Search Results (193)

Knee osteoarthritis (OA) is a prevalent condition in older adults that often results in impaired gait and balance, increased risk of falls, and reduced quality of life. Conventional clinical assessments may not adequately capture these deficiencies. This study investigated the gait and balance of elderly individuals with knee OA using wearable inertial measurement units (IMUs). Forty-four participants with Kellgren–Lawrence grade 2–3 knee OA (71.23 ± 5.75 years) and forty-five age-matched controls (70.87 ± 4.30 years) completed dynamic balance (balance board), static balance (single-leg stance), ‘timed up and go’ (TUG), and normal walking tasks. Between 2 and 8 IMUs, depending on the task, were placed on the head, chest, waist, knees, ankles, soles, and balance board to record kinematic data. Balance was quantified using absolute angular velocity and linear acceleration, with group differences analyzed by MANOVA and Bonferroni-adjusted univariate tests. The participants with knee OA exhibited greater gait asymmetry, although the difference was not significant. However, they consistently demonstrated higher absolute angular velocities than controls across most body segments during static and dynamic tasks, indicating reduced postural stability. No group differences were observed in TUG performance. These findings suggest that IMU-based measures, particularly angular velocity, are sensitive to balance impairment detection in knee OA. Incorporating IMU technology into clinical assessments may facilitate early identification of instability and guide targeted interventions to reduce fall risk. Full article

Background: Postural control in healthy young adults involves complex neuromuscular processes; however, the kinematic and kinetic consequences of small, forward leg perturbations in a defined population are not fully described. This study aimed to characterize the kinematic and kinetic consequences of forward leg perturbations during quiet standing. Methods: This investigation used a quasi-experimental repeated-measures design. Sixteen healthy young women (20.1 ± 0.7 years), all right-leg dominant, were tested using the Gait Real-Time Analysis Interactive Laboratory (GRAIL) system. Forward treadmill perturbations were applied to each limb during quiet standing, and joint angles, ground reaction forces, and torques were measured across baseline, perturbation, and response phases. As the data were non-normally distributed, paired comparisons were conducted using the Wilcoxon test, with significance set at p < 0.05 (Bonferroni corrected) and effect sizes (r) reported. Results: Joint angles remained symmetrical between limbs (no significant differences after correction). In contrast, kinetic measures showed clear asymmetries: at baseline, the dominant limb produced greater knee torque (p = 0.0003, r = 0.73), ankle torque (p = 0.0003, r = 0.76), and medio-lateral GRF (p = 0.0003, r = 0.87). During perturbation, it again generated higher knee (p = 0.0036, r = 0.43) and ankle torques (p = 0.0003, r = 0.53), with larger medio-lateral GRF (p = 0.0003, r = 0.87). In the response phase, the dominant limb showed greater hip torque (p = 0.0033, r = 0.43) and a small dorsiflexion shift at the ankle (p = 0.0066, r = 0.41). Anterior–posterior GRF changes were minor and non-significant after correction. Conclusions: Induced forward leg movements caused limb-specific kinetic adjustments while maintaining overall kinematic symmetry. The dominant leg contributed more actively to balance recovery, highlighting its role in stabilizing posture under small perturbations. These findings are specific to the studied demographic and should not be generalized to males, older adults, left-dominant individuals, or clinical populations without further research. Full article

Hemiparesis is a disabling consequence of stroke, causing abnormal gait patterns with biomechanical asymmetries. Gait mechanics for stroke survivors appear resistant to conventional rehabilitation. Auditory rhythmical cueing (ARC) represents an emerging intervention option. To determine effective gait interventions, objective measures of gait collected from real-world environments may be required in addition to standard clinical outcomes to aid understanding of gait mechanics. This study reports on the ability of wearable activity monitors to quantify an ARC intervention for fifty-nine stroke survivors randomised into an ARC gait and balance training programme or an equivalent training programme without ARC. Gait assessments were undertaken at baseline and at 6 weeks for 4-metre walks and continuously for 7 days following each home assessment using a wearable activity monitor. The success rates of data collection using the wearable activity monitors ranged from 64 to 95%. Forty-eight Digital Mobility Outcomes representing a broad range of gait mechanics were calculated. Visualisation of all DMOs using radar plots indicated changes from baseline in both groups, with individual data indicating large variability in response to the intervention and control programme. Including wearable activity monitors to evaluate gait interventions for stroke survivors provides additional value to traditional methods and aids understanding of individual responses; as such, they should be used for future intervention-based research. Full article

