Search Results (921)

Aim: This study examined how load size and symmetry affect trunk muscle activation patterns, vertical ground reaction forces, and estimated lumbar spine compression during overground walking in individuals with chronic low back pain (CLBP) and those without symptoms. Methods: Thirty male participants (15 with CLBP, 15 controls; ages 23–28 years) performed walking tests under four load conditions: symmetric and asymmetric carriage at 10% and 20% of body weight. Bilateral surface electromyography measured activation from seven trunk muscles (rectus abdominis, external oblique, internal oblique, latissimus dorsi, lumbar erector spinae, multifidus) and the thoracolumbar fascia region, normalized to maximum voluntary isometric contractions (%MVIC). Force plates recorded vertical ground reaction forces synchronized with heel-strike events. A repeated-measures ANOVA with Bonferroni corrections was used to analyze the effects of load configuration and magnitude. Results: Asymmetric loading at 20% body weight caused significantly higher peak vertical ground reaction forces compared to symmetric loading (mean difference = 47.3 N, p < 0.001), with a significant interaction between load magnitude and configuration (p = 0.004, ηp² = 0.26). Participants with CLBP showed consistently higher trunk muscle activation throughout the gait cycle (peak: 37% MVIC vs. 30% MVIC in controls; p < 0.001, d = 1.68), with maximum recruitment at shorter muscle lengths and 24% less activation at optimal length (95% CI: 18.2–29.8%). The lumbar erector spinae and multifidus muscles exhibited the highest activation during asymmetric 20% loading in CLBP participants (0.282 and 0.263%MVIC, respectively), indicating compensatory neuromuscular strategies. Conclusion: Asymmetric load carriage creates disproportionately high mechanical and neuromuscular demands, effects that are greatly amplified in individuals with CLBP. These findings support rehabilitation strategies that improve load distribution and restore motor control, thereby reducing compensatory strain and enhancing trunk stability. Full article

Background/Objectives: Pediatric cancer survivors frequently experience neuromuscular sequelae related to chemotherapy-induced neurotoxicity. Agents such as vincristine, methotrexate, and platinum compounds can lead to persistent gait alterations and sensorimotor deficits that impair mobility and quality of life. This study aimed to objectively assess gait in pediatric cancer survivors after the completion of oncological pharmacological treatment to identify specific spatiotemporal, kinematic, and kinetic alterations and characterize neuromechanical patterns associated with neurotoxic exposure. Methods: A cross-sectional observational study was conducted including pediatric cancer survivors (6–18 years) who had completed chemotherapy and age- and sex-matched healthy controls. Gait was analyzed using a Vicon^®3D motion capture system, with reflective markers placed on standardized anatomical landmarks. Spatiotemporal, kinematic, and kinetic variables were compared between groups using parametric tests and statistical parametric mapping (SPM) with Holm–Bonferroni correction (α = 0.05). Results: Pediatric cancer survivors showed slower gait velocity (Mean Difference (MD) = 0.17, p = 0.018, Confidence Interval CI95% = 0.04; 0.4), shorter step (MD = 0.1, p = 0.015, CI95% = 0.01; 0.19) and stride length (MD = 0.17, p = 0.018, CI95% = 0.03; 0.31), as well as reduced single support time (MD = 0.1, p = 0.043, CI95% = 0.01; 0.19), along with significant alterations in pelvic, hip, knee, and ankle kinematics compared with controls. Increased pelvic elevation (MD = 0.92, p = 0.018, CI95% = 0.25; 1.58), reduced hip extension during stance (MD = −2.99, p = 0.039, CI95% = −5.19; −0.74), knee hyperextension in mid-stance (MD = −3.84, p < 0.001, CI95% = −6.18; −0.72), and limited ankle dorsiflexion (MAS MD = −4.04, p < 0.001, CI95% = −6.79; −0.86, LAS MD = −3.16, p < 0.001) and plantarflexor moments in terminal stance (MAS MD = −149.65, p = 0.018, CI95% = −259.35; −48.25, LAS MD = −191.81, p = 0.008, CI95% = −323.81; −57.31) were observed. Ground reaction force peaks during loading response (MAS MD = −16.86, p < 0.001, CI95% = −26.12; −0.72 LAS MD = −11.74, p = 0.001, CI95% = −19.68; −3.94) and foot-off (MAS MD = 10.38, p = 0.015, CI95% = 0.41; 20.53, LAS MD = 11.88, p = 0.01, CI95% = 3.15; 22.38) were also reduced. Conclusions: Children who have completed chemotherapy present measurable gait deviations reflecting persistent neuromechanical impairment, likely linked to chemotherapy-induced neurotoxicity and deconditioning. Instrumented gait analysis allows early detection of these alterations and may support the design of targeted rehabilitation strategies to optimize functional recovery and long-term quality of life in pediatric cancer survivors. Full article

