Search Results (250)

Rehabilitation devices such as actuated lower-limb exoskeletons can provide essential mobility assistance for pediatric patients with gait impairments. Enhancing their control systems under conditions of user variability and dynamic disturbances remains a significant challenge, particularly in active-assist modes. This study presents a human-in-the-loop control architecture for a pediatric lower-limb exoskeleton, combining outer-loop admittance control with robust inner-loop trajectory tracking via a non-singular terminal sliding-mode (NSTSM) controller. Designed for active-assist gait rehabilitation in children aged 8–12 years, the exoskeleton dynamically responds to user interaction forces while ensuring finite-time convergence under system uncertainties. To enhance adaptability, we augment the inner-loop control with a twin delayed deep deterministic policy gradient (TD3) reinforcement learning framework. The actor–critic RL agent tunes NSTSM gains in real-time, enabling personalized model-free adaptation to subject-specific gait dynamics and external disturbances. The numerical simulations show improved trajectory tracking, with RMSE reductions of 27.82% (hip) and 5.43% (knee), and IAE improvements of 40.85% and 10.20%, respectively, over the baseline NSTSM controller. The proposed approach also reduced the peak interaction torques across all the joints, suggesting more compliant and comfortable assistance for users. While minor degradation is observed at the ankle joint, the TD3-NSTSM controller demonstrates improved responsiveness and stability, particularly in high-load joints. This research contributes to advancing pediatric gait rehabilitation using RL-enhanced control, offering improved mobility support and adaptive rehabilitation outcomes. Full article

Multiple Sclerosis (MS) is a chronic neurological disorder affecting the central nervous system that significantly impairs postural control and functional abilities. Robotic-assisted gait training mitigates this functional deterioration. This preliminary study aims to investigate the effects of a four-week gait training with the ExoAtlet II exoskeleton on static balance control and functional mobility in five individuals with MS (Expanded Disability Status Scale ≤ 2.5). Before and after the training, they were assessed in quiet standing under Eyes Open (EO) and Eyes Closed (EC) conditions and with the Timed Up and Go (TUG) test. Center of Pressure (CoP) Sway Area, Antero–Posterior (AP) and Medio–Lateral (ML) CoP displacement, Stay Time, and Total Instability Duration were computed. TUG test Total Duration, sit-to-stand, stand-to-sit, and linear walking phase duration were analyzed. To establish target reference values for rehabilitation advancement, the same evaluations were performed on a matched healthy cohort. After the training, an improvement in static balance with EO was observed towards HS values (reduced Sway Area, AP and ML CoP displacement, and Total Instability Duration and increased Stay Time). Enhancements under EC condition were less marked. TUG test performance improved, particularly in the stand-to-sit phase. These preliminary findings suggest functional benefits of exoskeleton gait training for individuals with MS. Full article

Background: Aquatic therapy (AT), immersion in hot water, and supported standing are frequently used to manage spasticity, contractures, and joint retractions in children with cerebral palsy (CP). Recently, the use of exoskeletons has been offering a new treatment option for severe CP. This study aimed to compare the post-immediate effects of four treatments on spasticity, range of motion, and the heart rate of children with severe CP. Methods: Three children with spastic CP (levels IV and V GMFCS) received a single 30-min session in consecutive weeks of robot-assisted gait training (RAGT), AT, supported standing, and immersion in hot water. Post-immediate assessments included knee flexor spasticity (modified Ashworth scale, MAS, and modified Tardieu scale, MTS); knee range of motion (ROM, in degrees (°)); and heart rate (HR). Results: AT and supported standing induced greater reductions in spasticity based on MAS scores. RAGT demonstrated superior spasticity reduction using MTS and yielded the greatest improvement in popliteal angle (mean increase: 27°). AT and RAGT induced a 14 beats-per-minute change in HR, indicating moderate cardiovascular engagement. Conclusions: RAGT appears particularly effective in improving spasticity and ROM in children with severe CP. Nonetheless, conventional treatments still offer an effective option when addressing spasticity. Full article

Cerebral palsy is the leading cause of motor disability in early childhood, with no curative treatment currently available. To mitigate its effects and promote motor rehabilitation, robotic-assisted therapies have emerged as a complement to conventional physiotherapy. In particular, cable-driven exoskeletons offer notable advantages, providing patients with additional mobility and interaction with their environment while preserving motion assistance. Within this context, the Discover2Walk project introduces a modular cable-driven robotic platform designed for early-stage gait rehabilitation. This article presents a novel ankle control module capable of actuating 3 degrees of freedom: 2 translational (in the x and z directions) and 1 rotational (dorsiflexion/plantarflexion). Experimental results confirm the technical feasibility of the approach and its effectiveness in guiding motion within the targeted degrees of freedom. Full article