Differentiating genuine hemiplegic gait (HG) in stroke survivors from hemiplegic-like gait voluntarily imitated by healthy adults (MHG) is essential for reliable assessment and intervention planning. Treadmill-based gait data were obtained from 79 participants—39 stroke patients (HG) and 40 healthy adults—instructed to mimic HG (MHG). Forty-eight spatiotemporal and force-related variables were extracted. Random Forest, support vector machine (SVM), and logistic regression classifiers were trained with (i) the full feature set and (ii) the 10 most important features selected via Random Forest Gini importance. Performance was assessed with 5-fold stratified cross-validation and an 80/20 hold-out test, using accuracy, F1-score, and the area under the receiver operating characteristic curve (AUC). All models achieved high discrimination (AUC > 0.93). The SVM attained perfect discrimination (AUC = 1.000, test set) with the full feature set and maintained excellent accuracy (AUC = 0.983) with only the top 10 features. Temporal asymmetries, delayed vertical ground reaction force peaks, and mediolateral spatial instability ranked highest in importance. Reduced-feature models showed negligible performance loss, highlighting their parsimony and interpretability. Supervised machine learning algorithms can accurately distinguish true hemiplegic gait from mimicked patterns using a compact subset of gait features. The findings support data-driven, time-efficient gait assessments for clinical neurorehabilitation and for validating experimental protocols that rely on gait imitation. Full article

This cross-sectional study analyzed the lower limb (LL) behavior in terms of gait asymmetry and joints’ kinematic parameters, comparing people with multiple sclerosis (pwMS) and unaffected individuals. Methods: Data from 15 patients, EDSS ≤ 4.5, and 15 healthy control volunteers were gathered. The VICON Motion Capture System (14 infrared cameras), NEXUS software, Plug-in–Gait skeleton model and reflective markers were used to collect data for each subject during five gait cycles on a plane surface. Biomechanical analysis included evaluation of LL joints’ range of motion (ROM) bilaterally, as well as movement symmetry. Results: Comparative biomechanical analysis revealed a hierarchy of vulnerability between the groups: the ankle is the most affected joint in pwMS (p = 0.008–0.014), the knee is moderately affected (p = 0.015 in swing phase), and the hip is the least affected (p > 0.05 in all phases). The swing phase showed the most significant left–right asymmetry impairment, as reflected by root mean square error (RMSE) values: swing-phase RMSE = 9.306 ± 4.635 (higher and more variable) versus stance-phase RMSE = 6.363 ± 2.306 (lower and more consistent). Conclusions: MS does not affect the joints structurally; rather, it eliminates the ability to differentiate the fine-tuning control between them. The absence of significant left–right joint asymmetry differences during complete gait cycle indicates dysfunction in the global motor control. Full article

Parkinson’s disease (PD) impairs balance and gait through neuromotor dysfunction, yet conventional assessments often overlook subtle postural deficits during dynamic tasks. This study evaluated the diagnostic utility of center-of-pressure (CoP) features captured by pressure-sensing insoles during the Timed Up and Go (TUG) test. Using 39 PD and 38 control participants from the recently released open-access WearGait-PD dataset, the authors extracted 144 CoP features spanning positional, dynamic, frequency, and stochastic domains, including per-foot averages and asymmetry indices. Two scenarios were analyzed: the complete TUG and its 3 m walking segment. Model development followed a fixed protocol with a single participant-level 80/20 split; sequential forward selection with five-fold cross-validation optimized the number of features within the training set. Five classifiers were evaluated: SVM-RBF, logistic regression (LR), random forest (RF), k-nearest neighbors (k-NN), and Gaussian naïve Bayes (NB). LR performed best on the held-out test set (accuracy = 0.875, precision = 1.000, recall = 0.750, F1 = 0.857, ROC-AUC = 0.921) using a 23-feature subset. RF and SVM-RBF each achieved 0.812 accuracy. In contrast, applying the identical pipeline to the 3 m walking segment yielded lower performance (best model: k-NN, accuracy = 0.688, F1 = 0.615, ROC–AUC = 0.734), indicating that the multi-phase TUG task captures PD-related balance deficits more effectively than straight walking. All four feature families contributed to classification performance. Dynamic and frequency-domain descriptors, often appearing in both average and asymmetry form, were most consistently selected. These features provided robust magnitude indicators and offered complementary insights into reduced control complexity in PD. Full article

