Search Results (159)

Stroke is the second leading cause of death globally and a major contributor to lower-limb disability, affecting gait, balance, and functional independence in elderly populations. While robot-assisted rehabilitation has demonstrated effectiveness in motor recovery, access remains limited due to high costs and geographic barriers, particularly in Latin America. This study presents ExoKnee, a low-cost knee exoskeleton designed through biomimetic principles and 3D-printed fabrication as a proof-of-concept device targeting gait rehabilitation in elderly adults. The system integrates a single-degree-of-freedom pneumatic actuator controlled by electromyography (EMG) signals from the quadriceps muscle, enabling knee flexion and extension (90° to 180°). The design was evaluated through finite element analysis and dynamic simulations in MATLAB/Simulink R2024a under constant, stepwise, and sinusoidal reference inputs in a digital-twin environment. Expert validation using the Content Validity Coefficient yielded a mean score of 0.8747, reflecting preliminary expert agreement on the conceptual design’s coherence and relevance. The prototype demonstrated controlled movements through a 6-bar pneumatic system with EMG-triggered relay activation, validated at the proof-of-concept level through simulation and single-subject threshold calibration. ExoKnee addresses critical gaps by offering an anthropometrically informed, biosignal-driven, and locally manufacturable rehabilitation platform for low- and middle-income countries, pending clinical validation. Future work will focus on clinical trials and adaptive EMG control strategies. Full article

Background/Objectives: Well-leg compartment syndrome is a rare but potentially life-threatening complication associated with the lithotomy position during pelvic or lower abdominal surgery. While previous studies have examined this condition in specific surgical fields, comprehensive studies focusing on minimally invasive surgery, including laparoscopic and robot-assisted surgery, have not been conducted. This scoping review aimed to summarize the latest evidence on this condition, identify risk factors, and evaluate prevention strategies. Methods: This scoping review was conducted according to the PRISMA-ScR guidelines. A comprehensive literature search was performed using MEDLINE, Embase, and CENTRAL. Data were extracted from studies focusing on patients who underwent minimally invasive surgery in the lithotomy position. Results: A total of 25 studies, including cohort studies and case reports, were included. The majority of cases were observed in procedures exceeding 4 h in duration, with a notable prevalence in the left lower extremity during gastrointestinal surgical procedures. Fasciotomy was required in the majority of reported cases. Risk factors included high body mass index, large calf circumference, prolonged operative time, peripheral vascular disease, and specific surgical positions such as head-down or head-down plus right-sided tilting. Preventive measures included intraoperative lower limb pressure monitoring, leg positioning, use of improved support devices, and reduction of operative time in the lithotomy position. Conclusions: This review identified key risk factors and preventive measures for compartment syndrome of the unaffected lower limb in minimally invasive pelvic surgery. However, evidence for minimally invasive surgery is limited, and standardized guidelines do not exist. Further multicenter studies are needed to establish optimal preventive measures and improve patient safety. Full article

Robust exercise recognition is essential for robot-assisted lower-limb rehabilitation, where misclassifications of sensor-derived movements can degrade therapy execution and supervision. This study proposes a novel hybrid weighted stacking ensemble to increase the efficiency of the intelligent module of the LegUp parallel robotic system for lower limb rehabilitation. The approach combines a Residual Multilayer Perceptron (ResMLP) and an optimized Kernel Extreme Learning Machine (KELM), where model hyperparameters are tuned using Optuna and the base-model probability outputs are fused through optimized weighting and a meta-learner. Experiments were conducted on a five-class dataset built from nine IMU orientation features acquired from three sensors placed on the healthy limb. Four meta-learners were evaluated (Logistic Regression, Random Forest, Gradient Boosting, and AdaBoost), with AdaBoost providing the best overall performance. To further assess the robustness and generalization capability of the proposed approach, a 5-fold cross-validation procedure was performed for the ResMLP, KELM, and the hybrid ensemble models. The proposed stacking hybrid ensemble consistently surpassed the performance of the strongest individual classifiers as well as the original LegUp Multilayer Perceptron model. These results indicate that combining residual learning with kernel-based classification in a weighted stacking framework yields a stable and high-performing solution for multi-class rehabilitation exercise recognition. Full article

