Search Results (2,533)

Background: Flatfoot deformity is associated with altered lower extremity biomechanics and functional impairment during gait. However, evidence describing spatio-temporal gait alterations remains heterogeneous and has not been consistently synthesized across studies. Methods: A systematic review was conducted in accordance with PRISMA guidelines. MEDLINE (via PubMed) and Scopus were searched through 24 March 2025 for studies evaluating gait characteristics in individuals with flatfoot or progressive collapsing foot deformity. Studies reporting spatio-temporal parameters in both flatfoot and healthy control cohorts were included in quantitative synthesis. Random-effects meta-analyses were performed to evaluate gait velocity, stance duration, stride length, and cadence. Results: Fifteen studies met inclusion criteria, of which five provided sufficient data for meta-analysis. Compared with healthy controls, individuals with flatfoot demonstrated longer stance duration and shorter stride length. No differences were observed in gait velocity or cadence. Substantial heterogeneity was present across all pooled outcomes (I² > 80%), reflecting variability in study populations, disease characteristics, and gait analysis methodologies. Conclusions: Flatfoot is associated with consistent spatio-temporal gait adaptations characterized by longer stance duration and reduced stride length. Despite heterogeneity among included studies, these findings suggest consistent spatio-temporal gait adaptations that may serve as clinically relevant markers of altered gait mechanics and functional impairment. Further studies with standardized protocols are needed to refine the role of gait analysis in the assessment and management of flatfoot. Full article

Background/Objectives: Ambroxol is a mucolytic agent widely used in the treatment of respiratory diseases; however, evidence in the literature indicates anti-inflammatory, analgesic, and immunomodulatory properties, suggesting potential for therapeutic repositioning. This study aimed to evaluate the analgesic and anti-inflammatory effects of ambroxol in an experimental model of osteoarthritis (OA). Methods: Adult male Wistar rats underwent OA induction on day zero (D0) by sodium monoiodoacetate (MIA) injection and were allocated into the following groups: Healthy, negative control (CTRL−), and groups treated with meloxicam (2 mg/kg) or ambroxol (10, 50, and 100 mg/kg). Treatments were administered orally (gavage) once daily for 28 days. Behavioral tests were performed, including rotarod, walkway gait analysis, weight-bearing, Von Frey, and Rat Grimace Scale assessments, along with radiographic and histopathological analyses and quantification of pro- and anti-inflammatory cytokines (IL-1β, IL-6, and IL-10). Results: Ambroxol treatment improved nociceptive parameters and motor function, reduced radiographic and histopathological scores, and showed performance comparable to meloxicam in several tests. There was a marked reduction in IL-1β and IL-6 levels, while IL-10 levels were lower in ambroxol-treated groups, suggesting early control of the inflammatory response. Conclusions: The results indicate that ambroxol exhibits antinociceptive and anti-inflammatory actions and suggest a potential chondroprotective effect, reinforcing its viability as a candidate for therapeutic repositioning in osteoarthritis. Further studies are required to more precisely elucidate its mechanisms of action, define optimal dosing and treatment duration, and support translation to clinical models. Full article

Background: The use of inertial measurement units (IMUs) in the Timed Up and Go (TUG) test enables the quantitative assessment of functional performance and mobility. It allows for the determination not only of the total test completion time, but also of the durations of individual phases, as well as the derivation of spatiotemporal gait parameters and turning velocity. The aim of this review article was to compile parameters of the instrumented Timed Up and Go (iTUG) test and to identify the parameters most commonly analyzed in populations of healthy adults, older adults, and patients with neurological disorders. Methods: A systematic literature search was conducted in the PubMed, Scopus, and ScienceDirect databases. The authors included studies in which commercial IMUs were used during the TUG test and quantitative parameters were analyzed. Methodological quality was assessed using the JBI Critical Appraisal Checklist for cross-sectional studies. Results: A total of 36 studies were included in the review. Only those disease entities represented by at least four studies were included in the tabular analysis. The study presents results for a total of 1268 individuals, including 192 healthy adults, 514 older adults, 230 patients with multiple sclerosis (MS), and 332 patients with Parkinson’s disease (PD). The analysis showed that temporal parameters, particularly the total test duration and the durations of individual phases, were the most commonly reported across all populations. Conclusions: Turning-related parameters were analyzed frequently, whereas spatiotemporal parameters were assessed less often. The results indicate a lack of standardization both in the selection of iTUG parameters as well as in the measurement methods and systems used. Full article