We propose a speed-adapted treadmill that can be incorporated into a rehabilitation trainer that applies neurodevelopmental treatment (NDT) for patients with stroke. NDT practice is effective for post-stroke patients, but its requirement for therapists’ participation can limit the patients’ rehabilitation during the golden period of recovery. Previous studies have proposed a trainer that can automatically reiterate therapists’ interventions. However, that trainer employed a constant-speed treadmill, which required the users to frequently adjust their walking speeds during rehabilitation. This paper develops a speed-adapted treadmill that can regulate the treadmill motor to maintain the subject’s position during the training process. First, we derive models of the treadmill and cable motors through experiments. Then, we design robust controls for the two systems and simplify them as proportional-integral-derivative controllers for hardware implementation. Finally, we integrate the system and invite healthy and stroke subjects to participate in clinical experiments. Among ten stroke subjects, all subjects’ walking speeds and nine subjects’ stride lengths were improved, while eight subjects showed improvement in the swing-phase asymmetry and pelvic rotation after receiving the NDT rehabilitation employing the speed-adapted treadmill. Our findings indicate that the NDT trainer effectively enhances users’ gait characteristics, including swing-phase symmetry, pelvic rotation, walking speed, and stride length. Full article

Background/Objectives: Multiple sclerosis (MS) often leads to gait impairments, even in early stages, and can affect autonomy and quality of life. Traditional assessment methods, while widely used, have been criticized because they lack sensitivity to subtle gait changes. This scoping review aims to map the landscape of advanced gait analysis technologies—both wearable and non-wearable—and evaluate their application in detecting, characterizing, and monitoring possible gait dysfunction in individuals with MS. Methods: A systematic search was conducted across PubMed and Scopus databases for peer-reviewed studies published in the last decade. Inclusion criteria focused on original human research using technological tools for gait assessment in individuals with MS. Data from 113 eligible studies were extracted and categorized based on gait parameters, technologies used, study design, and clinical relevance. Results: Findings highlight a growing integration of advanced technologies such as inertial measurement units, 3D motion capture, pressure insoles, and smartphone-based tools. Studies primarily focused on spatiotemporal parameters, joint kinematics, gait variability, and coordination, with many reporting strong correlations to MS subtype, disability level, fatigue, fall risk, and cognitive load. Real-world and dual-task assessments emerged as key methodologies for detecting subtle motor and cognitive-motor impairments. Digital gait biomarkers, such as stride regularity, asymmetry, and dynamic stability demonstrated high potential for early detection and monitoring. Conclusions: Advanced gait analysis technologies can provide a multidimensional, sensitive, and ecologically valid approach to evaluating and detecting motor function in MS. Their clinical integration supports personalized rehabilitation, early diagnosis, and long-term disease monitoring. Future research should focus on standardizing metrics, validating digital biomarkers, and leveraging AI-driven analytics for real-time, patient-centered care. Full article