Gait, the fundamental form of human locomotion, occurs across diverse environments. The technology for recognizing environmental changes during walking is crucial for preventing falls and controlling wearable robots. This study collected gait data on level ground (LG), ramps, and stairs using a feed-forward neural network (FFNN) to classify the corresponding gait environments. Gait experiments were performed on five non-disabled participants using an inertial measurement unit, a galvanic skin response sensor, and a smart insole. The collected data were preprocessed through time synchronization and filtering, then labeled according to the gait environment, yielding 47,033 data samples. Gait data were used to train an FFNN model with a single hidden layer, achieving a high accuracy of 98%, with the highest accuracy observed on LG. This study confirms the effectiveness of classifying gait environments based on signals acquired from various wearable sensors during walking. In the future, these research findings may serve as basic data for exoskeleton robot control and gait analysis. Full article

This review provides a comprehensive analysis of recent advancements in lower limb exoskeleton systems, focusing on applications, control strategies, hardware architecture, sensing modalities, human-robot interaction, evaluation methods, and technical innovations. The study spans systems developed for gait rehabilitation, mobility assistance, terrain adaptation, pediatric use, and industrial support. Applications range from sit-to-stand transitions and post-stroke therapy to balance support and real-world navigation. Control approaches vary from traditional impedance and fuzzy logic models to advanced data-driven frameworks, including reinforcement learning, recurrent neural networks, and digital twin-based optimization. These controllers support personalized and adaptive interaction, enabling real-time intent recognition, torque modulation, and gait phase synchronization across different users and tasks. Hardware platforms include powered multi-degree-of-freedom exoskeletons, passive assistive devices, compliant joint systems, and pediatric-specific configurations. Innovations in actuator design, modular architecture, and lightweight materials support increased usability and energy efficiency. Sensor systems integrate EMG, EEG, IMU, vision, and force feedback, supporting multimodal perception for motion prediction, terrain classification, and user monitoring. Human–robot interaction strategies emphasize safe, intuitive, and cooperative engagement. Controllers are increasingly user-specific, leveraging biosignals and gait metrics to tailor assistance. Evaluation methodologies include simulation, phantom testing, and human–subject trials across clinical and real-world environments, with performance measured through joint tracking accuracy, stability indices, and functional mobility scores. Overall, the review highlights the field’s evolution toward intelligent, adaptable, and user-centered systems, offering promising solutions for rehabilitation, mobility enhancement, and assistive autonomy in diverse populations. Following a detailed review of current developments, strategic recommendations are made to enhance and evolve existing exoskeleton technologies. Full article

Background/Objectives: Robotic-assisted gait training (RAGT) is a promising adjunct to conventional rehabilitation for stroke survivors. However, its additive benefit over standard therapy remains to be fully clarified. This systematic review and meta-analysis evaluated the effectiveness of combining RAGT with conventional rehabilitation in improving gait-related outcomes among individuals with stroke. Methods: We searched PubMed, Embase, CINAHL, and Cochrane CENTRAL through September 2024 for randomized controlled trials (RCTs) comparing combined RAGT and conventional rehabilitation versus conventional rehabilitation alone in adults post-stroke. Data were synthesized using a random-effects model, and subgroup analyses examined effects by intervention duration, stroke chronicity, and robotic system type. Results: Twenty-three RCTs (n = 907) were included. The combined intervention significantly improved gait function (SMD = 0.51, p = 0.001), gait speed (SMD = 0.47, p = 0.010), balance (MD = 4.58, p < 0.001), and ADL performance (SMD = 0.35, p = 0.001). Subgroup analyses revealed that end-effector robotic systems yielded superior outcomes compared to exoskeletons, particularly in subacute stroke patients. The most pronounced benefits were seen in gait velocity and dynamic balance, especially with ≤15 training sessions. Conclusions: Integrating RAGT with conventional rehabilitation enhances motor recovery and functional performance in stroke survivors. End-effector devices appear most effective in subacute phases, supporting individualized RAGT application based on patient and device characteristics. Full article

