar declination angle (TDA) as reference labels. Ensemble-based methods showed superior performance, with XGBoost achieving perfect classification (Accuracy = 1.000) under all three labels for the left foot and 0.996–1.000 for the right foot, while Random Forest reached 0.986–1.000 across all experiments. Logistic Regression and SVM yielded moderate accuracies (0.905–0.973), whereas KNN consistently performed the weakest (0.905–0.964), particularly in the pes cavus subgroup. The near-perfect accuracy obtained when the labeling angle was itself included among the predictors reflects, at least in part, the algebraic reconstruction of the threshold rule from a same-source variable rather than genuine diagnostic generalization; results should therefore be interpreted with this in mind. Conclusions: This study demonstrates that machine learning, particularly ensemble methods such as XGBoost and Random Forest, provides high accuracy and consistency in diagnosing foot arch deformities based on radiographic angle measurements. Traditional models, such as Logistic Regression, still hold value in terms of clinical interpretability despite their lower performance. The findings suggest that machine learning-based approaches can offer objective, rapid, and reliable decision support tools for diagnosing pes planus and pes cavus, but external validation studies are necessary for clinical generalizability. Full article

(1) Background: Changes in midsole hardness may affect lower-limb impact loading during forefoot strike (FFS) running in children, yet the biomechanical basis for discriminating elevated VALR-related loading remains unclear. (2) Methods: Fourteen school-aged boys performed FFS running tests in experimental shoes with four midsole hardness levels (37, 42, 47, and 52 Shore C). Lower-limb kinematics and surface electromyography (sEMG) data were collected during the dominant leg stance phase. After preprocessing, VALR was calculated from 336 valid trials, and 28 stance-phase biomechanical features were extracted, yielding a final machine-learning dataset of 324 trials after excluding incomplete feature data. VALR was used to compare loading changes and define trial-level elevated-loading labels based on the median VALR value. Classification models were evaluated under participant-level GroupKFold validation, and XGBoost was retained for exploratory SHAP analysis. (3) Results: VALR showed an upward trend with increasing hardness, but no statistically supported change point was identified. XGBoost achieved an accuracy of 75.93%, precision of 74.14%, recall of 79.63%, F1-value of 0.768, and pooled out-of-fold AUC of 0.738. SHAP analysis indicated that distal and non-sagittal kinematic features contributed most to model classification. (4) Conclusions: Elevated VALR-related loading during children’s FFS running may be characterized by a multi-feature model-based pattern rather than a fixed midsole hardness threshold. Full article

This study investigated balance control during the half squat by analyzing the relationship between the center of mass (CoM) and the center of pressure (CoP) in five experienced male weightlifters performing segmented squats at five load levels (20–80% 1 RM) across four Power-Based Training (PBT) exercises. The area of the 95% confidence ellipse was quantified using the Vicon motion capture system in conjunction with AMTI force plates. Given the small sample size (n = 5), a dual inference approach was implemented—frequentist repeated-measures analysis of variance (ANOVA) complemented by a unified adaptive Bayesian hierarchical model—to mitigate Type II error in low-power scenarios. Regarding the movement phase, a marked effect on center of pressure (CoP) stability was observed, as evidenced by both statistical approaches (frequentist: F(1.65, 6.59) = 19.44, p = 0.002, ηp² = 0.829; Bayesian: P(β_phase < 0) > 0.999). Although external load did not reach statistical significance in the frequentist analysis (p = 0.177, achieved power = 0.27), the Bayesian model provided moderate evidence of a positive impact (β_load = 0.059, 95% HDI [0.005, 0.115], p = 0.981). The area of the center of mass (CoM) ellipse showed no effects of interest. Limb asymmetries were significant and consistent throughout the experiment (frequentist: 48.01 ± 30.13%; Bayesian: 69.48%, 95% HDI [55.86%, 81.44%], P(AI > 20%) = 1.000) and were not modulated by the experimental condition. CoP-CoM coupling was stronger in the mediolateral direction than in the anteroposterior direction. The findings reveal that phase is the primary factor in postural stability, exerting a modest positive influence discernible only through low-powered probabilistic inference, and that the dual framework strengthens inferential robustness in small-sample biomechanical studies. Confirmatory studies with larger samples are recommended. Full article

