Search Results (429)

Background: Our objective was to evaluate the clinical effects on walking function and symmetry of combined knee-powered exoskeleton-assisted gait training (EAGT) and conventional physiotherapy versus conventional therapy alone in patients with subacute stroke. Methods: This randomized pilot trial enrolled patients 1–3 months post-stroke with Functional Ambulation Classification scores of 3 or 4. Participants were randomly assigned in a 1:1 ratio to receive EAGT plus conventional therapy or conventional therapy alone for 4 weeks. Outcomes included gait speed (10MWT), endurance (6MWT), and motor function (FMA-LE), assessed at baseline, 4 weeks, and 16 weeks. Accelerometer-based spatiotemporal gait parameters were analyzed in the EAGT group. Results: Twenty-four participants (EAGT: 17, control: 7) completed the study. No significant group × time interaction was observed for gait speed, walking endurance, or lower-extremity motor function, indicating that changes over time did not differ significantly between the EAGT and control groups. In the EAGT group, increased gait speed was accompanied by augmented cadence (p < 0.001) without significant changes in stride length or gait symmetry. Conclusions: From this preliminary pilot study, knee-powered EAGT improved walking performance in subacute stroke but did not demonstrate superiority over conventional rehabilitation. Gains were accompanied by cadence augmentation without restoration of gait symmetry. Larger randomized trials are needed to validate these preliminary findings. Full article

Background: Gait disturbances are among the most disabling manifestations of cerebral palsy (CP) and substantially affect functional mobility and quality of life. Robot-assisted gait training (RAGT) has been introduced to improve gait performance; however, its effects on lower-limb kinematics remain inconsistent. The aim of this study was to evaluate changes in sagittal-plane gait kinematics in children with CP following a four-week gait rehabilitation program using the PRODROBOT robotic gait trainer. Methods: A prospective quasi-experimental pre–post study with a reference group was conducted. Ten children with spastic cerebral palsy at GMFCS level III aged 8–13 years completed a four-week intervention comprising 20 sessions of robot-assisted gait training. Three-dimensional gait analysis (Vicon) was performed before (KF1) and within 72 h after the end of the intervention (KF2); for each participant, 15 gait cycles were averaged. Eighteen typically developing children aged 7–14 years constituted the normative reference group (CG) and were assessed once. Sagittal plane angular waveforms for the hip, knee, and ankle joints were compared using the difference factor (f₁) and the similarity factor (f₂). Results: The intervention produced selective changes in lower-limb kinematics. At the ankle joint, the range of motion deficit in the sagittal plane relative to the CG decreased from 4.5° to 2.9°, and the difference modulus at 55–60% of the gait cycle decreased from 7.5% to 4.0%. At the knee joint, the difference factor relative to the CG decreased from f₁ = 22.2% to f₁ = 17.4%, with a reduction of the difference modulus by 2–3 percentage points during the loading response phase, terminal stance phase, and terminal swing phase. At the hip joint, the profiles before and after the intervention were respectively f₁ = 2.0% and f₂ = 95.6, and the deviation from the reference pattern remained at the level of f₁ = 54.1% vs. 53.0%; f₂ = 50.2 in both comparisons, with persistent flexion of approximately 10° and a lack of extension in the terminal stance phase. Conclusions: Robot-assisted gait training with the PRODROBOT system produced selective improvements in sagittal-plane gait kinematics in children with cerebral palsy, with the most pronounced effects observed at the ankle joint and, to a lesser extent, the knee. The intervention did not restore a normal gait pattern, particularly at the hip joint. These findings suggest that robotic gait training may serve as a valuable component of comprehensive rehabilitation but should be combined with interventions targeting muscle strength, joint mobility and postural control. Larger randomized studies with long-term follow-up are needed to confirm the clinical effectiveness of this approach. Full article

Lower-limb rehabilitation exoskeletons have emerged as a promising complementary technology to conventional therapy, enabling repetitive, intensive, and personalized gait training. However, achieving accurate trajectory tracking while maintaining robustness against parametric uncertainties, external disturbances, and unpredictable human–robot interaction remains a significant control challenge due to the highly nonlinear dynamics of coupled human–exoskeleton systems. This paper presents an experimental performance comparison of five control strategies for gait rehabilitation exoskeletons, including a classical proportional–integral–derivative (PID) controller, a model-based proportional–derivative controller with gravity compensation (PD+G), a computed torque sliding mode controller (CT-SMC), a computed torque–super-twisting sliding mode controller (CT–ST-SMC) and a hybrid backstepping–super-twisting sliding mode controller (BS–ST-SMC). All the controllers were implemented on the same lower-limb rehabilitation exoskeleton under identical operating conditions. The experimental results demonstrate that the proposed BS–ST-SMC architecture outperforms classical and traditional robust approaches, particularly in mitigating chattering and managing human–robot interaction uncertainties. Specifically, the BS–ST-SMC achieved the highest tracking precision with a mean squared position error (MSEp) of $1.32\times {10}^{-3}{\mathrm{rad}}^2$ and effectively synchronized with the user by reducing the phase lag to just 4.22° at the knee joint. Their overall performance was evaluated using the following metrics: mean squared position error (MSEP), mean squared velocity error (MSEv), peak error, phase lag, jerk index, peak torque, and peak power. Full article