The Timed Up and Go (TUG) test is a clinical gold standard for assessing elderly mobility, yet its automated deployment in home-monitoring and resource-limited areas is hindered by high hardware costs and expert calibration requirements. This study introduces a Physics–Data Hybrid framework specifically designed for uncalibrated consumer-grade CMOS cameras, enabling a “plug-and-play” solution for early Parkinson’s disease (PD) risk screening. The proposed pipeline integrates learning-based pose perception with a self-evolving physics model to recover absolute metric-scale motion without manual checkerboard calibration. A noise-adaptive fusion strategy is implemented to reconcile 2D pixel dynamics with 3D kinematic consistency, overcoming the inherent scale ambiguity of monocular vision. Crucially, this framework enables the extraction of high-dimensional spatiotemporal parameters—such as stride length coefficient of variation and mean gait velocity—which provide a finer diagnostic resolution for capturing subtle motor fluctuations than conventional timing-only systems. Results from our pilot study with a cohort of 10 subjects demonstrate that these extracted metric features serve as decisive markers for risk staging simulated by dual-task-induced cognitive-motor-interference, achieving 98% screening accuracy and an overall classification accuracy of 87.32%. This framework provides a robust, low-cost tool for ubiquitous telehealth, potentially supporting early PD risk assessment in underserved populations. Full article

Transparent clinical decision-making remains a critical barrier to deploying deep learning in medical diagnosis. Post hoc explanation methods approximate model behaviour after training but cannot guarantee that explanations faithfully reflect the underlying reasoning. This study proposes a Self-Explaining Neural Network (SENN) for Parkinson’s Disease (PD) screening via Ground Reaction Force (GRF) gait analysis, enforcing intrinsic interpretability through learnable basis concepts and input-dependent relevance scores computed jointly with the prediction. The architecture combines a four-block residual CNN backbone with stochastic depth regularisation, a 16-concept encoder with diversity and stability constraints, and temperature-scaled probability calibration for reliable clinical operating points. Evaluated on the PhysioNet Gait in Parkinson’s Disease dataset (306 subjects, 16 GRF sensors per foot), SENN achieves a subject-level ROC-AUC of 0.916 [95% CI: 0.867–0.964], sensitivity of 0.913 [0.862–0.963], specificity of 0.671 [0.485–0.858], and Average Precision of 0.942 [0.918–0.967], reported across five independent random seeds. Comparative evaluation against four deep learning baselines—CNN-Residual, BiLSTM, CNN-LSTM, and CNN-Attention—confirms that the interpretability constraints impose no statistically significant reduction in discriminative performance, with all pairwise ROC-AUC confidence intervals overlapping. Concept-level analysis reveals that the three most discriminative concepts correspond to disrupted midfoot loading patterns, increased step-length variability, and bilateral cadence asymmetry—all established biomechanical hallmarks of parkinsonian gait—providing clinically grounded, patient-specific explanations without post hoc approximation. These findings demonstrate that rigorous intrinsic interpretability and competitive predictive accuracy are simultaneously achievable in deep gait analysis, supporting the clinical adoption of transparent diagnostic AI. Full article

This study addresses the analysis of velocity–force duality in redundantly actuated systems under actuation constraints. While velocity and force capabilities have typically been evaluated separately, their coupled relationship has not been systematically investigated. To this end, an optimization-based framework is developed to characterize achievable velocity and force along specified directions by incorporating system kinematics and actuator limits. A distributed actuation mechanism is considered as a representative system to demonstrate the proposed formulation. The resulting feasible velocity and force boundaries are interpreted as directional performance limits and are used to examine their intrinsic trade-off. The analysis reveals that velocity and force exhibit complementary characteristics depending on the actuation configuration, providing a basis for performance-oriented selection under task requirements. The proposed framework is further applied to a gait-inspired motion generation problem, where force-oriented characteristics are assigned to the stance phase and velocity-oriented characteristics to the swing phase. The generated motion reflects phase-dependent features consistent with human gait. These results demonstrate that the proposed framework provides a systematic approach for analyzing and utilizing velocity–force duality in redundantly actuated systems. Full article