Integrating neural and muscular signals into wearable robotics enables adaptive assistance during real-world tasks. This study proposes a multimodal neural interface for passive exoskeletons that combines electroencephalography (EEG) and electromyography (EMG) signals to classify motor gestures and estimate real-time cognitive and muscular effort, supported by finite-element-based biomechanical modeling. The system was implemented on the Ottobock Shoulder X passive exoskeleton© and validated using synchronous EEG–EMG acquisition via the LiveAmp platform©, a commercially available platform that was not developed specifically for this study. A hybrid CNN–LSTM architecture with deep fusion was employed to enhance robustness and responsiveness under realistic operating conditions. This study proposes a multimodal neural interface for the software-level adaptive assistance of passive upper-limb exoskeletons. While the physical device maintains a static mechanical profile, the proposed digital framework achieves adaptation by interpreting the user’s physiological and motor states. Ten healthy participants performed three functional tasks (screwing, moving the box, and lifting the box) under five assistive conditions. Finite element modeling (FEM) was used to characterize the torque–angle relationship of the passive exoskeleton and to support the interpretation of experimentally observed assistive torque profiles. The FEM model, used as an offline biomechanical analysis tool to aid in the interpretation of experimental results, has not been integrated into the real-time control loop. Results showed an average classification accuracy of 90%, an F1-score of 0.85, and inference latency below 180 ms, confirming real-time applicability. Cognitive indices such as the Cognitive Load Index (CLI) and Frontal Asymmetry Index (FAI) enabled adaptive modulation of assistance strategies without requiring active actuation, thereby preserving the device’s intrinsic passive nature. Comparative torque analysis highlighted the ergonomic benefits of passive systems in mid-range postures, while Finite Element Method (FEM) supported analysis clarified their limitations under highly dynamic loads compared to active solutions. These findings advance multimodal brain–machine interfaces for wearable robotics by integrating physiological sensing, deep learning, and biomechanical modeling, offering a safe, energy-efficient, and adaptive approach with potential rehabilitation, occupational ergonomics, and human–robot applications. Full article

Sacroiliac joint (SIJ) disruption alters posterior pelvic ring stability and can produce abnormal sacral stress redistribution; the symmetry of sacral load transfer following different fixation strategies remains controversial. This study compared sacral stress patterns under unilateral and bilateral SIJ instability for three fixation constructs using a three-dimensional finite element (FE) model. A lumbosacral–pelvic FE model was reconstructed from computed tomography data of a healthy adult and validated against previously published pelvic biomechanical data. SIJ instability was simulated by reducing the friction coefficient to represent ligamentous failure. Three fixation constructs were analyzed: anterior plate combined with posterior screw fixation (Model 1), spinopelvic fixation (Model 2), and hybrid fixation (Model 3). A 750 N axial compressive load was applied to simulate static standing. Peak sacral von Mises stress, stress amplification factors (SAFs), and left–right asymmetry ratios were computed and compared with the intact reference. Model 1 produced the highest sacral stress amplification (SAF = 3.46 under unilateral instability; peak stress 265.40 MPa). Model 2 reduced peak sacral stress (125.66 MPa under bilateral instability; SAF = 1.64), but values remained above the intact-model baseline. Model 3 yielded sacral stress closest to the intact condition under bilateral instability (81.64 MPa; SAF = 1.06), with near-symmetric load distribution in the bilateral injury configuration. Fixation topology strongly influenced sacral load transfer: hybrid fixation (Model 3) produced sacral stress magnitudes closest to the intact model, particularly under bilateral instability, whereas spinopelvic fixation (Model 2) showed more consistent left–right symmetry under unilateral injury. No single construct was superior across all symmetry-related outcomes. Hybrid stabilization may provide a biomechanically balanced approach to highly unstable posterior pelvic ring injuries under the simulated static axial-loading conditions. Full article

Background/Objectives: The stability of complete dentures is strongly influenced by the biomechanical properties of the oral mucosa, whose heterogeneity results in non-uniform load distribution, while its clinical evaluation remains predominantly qualitative. This article proposes a theoretical differential hypothesis aimed at providing a conceptual mathematical framework for interpreting the relationship between mucosal resilience and load distribution in complete dentures. Methods: The denture-mucosa system was represented along a one-dimensional coordinate, defining resilience R(x) and pressure P(x) as continuous functions related by a first-order differential equation, interpreted through elementary principles of differential calculus. Results: A theoretical simulation based on physiological parameters (F = 50 N, Young’s modulus 19.75 MPa, R = 2 mm) highlights that areas of thinner mucosa tend to behave as stress concentration points, while spatial variability of resilience generates deformation gradients potentially associated with prosthetic instability. Conclusions: The model, although simplified and non-predictive, provides a coherent interpretative framework and can support the integration of biomechanical parameters into clinical reasoning and prosthetic planning. No clinical recommendations should be derived from this model until experimental validation has been performed. Full article