Background: Sex-based variations in brain structure, hormonal balance, and neurochemistry may influence symptom presentation and recovery after mild traumatic brain injury (mTBI). This systematic review investigated sex-related differences in mTBI severity, symptoms, and recovery outcomes across different injury mechanisms. Methods: This review followed PRISMA 2020 guidelines and was registered with PROSPERO (CRD420251011379). Searches were conducted in PubMed, SPORTDiscus, Web of Science, and Scopus for articles published between 2000 and 2024. Eligible studies included adults (≥18 years) diagnosed with mTBI or concussion (Glasgow Coma Scale 13–15) with quantifiable outcome data for both sexes. Data extraction and quality assessment followed the JBI critical appraisal tools. Results: Forty-one studies involving 15,656 participants (8671 males; 6985 females) met the inclusion criteria. Female participants reported a greater symptom burden, higher pain intensity, and longer recovery times for gait abnormalities and return to activity compared with males. Neuroimaging studies showed more extensive white matter alterations in females, whereas males displayed greater reductions in cerebral blood flow. Cognitive and neurosensory outcomes revealed poorer cognitive performance, slower reaction times, and higher rates of vestibular–ocular and visual abnormalities in females. A limited number of studies explored electrophysiological measures, indicating sex-based differences in early brain responses to emotional stimuli. Conclusions: Sex plays an important role in symptom presentation and recovery after mTBI. Female patients demonstrate heightened vulnerability across several clinical domains, likely due to biological and neurochemical differences. Recognising these sex-specific patterns can support more targeted diagnostic and rehabilitation strategies. Future research should further explore the structural and biochemical mechanisms underlying these differences to improve precision in mTBI management. Full article

Background: Electromyography (EMG) is increasingly used to characterize neuromuscular alterations after total hip arthroplasty (THA), yet available evidence remains fragmented and inconsistent. This systematic review synthesizes postoperative EMG findings during gait, functional tasks, and static assessments, highlighting clinical implications and future research needs. Methods: Peer-reviewed studies employing surface, needle, or high-density EMG after THA were systematically examined. Extracted variables included activation amplitude, timing (onset, offset, burst duration), co-activation patterns, and the influence of surgical approach. Methodological rigor, normalization procedures, and the extractability of quantitative EMG metrics were also assessed. Results: Across studies, postoperative EMG consistently revealed non-physiological activation patterns, including delayed or prolonged gluteus medius activity and excessive recruitment of posterior chain muscles. These abnormalities persisted for up to 12 months and, in isolated cases, beyond a decade. Comparisons of surgical approaches demonstrated early denervation signs and impaired recruitment following lateral-based incisions, whereas later adaptations differed between lateral and posterior approaches but remained abnormal in both. Needle EMG studies confirmed transient involvement of muscles innervated by the superior gluteal nerve, while high-density EMG identified persistent deficits in spatial and temporal organization despite clinical improvement. Load-bearing and assisted-task studies showed that cane use and balance challenges modulate abductor demand yet continue to expose asymmetries and elevated stabilization requirements. Nonetheless, comparability across investigations remains limited because few studies adopted standardized normalization procedures or reproducible locomotor tasks. Conclusions: Neuromuscular recovery after THA appears incomplete and asymmetric, characterized by compensatory strategies not detectable through clinical or kinematic assessments alone. Improved diagnostic sensitivity and clinical applicability will require protocol standardization and the broader adoption of advanced EMG approaches. Full article