The aging population and the high incidence of neurological disorders have driven an increasing demand for lower-limb motor dysfunction rehabilitation. Traditional rehabilitation methods suffer from limitations such as low efficiency and a lack of personalization. Lower-limb rehabilitation exoskeleton robots have emerged as a critical solution, with human–robot intelligent fusion serving as the core theoretical framework and technological pathway for performance enhancement. From the unique perspective of human–robot intelligent fusion, this paper systematically reviews the application and recent advances of artificial intelligence in three key aspects—intention perception, intelligent control, and human–robot integration—based on a layered architecture of “fusion perception, fusion decision-making, and fusion execution”. The definition, connotations, and realization mechanisms of human–robot intelligent fusion are clarified. Furthermore, this review analyzes the fusion mechanisms, applicable scenarios, and technical characteristics of different AI technologies and summarizes the human–robot intelligent fusion modes and clinical application status of representative products such as EksoNR, MyoSuit, and AiLegs. In addition, key challenges are identified from the perspectives of fusion generalization capabilities, the trade-off between real-time performance and robustness, algorithm interpretability, and multimodal deep fusion mechanisms. This paper provides a systematic theoretical reference and technical roadmap for establishing a unified human–robot intelligent fusion framework for lower-limb rehabilitation exoskeletons. Full article

The number of patients with lower-limb dysfunction is increasing each year due to aging, illness, accidents, and other factors. Without timely rehabilitation and rapid recovery of walking function, further physical and mental deterioration may be accelerated, potentially leading to long-term bedriddenness. This study discusses gait training in rehabilitation therapy from the perspectives of kinesiology, cognitive science, walking function, and safety, and an omnidirectional gait training robot was developed. This study proposed a control system construction method for an omnidirectional gait training robot based on both prescription-based training and autonomous training. In the prescription-based training system, the target values are derived from the training prescription, and the control objective is to guide the patient to walk along the robot’s prescribed path and speed. In the autonomous training system, the target values are automatically generated based on the patient’s walking intentions, and the control objective is for the robot to safely follow the patient’s movement. A necessary condition for robot-assisted autonomous gait training is effective fall prevention. A fall prediction strategy based on foot position information and handrail pressure data was developed. Using this strategy, the robot can predict falls immediately before they occur, similar to a physical therapist, thereby reducing the risk of falls during gait training. Experimental results demonstrate the feasibility of implementing this strategy. Full article

Gait dysfunction is a common and disabling consequence of stroke, frequently associated with impaired lower-limb torque generation and reduced balance. Robot-assisted gait training (RAGT) has emerged as a promising intervention; however, its phase-specific biomechanical effects remain incompletely characterized. This pilot mechanistic study investigated the effects of Walkbot-assisted gait training on phase-specific lower-limb torque and clinical outcomes in individuals with unilateral hemiplegia. Fifteen patients with hemiplegia underwent Walkbot-assisted gait training. Joint torque values were normalized to body mass (Nm/kg). Phase-specific torque was analyzed during the swing and stance phases for the affected and unaffected limbs. Pre–post differences were evaluated using the Wilcoxon signed-rank test. Functional balance and motor impairment were assessed using the Berg Balance Scale (BBS) and the Fugl–Meyer Assessment—Lower Extremity (FMA-LE). Significant torque increases were observed in both gait phases. Median swing-phase torque increased from 0.261 to 0.361 Nm/kg in the affected limb and from 0.254 to 0.334 Nm/kg in the unaffected limb (p ≤ 0.017). Stance-phase torque increased from 0.197 to 0.454 Nm/kg in the affected limb and from 0.158 to 0.471 Nm/kg in the unaffected limb. Clinical outcomes improved significantly, with median BBS scores increasing from 22.0 to 34.0 and FMA-LE scores from 14.0 to 24.0 (p = 0.001). Walkbot-assisted gait training was associated with significant phase-specific torque gains, accompanied by improvements in balance and lower-limb motor recovery. Full article

