Search Results (2,334)

Background/Objectives: Ankle dorsiflexion plays a fundamental role in gait stability, impact absorption, and the stance-to-swing transition, and its impairment is a major limitation in transtibial prostheses. This study proposes and evaluates a lightweight two-stage pipeline for generating ankle-dorsiflexion references using a single shank-mounted inertial measurement unit (IMU). Methods: In the first stage, a deep neural network (DNN) estimates the shank pitch waveform from raw three-axis accelerations and angular velocities. In the second stage, the estimated shank pitch is transformed into an ankle-dorsiflexion waveform using a temporal mapping model. The approach was evaluated on a multisubject subset of the NONAN GaitPrint database comprising 35 healthy young adults, 598 walking trials, and approximately 122,468 gait cycles, using a strict subject-held-out protocol. Results: A feature-based Random Forest baseline showed limited performance, whereas the waveform-based DNN achieved high accuracy for shank pitch estimation, with test R² values up to 0.97. A conventional polynomial mapping between shank pitch and dorsiflexion yielded weak performance, whereas a temporal mapping model substantially improved the estimation of ankle dorsiflexion, with test R² values up to 0.85. The resulting ankle reference was integrated into a Gazebo/Robot Operating System 2 (ROS 2) simulation of a transtibial prosthesis, where the generated trajectories were executed in a software integration test under open-loop position control, confirming stable and consistent trajectory execution. Conclusions: These results indicate that combining accurate shank pitch estimation with temporal mapping enables feasible ankle-dorsiflexion reference generation from a single sensor in able-bodied gait, offering a preliminary, simulation-based pathway for single-sensor artificial intelligence (AI) pipelines in prosthetic development. The framework supports waveform-level feasibility, not clinical readiness or functional prosthetic control. Full article

Quantifying metabolic cost during short transitional movements is challenging because conventional metabolic measurements have limited temporal resolution. This proof-of-concept study examined whether model-derived metabolic cost during the sit-to-walk (STW) transition could be exploratorily stratified using interpretable ensemble learning. Forty-nine healthy adults completed the STW phase of the Timed Up and Go task with synchronized three-dimensional kinematics, ground reaction forces, and eight-channel surface electromyography. Individually scaled OpenSim gait2392 models and the Umberger metabolic model were used to estimate metabolic power from seat-off to the end of the first complete gait cycle. Window-averaged metabolic power was stratified into low-, medium-, and high-cost levels. Window-level biomechanical features were extracted from kinematic, kinetic, and muscle-state time series. Seven classifiers were trained using a subject-level 7:3 train–test split and stratified five-fold cross-validation within the training set, and their probability outputs were integrated through TOPSIS-weighted classifier fusion. SHapley Additive exPlanations were used for class-specific feature attribution. The fused ensemble achieved an AUC of 0.870, F1 score of 0.703, accuracy of 0.705, and specificity of 0.853 on the independent test set. Discrimination was stronger for the low- and high-cost levels than for the medium-cost level. SHAP-based attribution highlighted force-related changes and knee-angle variability and amplitude measures as prediction-relevant biomechanical features. These findings support a model-derived, interpretable workflow for extending STW assessment from task performance to task cost, while indicating the need for further validation in larger and clinical datasets. Full article

As accessibility development in public buildings has gradually shifted from facility compliance toward experience- and performance-oriented evaluation, the quantitative assessment of indoor mobility experiences among blind users still lacks a systematic sensor-supported analytical framework. To address this gap, this study proposes an indoor accessibility assessment approach that integrates multi-sensor data acquisition with explainable machine learning, using a tactile museum as the experimental setting. Sixty-four participants with first-level blindness were recruited to complete a real-world directed walking task. A multimodal database was constructed by integrating objective data collected from an ultra-wideband (UWB) indoor positioning system, an intelligent gait analysis system, and video-based behavioral recording, including spatiotemporal trajectories, gait characteristics, and behavioral events, together with post-task accessibility satisfaction ratings. Based on this dataset, a random forest model was developed using the Overall Accessibility Satisfaction Score (OAS) as the response variable. SHAP, partial dependence analysis, and GAM smoothing were further applied to interpret the associations between key variables and predicted satisfaction. The results showed that walking distance, number of turns, self-reported collision perception, and selected gait indicators made relatively high contributions to the model interpretation, and these variables exhibited certain nonlinear associations with predicted satisfaction. These findings suggest that combining multi-source sensor-based behavioral measurement with explainable machine learning has potential for sensor-supported post-occupancy evaluation of indoor accessibility environments and can provide exploratory references for the quantitative assessment and optimization of accessibility in public buildings. Full article

