Search Results (545)

The Gross Motor Function Measure-66 (GMFM-66, range of values: 0 to 100 points) is one of the most widely used clinical tests to quantify motor function in children with cerebral palsy (CP). A disadvantage of the GMFM-66 is that it can take up to one hour to complete. The aim of the study was to evaluate whether the GMFM-66 can be predicted with sufficient accuracy by the results of an instrumental gait analysis (IGA) in ambulant children with CP. A retrospective analysis was conducted on n = 256 ambulant children with CP enrolled in a rehabilitation program between 2018 and 2023. The sample consisted of 97 females and 159 males, with a mean age of 9.0 years (SD 3.6 years). The IGA was performed with a Zebris FDM pressure plate. For the prediction of the GMFM-66, different statistical models were used (multiple linear regression and machine learning algorithms). Among the models considered, the XGBoost model had the best predictive performance (mean absolute error 6.32 (95%CI 5.35–7.28)). Agreement between results from gait analyses by the Zebris FDM pressure plate and GMFM-66 is not yet sufficient to predict the GMFM-66 score with acceptable accuracy for clinical purposes. Full article

The ability to quantify equine kinematics is essential for clinical evaluation, research, and performance feedback. However, current methods are challenging to implement. This study presents a motion capture methodology for horses, where three-dimensional, full-body kinematics are calculated without instrumentation on the animal, offering a more scalable and labor-efficient approach when compared with traditional techniques. Kinematic trajectories are calculated from multi-camera video data. First, a neural network identifies skeletal landmarks (markers) in each camera view and the 3D location of each marker is triangulated. An equine biomechanics model is scaled to match the subject’s shape, using segment lengths defined by markers. Finally, inverse kinematics (IK) produces full kinematic trajectories. We test this methodology on a horse at three gaits. Multiple neural networks (NNs), trained on different equine datasets, were evaluated. All networks predicted over 78% of the markers within 25% of the length of the radius bone on test data. Root-mean-square-error (RMSE) between joint angles predicted via IK using ground truth marker-based motion capture data and network-predicted data was less than 10 degrees for 25 to 32 of 35 degrees of freedom, depending on the gait and data used for network training. NNs trained over a larger variety of data improved joint angle RMSE and curve similarity. Marker prediction error, the average distance between ground truth and predicted marker locations, and IK marker error, the distance between experimental and model markers, were used to assess network, scaling, and registration errors. The results demonstrate the potential of markerless motion capture for full-body equine kinematic analysis. Full article

Background: Gait kinematics address the analysis of joint angles and segment movements during walking. Although there is work in the literature to solve the problems of forward (FK) and inverse kinematics (IK), there are still problems related to the accuracy of the estimation of Cartesian and joint variables, singularities, and modeling complexity on gait analysis approaches. Objective: In this work, we propose a framework for two-dimensional gait analysis addressing the singularities in the estimation of the joint variables using quaternion-based kinematic modeling. Methods: To solve the forward and inverse kinematics problems we use the dual quaternions’ composition and Damped Least Square (DLS) Jacobian method, respectively. We assess the performance of the proposed methods with three gait patterns including normal, toe-walking, and heel-walking using the RMSE value in both Cartesian and joint spaces. Results: The main results demonstrate that the forward and inverse kinematics methods are capable of calculating the posture and the joint angles of the three-DoF kinematic chain representing a lower limb. Conclusions: This framework could be extended for modeling the full or partial human body as a kinematic chain with more degrees of freedom and multiple end-effectors. Finally, these methods are useful for both diagnostic disease and performance evaluation in clinical gait analysis environments. Full article

Background: Charcot–Marie–Tooth disease (CMT) is a genetically and phenotypically heterogeneous hereditary neuropathy. Axonal CMT type 2 (CMT2) subtypes often exhibit overlapping clinical features, which makes molecular genetic analysis essential for accurate diagnosis and subtype differentiation. Methods: This retrospective study included five pediatric patients who presented with gait disturbance, muscle weakness, and foot deformities and were subsequently diagnosed with axonal forms of CMT. Clinical data, electrophysiological studies, neuroimaging, and genetic analyses were evaluated. Whole exome sequencing (WES) was performed in three sporadic cases, while targeted CMT gene panel testing was used for two siblings. Variants were interpreted using ACMG guidelines, supported by public databases (ClinVar, HGMD, and VarSome), and confirmed by Sanger sequencing when available. Results: All had absent deep tendon reflexes and distal muscle weakness; three had intellectual disability. One patient was found to carry a novel homozygous frameshift variant (c.2568_2569del) in the IGHMBP2 gene, consistent with CMT2S. Other variants were identified in the NEFH (CMT2CC), DYNC1H1 (CMT2O), and MPV17 (CMT2EE) genes. Notably, a previously unreported co-occurrence of MPV17 mutation and congenital heart disease was observed in one case. Conclusions: This study expands the clinical and genetic spectrum of pediatric axonal CMT and highlights the role of early physical examination and molecular diagnostics in detecting rare variants. Identification of a novel IGHMBP2 variant and unique phenotypic associations provides new insights for future genotype–phenotype correlation studies. Full article