Falls among older adults remain a major public health concern, yet scalable and interpretable sensing approaches for functional fall-risk stratification remain limited. This study presents a lightweight contactless framework for five-level Short Physical Performance Battery (SPPB)-aligned functional fall-risk stratification using monocular RGB video. A total of 688 community-dwelling older adults completed SPPB-aligned assessments, including balance, five-times sit-to-stand, and 3 m gait tasks. Because prospective fall-event outcomes were unavailable, supervised labels were constructed from a pre-specified SPPB-aligned functional risk index rather than observed future falls. BlazePose-based two-dimensional keypoints were extracted, normalized using pelvis-centered and height-scaled transformations, and represented as temporal skeletal trajectories. Biomechanical descriptors were fused with embeddings from the proposed Temporal Convolutional Artificial Intelligence Fall-Risk Network (TCAI-FallNet). Participant-level data partitioning was used to reduce data leakage. TCAI-FallNet achieved a macro-averaged area under the curve of 0.91 and an overall accuracy of 81.3%. The trained model had a footprint under 3 MB, and TCN inference latency was below 20 ms per sequence under workstation-based evaluation. These findings suggest that TCAI-FallNet may support contactless SPPB-aligned functional mobility risk stratification, while prospective fall-event validation remains necessary. Full article

Introduction: Pedestrians lying on the road—collapsed through medical emergency, intoxication, or displacement following a prior collision—represent a disproportionately lethal and underaddressed category in road traffic safety. Forensic database analyses derived from Japan’s national police records document a fatality rate of 33.0% for collisions involving pedestrians lying on the road, more than double the rate for upright pedestrian collisions. Standard Advanced Driver-Assistance Systems (ADAS) yield a True Positive Rate (TPR) of only 21.4% for detecting pedestrians lying on the road under night conditions—a classification gap of 73.3 percentage points. Methods: In simulation trials, we evaluated the Advanced Falling Object Detection System (AFODS—where “falling object” denotes the low-profile human form at road level, distinguishing the prone pedestrian from the upright postures addressed by conventional ADAS) on a composite dataset of 3200 annotated fall events and 12,000 negative samples (training/validation), with 320 independent controlled simulation trials used for performance evaluation, spanning real-world, forensic-reconstruction, and Total Human Body Model for Safety (THUMS)-validated synthetic scenarios. No physical prototype has been evaluated; all performance data are derived from simulation, and 37.5% of positive samples are synthetically generated. These simulation conditions represent a first feasibility demonstration pending real-world hardware validation. This paper introduces three original contributions absent from prior work: a three-stage quantitative injury-risk model, a formal ISO 26262 Hazard Analysis and Risk Assessment (HARA), and a medicolegal SHAP interpretability framework. The injury-risk model translated detection latency via impact velocity to Head Injury Criterion (HIC) and estimated fatal injury probability (AIS ≥ 5); these model outputs should be interpreted as exploratory estimates pending ATD validation. Reporting follows principles consistent with the TRIPOD statement. Results: Under clear daytime conditions, AFODS demonstrated a TPR of 98.2% (95% CI: 97.4–98.8%) in simulation, decreasing to 95.6% under night dry-road conditions and 89.4% under night rain. The system achieved an AUC of 0.981 and a mean end-to-end latency of 46.5 ms, representing a 76.8 percentage-point improvement in simulation over the monocular RGB baseline (p < 0.001). The injury-risk model projects a reduction in estimated fatal head injury probability from 66.2% (Monte Carlo mean) (no detection, 50 km/h full-speed impact) to 0.7% under AFODS worst-case night/rain conditions, and to ≈0% under clear daytime simulation conditions. Conclusions: A 73.3 percentage-point classification gap places pedestrians lying on the road outside the effective detection envelope of current ADAS, compounded by the systematic exclusion of non-upright postures from regulatory test protocols and benchmark datasets. AFODS supports proof-of-concept feasibility under simulation conditions. Three translational steps are required: prototype validation on real-world hardware using instrumented Anthropomorphic Test Devices (ATDs); prone-posture biomechanical injury modelling using HIC and BrIC criteria; and regulatory extension of pedestrian AEB test standards to non-upright scenarios. Full article