Human gait analysis is a key component of rehabilitation medicine, enabling objective assessment of patient recovery. In crutch-assisted locomotion, however, conventional forearm crutches operate as passive devices, providing no quantitative information on load distribution or patient adherence to partial weight-bearing (PWB) prescriptions. This work presents the design and dynamic validation of a sensorized forearm crutch system for biomechanical monitoring during assisted gait. The proposed device combines a force-sensing module based on a full Wheatstone bridge strain-gauge configuration with a 6-axis inertial measurement unit (IMU) to capture both axial load and crutch orientation. Sensor fusion was implemented through a complementary filter to estimate pitch and roll angles under dynamic conditions. The system was calibrated through static loading procedures and validated against reference instrumentation, including an optoelectronic motion capture system and an instrumented dual-belt treadmill with force platforms. Unlike previous studies relying on stationary force platforms that capture discrete steps and may alter natural gait, this validation approach enabled continuous, stride-by-stride force and orientation measurements without restricting foot placement. Experimental trials were conducted with unimpaired participants performing assisted gait using 2-point and 3-point patterns at two partial weight-bearing levels (20% and 40% body weight) and two walking speeds (0.80 m/s and 1.20 m/s). Dynamic validation showed good agreement with the treadmill reference, with force RMSE values of $9.33\pm 1.70$ N for the left crutch and $12.90\pm 2.85$ N for the right crutch, and with coefficients of determination of $R^2=0.9956$ and $R^2=0.9927$, respectively. Orientation RMSE values were $1.08\pm 0.44$° (roll, right), $2.06\pm 0.56$° (roll, left), $1.79\pm 0.55$° (pitch, right), and $1.66\pm 0.37$° (pitch, left). Beyond validation accuracy, the system enabled extraction of a set of quantitative biomechanical descriptors directly from crutch signals, axial load, cadence, crutch contact variability, load asymmetry, pitch asymmetry, and crutch stance/swing asymmetries, characterizing walking stability, bilateral coordination, and gait regularity during continuous assisted locomotion. These results demonstrate the feasibility of integrating force and inertial sensors into forearm crutches to enable quantitative monitoring of assisted gait, with potential applications in rehabilitation assessment and real-time feedback. Full article

Background/Objectives: Sarcopenia may compound mobility limitations and fall vulnerability among older adults with coexisting knee osteoarthritis (KOA) and chronic spinal pain. This secondary analysis of a previously reported or substantially overlapping cohort estimated the proportion meeting Asian Working Group for Sarcopenia (AWGS) 2019 criteria and explored additional adjusted associations in a selected Vietnamese outpatient sample. Methods: A hospital-based secondary cross-sectional analysis included 88 outpatients aged ≥ 60 years (mean age, 70.5 ± 6.7 years; 69 women, 78.4%) with coexisting KOA and chronic spinal pain who were recruited by convenience sampling at Thai Binh General Hospital from May to October 2024. Source-record verification confirmed that all of the analytic participants had both diagnoses. Their muscle mass, grip strength, and gait speed were assessed using the InBody 770, an InGrip handgrip dynamometer, and a 15-foot walk test, respectively. The prespecified primary classification used AWGS 2019. The AWGS 2025 framework was considered during revision, but numerical reclassification was not feasible because the retained participant-level analytic dataset contained the derived AWGS 2019 outcome and covariates used in the reported regression and CHAID analyses, but not the original continuous age, appendicular skeletal muscle mass index, handgrip values, or a complete raw component record sufficient to independently reconstruct the AWGS 2019 status or apply AWGS 2025 thresholds. Multivariable logistic regression and CHAID were treated as exploratory. Results: Under AWGS 2019, 36/88 participants (40.9%) had sarcopenia, including 15 (17.0%) with severe sarcopenia. All 88 participants had both KOA and chronic spinal pain; therefore, diagnostic-category subgroup comparisons were not applicable. In the exploratory adjusted analysis, an age > 70 years (adjusted odds ratio [AOR]: 9.00, 95% confidence interval [CI]: 2.40–33.60), a history of falls (AOR: 6.33, 95% CI: 2.77–14.45), low educational attainment (AOR: 2.86, 95% CI: 1.46–5.61), and a higher Pittsburgh Sleep Quality Index score (AOR: 1.16, 95% CI: 1.02–1.32) remained associated with sarcopenia. Wide CIs and approximately 4.5 events per regression coefficient indicated substantial imprecision. Conclusions: This secondary report provides setting-specific descriptive evidence rather than independent replication, a validated prediction tool, or a fully auditable reconstruction of the original AWGS component measurements. Because AWGS 2025 reclassification could not be reconstructed from the retained dataset and raw component records, the AWGS 2019 estimate should not be treated as directly interchangeable with the estimates generated under the updated framework. The observed associations and within-sample subgroup patterns require confirmation in larger, prospectively auditable studies. Full article