This study aimed to evaluate how wearable hip exoskeleton assistance affects phase-dependent gait stability in healthy adults using a novel visualization technique known as gait tube analysis. Hip exoskeletons offer significant potential to enhance human locomotion through joint torque augmentation, yet their effects on gait stability across the gait cycle remain underexplored. This study introduces gait tube analysis, a novel method for visualizing center of mass velocity trajectories in three-dimensional state space, to quantify phase-dependent gait stability under hip exoskeleton assistance. We analyzed data from ten healthy adults walking under twelve conditions (ten powered with varying torque magnitude and timing, one passive, and one unassisted), assessing variability via covariance-based ellipsoid volumes. Powered conditions, notably HighLater and HighLatest, significantly increased vertical variability (VT) during early-to-mid stance (10–50% of the gait cycle), with HighLater showing the highest mean ellipsoid volume (99,937 mm³/s³; z = 2.3). Conversely, the passive PowerOff condition exhibited the lowest variability (47,285 mm³/s³; z = –1.7) but higher metabolic cost, highlighting a stability-efficiency trade-off. VT was elevated in 11 of 12 conditions (p ≤ 0.0059), and strong correlations (r ≥ 0.65) between ellipsoid volume and total variability validated the method’s robustness. These findings reveal phase-specific stability challenges and metabolic cost variations induced by exoskeleton assistance, providing a foundation for designing adaptive controllers to balance stability and efficiency in rehabilitation and performance enhancement contexts. Full article

This paper presents a novel design for a bionic knee exoskeleton equipped with a variable stiffness actuator based on rope-driven artificial muscles. To meet the varying stiffness requirements of the knee joint across different gait modes, the actuator dynamically switches between multiple rope bundle configurations, thereby enabling effective stiffness modulation. A mathematical model of the knee exoskeleton is developed, and the mechanical properties of the selected flexible aramid fiber ropes under tensile loading are analyzed through both theoretical and experimental approaches. Furthermore, a control framework for the exoskeleton system is proposed. Wearable experiments are conducted to evaluate the effectiveness of the variable stiffness actuation in improving compliance and comfort across various gait patterns. Electromyography (EMG) results further demonstrate that the exoskeleton provides a compensatory effect on the rectus femoris muscle. Full article

Background/Objective: Stroke is one of the leading causes of death and disability worldwide, with older survivors (aged > 65 years) bearing significant health and economic impacts, particularly in industrialized countries. While gait rehabilitation is a cornerstone in post-stroke recovery and robotic technologies offer promising tools to enhance its effectiveness, the existing literature has largely overlooked elderly populations. Most studies on robot-assisted gait training (RAGT)—which uses exoskeleton or end-effector devices to support and guide movement—either exclude older adults or do not analyze their outcomes separately. This review aims to critically evaluate the current evidence on RAGT in elderly post-stroke patients, addressing a significant gap in the literature and providing novel insights into the effectiveness and adaptability of RAGT for this specific population. Methods: The search included databases such as PubMed, Scopus, Embase, Web of Science, and ClinicalTrials. The inclusion criteria covered studies published up to March 2025, focusing on post-stroke individuals aged >65 years, who underwent RAGT. Results: 25 studies were included in the review, 21 involving exoskeleton and 4 end-effector devices. The primary focus was on motor outcomes, such as gait independence, gait parameters, and balance control. Only a few studies examined non-motor aspects, including cognitive and emotional functions, fatigue, pain, and neuroplasticity. Moreover, data on the long-term effects on the elderly population remain scarce. Conclusions: RAGT is an effective strategy for promoting motor recovery and improving functional outcomes, from independence in daily activities to quality of life, in the post-stroke elderly population. Early and high-intensity interventions are particularly useful with positive effects on neuronal plasticity, cognitive function, and well-being. Full article

The aim of this study is to examine the biomechanical interaction between an assistive wearable exoskeleton and the human body. For this purpose, a passive exoskeleton is designed to provide support during the transition from a squatting position to standing, while also enabling the resilient components to become active during the initial and mid-swing phases of level walking. The active period can be adjusted by a slot, which triggers the activation of the resilient components when the exoskeleton’s flexion angle exceeds a critical value. This study also compares the effect of using different passive powered components in the exoskeleton. Electromyography (EMG) signals and angular velocity during human motion are collected and analyzed. Experimental results indicate that the designed assistive exoskeleton effectively reduces muscle effort during squatting/standing motion, as intended. The exoskeleton reduces the flexion/extension (x-axis) angular velocity during both squatting/standing and the swing phase of gait. The oscillation of the angular velocity curve about the y-axis during gait is larger without the exoskeleton, suggesting that the exoskeleton may introduce interference but also a stabilizing effect in certain dimensions during gait. This study provides a stronger foundation for advancing the design of both passive and active powered exoskeletons. Full article