This paper presents a wearable soft strain sensor based on a commercially available conductive thermoplastic polyurethane (cTPU) 3D-printed as an auxetic soft metamaterial for human activity recognition. The growing demand for flexible and wearable electronics, driven by advances in artificial intelligence, highlights the importance of such sensors in healthcare, medical rehabilitation, soft robotics, and human–machine interfaces. The auxetic cTPU sensor was mechanically and electrically characterized through empirical measurements and validated against numerical simulations. A single sensor mounted on a knee brace was used to collect gait signals across four activities: running, walking, standing, and sitting. Two classification approaches were investigated. A Long Short-Term Memory (LSTM) network was trained directly on the raw time-series signal, with the best configuration achieving 96% accuracy using Relative Standard Deviation Normalization with 50 hidden units. Traditional machine learning models, namely Random Forest and XGBoost, were trained on 30 extracted time-domain and frequency-domain features per motion cycle, achieving 100% and 97.33% accuracy, respectively, under five-fold cross-validation. To enhance model transparency, explainability analysis using SHAP identified power spectral density and the first harmonic frequency as the most consistently influential features across both models, with dynamic activities driven by frequency characteristics and stationary activities distinguished by signal mean amplitude. The results demonstrate that auxetic cTPU soft strain sensors combined with machine learning and explainable artificial intelligence provide an accurate and interpretable solution for wearable human activity recognition, highlighting their potential for applications in robotics, healthcare, and human–robot interfaces. Full article

Background: The current evidence regarding the impact of augmented reality (AR) rehabilitation on motor function in children with cerebral palsy (CP) remains limited. Objectives: This study aimed to evaluate whether integrating AR motor training (ARMT) into conventional rehabilitation enhances gait and balance outcomes compared to conventional rehabilitation alone. Methods: Forty children aged 7 to 12 years with unilateral or bilateral spastic CP were randomly assigned to receive either ARMT integrated into a 4-week conventional rehabilitation program (n = 20) or conventional rehabilitation alone (n = 20). The ARMT replaced 20–30 min of standard motor therapy five days per week. Gait and balance were assessed pre- and post-intervention using an instrumented 10 m walk test with the G-Walk sensor, the MABC-2 one-leg balance task, and the Pediatric Balance Scale. Due to unavailability of data from five control-group participants, analyses were conducted on 35 children using available-case mixed-model approaches. Results: Nominally significant between-group differences in pre- to post-intervention changes were observed for left stride cycle duration (mean difference: −0.083 s; 95% confidence interval [CI]: [−0.166, −0.0002]; p = 0.049), right stride cycle duration (−0.095 s; 95% CI: [−0.185, −0.006]; p = 0.038), the coefficient of variation of the first double-support phase on the right side (−8.91 percentage points; 95% CI: [−17.39, −0.44]; p = 0.040), and the left propulsion index (2.42 m/s²; 95% CI: [0.39, 4.45]; p = 0.021). No statistically significant differences were detected between groups for balance measures, gait speed, stride length, gait quality, or symmetry. Conclusions: Incorporation of four weeks of ARMT into conventional rehabilitation did not yield substantial additional benefits for gait or balance in this cohort. The observed effects on gait timing, variability, and propulsion are exploratory and warrant validation in larger, adequately powered studies with pre-specified primary outcomes. Full article