Background: The Mercer Universal Prosthesis (MUP), designed with a default “neutral” (vertical) socket alignment, was developed to simplify transtibial prosthetic fitting, reduce labor costs, and improve access to prosthetic care in low-resource settings. Methods: This present longitudinal study evaluated biomechanical and functional outcomes at baseline, 6 months, and 12 months in 20 transtibial amputees fitted with the MUP. Results: Functional outcomes, assessed using the SF-36, showed significant improvement in overall health scores at 12 months (p < 0.001), while physical function and energy/fatigue domains remained unchanged (p = 0.686 and p = 0.211, respectively). Biomechanically, sagittal kinematics, measured using inertial motion capture, revealed significant limb × time interactions for hip flexion, knee flexion, and ankle plantarflexion. At 6 months, maximum hip flexion (−7°, p = 0.008) and knee flexion (−11°, p = 0.005) of the prosthetic limb were decreased versus baseline. At 12 months, the only observed difference was increased maximum ankle plantarflexion of the intact limb (+5° vs. baseline, p = 0.016). Muscle effort, quantified via the integral of EMG throughout the gait cycle, did not differ significantly between prosthetic and intact limbs across time points. Gait symmetry index (GSI) scores for hip, knee, and ankle range of motion trended toward gradual improvement but without statistical significance (p > 0.05). Conclusions: The MUP performance was maintained over 12 months, with stable biomechanical performance and meaningful quality-of-life gains. These findings support its potential as a cost-effective solution to expand prosthetic accessibility in low- and middle-income countries. Full article

To address the limited assistive performance and insufficient individual adaptability of hip exoskeletons, a multi-objective optimization-based assistive strategy is proposed. A parameterized assistive torque model is constructed based on human gait characteristics, with the objectives of reducing joint load and improving human–robot interaction coordination. The NSGA-II (Non-dominated Sorting Genetic Algorithm II) is employed to optimize the assistive parameters, and the optimized results are implemented in a gait phase-based control method to achieve synchronized torque output over the gait cycle. Experimental validation is conducted on a hip exoskeleton platform using motion capture and electromyography measurements. The results demonstrate that the proposed method effectively reduces hip joint torque, decreases muscle activation levels, and enhances human–robot interaction performance. Full article