Background: Juvenile Idiopathic Arthritis (JIA) is a chronic inflammatory condition that can disrupt joint function and biomechanics, often leading to altered gait patterns. When coexisting with secondary scoliosis—a common musculoskeletal complication in children with JIA—postural and movement impairments may be further exacerbated. However, limited research has investigated the combined impact of JIA and secondary scoliosis on gait characteristics. This study aimed to evaluate gait parameters in children diagnosed with JIA and secondary scoliosis and to compare them with age-matched healthy peers. Methods: A total of 50 children (25 with JIA and secondary scoliosis, 25 healthy controls) were included. Demographic data, plantar pressure distribution, temporal gait parameters, and center of mass (CoM) displacement were assessed using computerized gait analysis. Group comparisons were performed using appropriate statistical methods. Results: Children with JIA and secondary scoliosis exhibited significantly lower forefoot loading on both dominant and non-dominant sides compared to controls (p < 0.05). Maximum loading values were also reduced bilaterally in the JIA group (p < 0.001). The dominant side single-limb support duration was significantly shorter (p = 0.027), and CoM displacement was greater (p = 0.044) in the JIA group. No differences were observed in rearfoot loading or walking speed. Conclusions: Children with coexisting JIA and secondary scoliosis demonstrate altered gait mechanics, likely reflecting compensatory adaptations due to joint inflammation and postural asymmetries. Gait analysis may offer valuable insights for tailoring rehabilitation strategies in this patient population. Full article

Sarcopenia is highly prevalent among patients with chronic kidney disease (CKD) and chronic heart failure (CHF), yet the underlying immunometabolic mechanisms remain insufficiently understood. Short-chain fatty acids (SCFAs), inflammatory cytokines, and body-composition alterations may jointly contribute to the development of muscle dysfunction in this population. In this cross-sectional study, 80 patients with CKD and CHF underwent comprehensive clinical, biochemical, bioimpedance, inflammatory, and SCFA profiling. Sarcopenia was diagnosed according to EWGSOP2 criteria. Multivariable logistic regression, LASSO feature selection, correlation analysis, PCA, and Random Forest modeling were used to identify key determinants of sarcopenia. Sarcopenia was present in 39 (49%) participants. Patients with sarcopenia exhibited significantly lower body fat percentage, reduced ASM, and slower gait speed. Hexanoic acid (C6) showed an independent positive association with sarcopenia (OR = 2.24, 95% CI: 1.08–5.37), while IL-8 showed an inverse association with sarcopenia (OR = 0.38, 95% CI: 0.13–0.94), indicating that lower IL-8 levels were more frequently observed in individuals with sarcopenia. Correlation heatmaps revealed distinct SCFA–cytokine coupling patterns depending on sarcopenia status, with stronger pro-inflammatory clustering in C6-associated networks. The final multivariable model integrating SCFAs, cytokines, and body-composition metrics achieved excellent discrimination (AUC = 0.911) and good calibration. Sarcopenia in CKD–CHF patients represents a systemic immunometabolic disorder characterized by altered body composition, chronic inflammation, and dysregulated SCFA signaling. Hexanoic acid (C6) and IL-8 may serve as informative biomarkers of muscle decline. These findings support the use of multidimensional assessment and highlight potential targets for personalized nutritional, microbiota-modulating, and rehabilitative interventions. Full article