Background/Objectives: Ankle–foot orthoses (AFOs) are commonly used to improve gait in children with cerebral palsy (CP), but their effect on specific gait patterns is underreported. This study evaluates the utilization of the Gait Pattern Classification System for Children with Spastic CP (GaP-CP) to investigate the effects of ankle–foot orthoses on gait kinematics, spatio-temporal parameters and the energy cost of walking. Methods: In this retrospective study, 66 ambulatory children with spastic CP underwent 3D gait analysis with and without AFOs or functional electrical stimulation. Gait patterns were classified according to GaP-CP. AFOs were articulated, flexible, or rigid. Thirty-six children also performed a 5 min walk test with gas exchange measurements. Step length, walking speed, and the energy cost of walking were calculated. Gait kinematics were analyzed with statistical nonparametric mapping. Non-parametric statistics were used to investigate orthotic effects for the total group and for each gait pattern. Results: Ankle kinematics improved in swing phase and initial contact (10 degrees less plantarflexion, p < 0.05) for the total group, dropfoot and genu recurvatum. During the stance phase, reduced knee extension in genu recurvatum (by 3 degrees, p < 0.05) and increased knee extension in crouch (by 3 degrees, p < 0.05) were observed. Median changes in non-dimensional step length were clinically significant (>0.039, p ≤ 0.02, effect size ≥ 0.55) for the total group and the dropfoot, genu recurvatum, and crouch subgroups, while changes in most gait indices, walking speed and the energy cost of walking were not clinically significant. Conclusions: The combined use of GaP-CP and kinematic analysis provided new insights into the effects of ankle–foot orthoses on gait. Articulated and flexible orthoses may not have provided adequate support for genu recurvatum and crouch gait, showing a potential value in gait pattern specific orthotic design to optimize gait kinematics. Full article

Background: Frailty is a common finding in elderly subjects with severe aortic stenosis (AoS) and a strong predictor of mortality and disability after aortic valve surgery. The atherogenic index of plasma (AIP) is related to different cardiovascular (CV) risk factors, which in turn are correlated to the progression of frailty as well as of AoS. Aim: to analyze the association of AIP with different CV risk factors and frailty scores and its value as a determinant of mortality in older adults with severe AoS. Methods: The association of AIP with a multidimensional assessment of frailty by using Fried criteria and the following indices; timed up-and-go test (TUG) for gait function; Charlson Index (CI), basic activities of daily living (BADL) and instrumental activities of daily living (IADL) for disability; mini–mental state examination for cognitive function evaluation (MMSE); Geriatric Depression Score for mood disorder (GDS); Mini Nutritional Assessment (MNA) for nutritional status was assessed in 102 elderly AoS patients (33 males; mean age 83 ± 6 yrs). Moreover, the relationship between AIP and demographic, lifestyle, traditional CV risk factors and CV mortality was also evaluated. Results: Significant relationships between AIP and glycemia and inflammatory parameters (CRP, ESR and fibrinogen) as well as with troponin I were found. Moreover, AIP significantly correlates with CI, BADL, IADL and MNA. However, the Kaplan–Meier analysis did not show any significant difference for survival rates according to AIP intervals of risk, whereas ejection fraction remained the only significant determinant after multivariate adjustment for mortality at the Cox proportional hazard models analysis in this patient population. Conclusions: Higher AIP is significantly associated with cardiometabolic risk and increased physical dysfunction risk and frailty in AoS pts, evidencing its potential use as a simple biomarker in this clinical setting, although it did not represent a significant determinant for mortality in this population. Full article

The evaluation of biomechanical parameters in four-beat gaited horses remains limited by the subjectiveness and complexity of current standard methods. Through a deep learning approach, we aimed to infer dissociation % using only acoustic signals. A total of 268 audio samples were extracted from publicly available videos featuring three Brazilian horse breeds (Mangalarga Marchador, Campolina, and Piquira) performing marcha batida and marcha picada. Acoustic features, including root mean square energy (RMS), zero-crossing rate (ZCR), and 13 Mel-frequency cepstral coefficients (MFCCs), were extracted and used to train a long short-term memory (LSTM) neural network. The model accurately predicted the time intervals between successive hoof–ground contacts (R² = 0.98; MAE = 0.0071), enabling the calculation of the dissociation %. While no significant differences were found between gait types and dissociation %, breed-related differences in both mean hoof–ground contact interval and dissociation were observed, with 8 acoustic features demonstrating discriminative power. Our results suggest that hoof–ground contact patterns can be quantified objectively from audio alone, offering a practical and non-invasive method for gait analysis. The approach holds potential for applications in breed standardization, selection, and digital locomotion phenotyping of horse populations. Full article