Human Activity Recognition (HAR) plays a significant role in various applications, from learning a discipline to physical rehabilitation. In older adults, activity patterns can indicate levels of frailty, which helps inform the design of physical training programs to prevent falls and maintain mobility. HAR sensing ranges from wearable sensors such as IMUs and RGB cameras to video, specialized gait laboratories, perturbation units, VR, and other modalities. This paper presents a comprehensive study of depth-based, skeleton-driven HAR and age group recognition (AGR) using data collected from real-world nursing home environments. Depth sensors offer a privacy-preserving and non-invasive alternative to wearable and RGB-based systems, enabling continuous 24-h monitoring without requiring user compliance. We systematically evaluate multiple modeling paradigms, including classical machine learning models (DT, RF, KNN, SVM, HMM, HMM+SVM), sequence-based models (LSTM, TCN, ARNN), and graph-based approaches, using skeletal joint data extracted from depth images. Experiments are conducted on two heterogeneous datasets: NTU RGB+D (younger adults) and ETAP-DID (older adults). We analyze the impact of different joint subset configurations (full-body, limb-only, leg-only, and torso-only) and compare raw joint representations with handcrafted time-series features (TSFEL) for frame-based HAR. Beyond activity recognition, we introduce an AGR pipeline to distinguish younger from older adults based on skeletal motion patterns. We investigate multiple feature representations, including absolute joint positions, root-relative coordinates, bone vectors, and joint velocities, and provide interpretability through feature importance and saliency analysis to identify age-discriminative joints and motion cues. Our study provides a comprehensive analysis of various HAR models applied to depth data, examining model performance and the contribution of joint-based features to HAR and AGR. Our study highlights the potential for personalized privacy-preserved monitoring and intervention in nursing homes. Full article

Balance impairment threatens mobility and independence in older adults with cardiovascular disease, yet cardiac rehabilitation (CR) has traditionally prioritised aerobic capacity and cardiovascular outcomes. This narrative review examines the mechanisms and assessment of balance impairment, balance recovery within CR, and implications for clinical practice. Balance impairment reflects interactions among musculoskeletal, sensory, cognitive–motor, and cardiovascular constraints that affect different domains of postural control to varying extents. Standardised clinical measures do not capture these domains equally, and single scores or completion times can obscure the deficits underlying poor performance. Instrumented and wearable technologies extend clinical assessment by quantifying postural sway and gait, helping to distinguish broader mobility gains from recovery within specific balance domains. To translate this distinction into practice, this review recommends individualised, balance-focused CR, with assessment guiding task-specific training and virtual reality or exergaming providing graded practice and performance feedback to promote mobility and independence. Full article

Lower-limb rehabilitation exoskeletons have the potential to improve gait recovery after stroke by providing intensive and repetitive training. However, conventional control strategies often rely on fixed control parameters and exhibit limited adaptability to patient-specific characteristics, sensor noise, and dynamic uncertainties. This paper proposes an adaptive control framework that combines deep reinforcement learning (RL) with model-based impedance control for personalised lower-limb exoskeleton assistance. Patient-specific biological parameters are incorporated into the simulation environment and reward formulation to improve adaptability and robustness. Three state-of-the-art deep RL algorithms, Deep Deterministic Policy Gradient (DDPG), Twin Delayed Deep Deterministic Policy Gradient (TD3), and Soft Actor-Critic (SAC), are evaluated in a continuous control environment under varying signal-to-noise ratio (SNR) conditions ranging from 5 dB to noise-free conditions. Results demonstrate that TD3 achieves the most stable learning performance, obtaining a mean reward of −354.24 under noise-free conditions, while DDPG provides the highest joint-angle tracking accuracy with an RMSE of 0.0369 rad. SAC exhibits superior robustness in noisy environments, achieving the highest learning ratio of 0.51 at 5 dB SNR. Furthermore, the proposed personalised framework reduces tracking errors by up to 27% compared with non-personalised baseline approaches. The findings indicate that integrating patient-specific information with RL-based adaptive control can significantly enhance robustness, tracking performance, and personalisation in exoskeleton-assisted gait rehabilitation, providing a promising direction for future intelligent rehabilitation systems. Full article

Background/Objectives: Inclusion body myositis (IBM) causes progressive, quadriceps-predominant weakness that impairs mobility and increases fall risk, yet outcomes most relevant to independence remain unsynthesized for IBM. This review compiles direct IBM evidence across five domains (A1–A5): natural history, motor performance, falls and balance, sensory/peripheral-nerve function, and interventions. Methods: Eight databases and two trial registers were searched without date or language limits. Eligible studies enrolled adults with IBM based on recognized criteria reporting lower-limb strength or function, gait, transitional tasks, balance, falls, or intervention outcomes; mixed-myopathy cohorts required extractable IBM-specific data. Two reviewers independently screened, extracted, and appraised risk of bias, following PRISMA 2020. Results: Sixty-four studies were included; per-domain totals (A1: 16, A2: 9, A3: 6, A4: 2, and A5: 38) exceed 64 because studies may span domains. Quadriceps strength was the most sensitive progression marker, detected earlier by quantitative testing. Falls were near-universal and insufficiently managed. No drug showed convincing functional benefit in controlled trials, whereas exercise and orthotic/robotic assistance appeared to be safe in small studies. Sensory and peripheral-nerve dysfunction were common, but proprioceptive acuity and postural balance were unmeasured. Conclusions: IBM progression is best measured by quantitative quadriceps strength and function. Intervention evidence derives largely from small, uncontrolled and neutral trials. Primary myopathy is likely the principal driver of decline, but its downstream consequences—for balance, proprioception, and falls—remain underexplored and are the priority for future study. Full article