Recent advances in legged robotics have highlighted deep reinforcement learning (DRL)-based controllers for their robust adaptability to diverse, unstructured environments. While position-based DRL controllers achieve high tracking accuracy, they offer limited disturbance rejection, which degrades walking stability; torque-based DRL controllers can mitigate this issue but typically require extensive time and trial-and-error to converge. To address these challenges, we propose a Real-Time Motion Generator (RTMG). At each time step, RTMG kinematically synthesizes end-effector trajectories from target translational and angular velocities (yaw rate) and step length, then maps them to joint angles via inverse kinematics to produce imitation data. The RL agent uses this imitation data as a torque bias, which is gradually annealed during training to enable fully autonomous behavior. We further combine the RTMG-generated imitation data with a decaying action priors scheme to ensure both initial stability and motion diversity. The proposed training pipeline, implemented in NVIDIA Isaac Gym with Proximal Policy Optimization (PPO), reliably converges to the target gait pattern. The trained controller is Tensor RT-optimized and runs at 50 Hz on a Jetson Nano; relative to a position-based baseline, torso oscillation is reduced by 24.88% in simulation and 21.24% on hardware, demonstrating the effectiveness of the approach. Full article

Background/Objectives: Limited ankle dorsiflexion has been associated with compensatory movement patterns throughout the lower extremity kinematic chain. This study investigated relationships between weight-bearing dorsiflexion capacity and lower limb kinematics and plantar pressure patterns during gait. Methods: Twenty-seven healthy adults (age: 22.8 ± 3.4 years) performed a weight-bearing lunge test (WBLT) and walked at a standardized pace across a pressure-sensing walkway while wearing inertial measurement units. Statistical Parametric Mapping assessed correlations between WBLT dorsiflexion and kinematic variables throughout the stance phase. Partial correlations controlled for walking velocity and were used to examine relationships with discrete plantar pressure measurements. Results: Reduced dorsiflexion capacity during the WBLT showed bilateral moderate associations with less ankle dorsiflexion (LEFT: peak r = 0.53; RIGHT: peak r = 0.60) and knee flexion (LEFT: peak r = 0.56; RIGHT: peak r = 0.58) during terminal stance and push-off. Proximal compensations demonstrated limb-specific patterns. Hip abduction was strongly negatively correlated in the left leg only (peak r = −0.65), while pelvic tilt showed bilateral relationships with opposing temporal patterns (LEFT: peak r = −0.58 early stance; RIGHT: peak r = 0.62 terminal stance). Plantar pressure analysis revealed that reduced dorsiflexion was associated with decreased heel relative impulse bilaterally (r = 0.53–0.56) and altered temporal patterns of midfoot loading on the left leg (r = 0.56). Conclusions: Limited dorsiflexion under load is associated with compensatory movement patterns extending from the ankle to the pelvis bilaterally. The evaluation of loaded ankle mobility should be considered an essential component of lower extremity movement assessment. Full article

Early diagnosis of Parkinson’s disease (PD) is crucial for slowing its progression. Gait analysis is increasingly used to detect early symptoms, with smart insoles emerging as a cost-effective and convenient tool for daily monitoring. However, smart insoles have practical limitations, including durability concerns, limited battery life, and difficulties in minimizing the number of sensors. In this study, we designed a novel deep convolutional neural network model for accurately detecting abnormal gaits in patients with PD using a reduced number of sensors embedded in smart insoles. The proposed convolutional neural network (CNN) model was trained on a gait dataset collected from a total of 29 participants, including 13 healthy individuals, 9 elderly individuals, and 7 patients with Parkinson’s disease (PD). Instead of combining plantar pressure data from both feet, the model processes each foot independently through sequential layers to better capture gait asymmetries. The proposed CNN model achieved a classification accuracy of 90.35% using only 8 of the 32 plantar pressure sensors in the smart insole, outperforming a conventional CNN model by approximately 10%. The experimental results demonstrate the potential of our CNN model for effectively detecting abnormal gait patterns in patients with PD while minimizing sensor requirements, enhancing the practicality and efficiency of smart insoles for real-world use. Full article