Background/Objectives: Parkinson’s disease (PD) is a neurodegenerative disorder that impairs mobility. Treadmill training (TT) is a common rehabilitation strategy for improving gait parameters in individuals with PD. This systematic review evaluated the effectiveness of TT in improving motor function, walking ability, and overall functional mobility in PD patients. Methods: We compared TT to other forms of gait and motor rehabilitation, including conventional and robotic gait training. Trials that compared a treadmill training group with a non-intervention group were excluded from this review. We searched multiple databases for RCTs involving Parkinson’s patients until January 2025. The primary outcomes were motor function (UPDRS-III) and walking ability (6 MWT and TUG test). Results: We identified 285 articles; 199 were excluded after screening. We assessed the full text of 86 articles for eligibility, and 13 RCTs met the inclusion criteria. Some of them were included in the meta-analysis. The TT group showed a significant improvement in UPDRS-III scores [mean difference (MD): −1.36 (95% CI: −2.60 to −0.11)] and greater improvement in TUG performance [MD, −1.75 (95% CI: −2.69 to −0.81)]. No significant difference in walking capacity as assessed through the 6 MWT was observed [MD: 26.03 (95% CI: −6.72 to 58.77). Conclusions: The current study suggests that TT is effective in improving the motor symptoms and functional mobility associated with PD. Further studies are needed to develop protocols that consider the patients’ clinical characteristics, disease stage, exercise tolerance, and respiratory function. Full article

Background: Current methods of postural control assessments and interventions to improve postural stability and thereby prevent falls often fail to incorporate the hazardous perturbation situations that frequently accompany falls. Virtual environments can safely incorporate these hazards. The purpose of the study was to identify if virtual slip and trip perturbations can be used as an exposure paradigm in place of real slip and trip perturbations to improve postural control. Methods: Fifteen healthy young adults were included in this study. Two paradigms, real gait exposure (real) and virtual environment gait exposure (virtual), consisting of real and virtual slip and trip trials, were performed by each participant in a counterbalanced order to avoid order effects. At baseline and following real and virtual paradigms, the modified clinical test for sensory integration and balance (mCTSIB), limits of stability (LOS), and single-leg stance (SLS) using BTracks balance plate were administered. Separate one-way (baseline vs. Real vs. Virtual) repeated measures analysis of variance were conducted on response variables. Results: In the posterior left quadrant of the LOS, significant differences were found after the real paradigm compared to baseline (p = 0.04). For the anterior left quadrant and total LOS, significant differences post real paradigm (p = 0.002 and p < 0.001) and virtual paradigm (p = 0.007 and p < 0.001) compared to baseline were observed. For the SLS, the left-leg significant differences were observed post real paradigm (p = 0.019) and virtual paradigm (p = 0.009) compared to BL in path length, while significant main effects were found for mean sway velocity for the left leg only (p = 0.004). For the right leg, significant differences were only observed after the virtual paradigm (p = 0.01) compared to BL. Conclusions: Both virtual and real paradigms were identified to improve postural control. The virtual paradigm led to increased postural control in the right-leg SLS condition, while the real paradigm did not, without any adverse effects. Findings suggest virtual reality perturbation exposure acutely improves postural control ability compared to baseline among healthy young adults. Full article