This study proposes a model predictive control (MPC)-based Assist-as-Needed (AAN) strategy for upper-limb rehabilitation robots, with particular emphasis on mitigating motor slacking. In conventional error-based AAN approaches, robotic assistance is regulated through a single coefficient tied to the tracking error; thus, a reduction in voluntary effort is absorbed into the assistive channel and remains obscured by a small tracking error. The proposed method decouples this mechanism by introducing a two-channel admittance structure, in which the robotic-assistance gain $A_k$ and the user-participation-reflection gain $B_k$ are jointly optimized within a single convex MPC formulation. The cost function addresses trajectory tracking, participation-aware force alignment, assistance suppression, and passivity, enforced through energy-tank constraints. The controller was validated in two experiments on a mobile upper-limb rehabilitation robot. The first experiment confirmed differential adaptation of $A_k$ and $B_k$ across three instructed contribution levels, with the participation ratio increasing from 0.103 to 0.879 as the contribution shifted from insufficient to appropriate. The second experiment compared the controller with an error-based AAN baseline and a forgetting-factor AAN baseline under an induced motor-slacking condition, in which the task-direction contribution was reduced to 45%. Under an identical synthesized input, the proposed controller yielded a lower aggregate human-contribution ratio of 0.282, compared with 0.595 and 0.535 for the two baselines, respectively. This indicates that the externally imposed reduction in participation was represented more explicitly in the controller allocation, rather than being masked by error-driven assistive compensation. These results suggest that the proposed approach extends AAN control toward a participation-preserving, anti-slacking strategy for robot-assisted rehabilitation. Full article

Compliant mechanisms have contributed to many advances in soft robotics, and there is strong motivation to translate these ideas to assistive devices where adaptive motion at the human interface is required. This work presents novel reconfigurable compliant joints (RCJs) as a parameterized joint element for functional biomimicry in lower-extremity joints for prosthetic knees and ankle–foot orthoses, with concepts that extend to other limb joints. The RCJ uses a rigid hub and outer ring joined by an array of flexible links with centerlines defined by cubic Bézier curves. Link shapes are organized into four Bézier classes (A–D), with base types using 10, 12, or 14 uniformly distributed link slots and variants generated by modifying active-link count and distribution, forming a structured morphology space of 12 configurations for machine design. Dual-extrusion 3D-printed prototypes are characterized by a custom testing apparatus using a 2.2 kN load cell at 25 mm/s over a 0–90° rotation range across six recorded load cycles to measure torque–angle curves and stiffness under large deformations. Angle-dependent stiffness is evaluated over three fixed intervals (0–30°, 30–60°, and 60–90°) to quantify multi-stage behavior. A 2-dimensional corotational frame model and a Simscape Multibody model, including a rolling-contact knee configuration, use the same parameterization to relate geometry, nonlinear mechanics, and system-level motion. Experiments and simulations show multi-stage torque–angle profiles and predictable stiffness modulation across all configurations, with both magnitude and transition angle tunable through Bézier class and active-link distribution, positioning the RCJ as a CAD/CAE-compatible joint architecture for assistive devices or wearable robotic systems and a basis for advancing functional biomimicry in compliant mechanism design. Full article

Lower-limb exoskeleton robots play a pivotal role in rehabilitation medicine and assistive augmentation, where precise dynamic modelling and trajectory tracking control are fundamental to effective assistance. Existing models predominantly focus on hip and knee rotational degrees of freedom, with insufficient attention to ankle dynamics and pelvic translation. To address these limitations, this paper establishes a sagittal-plane dynamic model comprising nine generalised coordinates, treating the human lower limb and exoskeleton as an integrated coupled system. A seven-segment kinematic model encompassing the trunk, bilateral thighs, shanks, and feet is constructed via a modified Denavit–Hartenberg parameter method, and dynamic equations are derived using Lagrangian formulation. Three control strategies—PD control, PD with gravity compensation, and the computed torque method—are designed and evaluated through simulations using gait data from five subjects (two self-collected, three from a public dataset) acquired via Vicon motion capture. Results demonstrate that the computed torque method achieves a joint angle tracking root mean square error (RMSE) of 0.59°, representing an 86.3% improvement over conventional PD control, while maintaining a low control torque RMS of 4.44 N·m. The controller exhibits stable tracking performance across walking speeds of 0.4–1.45 m/s, validating the effectiveness of the proposed model and control strategies. Full article