This study aimed to assess the effect of a downhill-running (DR) bout on muscle damage biomarkers. It also examined whether training background and gait kinematics may influence DR-induced muscle damage and strength loss. Thirty-six experienced trail runners (25 men, 11 women), participants of a 106 km ultra-trail, performed a 5 km DR bout at 15% decline and at an intensity equivalent to their first ventilatory threshold. Muscle damage biomarkers (creatine kinase, lactate dehydrogenase, and myoglobin) were analyzed before and 30 min after the DR protocol, and also before and after the UT race. Isometric strength was assessed before and after DR, and gait parameters were recorded during DR. All muscle damage biomarkers increased following DR (d = 0.19 to 1.85). Lactate dehydrogenase concentrations after the race and DR were associated (r = 0.64). Athletes who habitually performed downhill repetitions showed reduced creatine kinase (182 ± 73 U/L vs. 290 ± 192 U/L; p < 0.05; d = 0.64) and greater squat strength retention (4 ± 10% vs. −9.1 ± 16.8%; p <0.05; d = 0.87). Ankle plantar flexion and squat strength retention were inversely correlated with vertical oscillation (r = −0.44) and step length (r = −0.37), respectively. In summary, lactate dehydrogenase response to a short DR bout could indicate an athlete’s readiness to handle ultra-trail-induced muscle damage, although further research is needed to confirm it. In addition, despite the exploratory nature of the study, regularly performing downhill intervals and adopting a more terrestrial gait pattern appear to soften strength loss and muscle damage response to DR. Full article

The center of pressure (CoP) is a key biomechanical indicator for assessing balance and fall risk; however, force plates, the gold standard for CoP measurement, are costly and impractical for widespread use. Low-cost alternatives such as inertial units or pressure sensors are limited by drift, sparse sensor coverage, and directional performance imbalances, with previous supervised learning approaches reporting ML-AP NRMSE differences of 3.2–4.7% using 1D time-series models on sparse sensor arrays. Therefore, we propose a tactile sensor-based CoP estimation system using deep learning models that can extract 2D spatial features from each pressure distribution image with CNN/ResNet encoders followed by a Bi-LSTM for temporal patterns. Using data from 23 healthy adults performing four balance protocols, we compared ResNet-Bi-LSTM and CNN-Bi-LSTM with baseline CNN-LSTM and Bi-LSTM models used in previous studies. Model performance was validated using leave-one-out cross-validation (LOOCV) and evaluated with RMSE, NRMSE, and R². The ResNet-Bi-LSTM with angular features achieved the best performance, with RMSE values of 18.63 ± 4.57 mm in the mediolateral (ML) direction and 17.65 ± 3.48 mm in the anteroposterior (AP) direction, while reducing the ML/AP NRMSE difference to 1.3% compared to 3.2–4.7% in previous studies. Under dynamic protocols, ResNet-Bi-LSTM maintained the lowest RMSE across models. These findings suggest that tactile sensor-based systems may provide a cost-effective alternative to force plates and hold potential for applications in gait analysis and real-time balance monitoring. Future work will validate clinical applicability in patient populations and explore real-time implementation. Full article

Classifying demographic groups of humans from gait patterns is desirable from several long-standing diagnostic and monitoring perspectives. IMU recorded gait patterns are mapped into a nonlinear dynamic representation space using criticality analysis and subsequently classified using standard Support Vector Machines. Inertial-only gait recordings were found to readily classify in the CA representations. Accuracies across age categories for female versus male were 72.77%, 78.95%, and 80.11% for $\sigma =0.1$, 1, and 10, respectively; within the female group, accuracies were 73.36%, 76.70%, and 78.90%; and within the male group, 77.65%, 81.48%, and 81.05%. These results show that dynamic biological data are easily classifiable when projected into the nonlinear space, while classifying the data without this is not nearly as effective. Full article