Background/Objectives: Hip-level amputees face ambulatory challenges due to the lack of a lower limb and prosthetic hip power. Some hip-level amputees restore mobility by using a prosthesis with hip, knee, and ankle joints. Powered prosthetic joints contain an actuator that provides external flexion-extension moments to assist with movement. Powered knee and powered ankle-foot units are on the market, but no viable powered hip unit is commercially available. This research details the development of a novel powered four-bar prosthetic hip joint that can be integrated into a full-leg prosthesis. Methods: The hip joint design consisted of a four-bar linkage with a harmonic drive DC motor placed in the inferior link and an additional linkage to transfer torque from the motor to the hip center of rotation. Link lengths were determined through engineering optimization. Device strength was demonstrated with force and finite element analysis and with ISO 15032:2000 A100 static compression tests. Walking tests with a wearable hip-knee-ankle-foot prosthesis simulator, containing the novel powered hip, were conducted with three able-bodied participants. Each participant walked back and forth on a level 10 m walkway. Custom hardware and software captured joint angles. Spatiotemporal parameters were determined from video clips processed in the Kinovea software (ver. 0.9.5). Results: The powered hip passed all force and finite element checks and ISO 15032:2000 A100 static compression tests. The participants, weighing 96 ± 2 kg, achieved steady gait at 0.45 ± 0.11 m/s with the powered hip. Participant kinematic gait profiles resembled those seen in transfemoral amputee gait. Some gait asymmetries occurred between the sound and prosthetic legs. No signs of mechanical failure were seen. Most design requirements were met. Areas for powered hip improvement include hip flexion range, mechanical advantage at high hip flexion, and device mass. Conclusions: The novel powered four-bar hip provides safe level-ground walking with a full-leg prosthesis simulator and is viable for future testing with hip-level amputees. Full article

Based on fundamental mechanics, movement and force associate head and pelvic movement asymmetry with asymmetry of force production. We investigate, how often racehorses undergoing strenuous training regimens show evidence of switching between “preferred” limbs, i.e. one limb producing increased force, when assessed at monthly intervals? We hypothesize that clinical asymmetry thresholds designed for “detecting lameness” are frequently exceeded and that when applying previously established Thoroughbred-specific repeatability values, horses rarely switch between showing left- and right-sided asymmetry. Monthly gait assessments (inertial sensors) were conducted in 256 Thoroughbred racehorses at least twice per horse (up to 16 times per horse). Descriptive statistics for absolute differences for head and pelvic movement were compared to published Thoroughbred-specific repeatability values. The percentage of left–right switches between repeat assessments was calculated in comparison to three different levels of pre-defined thresholds (perfect symmetry, clinical lameness thresholds, previously established Thoroughbred-specific repeatability values) and switch frequencies compared between the three thresholds. Ranges containing 95% of monthly differences were higher than published daily and weekly values except for pelvic vertical range of motion. Approximately 30% of monthly differences in individual symmetry parameters showed left–right switches around “perfect symmetry”. Utilizing clinical lameness thresholds for categorizing left–right switches, a significantly (p < 0.001) reduced percentage of 4–11% of measurements for head movement and 7–17% for pelvic movement showed switches. Using daily repeatability values for categorization, a further significantly (p < 0.001) reduced percentage of switches was observed: 0.3–3.6% for head movement and 0.6–7.0% for pelvic movement. While racehorses in training regularly switch between small left- or right-sided movement symmetries, they less frequently switch between more pronounced left- and right-sided movement symmetries defined based on daily variations. Further studies should investigate the reasons for these rare switches. Full article

Symmetry and asymmetry significantly influence running biomechanics, performance, and injury risk. Given the practical, ethical, and methodological constraints inherent in human-subject studies, computational modeling emerges as a valuable alternative for exploring biomechanical asymmetries in detail. This study systematically evaluated the mechanical effects of lower limb imbalance during running using a simulation-based musculoskeletal framework in OpenSim. A total of 130 simulations were performed, incorporating controlled asymmetries in limb strength, stride length, and ground reaction forces (±5% and ±10%), to quantify alterations in joint moments, ground reaction forces (GRF), and muscular activation patterns. Results demonstrated clear biomechanical deviations under asymmetric conditions. Vertical ground reaction forces (GRF) decreased on the weaker limb and increased on the stronger limb, with peak knee joint moments rising by up to 20% under pronounced asymmetry. Muscle activation in major lower limb muscles, including the gastrocnemius and quadriceps, increased substantially on the stronger side, reflecting compensatory mechanical loading. These findings highlight the negative consequences of uneven limb loading and support the use of computational modeling to guide personalized training, rehabilitation, and injury prevention strategies. Full article