Introduction: Although gait alterations associated with excess body weight have been widely studied, most available evidence comes from laboratory-based analyses, which limit ecological validity and the translation of findings into clinical practice. This study addresses this gap by examining gait biomechanics across BMI categories using portable sensor-based insoles that allow gait assessment in real-world conditions. Methods: A cross-sectional study including 96 adults categorized as normal weight (NW), overweight (OW), or obese (OB) was conducted. Gait biomechanics were recorded using PODOSmart^® intelligent insoles, which capture spatiotemporal and angular parameters during natural walking. Foot health, quality of life and comorbildities were evaluated throught valeted questionnarires. Differences between groups were analyzed using ANOVA and chi-square tests. Age and sex, known to influence gait, were comparable across BMI groups and were considered in the interpretation of the results. Results: Overall, the participants in the OB group exhibited reduced stride length, gait speed, and swing time, increased double-support time, and greater pronation–supination and progression angles than OW and NW participants. Partial eta-squared values (η²p) were predominantly medium to large, reinforcing the robustness of these between-group differences (e.g., double-support time, p > 0.001; η²p = 0.19). Individuals with obesity reported poorer general and foot health and more difficulty finding suitable footwear. BMI was also significantly associated with hypertension, dyslipidemia, arthritis, and depression (all p <0.05), whereas diabetes, cardiopathies, knee pain, and fatigue andwalking or social activity limitations showed no significant differences. Conclusions: By using portable gait analysis technology in ecological conditions, this study provides novel evidence of clinically meaningful gait impairments across BMI groups. Higher BMI is associated with clinically relevant gait impairments, poorer perceptions of foot and general health, and a higher prevalence of several comorbidities. Full article

The Chopart amputation is a controversial amputation level and has been described as a relatively non-functional amputation due to the increased risk for stump deformity and subsequent complications. Recent literature has shown that a tibiotalocalcaneal arthrodesis can subjectively increase function in the Chopart amputation. We describe the use of a tibiotalocalcaneal arthrodesis in a 44-year-old diabetic male with a Chopart amputation for the purpose of limb and gait salvage. After 292 days and an uneventful postoperative course, the patient consented to gait analysis. The case allows us to objectively conclude that the tibiotalocalcaneal arthrodesis can quantitatively increase the functionality of the Chopart amputation and, in turn, expand its indication for use within the more functional populations. Full article

Abnormal gait associated with neuromuscular and musculoskeletal disorders represents a growing clinical burden, particularly in aging populations. This study presents a modular, low-cost Smart Rehabilitation Walker (SRW) that integrates multimodal sensing and real-time haptic feedback to enable simultaneous gait monitoring and corrective intervention in both clinical and home environments. The system combines force-sensing resistors for bilateral load symmetry assessment, inertial measurement units for fall detection, and surface electromyography (sEMG) for neuromuscular activity monitoring within a closed-loop assistive feedback architecture. A 15-day pilot study involving ten individuals with rheumatoid arthritis and clinically observed neurological gait abnormalities demonstrated measurable improvements in gait biomechanics. The Force Symmetry Index (FSI), calculated using the Robinson symmetry metric, decreased from an average of 0.9691 to 0.2019, corresponding to a 79.26% average reduction in inter-limb load asymmetry. Concurrently, sEMG measurements showed a substantial increase in neuromuscular activation (ΔEMG = 4.28), with statistical analysis confirming a significant improvement across participants (paired t-test: t(9) = 13.58, p < 0.001). To model rehabilitation trajectories, a nonlinear predictive framework based on Gaussian Process Regression achieved high predictive accuracy (R² ≈ 0.9, with a mean RMSE of 0.0385), while providing uncertainty-aware trend estimation. Validation using an independent amyotrophic lateral sclerosis gait dataset further demonstrated the transferability of the analytical pipeline. These results highlight the potential of sensor-enabled assistive walkers as scalable platforms for quantitative gait rehabilitation, adaptive feedback, and long-term mobility monitoring. Full article