This paper proposes a hierarchical control architecture that is established at both lower limb joint and platform levels based on a simplified admittance model, where compliance is regulated through virtual mass and damping. At the lower limb joint level, admittance control governs lower limb motion tracking, while at the platform level it adjusts the omnidirectional mobile platform velocity in response to human interaction forces. Within this framework, a Sarsa-based reinforcement learning agent dynamically optimizes the parameters of a Sigmoid function using dual state inputs. Based on hip joint angle error and human–robot interaction force, the controller dynamically adjusts virtual mass and damping to optimize the trade-off between tracking error and dynamic compliance. The simulation and experimental results on the prototype system demonstrate that, compared with traditional Sigmoid parameter-tuned admittance control, the proposed approach significantly enhances gait smoothness (dimensionless squared jerk reduced by 65.62% and 36.74% for hip and knee joints), and increases human–robot interaction compliance (RMS interaction force was reduced from 3.2502 N to 2.5109 N; EPUD decreased from 12.14 to 9.53). Moreover, the proposed strategy achieves smaller maximum overshoot (0.45° vs. 0.9°) and faster settling time (2.6 s vs. 4.59 s). These findings indicate that integrating reinforcement learning with Sigmoid parameter adaptation provides a systematic and effective solution for adaptive compliance regulation in mobile exoskeleton systems, enhancing adaptability, safety, and functional relevance for stroke patients undergoing lower limb rehabilitation training. Full article

Background/Objectives: Sensor-based movement assessment is increasingly used to quantify movement execution quality and support feedback-guided rehabilitation exercise, particularly in home-based and remote rehabilitation contexts. However, exercise adherence, movement execution quality, and rehabilitation progress remain difficult to monitor continuously and objectively outside direct therapist supervision. This scoping review aimed to map the current applications of sensor-based biofeedback and movement-quality assessment systems for rehabilitation exercise and to identify evidence gaps. Methods: This review followed established scoping review methodology and PRISMA-ScR guidance. PubMed/MEDLINE, Web of Science Core Collection, and IEEE Xplore were searched, and Google Scholar was used for supplementary searching. English-language studies published from January 2014 to May 2026 were eligible if they involved rehabilitation-related populations, sensor-based movement assessment, biofeedback, or training guidance. Data were charted and narratively synthesized according to rehabilitation context, sensor technology, movement-quality metrics, computational approaches, feedback strategies, real-time or remote functions, and reported outcomes. Results: Fifty-five studies published between 2015 and 2026 were included. The evidence covered neurological, musculoskeletal and orthopedic, balance and vestibular, fall-prevention, home-based, and telerehabilitation applications. Technologies included inertial sensors, smartphones, vision/depth cameras, surface electromyography, pressure/force sensors, and multisensor systems. Movement-quality metrics included range of motion, postural stability, gait characteristics, loading, muscle activation, movement correctness, repetition count, and task completion quality. Feedback was visual, auditory, vibrotactile, app-based, avatar-based, therapist-facing, or remote-platform-based. Most evidence came from feasibility, technical validation, algorithmic validation, and small-sample clinical studies. Conclusions: Sensor-based systems may help translate rehabilitation exercise performance into quantifiable and feedback-enabled information. Future research should strengthen real-world validation, standardize task-specific movement-quality metrics, and clarify how feedback mechanisms can support individualized rehabilitation progression. Full article

Gait asymmetry and reduced gait speed (GS) are common after stroke. This systematic review and meta-analysis evaluated the effects of gait training with lower-limb robotic exoskeletons or exoskeleton-type devices on gait asymmetry and GS compared with conventional rehabilitation or non-robotic gait training. PubMed, Embase, Web of Science, the Cochrane Library, and Scopus were searched from inception to 21 June 2026. Randomized controlled trials (RCTs) reporting spatial gait asymmetry (SGA), temporal gait asymmetry (TGA), or GS were included. Standardized mean differences (SMDs; Hedges’ g) and 95% confidence intervals (CIs) were pooled using random-effects models. Eleven RCTs involving 532 randomized participants were included. Training with these devices reduced SGA (SMD = −0.68, 95% CI −1.13 to −0.22, p < 0.01) and improved GS (SMD = 0.46, 95% CI 0.12 to 0.81, p = 0.01), but did not significantly affect TGA (SMD = −0.85, 95% CI −1.90 to 0.20, p = 0.11). The certainty of evidence, assessed using the Grading of Recommendations Assessment, Development and Evaluation (GRADE) framework, was very low for all three outcomes. Gait training with lower-limb robotic exoskeletons or exoskeleton-type devices may reduce SGA and improve GS after stroke, whereas its effect on TGA remains uncertain. Full article