Gait analysis is a valuable tool for aiding in the diagnosis of neurological diseases, providing objective measurements of human gait kinematics and kinetics. These data enable the quantitative estimation of movement abnormalities, which helps to diagnose disorders and assess their severity. In this regard, machine learning techniques and explainability methods offer an opportunity to enhance anomaly detection in gait measurements and support a more objective assessment of neurodegenerative disease, providing insights into the most relevant gait parameters used for disease identification. This study employs several classifiers and explainability methods to analyze gait data from a public dataset composed of patients affected by degenerative neurological diseases and healthy controls. The work investigates the relevance of spatial, temporal, and kinematic gait parameters in distinguishing such diseases. The findings are consistent among the classifiers employed and in agreement with known clinical findings about the major gait impairments for each disease. This work promotes the use of data-driven assessments in clinical settings, helping reduce subjectivity in gait evaluation and enabling broader deployment in healthcare environments. Full article

Weakness, spasticity, and muscle shortening are common in children with cerebral palsy (CP), leading to deficits in gross motor, gait, and selective motor functions. While traditional assessments, such as the Gross Motor Function Measure (GMFM-66), instrumented gait analysis, and the Selective Control Assessment of the Lower Extremity (SCALE), are widely used, they are often limited by the resource-intensive nature of hospital-based evaluations. We employed cyclogram-based analysis, utilizing simple hip and knee joint kinematics to assess clinical measures, including GMFM-66, normalized gait speed, the gait deviation index (GDI), and the gait profile score (GPS). Principal component analysis was used to quantify the cyclogram shape characteristics. A total of 144 children with ambulatory spastic CP were included in the study. All the cyclogram parameters were significantly correlated with GMFM-66, gait speed, the GDI, and the sagittal plane subscore of the GPS for the hip and knee, with the swing phase area showing the strongest correlation. Regression models based on the swing phase area were used to estimate the GMFM-66 (R² = 0.301) and gait speed (R² = 0.484). The PC1/PC2 ratio showed a moderate correlation with selective motor control, as measured by the SCALE (R² = 0.320). These findings highlight the potential of hip–knee cyclogram parameters to be used as accessible digital biomarkers for evaluating motor control and gait function in children with bilateral spastic CP. Further prospective studies using wearable sensors, such as inertial measurement units, are warranted to validate and build upon these results. Full article

Accurate gait assessment is essential for managing pathological locomotion, especially in elderly patients recovering from hip joint surgeries. Inertial measurement units (IMUs) provide real-time, objective data in clinical settings. This study examined pelvic oscillations in sagittal, frontal, and transverse planes using a wearable IMU system in two groups: Group A (n = 15, osteosynthesis metallica) and Group B (n = 34, arthroplasty), all over age 65. Gait analysis was conducted during assisted and unassisted walking. In the frontal plane, both groups showed statistically significant improvements: Group A from 46.4% to 75.2% (p = 0.001) and Group B from 52.6% to 72.2% (p = 0.001), reflecting enhanced lateral stability. In the transverse plane, Group A improved significantly from 47.7% to 80.2% (p = 0.001), while Group B showed a non-significant increase from 73.0% to 80.5% (p = 0.068). Sagittal plane changes were not statistically significant (Group A: 68.8% to 71.1%, p = 0.313; Group B: 76.4% to 69.1%, p = 0.065). These improvements correspond to better pelvic symmetry and postural control, which are critical for a safe and stable gait. Improvements were more pronounced during unassisted walking, indicating better pelvic control. These results confirm the clinical utility of IMUs in capturing subtle gait asymmetries and monitoring recovery progress. The findings support their use in tailoring rehabilitation strategies, particularly for enhancing frontal and transverse pelvic stability in elderly orthopedic patients. Full article

Background: Lokomat-assisted robotic rehabilitation is increasingly used for gait restoration in patients with spinal cord injury (SCI). However, the objective evaluation of treatment effectiveness through biomechanical parameters and machine learning approaches remains underexplored. Methods: This study analyzed data from 29 SCI patients undergoing Lokomat-based rehabilitation. A dataset of 46 variables including range of motion (L-ROM), joint stiffness (L-STIFF), and muscular force (L-FORCE) was examined using statistical methods (paired t-test, ANOVA, and ordinary least squares regression), clustering techniques (k-means), dimensionality reduction (t-SNE), and anomaly detection (Isolation Forest). Predictive modeling was applied to assess the influence of age, speed, body weight, body weight support, and exercise duration on biomechanical outcomes. Results: No statistically significant asymmetries were found between left and right limb measurements, indicating functional symmetry post-treatment (p > 0.05). Clustering analysis revealed a weak structure among patient groups (Silhouette score ≈ 0.31). Isolation Forest identified minimal anomalies in stiffness data, supporting treatment consistency. Regression models showed that body weight and body weight support significantly influenced joint stiffness (p < 0.01), explaining up to 60% of the variance in outcomes. Conclusions: Lokomat-assisted robotic rehabilitation demonstrates high functional symmetry and biomechanical consistency in SCI patients. Machine learning methods provided meaningful insight into the structure and predictability of outcomes, highlighting the clinical value of weight and support parameters in tailoring recovery protocols. Full article