This study addresses gait reference trajectory tracking control in a 13-degree-of-freedom lower-limb rehabilitation robot, where patients exhibit nonlinear perturbations in lower-limb muscle groups and gait irregularities during exoskeleton-assisted walking. We propose a novel control strategy integrating iterative learning with RBF neural network-based sliding mode control, featuring a single hidden-layer pre-feedback architecture. The RBF neural network effectively approximates uncertainties arising from lower-limb muscle perturbations, while adaptive regulation through parameter simplification ensures precise torque tracking at each joint, meeting real-time rehabilitation requirements. MATLAB 2021 simulations demonstrate the proposed algorithm’s superior trajectory tracking performance compared to conventional sliding mode control, effectively eliminating control chattering. Experimental results show maximum angular errors of 1.77° (hip flexion/extension), 1.87° (knee flexion/extension), and 0.72° (ankle dorsiflexion/plantarflexion). The integrated motion capture system enables the development of patient-specific skeletal muscle models and optimized gait trajectories, ensuring both training efficacy and safety for spasticity patients. Full article

(1) Background: Peripheral artery disease (PAD) and related conditions significantly impair walking ability. Previous studies demonstrated that passive lightweight exosuits can improve walking biomechanics. However, most of these devices focus on assisting hip flexion. The aim of this study was to investigate the effects of flexion and extension assistance on joint kinetics and muscle activation. We hypothesized that there would be an optimal combination of flexion and extension assistance for measured parameters. (2) Methods: Four patients with PAD and six healthy individuals walked on a treadmill while wearing a passive exosuit with adjustable hip flexion and extension assistance. Lower limbs’ power, moment, and muscle activation were recorded. (3) Results: We found that passive hip assistance effectively reduced hip kinetics in both healthy and PAD participants. We also found different effects between the groups, with the PAD group utilizing the exosuit to reduce plantarflexion kinetics and gastrocnemius activity. (4) Conclusions: These findings suggest that patients with PAD can leverage the exosuit to ameliorate impairment-specific deficits. Future research should explore more real-world applicability of passive exosuits. Full article

To address lower limb fatigue in workers engaged in prolonged standing, this study proposes a structural design for a medial-support passive exoskeleton seat. The design incorporates support rods positioned along the medial aspect of the user’s lower limbs and features an adaptive telescopic rod system, enhancing sitting stability and reducing collision risks in workplace environments. Human motion capture technology was used to collect kinematic data of the lower limbs, and a mathematical model of center-of-gravity variation was developed to calculate and optimize the exoskeleton’s structural parameters. Static analysis was performed using ANSYS software (2025 R1) to evaluate the structural integrity of the design. The effectiveness of the exoskeleton seat was validated through surface electromyography (sEMG) experiments, with results showing that the exoskeleton significantly reduces lower limb muscle load by 49.2% to 72.9%. Additionally, force plate experiments demonstrated that the exoskeleton seat improves stability, with a 39.2% reduction in the average displacement of the center of pressure (CoP), confirming its superior postural alignment and balance. The design was also compared with existing exoskeleton chairs, showing comparable or better performance in terms of muscle load reduction, stability, and overall effectiveness. Full article

To overcome the problem of insufficient adaptability to the motion environment of lower limb exoskeleton robots, this paper introduces computer vision technology into the motion control of lower limb exoskeleton robots and studies an obstacle-crossing-motion method based on detecting obstacle feature information. Considering the feature information of different obstacles and the distance between obstacles and robots, a trajectory planning method based on direct point matching was used to generate offline adjusted gait trajectory libraries and obstacle-crossing gait trajectory libraries. A lower limb exoskeleton robot obstacle-crossing motion decision-making algorithm based on obstacle feature information is proposed by combining gait constraints and motion constraints, enabling it to select appropriate motion trajectories in the trajectory library. The proposed obstacle-crossing-motion method was validated at three distances between the obstacle and the robot and with the feature information of four obstacles. The experimental results show that the proposed method can select appropriate trajectories from the trajectory library based on the detected obstacle feature information and can safely complete obstacle-crossing motions. Full article