Intelligent lower-limb exoskeleton rehabilitation robots are increasingly superseding traditional rehabilitation equipment, making them a focus of research in this field. However, existing systems remain challenged by dynamic instability resulting from various disturbances during actual walking. To address this limitation, this study proposes a novel dynamic stability criterion. Through an analysis of the principles and limitations of the traditional zero-moment point (ZMP) stability criterion, particularly during the late single-leg support phase, a new stability criterion is introduced, which is founded on the swing projection polygon during single-leg support. This approach elucidates the variation patterns of the stability polygon during a single-step motion and facilitates a qualitative analysis of the stability characteristics of the human–robot system in multiple postures. To further enhance the stability and smoothness of gait trajectories in lower-limb exoskeleton rehabilitation robots, the shortcomings of conventional gait planning approaches, namely their non-intuitive nature and discontinuity, are addressed. A recurrent gait planning method leveraging Long Short-Term Memory (LSTM) neural networks is proposed. The integration of the periodic motion characteristics of human gait serves to validate the feasibility and correctness of the proposed method. Finally, based on the recurrent gait planning method, the dynamic stability of walking postures is verified through theoretical analysis and experimental comparisons, accompanied by an in-depth analysis of key factors influencing dynamic stability. Full article

(1) Background: Navigating surfaces during walking can alter gait patterns. This study aims to develop tools for automatic walking condition classification using inertial measurement unit (IMU) and foot pressure sensors. We compared sensor modalities (IMUs on lower-limbs, IMUs on feet, IMUs on the pelvis, pressure insoles, and IMUs on the feet or pelvis combined with pressure insoles) and evaluated whether gait cycle segmentation improves performance compared to a sliding window. (2) Methods: Twenty participants performed flat, stairs up, stairs down, slope up, and slope down walking trials while fitted with IMUs and pressure insoles. Machine learning (ML; Extreme Gradient Boosting) and deep learning (DL; Convolutional Neural Network + Long Short-Term Memory) models were trained to classify these conditions. (3) Results: Overall, a DL model using lower-limb IMUs processed with gait segmentation performed the best ($F1=0.89$). Models trained with IMUs outperformed those trained on pressure insoles ($p<0.01$). Combining sensor modalities and gait segmentation improved performance for ML models ($p<0.01$). The best minimal model was a DL model trained on IMU pelvis + pressure insole data using sliding window segmentation ($F1=0.83$). (4) Conclusions: IMUs provide the most discriminative features for automatic walking condition classification. Combining sensor modalities may be helpful for some model architectures. DL models perform well without gait segmentation, making them independent of gait event identification algorithms. Full article

This two-case proof-of-concept study evaluated the feasibility and clinical utility of a smartphone-based markerless motion capture system for quantitative gait assessment in pediatric Guillain–Barré syndrome (GBS). Two children with GBS underwent overground gait analysis using a dual-smartphone setup (OpenCap), and three-dimensional hip, knee, and ankle kinematics were computed via OpenSim. Case 1, a boy with treatment-related fluctuation, demonstrated marked abnormalities in swing-phase limb advancement and ankle push-off that improved after six weeks of rehabilitation in parallel with gains in muscle strength, balance, and ambulation. Case 2, a girl recovering from acute inflammatory demyelinating polyneuropathy, exhibited residual reductions in hip and knee flexion and impaired ankle control despite normal strength, consistent with vestibular dysfunction. All assessments were completed within routine clinical time constraints and produced analyzable kinematic data using only two smartphones. These findings indicate that smartphone-based markerless motion capture is a feasible and informative method for detecting gait impairment and recovery patterns in pediatric GBS and may serve as an accessible digital biomarker to complement standard clinical evaluations. Full article