This review provides a comprehensive analysis of recent advancements in lower limb exoskeleton systems, focusing on applications, control strategies, hardware architecture, sensing modalities, human-robot interaction, evaluation methods, and technical innovations. The study spans systems developed for gait rehabilitation, mobility assistance, terrain adaptation, pediatric use, and industrial support. Applications range from sit-to-stand transitions and post-stroke therapy to balance support and real-world navigation. Control approaches vary from traditional impedance and fuzzy logic models to advanced data-driven frameworks, including reinforcement learning, recurrent neural networks, and digital twin-based optimization. These controllers support personalized and adaptive interaction, enabling real-time intent recognition, torque modulation, and gait phase synchronization across different users and tasks. Hardware platforms include powered multi-degree-of-freedom exoskeletons, passive assistive devices, compliant joint systems, and pediatric-specific configurations. Innovations in actuator design, modular architecture, and lightweight materials support increased usability and energy efficiency. Sensor systems integrate EMG, EEG, IMU, vision, and force feedback, supporting multimodal perception for motion prediction, terrain classification, and user monitoring. Human–robot interaction strategies emphasize safe, intuitive, and cooperative engagement. Controllers are increasingly user-specific, leveraging biosignals and gait metrics to tailor assistance. Evaluation methodologies include simulation, phantom testing, and human–subject trials across clinical and real-world environments, with performance measured through joint tracking accuracy, stability indices, and functional mobility scores. Overall, the review highlights the field’s evolution toward intelligent, adaptable, and user-centered systems, offering promising solutions for rehabilitation, mobility enhancement, and assistive autonomy in diverse populations. Following a detailed review of current developments, strategic recommendations are made to enhance and evolve existing exoskeleton technologies. Full article

Background: Marker-based motion capture is the current gold standard for three-dimensional (3D) gait analysis. This is a highly technical analysis that is time-consuming, and marker application can trigger anxiety in children. One potential solution is to use markerless camera systems instead. The objective of this study was to compare 3D lower limb gait kinematics in children using both marker-based and markerless motion capture methods. Methods: Ten typically developing children (age 6–13 yrs) completed five barefoot walks at a self-selected speed. A 10-camera marker-based system (Oqus, Qualisys) and a 7-camera markerless system (Miqus, Qualisys) captured synchronised gait data at 85 Hz. Generalised Additive Mixed Models were fitted to the data to identify the random effects of measurement systems, age, and time across the gait cycle. The root-mean-square difference (RMSD) was used to compare the differences between systems. Results: Significant interactions and differences were observed between the marker-based and markerless systems for most joint angles and planes of motion, particularly with regard to time and age. Conclusions: Despite differences across all kinematic profiles, the RMSD in this study was comparable to previously published results. Alternative model definitions and kinematic crosstalk in both systems likely explain the differences. Age differences were not consistent across joint levels, suggesting a larger sample size is required to determine how maturation may affect markerless tracking. Further investigation is required to understand the deviations and differences between systems before implementing markerless technology in a clinical setting. Full article

Background: The efficacy of postoperative exercise rehabilitation after spine surgery is controversial, and a protocol for exercise treatment and detailed outcomes based on functional activity have not yet been established. This study aimed to determine the efficacy of exercise rehabilitation after lumbar spine surgery. Methods: A prospective, randomized controlled trial was conducted in 40 patients who underwent lumbar spine surgery (20 patients each in the exercise and control groups) for 12 weeks. Clinical outcomes were assessed using the visual analog scale (VAS) for pain and EuroQol-5 Dimensions 5-Level version (EQ-5D-5L). Body proportions, including body mass index, total muscle mass, and body fat percentage were analyzed. Functional activity was evaluated based on the range of motion of the lumbar spine, strength and endurance of lumbar flexion/extension, flexibility, 6 min walking test, single-leg stance, coordination, and gait pattern analysis. Results: The exercise group showed significantly greater improvement in VAS for pain (66.67% versus 20.00%, p < 0.001) and EQ-5D-5L (45.56% versus 20.00, p = 0.039) compared to the control group. Serial assessment revealed significant improvement in strength of lumbar flexion/extension, 6 min walking test, single-leg stance, coordination, and gait patterns in the exercise group compared to the control group. In particular, the single-leg stance time for the affected leg improved more markedly in the exercise group (280.9% versus 48.7%, p < 0.001). Conclusion: Tailored postoperative exercise after lumbar spine surgery is effective in reducing pain and enhancing functional recovery, including strength and balance. Full article