Background/Objectives: Dysphagia may adversely affect nutritional status in patients with Multiple Sclerosis (MS). This study aimed to investigate the associations between dysphagia severity and (i) nutritional status, assessed by the Malnutrition Universal Screening Tool (MUST) and Global Leadership Initiative on Malnutrition (GLIM) criteria, and (ii) secondary sarcopenia indicators according to the European Working Group on Sarcopenia in Older People-2 (EWGSOP2) framework. Materials and Methods: This cross-sectional study enrolled 32 consecutive adult outpatients with confirmed MS and self-reported dysphagia (DYMUS ≥ 1). Dysphagia severity was evaluated using the Dysphagia in Multiple Sclerosis (DYMUS) questionnaire, the Eating Assessment Tool-10 (EAT-10), and the Yale Swallow Protocol. Nutritional assessment included MUST screening and GLIM-based malnutrition diagnosis. Muscle function was evaluated via handgrip strength, calf circumference, and 4-metre gait speed. Results: GLIM-defined malnutrition was identified in 12 (37.5%) patients. Dysphagia severity was significantly associated with MUST score (ρ = 0.596, p < 0.001) and the presence of GLIM-defined malnutrition (median DYMUS 6.5 vs. 4.0; p = 0.012). In exploratory logistic regression, higher DYMUS scores were associated with GLIM-defined malnutrition. Conversely, no significant associations were found between dysphagia severity and handgrip strength, calf circumference, or sarcopenia classification (p > 0.30 for all). The categorical severe-sarcopenia rate was not considered reliably interpretable because of a pronounced gait speed floor effect. Conclusions: In ambulatory MS patients with dysphagia, dysphagia severity was associated with nutritional risk indicators and GLIM-defined malnutrition, but not with the primary muscle strength and mass indicators evaluated. Because MUST and GLIM reflect composite nutritional risk rather than confirmed protein–energy deficiency, these findings should be regarded as exploratory and hypothesis-generating. The present data did not permit a reliable estimate of sarcopenia prevalence because of a pronounced gait speed floor effect and the absence of body composition measurement. As a preliminary practical consideration, these findings may support combined dysphagia and nutritional screening in multidisciplinary MS outpatient care, pending confirmation in larger prospective cohorts. Full article

Robotic lower-limb exoskeletons are an increasingly important tool in the rehabilitation of patients with motor impairments, and their effectiveness depends on how faithfully the device reproduces the natural gait pattern. Inertial measurement units (IMUs) are widely used to acquire body-worn kinematic data for gait monitoring, but compact, interpretable models linking IMU-derived hip- and knee-flexion features to gait phase under exoskeleton-assisted conditions are still lacking. We collected gait data from two independent experiments: Experiment 1, 20 healthy adults (10 M, 10 F; 22.2 ± 1.9 years) walking freely on level ground, stairs and a ramp with seven Noraxon IMUs; and Experiment 2, six healthy adults (4 M, 2 F; 31.0 ± 8.9 years) walking with and without the Exowalk (HR-02) over-ground exoskeleton with five IMUs. Eight bilateral hip- and knee-flexion features were extracted, and a binary logistic-regression model with stance/swing as the dependent variable was fitted on Experiment 1 and externally cross-validated on Experiment 2. The model classified gait phases with an accuracy of 90.83% (sensitivity 87.50%, specificity 92.50%, positive predictive value 85.37%) on Experiment 1. External validation retained 91.7% accuracy during free walking but dropped to 41.7% under Exowalk-assisted walking, indicating that the device alters the inertial signature of gait. The findings identify swing-phase hip flexion and the minimum swing-phase knee flexion as the kinematic descriptors most predictive of gait phase, and provide quantitative design and control targets for next-generation IMU-instrumented lower-limb rehabilitation exoskeletons. Full article

Background/Objectives: This study aimed to evaluate longitudinal changes in physical and cognitive function in individuals with chronic stroke over five years and to explore factors associated with long-term outcomes. Methods: This ambispective cohort study included individuals with chronic stroke who had participated in a previous cross-sectional study conducted between 2018 and 2019. Assessments were performed at baseline and five-year follow-up (2023–2024). Primary outcomes were physical function, assessed using the six-minute walk test (6MWT), comfortable and fast gait speeds measured by the ten-meter walk test (10MWT), and cognitive function, assessed using the Mini-Mental State Examination (MMSE). Results: Thirty-two individuals participated (mean age 63.5 ± 10.3 years; median time since stroke 7.0 years). The six-minute walk distance declined by 22% (263.7 to 206.8 m, p < 0.001), whereas no significant changes were observed in gait speed or cognitive function. Age, baseline National Institutes of Health Stroke Scale (NIHSS) score, baseline values of the 6MWT and 10MWT, and nutritional status (Mini Nutritional Assessment–Short Form; MNA-SF) showed associations with physical outcomes. For cognitive outcomes, baseline NIHSS score, baseline MMSE score, MNA-SF score, and education level showed associations. However, sensitivity analyses suggested that the associations involving MNA-SF and education level were not robust. Conclusions: Physical function declines over five years in individuals with chronic stroke, highlighting the importance of long-term follow-up. While global cognition (MMSE) remained stable, domain-specific declines cannot be ruled out. Baseline stroke severity, nutritional status, and initial functional and cognitive performance may be associated with long-term outcomes. Full article