Background/Objectives: Implant placement in transnasal and paranasal regions of the severely atrophic maxilla is challenged by complex anatomy and proximity to critical structures, particularly the nasolacrimal duct (NLD). While cortical anchorage is considered important for implant stability, structured methods for evaluating anatomical eligibility and anatomical risk during planning remain limited. This proof-of-concept study aimed to describe a segmentation-assisted workflow for anatomical assessment of potential paranasal implant trajectories. Methods: A single-case proof-of-concept workflow was developed using CBCT imaging and multi-component anatomical bone segmentation (MCABS). Segmented anatomical structures were used to selectively visualize cortical pathways within the anterior maxilla. Implant planning was performed using axial, non-tilted trajectories. Particular attention was directed toward visualization of the spatial relationship between the planned implant pathway and the nasolacrimal duct. Workflow feasibility was further explored through study-model fabrication, guided implant insertion, and axis-based verification. Results: The proposed workflow enabled selective visualization of cortical structures and facilitated identification of anatomically favorable implant trajectories within the paranasal region. The relationship between the planned implant pathway and the nasolacrimal duct could be directly assessed using the segmented anatomical model. Guided insertion in the study model demonstrated concordance between planned and executed implant axes, supporting the technical feasibility of the workflow. Conclusions: Within the limitations of a single-case proof-of-concept study, the proposed segmentation-assisted workflow may contribute to preoperative anatomical assessment of potential paranasal implant trajectories and their relationship to adjacent anatomical structures. The workflow should be regarded as a methodological demonstration rather than a validated clinical protocol. Further anatomical, reproducibility, biomechanical, and clinical studies are required before broader clinical adoption can be considered. Full article

Background/Objectives: Patient-specific subperiosteal implants are increasingly used to treat severely atrophic ridges due to advances in digital planning and additive manufacturing. This study aimed to evaluate the effects of material type and implant configuration on stress distribution in subperiosteal implant systems and to compare their overall biomechanical performance using a multi-criteria decision framework. Methods: A three-dimensional model of a severely atrophic maxilla was reconstructed to simulate four clinical scenarios combining two configurations (one-piece and two-piece) and two materials (titanium and 60% carbon fiber-reinforced polyetheretherketone). Finite element analysis was conducted to assess stress distribution within the implant body, fixation screws, prosthetic framework, and surrounding bone under vertical and oblique loading conditions. Maximum and minimum principal stresses were evaluated in bone, whereas von Mises stresses were calculated for implant components. The resulting biomechanical indicators were subsequently integrated using an entropy weight–TOPSIS multi-criteria decision analysis. Results: Principal stresses in the surrounding bone showed minimal variation between titanium and 60% carbon fiber-reinforced polyetheretherketone across all configurations. Implant configuration had a more pronounced effect on implant body stress. Under oblique loading, the two-piece configuration demonstrated substantially higher implant stresses than the one-piece design, whereas under vertical loading, lower implant stresses were observed in the two-piece configuration. The multi-criteria analysis ranked the one-piece titanium model highest under oblique loading and the two-piece titanium model highest under vertical loading. Conclusions: Implant configuration and loading direction influenced biomechanical behavior more than material selection in patient-specific subperiosteal implants. Full article

In today’s manufacturing industry, work-related musculoskeletal disorders (WMSDs) remain among the most prevalent occupational health issues. Collaborative robots (cobots) represent a promising technology to address this challenge. Consequently, ergonomics assessment in human–robot collaboration (HRC) has gained increasing attention in recent years. This study investigates the feasibility of using a coupled digital twin system consisting of a digital human model (DHM) and a cobot digital twin to assess detailed ergonomic parameters such as muscle activations and joint reaction forces in an HRC task. Selected parameters are used to develop an ergonomics map that condenses the effects of human–robot interaction into a single scalar value for each working position in the coronal plane in front of the user. The ergonomics mapping approach is presented, key influencing factors are identified, and critical workspace design implications are discussed. Full article