Background/Objectives: Abnormal muscle tone and impaired motor control commonly limit gait recovery after stroke. Robot-assisted gait training has been introduced to augment conventional rehabilitation; however, its effects on stage-based motor recovery, functional ambulation, and muscle tone during the subacute phase remain unclear. Methods: This prospective, single-center, randomized controlled trial enrolled 30 patients with subacute stroke who received robot-assisted gait training plus conventional rehabilitation (R-BoT Plus group, n = 15) or conventional rehabilitation alone (control group, n = 15) over 4 weeks. The primary outcome was the change in Brunnstrom recovery stage of the lower extremities (BRS-LE). Secondary outcomes included Functional Ambulation Category (FAC), Fugl–Meyer Assessment for the Lower Extremity (FMA-LE), clinical spasticity measures (Modified Ashworth Scale and Modified Tardieu Scale), and muscle mechanical properties (MyotonPRO). Exploratory analyses were conducted to examine the associations between changes in stage-based motor recovery (ΔBRS-LE), functional ambulation (ΔFAC), and MyotonPRO parameters. Within-group changes were assessed using the Wilcoxon signed-rank test. Between-group effects were primarily evaluated using baseline-adjusted ANCOVA with HC3 robust standard errors, with Wilcoxon rank-sum tests on change scores as sensitivity analyses. Associations between changes in clinical outcomes and MyotonPRO parameters were evaluated using Spearman’s rank correlation coefficient (ρ). Results: BRS-LE (p = 0.014) and functional ambulation (p = 0.041) were significantly improved in the R-BoT Plus group. Changes in FMA-LE and clinical spasticity measures did not differ significantly between groups. Quantitative myotonometry revealed selective muscle- and parameter-specific changes. No robust correlations were observed between MyotonPRO parameters and changes in BRS-LE. Conclusions: The addition of robot-assisted gait training to conventional rehabilitation was associated with greater improvements in stage-based lower-limb motor recovery and functional ambulation in patients with subacute stroke. In contrast, cumulative impairment scores and conventional clinical spasticity measures demonstrated limited changes between groups. Quantitative muscle mechanical assessment revealed selective muscle-specific adaptations, supporting its role as a complementary tool for mechanistic characterization rather than as a surrogate marker of motor recovery. Future studies incorporating dose-matched designs and longer follow-up periods are warranted to clarify the independent and long-term effects of robot-assisted gait training. Full article

Surface electromyography (sEMG) provides a non-invasive measure of the neural drive transmitted from the central nervous system to muscles by capturing the spatiotemporal summation of motor unit action potentials at the skin surface, and is therefore widely used to study neuromuscular coordination during motor tasks. By reflecting neural drive transmitted from the central nervous system to peripheral muscles, sEMG provides valuable insights for investigating neuromuscular coordination during upper-limb motor tasks. Within the framework of modular motor control, muscle synergy analysis has been increasingly applied to characterize coordinated muscle activation patterns extracted from multi-channel sEMG recordings. In this study, sEMG signals were collected from twelve stroke patients and nine healthy subjects during robot-assisted upper-limb training, involving two movement trajectories (straight and rectangular) and multiple robot-assisted levels. Muscle synergies were extracted using non-negative matrix factorization (NMF). A synergy merging–splitting model, combined with a Functional Driving Ratio (FDR), was employed to characterize both the muscle synergy reorganization and the relative activation contributions of driving versus stabilizing muscle components in terms of motor control strategy. The results showed that healthy subjects maintained consistent muscle coordination patterns across different assistive levels, while making task-dependent adjustments to muscle activation to adapt to variations in movement trajectories. For stroke patients, higher functional status was correlated with more differentiated coordination patterns and relatively higher FDR values, suggesting greater reliance on task-relevant agonist muscles during movement execution. In contrast, lower-function patients exhibited less differentiated coordination patterns accompanied by reduced FDR values, indicating the increased involvement of stabilizing or antagonist muscles. This shift may reflect compensatory control strategies and the reduced efficiency of neuromuscular coordination during assisted upper-limb movements. These findings suggest that sEMG-based muscle synergy features and the FDR may provide quantitative, sensor-derived support for characterizing neuromuscular coordination during robot-assisted rehabilitation. Full article

Rehabilitation is a critical component in the recovery of patients with either complete or partial loss of motor movements. Repeated and slow limb movements are usually advised by practitioners. Advanced robotic systems can help to configure monotonous movements and accelerate the recovery process as an alternative to therapist-assisted motions, especially during the later phase of recovery. In this work, robotic-assisted human limb movements are engineered and augmented with a novel electromyography (EMG) signal to characterize the movements. The proposed lower- and upper-limb assistive system is designed on a wheelchair platform and is IoT-enabled. The proposed assistive system is designed for patients affected with hemiplegia, paraplegia and tetraplegia. Existing state-of-the-art (SOTA) systems are typically focused on either the upper or lower limbs, with limited degrees of freedom (DoF). The IoT framework for remote access enables the possibility of home-based rehabilitation. A prototype was successfully developed and experiments to characterize various muscle movements using the proposed system were performed. Full article