Body asymmetry is often analysed in the context of low back pain (LBP). To date, research has mainly focused on the general relationships between asymmetry and pain, with less attention paid to issues related to pressure distribution and its potential impact on the occurrence of LBP. The aim of this study was to compare biomechanical parameters in people with anatomical leg length discrepancy with and without LBP to identify overloads that may lead to pain. Early detection of common abnormalities in these parameters in both groups may influence the early prevention of 0LBP in the course of LLD. Materials and methods: This study included 60 patients with diagnosed LLD, of whom 30 had LBP (group 1, NP) and 30 were pain-free (group 2, NwP). Body weight distribution during standing and walking was analysed using pedobarography. The analysis was carried out in two stages, the first being the analysis of the biomechanical parameters for the whole study population, for group 1 with LBP and group 2 without LBP, while the second stage focused on the main issue, i.e., the comparison of the group with LBP with the group without LBP. The study included standing and walking tests. Left–right pressure distribution and ground contact time were analysed. In addition, the angle of foot abduction was analysed to indirectly assess compensatory mechanisms resulting from the asymmetry. Results: The standing test showed significantly greater pressure on the longer limb (p = 0.022) in the whole study population (N = 60). When divided into groups, it was found that in those with LBP (NP = 30), the difference was not statistically significant (p = 0.359), whereas in those without pain (NwP = 30), the pressure on the longer limb was significantly greater (p = 0.002). No differences were found between the groups in the comparative analysis. The angle of foot abduction was greater than normal across the study population (N = 60), with greater values in the shorter limb (12.83° vs. 11.04°), which was close to significance (p = 0.065). The group with LBP (NP = 30) showed a similar trend, also close to statistical significance (p = 0.054), with significantly higher values of abduction angle in both legs compared to the group without LBP (NwP = 30). In the walking test, the left–right load distributions were significantly dispersed. The mean pressure on the longer limb was significantly higher in group 1 (NP = 30) (p = 0.031), whereas this difference was not statistically significant in group 2 (NwP = 30). For mean peak pressure, there were no significant differences in any of the groups tested. In addition, the mean ground contact time during gait was longer for the longer limb in the whole study population (N = 60) (938.8 ms vs. 915 ms), but again, this difference did not reach statistical significance (p = 0.305). Comparative analysis showed no differences between the groups. Conclusions: This study showed that in people with anatomical LLD, both with and without LBP, most parameters reflected marked asymmetries in peak and mean pressures and abduction angles. A prolongation of ground contact time has also been shown, and even though some parameters were not statistically significant, it is important to note the high dispersion of left–right loading, which provides information on body load asymmetries in patients with anatomical LLD. Given that there were no differences between the groups for most of the parameters, it is important for both clinical practice and further research that the abnormalities observed in both groups (NP = 30, NwP = 30) may have been a significant predictor of the development of LBP, as the abnormalities preceded the onset of pain. This should be taken into account in diagnostic and preventive measures. Full article

This research addresses critical challenges in clinical gait analysis by developing an automated video quality assessment framework to support Edinburgh Visual Gait Score (EVGS) scoring. The proposed methodology uses the MoveNet Lightning pose estimation model to extract body keypoints from video frames, enabling detection of multiple persons, tracking the person of interest, assessment of plane orientation, identification of overlapping individuals, detection of zoom artifacts, and evaluation of video resolution. These components are integrated into a unified quality classification system using a random forest classifier. The framework achieved high performance across key metrics, with 96% accuracy in detecting multiple persons, 95% in assessing overlaps, and 92% in identifying zoom events, culminating in an overall video quality categorization accuracy of 95%. This performance not only facilitates the automated selection of videos suitable for analysis but also provides specific video improvement suggestions when quality standards are not met. Consequently, the proposed system has the potential to streamline gait analysis workflows, reduce reliance on manual quality checks in clinical practice, and enable automated EVGS scoring by ensuring appropriate video quality as input to the gait scoring system. Full article