Background/Objectives: Achilles tendon injuries exhibit slow and incomplete healing due to limited vascularity, cellularity, and a complex extracellular matrix (ECM). Biologically derived ECM-based therapies have emerged as promising strategies to enhance tendon repair by providing native biochemical cues. In this study, the efficacy of injectable human tendon-derived ECM (hT-ECM) was investigated using a rat Achilles tenotomy model. Methods: Percutaneous tenotomy of the right Achilles tendon in 50 rats was performed. The animals were subjected to either no treatment as a control group (CON) or hT-ECM treatment group (ECM). Tendon healing was evaluated at 1 and 6 weeks using biomechanical, histological, transmission electron microscopy, Western blot, and immunohistochemical analysis. Results: At 6 weeks, the ECM group exhibited higher load to failure (54.5 ± 15.0 N) than the CON group (28.6 ± 13.7 N) (p < 0.05). Histological evaluation revealed progressive restoration of the injured tendon in both groups over the healing period, with comparable results observed between the groups. Ultrastructural analysis revealed that the ECM group exhibited significantly increased collagen fibrils diameters (74.1 ± 19.1 nm) than the CON group (53.8 ± 13.8 nm) (p < 0.001). Western blot exhibited that collagen I (COL I) expression at 6 weeks was significantly higher in the ECM group compared with the CON group (p < 0.05). Conclusions: These outcomes suggest that injectable hT-ECM improves early repair responses in a rat Achilles tenotomy model. Molecular analyses suggested that the therapeutic effects of hT-ECM injection could be linked to enhanced collagen type I production. Based on the study, hT-ECM injection might be a good adjuvant option for the treatment of Achilles tendon injuries. Full article

Background: Reactive agility (RA) and change of direction speed (CODs) are fundamental abilities that determine performance effectiveness in football. Biomechanical and motor factors, such as lower limb strength, asymmetry, and speed, may influence the level of these abilities. Moreover, reactive agility is a complex ability and may be partially dependent on the level of CODs. Methods: This study aimed to identify the key motor abilities responsible for shaping reactive agility in young football players. The study involved 55 boys aged $15.63\pm 1.56$ years. The following tests were used: the isometric mid-thigh pull (IMTP) for strength assessment, a 30 m sprint test, the 505 change of direction test, and reactive agility tests with and without a ball, utilizing the Skillcourt system. Results: Isometric strength and sprint speed were significantly correlated with the results of the 505 test. However, asymmetry in lower limb strength did not cause statistically significant changes in the analyzed parameters. Reactive agility without the ball showed significant correlations with speed, change of direction performance, and isometric strength. In the case of reactive agility with the ball, significant correlations were observed primarily with change of direction performance and reactive agility without the ball. Additionally, stepwise regression models revealed significant models for the 50 Random Run without the ball, the Star Run Random without the ball, and the 50 Random Run with the ball. Conclusions: Asymmetry in the isometric lower limb strength does not significantly affect the level of reactive agility or the ability to quickly CODs. Similarly, asymmetry in directional changes during running did not significantly impact the level of reactive agility. However, it may still be a contributing factor to increased injury risk in young football players. Full article

Background: Sports injuries impose a substantial burden on athletes. Machine learning (ML) and deep learning (DL) methods, collectively referred to as artificial intelligence (AI), are increasingly applied to develop predictive models and targeted prevention strategies. Objective: This scoping review aimed to map contemporary trends in AI applications for sports injury prediction and personalised prevention strategies, critically appraising the existing methodological approaches and identifying future research directions. Methods: Following PRISMA-ScR guidelines, we systematically searched five electronic databases, i.e., PubMed, Web of Science, Institute of Electrical and Electronics Engineers Xplore, Scopus, and Google Scholar, for peer-reviewed studies published up to February 2026 that applied AI methods for injury prediction and/or prevention in athletic populations. Results: Thirty-nine studies were included. Tree-based ML algorithms were the most common (59% of studies) methods used, with reported area under the curve values ranging from 0.82 to 0.95. DL was used in 18% of studies, with one hybrid model reporting 92% accuracy. Integrating multi-modal data was associated with improved model performance in 37% of studies. Among included studies, AI-informed prevention strategies were associated with injury reductions ranging from 23% to 42%, derived from synthesis-level and single-centre intervention evidence, respectively. The key challenges identified were heterogeneous injury definitions, small sample sizes, and data privacy concerns. Conclusions: AI models can inform personalised injury prevention, but their clinical use is limited by methodological issues. Key limitations include heterogeneous injury definitions, small sample sizes, and a lack of external validation. Standardised protocols are needed to improve the reliability and application of these models in practice. Full article