Background/Objectives: Previous studies have measured a limited number of biomechanical parameters during medical rehabilitation of an anterior cruciate ligament (ACL) rupture. This study aimed to quantitatively assess changes in gait biomechanics, knee function, and lower-extremity muscle activity during after ACL reconstruction. Methods: The study included 32 patients after arthroscopic ACL reconstruction. The patients were divided into three groups based on postoperative time points: 0.5 year (12 men), 1 year (7), and over 1 year (9). Gait analysis at both self-selected and fast speeds was performed using an inertial system. Statistical analysis was performed using rank models and full-factorial orthogonal designs. Results: After 0.5 year, the timing of the gait cycle at self-selected speed was within the control group’s range and showed no significant asymmetry. With increasing speed, a decrease in knee joint range of motion was observed in the 0.5 year and 1-year groups, without achieving a full physiological increase in range of motion at long-term follow-up. Multivariate analysis revealed the greatest biomechanical imbalance during fast walking at one year and a phase-dependent effect of time after surgery, speed, and limb status on kinematics and EMG, particularly in the quadriceps. Conclusions: Basic temporal gait parameters during self-selected walking were within the control range by 0.5 year, but load-dependent knee kinematic and EMG abnormalities persisted. The knee joint’s response to increased loads remained impaired for at least one year. The persistence of phase-specific compensatory changes in kinematics and muscle activity at later stages can be assessed using exercise testing. Full article

Preclinical testing is essential for evaluating new implant designs and materials for total knee replacement (TKR). Standardized wear tests, such as ISO 14243, are widely accepted but only partially represent physiological kinematics and kinetics, as they do not account for all six degrees of freedom of the knee joint. More advanced setups, including robotic systems and joint simulators, enable complex load cases; however, the influence of lubrication conditions and testing speeds remains insufficiently standardized. This study investigated the kinematic and kinetic effects of different lubrication conditions (dry, synthetic synovial fluid, silicone oil) and speeds (static, 10%, 50%, 100% of normal gait) in a joint simulator setup using a posterior cruciate ligament-retaining TKR during level walking. Complementary pin-on-disk measurements revealed significant dependencies on both lubrication and speed. During joint simulator tests, omitting lubrication resulted in more than double the maximum flexion–extension moment, while the range of anterior–posterior femoral translation increased by approximately 73%. At 50% and 100% speed, silicone lubrication yielded results comparable to static tests, in contrast to the dry and synthetic synovial fluid conditions. These findings demonstrate that physiologically relevant lubrication and appropriate test speeds are essential for obtaining reliable results in experimental studies of TKR dynamics. Full article

Sarcopenia is highly prevalent among stroke patients and is associated with poor functional outcomes; however, differences across stroke stages and age groups remain unclear. This cross-sectional study enrolled 80 stroke patients from a regional teaching hospital in Taiwan, categorized into chronic (n = 40) and post-acute care (PAC) groups (n = 40), and further stratified into younger (40–64 years, n = 44) and older (≥65 years, n = 36) groups. Assessments included body composition, muscle strength, ultrasound-measured muscle thickness, gait speed, calf circumference, sarcopenia screening (SARC-F), nutritional status, and health-related quality of life. No significant differences were observed in muscle mass, muscle strength, or ultrasound-derived muscle thickness between the chronic and PAC groups. However, the PAC group demonstrated poorer functional outcomes and health-related quality of life, including lower gait speed (p = 0.018), and lower EQ-5D index and visual analogue scale scores (p = 0.006 and p = 0.002, respectively). In contrast, the chronic group showed a higher prevalence of sarcopenia (p < 0.001), a higher mean SARC-F scores (p = 0.004), a greater proportion of low appendicular skeletal muscle mass index (ASMMI, p = 0.025), and reduced calf circumference (p < 0.001). Age-stratified analysis revealed that older patients had lower muscle mass and structural parameters, including ASMMI (p < 0.001), fat-free mass (p < 0.001), quadriceps thickness (p < 0.001), and calf circumference (p = 0.002), along with a higher prevalence of sarcopenia (p < 0.001). These findings indicate that stroke stage is more closely associated with functional impairment, whereas aging predominantly affects muscle mass and sarcopenia severity. Full article