Peripheral nerve injuries often lead to incomplete recovery despite surgical repair. Vitamin B12 and folic acid have been implicated in nerve regeneration, but their comparative effects have not been systematically evaluated. Twenty-four male Wistar rats underwent femoral nerve transection and were assigned to three groups: control, vitamin B12 (2500 µg/kg weekly, subcutaneous), and folic acid (40 mg/L in drinking water). Functional recovery was assessed over eight weeks using foot-base angle (FBA) during beam walking. Histological analysis evaluated axon counts and myelination (g-ratio). Both treatments accelerated early gait recovery compared to controls, with significant FBA improvement at week 4 (p < 0.05). Vitamin B12 produced sustained functional benefits through week 8 and superior myelination (lower g-ratio, p < 0.0001), whereas folic acid increased axon numbers but did not enhance myelin thickness or late-phase recovery. High-dose vitamin B12 significantly improves structural and functional outcomes after femoral nerve injury, while folic acid primarily supports early axonal regrowth. Vitamin B12 represents a promising pharmacological adjunct for peripheral nerve repair. Further research should explore optimal dosing strategies and long-term effects in clinical settings. To our knowledge, no prior study has directly compared the effects of folic acid and vitamin B12 supplementation within the rat femoral-nerve model, providing the rationale for the present head-to-head design. Full article

In-pipe robots must navigate narrow, curved passages where rigid mechanisms often require bulky steering units. Soft crawlers offer better compliance but typically rely on multiple actuators or reconfigurable contacts to achieve multi-directional motion. Drawing inspiration from biological soft crawlers that exploit directional friction and coordinated anchor–slip patterns, this study focuses on locomotion principles observed in caterpillars, water boatmen, and whirligig beetles. Based on these bioinspired concepts, we present a tendon-driven soft in-pipe robot that combines continuum bending–twisting deformation with modular anisotropic friction pads (AFPs), enabling three locomotion modes using only two motors. AFP inclination, curvature, and ridge geometry were optimized through friction tests, constant-curvature modeling, and finite element analysis to enhance directional adhesion on flat and curved surfaces. A deformation-based locomotion framework was developed to couple tendon actuation with friction orientation, achieving longitudinal crawling, transverse translation, in-place rotation, and smooth transitions via programmed twisting. Driving experiments demonstrated repeatable anchor–slip locomotion with average speeds of 28.6 mm/s, 15.7 mm/s, and 11.5°/s for the three modes. Pipe tests in straight, curved, and T-junction sections further validated stable contact and reliable gait transitions. These findings highlight the potential of friction-programmed continuum robots as compact, bioinspired platforms for advanced in-pipe inspection and diagnostic tasks. Full article

This study introduces a novel unsupervised framework, ICR-LLS, for detecting pathological gait patterns using instantaneous center of rotation (ICR) trajectories of the shank in the sagittal plane. ICR trajectories were computed from two-dimensional kinematic data captured at the lateral femoral epicondyle and lateral malleolus for both shanks, producing four-dimensional multivariate time series for each gait trial. Pairwise trajectory dissimilarities were quantified using circularly aligned Dynamic Time Warping (DTW), preserving temporal and spatial structure. The resulting dissimilarity matrix was embedded into a three-dimensional space using a force-directed network layout, enabling intuitive visualization of inter-subject gait relationships. Density-based clustering (DBSCAN), enhanced with a consensus-based ensemble approach, was employed to automatically identify clusters representing typical (healthy) gait patterns and outliers corresponding to pathological deviations. The framework is evaluated on a public dataset comprising individuals with Parkinson’s disease (PD) and healthy controls, achieving a normalized mutual information (NMI) of 0.449 and a Separation-to-Compactness Ratio (SCR) of 6.754, indicating a meaningful cluster structure. In addition, classification-oriented metrics yield an accuracy of 90%, sensitivity of 70%, and specificity of 96.7%, supporting the method’s effectiveness in distinguishing pathological gait. By combining minimal 2D kinematic inputs with unsupervised learning, ICR-LLS provides an interpretable framework for the exploratory analysis of gait variability, and although further validation is required, the findings suggest that ICR trajectories may serve as a meaningful biomechanical descriptor for characterizing pathological locomotion. Full article