Stroke is a significant global health concern characterized by the abrupt disruption of cerebral blood flow, leading to neurological impairment. Accurate and timely diagnosis—enabled by imaging modalities such as computed tomography (CT) and magnetic resonance imaging (MRI)—is essential for differentiating stroke types and initiating interventions like thrombolysis, thrombectomy, or surgical management. In parallel, recent advancements in wearable technology, particularly smart clothing, offer new opportunities for stroke prevention, real-time monitoring, and rehabilitation. These garments integrate various sensors, including electrocardiogram (ECG) electrodes, electroencephalography (EEG) caps, electromyography (EMG) sensors, and motion or pressure sensors, to continuously track physiological and functional parameters. For example, ECG shirts monitor cardiac rhythm to detect atrial fibrillation, smart socks assess gait asymmetry for early mobility decline, and EEG caps provide data on neurocognitive recovery during rehabilitation. These technologies support personalized care across the stroke continuum, from early risk detection and acute event monitoring to long-term recovery. Integration with AI-driven analytics further enhances diagnostic accuracy and therapy optimization. This narrative review explores the application of smart clothing in conjunction with traditional imaging to improve stroke management and patient outcomes through a more proactive, connected, and patient-centered approach. Full article

Background and Objective: This study presents a combined osteometric and biomechanical analysis of a Korean female cadaver exhibiting bilateral lower limb bone asymmetry with abnormal curvature and callus formation on the left femoral midshaft. Methods: To investigate bilateral bone length differences, osteometric measurements were conducted at standardized landmarks. Additionally, we developed three gait models using Meta Motivo, an open-source reinforcement learning platform, to analyze how skeletal asymmetry influences stride dynamics and directional control. Results: Detailed measurements revealed that the left lower limb bones were consistently shorter and narrower than their right counterparts. The calculated lower limb lengths showed a bilateral discrepancy ranging from 39 mm to 42 mm—specifically a 6 mm difference in the femur, 33 mm in the tibia, and 36 mm in the fibula. In the gait pattern analysis, the normal model exhibited a straight-line gait without lateral deviation. In contrast, the unbalanced, non-learned model demonstrated compensatory overuse and increased stride length of the left lower limb and a tendency to veer leftward. The unbalanced, learned model showed partial gait normalization, characterized by reduced limb dominance and improved right stride, although directional control remained compromised. Conclusions: This integrative approach highlights the biomechanical consequences of lower limb bone discrepancy and demonstrates the utility of virtual agent-based modeling in elucidating compensatory gait adaptations. Full article

In this study, the effects of walking speed on lower limb muscle activity and gait parameters during over-ground walking were investigated in individuals with chronic stroke. Twenty-four patients with chronic stroke participated in a cross-sectional repeated-measures study, walking 20 m at three different speeds: slow (80% of self-selected speed), self-selected, and maximal speed. Surface electromyography was used to measure muscle activity in five paretic-side muscles (rectus femoris, biceps femoris, tibialis anterior, gastrocnemius, and gluteus medius), while gait parameters, including stride length, stance and swing phases, single-limb support time, and the gait asymmetry index were assessed using a triaxial accelerometer. As walking speed increased, activity in the rectus femoris, biceps femoris, and gastrocnemius muscles significantly increased during the stance and swing phases (p < 0.05), whereas the gluteus medius activity tended to decrease. Stride length on the paretic and non-paretic sides significantly increased with faster walking speed (p < 0.05); however, no significant improvements were observed in other gait parameters or gait asymmetry. These findings suggest that although increasing walking speed enhances specific muscle activities, it does not necessarily improve overall gait quality or symmetry. Therefore, rehabilitation programs should incorporate multidimensional gait training that addresses speed and neuromuscular control factors such as balance and proprioception. 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