This systematic review examined the validity and reliability of wearable inertial sensor systems to quantify spatiotemporal gait parameters in post-stroke adults, a population in which gait asymmetry and altered motor control challenge accurate measurement. Sixteen studies involving 300 participants were included. Spatial parameters gait speed, cadence, and step/stride length showed consistently good-to-excellent agreement with reference systems (ICC 0.85–0.98; 95% LoA ±0.03–0.08 m/s for gait speed, ±4–10 steps/min for cadence, and ±3–8 cm for step/stride length) and high test–retest reliability. Temporal parameters demonstrated greater heterogeneity, with larger errors and lower concordance (ICC 0.40–0.85; LoA ±0.04–0.12 s), particularly for swing time (ICC 0.40–0.70; LoA up to ±0.15 s). Paretic-side measurements showed 10–20% lower concordance and 30–50% wider limits of agreement compared with the non-paretic side, although within-subject reliability remained moderate to high. No consistent influence of sensor number on measurement accuracy was observed. Overall, wearable inertial sensors provide robust estimates of spatial gait parameters, whereas temporal outcomes especially swing time remain limited due to challenges in gait event detection under stroke-related biomechanical alterations. These findings highlight the need for standardized protocols and improved algorithms to enhance comparability across studies and support broader clinical adoption. Full article

Walking is not merely locomotion but a window into the nervous system, integrating cortical, subcortical, cerebellar, spinal, and peripheral networks into a unified motor behavior. Across neurological diseases—including Parkinson’s disease, atypical parkinsonism, cerebellar ataxias, stroke, multiple sclerosis, neuropathies, neuromuscular disorders, and functional gait syndromes—gait disturbances are among the most disabling clinical features, contributing to falls, loss of independence, institutionalization, and premature mortality. Traditional bedside observation remains indispensable, but it lacks the sensitivity and reproducibility needed to capture subtle, episodic, or prodromal abnormalities. Over the past decade, advances in wearable sensors, marker-based and markerless motion capture, pressure-sensitive walkways, force plates, artificial intelligence, and machine learning have positioned digital mobility outcomes as promising, ecologically valid biomarkers of neurological function. These measures can support differential diagnosis, provide prognostic information on falls and survival, and serve as sensitive endpoints in therapeutic trials. They may also detect early abnormalities, such as increased stride-to-stride variability or prolonged double-support time, before overt clinical deterioration becomes evident. Clinical applications are increasingly evident across disorders, including distinguishing Parkinson’s disease from atypical parkinsonism, quantifying treatment response in normal-pressure hydrocephalus, tracking progression in ataxia and multiple sclerosis, predicting functional decline in motor neuron disease, and guiding rehabilitation after stroke. Integration with neuroimaging, electrophysiology, and molecular biomarkers is beginning to reveal the circuits underlying variability, instability, and freezing, positioning gait as a systems-level marker of neural integrity. Nevertheless, methodological heterogeneity, limited disease-specific validation, insufficient longitudinal data, and lack of consensus on clinically meaningful parameters continue to constrain translation. Cognitive, affective, and environmental influences also remain insufficiently represented in digital frameworks, while equity, accessibility, algorithmic bias, and privacy require careful ethical governance. Reconceptualizing gait as a “sixth vital sign” reframes mobility as a multidimensional biomarker of neural and systemic health. With harmonized protocols, robust validation, multimodal integration, and appropriate ethical frameworks, gait analysis could become a cornerstone of precision neurology. Full article