Background and Objectives: Learning curves robotic arm-assisted total knee arthroplasty (TKA) are well-documented for semi-active systems, but evidence for advanced fully active robotic systems remains scarce. This study aimed to characterize the learning curve for operative time, implant positioning, and lower-limb alignment using a fully active robotic TKA system, specifically accounting for the impact of rotating resident involvement in a tertiary center. Materials and Methods: Sixty consecutive primary TKAs were performed using the advanced active robotic system (CUVIS-Joint^®). The learning curve for operative time was evaluated using cumulative summation (CUSUM) analysis. To identify independent predictors of surgical duration and radiographic precision, a multivariate linear regression model was constructed, including case number, implant type, and resident rotation period as variables. Results: CUSUM analysis identified a statistically significant inflection point at the 39th case. Beyond this point, mean operative time decreased approximately 20 min (133.3 ± 13.5 vs. 113.8 ± 7.9 min, p < 0.001). Multivariate regression confirmed that case number was the sole independent predictor of operative time (p < 0.001). Notably, implant positioning and lower-limb alignment showed no detectable difference across the sequential cases (p > 0.05), maintaining high precision from the outset. Conclusions: Active robotic TKA demonstrated a learning curve for operative time that stabilized after 39 cases within a clinical setting of rotational resident participation. Radiographic accuracy remained consistent despite these educational requirements, supporting the technical feasibility and reliability of this advanced system for the management of end-stage knee osteoarthritis Full article

For safe and efficient human–robot interaction, lower-limb exoskeletons used for assistance and rehabilitation need to be precisely and energy-efficiently controlled. By creating an adaptive neuro-fuzzy fractional-order PID (ANFIS-FOPID) controller, this project seeks to improve tracking accuracy, robustness, and energy efficiency in a two-degree-of-freedom hip–knee exoskeleton. The Euler–Lagrange formulation is used to derive a nonlinear dynamic model, and a Lyapunov-based stability analysis is used to show that the closed-loop system remains uniformly ultimately bounded under disturbances and parameter uncertainties. The suggested controller performs noticeably better than traditional PID and fixed-parameter FOPID controllers, according to numerical simulations conducted under both normal and perturbed conditions. The ANFIS FOPID achieves root mean square errors below 0.028 rad and lowers the integral absolute errors at the hip and knee joints to 0.1454 and 0.1480, as opposed to 0.3496–0.3712 for PID controllers. Under ±10% parameter uncertainty, the total control-energy proxy drops from 2870.0 (PID) to 936.25, a 67.4% decrease, and stays at 1587.93. Statistically significant variations in energy consumption are confirmed by one-way ANOVA (p < 10⁻¹⁷⁶). Large effect sizes are found (η² = 0.237–0.314). These results demonstrate the superior tracking performance, robustness, and energy efficiency of the ANFIS-FOPID controller. The results set a quantitative standard for future experimental validation and hardware-in-the-loop implementation, despite being based on high-fidelity simulations. Full article

Background: Cerebral palsy (CP) is the most common cause of disability in developmental age, affecting motor and postural skills. With growth, lower-limb orthopedic surgery often becomes necessary. Post-surgical walking rehabilitation programs generally involve conventional therapy with only limited evidence on the use of robot-assisted gait training (RAGT). The aim of the present pilot study is to assess the feasibility and the preliminary functional outcomes of an intensive 3-week rehabilitation of 15 sessions with Lokomat combined with 15 sessions of conventional physiotherapy. Methods: In total, 27 patients with diplegic cerebral palsy who underwent orthopedic surgery were recruited. Outcomes collected: the 6 min walking test (primary outcome), the Gross Motor Function Measure-88, the Gillette Functional Assessment Questionnaire, 3D gait analysis, and spasticity and force metrics of the lower limbs. Paired statistical tests were used to assess pre–post changes. Results: A pre–post statistically significant improvement was observed in gait endurance in the 6MWT (Δ = 28.56 ± 34.28 m; p < 0.001) and in gross motor functional skills. Gait parameters showed some functional and structural improvements, and joint stiffness was reduced in some measures. Conclusions: This combined rehabilitative approach seems to be promising in postoperative patients with CP. Future studies, involving a control group and larger sample size, are needed to generalize our results. Full article