Background: Lower limb prosthesis users commonly exhibit gait asymmetry that may increase metabolic demand. Gait asymmetry, therefore, is frequently targeted in rehabilitation to improve walking efficiency. This review aimed to investigate whether interventions that improve gait asymmetry reduce metabolic demand in unilateral lower limb prosthesis users. Methods: A search of relevant English-language articles was conducted using PubMed and CINAHL, yielding a total of 1067 records. Following title, abstract, and full-text screening, 10 studies met eligibility requirements and were included in the qualitative synthesis. Data extraction focused on participant characteristics, gait asymmetry measures, metabolic outcomes, and intervention characteristics. Results: Four main intervention categories were identified: advanced prosthetic knees and ankles, prosthetic mass manipulation, feedback-based gait interventions, and exercise-based training. Across studies, changes in gait symmetry were not consistently associated with reductions in metabolic demand. Interventions targeting ankle function showed the most consistent reductions in metabolic demand, particularly during demanding walking tasks. In contrast, distal prosthetic mass addition consistently increased metabolic demand and worsened gait symmetry. Conclusion: Current evidence does not support a consistent or direct relationship between gait symmetry and metabolic demand in lower limb prosthesis users. Gait asymmetry may not be inherently metabolically disadvantageous and may represent an energetically optimal adaptation. Full article

Cerebral palsy is a leading cause of childhood motor disability, and ambulatory children with spastic hemiplegia commonly present with dynamic equinus, calf muscle overactivity, and gait impairment. Evidence on focal vibration after botulinum toxin type A injection remains limited. This exploratory proof-of-concept pilot study assessed short-term changes following a four-week multimodal rehabilitation program incorporating focal vibration. In this uncontrolled single-group pre–post study, nine ambulatory children (mean age, 12.1 years), all classified as level II on the Gross Motor Function Classification System, received onabotulinumtoxinA injections into the affected gastrocnemius muscles, followed by twelve outpatient sessions combining focal vibration, conventional physiotherapy, and robotic gait training. Passive ankle dorsiflexion increased, calf muscle spasticity decreased, and selected gait parameters improved. Walking speed increased from 0.79 to 0.90 m per second, an absolute change of 0.11 m per second, comparable in magnitude to published estimates of clinically important change in ambulatory children with cerebral palsy. After Bonferroni correction, only walking speed and affected-limb stride length remained significant. No adverse events occurred. These findings describe outcomes associated with the combined program but do not establish the efficacy or independent contribution of focal vibration. Full article

Background: Exercise rehabilitation (ER) has become an important treatment regimen during the recovery phase of acute ischemic stroke (AIS). Optimal exercise prescriptions for post-stroke motor recovery remain controversial. Methods: PubMed, Embase, the Cochrane Library, and Web of Science were systematically searched through November 2025. The Cochrane risk-of-bias (RoB 1) assessment tool was utilized to assess the risk of bias in the original studies. Results: This study included 66 trials comprising 3675 patients. For balance, whole-body tilting postural training (WTPT), unilateral strength training (UST) and robot-assisted unilateral gait training (RAUGT) showed high SUCRA rankings. Activities of daily living likely improved most with robot-assisted Tai Chi training (RATCT), home rehabilitation guidance (HRG) and robot-assisted gait training (RAGT). Fugl-Meyer Assessment (FMA) gains appeared greatest following robot-assisted hand training with electrical stimulation (RAHT + ES), neural inhibition therapy (NIT) and graded motor imagery with electrical stimulation (GMI + ES). Walking endurance seemed most responsive to visual feedback training (VFT), UST and visual aids training (VAT), while functional mobility showed greatest improvement with tilt sensor-assisted gait training with electrical stimulation (TAGT + ES), gait training (GT) and virtual reality therapy with motor imagery (VRT + MI). Conclusions: This study provides the first comprehensive evidence synthesis evaluating the comparative effectiveness of specific exercise modalities across distinct recovery domains after AIS. Rather than supporting generic interventions, the findings highlight the distinct comparative advantages of various emerging technologies and traditional practices. These results offer an evidence-based framework for clinicians to optimize post-stroke rehabilitation protocols and highlight priority areas for future research on dose–response and long-term outcomes. Full article