Search Results (1,204)

In this paper, we propose an assist-as-needed (AAN) backstepping control scheme for a lower-limb exoskeleton with nonlinear dynamics and uncertain human–robot interactions. The main objective is to achieve a good trajectory tracking capability while adaptively controlling the assistance of the robot according to the user’s effort. The adopted dynamic model is nonlinear, which includes joint dynamics and external human interaction torque. This allows for the derivation of the tracking error formulation. The backstepping control law, formulated based on the filtered tracking error, ensures stable closed-loop performance with bounded tracking errors. We incorporate an AAN scaling framework based on estimated human effort to regulate the overall control torque as a convex combination of the nominal backstepping torque and the impedance-based assistance torque. The proposed controller was tested by numerical simulations and was compared with the sliding mode control (SMC) and the proportional–integral–derivative (PID) control. The overall root-mean-square tracking error for the proposed controller was 0.0962 rad, while for the SMC controller and PID controller, it was 0.0819 rad and 0.1246 rad, respectively. Moreover, the proposed controller reduced the peak human–robot interaction torque to 14.68 N·m compared to 15.36 N·m for SMC and 15.81 N·m for PID, adaptively controlling assistance based on the applied effort of the user. The assistance ratio went down from an average of 0.7988 in the low-effort condition to 0.6960 in the higher-effort condition, indicating effective adaptation while maintaining stable tracking performance. Although the PID controller achieved the lowest torque-variation index, the proposed controller achieved a more favorable trade-off among tracking accuracy, adaptive assistance, and acceptable torque smoothness. Finally, the proposed AAN backstepping controller achieved a practical trade-off between tracking accuracy, adaptive assistance, torque smoothness, and interaction safety, suggesting its potential in rehabilitation and assistive exoskeleton applications. Full article

Objective: This study aimed to determine whether robot-assisted therapy, low-frequency rTMS, or intensive therapist-guided upper-limb training provides additional recovery beyond self-directed training in individuals 4–9 months post-stroke. Design: A five-phase single-case experimental study was conducted with two baseline phases and three randomized 3-week intervention phases. Subjects/Patients: Sixteen adults with moderate-to-severe upper-limb motor impairment in the late subacute phase after stroke (4–9 months post-stroke) were included. Methods: Participants completed intensive task-oriented training, low-frequency rTMS, and robot-assisted therapy in a randomized order. Primary outcomes were Fugl–Meyer Assessment for the Upper Extremity (FMA-UE), active range of motion, and muscle strength. Secondary outcomes included EQ-5D and WHODAS 2.0. Results: FMA-UE motor scores improved significantly over the study period (8–10 points). However, comparable gains occurred during baseline phases. rTMS and intensive training produced within-phase improvements, whereas robotic therapy did not. Participants with higher initial FMA-UE scores improved, while those with severe paresis showed minimal benefit and occasional decline during rTMS. Disability and quality-of-life measures remained stable. Gains were maintained at 1-year follow-up. Conclusions: In the late subacute phase after stroke, modest upper-limb motor improvements occurred, but effects were not clearly attributable to specific interventions beyond ongoing recovery. Treatment response depended strongly on baseline motor severity, with limited benefit in severe paresis. Full article

Robotic systems for pediatric rehabilitation must provide precise mechanical assistance while ensuring clinically appropriate risk control for vulnerable populations. In low- and middle-income countries (LMICs), academic medical robotics projects frequently fail to progress beyond intermediate Technology Readiness Levels (TRLs 3–5) due to limited translational planning. This study proposes and evaluates an integrated governance framework for academic pediatric rehabilitation robotics in LMIC settings, applied through the development of the AMCOR robotic orthosis for pediatric arthrogryposis multiplex congenita (AMC). The framework combines multiple national and international medical devices development standards and a dual regulatory pathway separating academic development from future translational stages. The framework is structured around four principles—auditability, TRL-proportional documentation, binding decision criteria, and regulatory separation—and is operationalized through a six-gate process. Across the first two gates (G0–G1), 38 traced requirements and 12 failure modes were documented. Five internal audits confirmed operational implementation of the quality management structure. The framework application also shaped core engineering decisions. The low amplitude and poor signal-to-noise ratio of sEMG signals observed in pediatric AMC patients rendered the original single-layer control strategy inadequate, prompting a framework-governed redesign toward a three-layer adaptive architecture based on signal quality thresholds and fallback safety logic. These findings demonstrate that a prospective, multi-standard governance model can improve early-stage academic medical robotics in resource-constrained settings. Generalizability beyond the single-center, two-gate application reported here requires further validation; however, the framework provides a replicable foundation for adoption in comparable LMIC contexts. Full article

Background/Objectives: Spinal cord injury (SCI) causes heterogeneous motor, sensory, autonomic, and participation limitations; recovery priorities vary by injury level, completeness, time since injury and residual function. Robotic rehabilitation has expanded from assistive technology to restorative, compensatory and health-promoting interventions, but patient-tailored prescription frameworks remain underdeveloped. Methods: PubMed/MEDLINE was searched from database inception to May 2026 using predefined domain-specific strategies, and findings were synthesized narratively to integrate mechanistic, clinical, safety and implementation evidence. Results: Robotic systems can increase task-specific repetition, sensorimotor feedback, active engagement and quantitative monitoring. Upper-limb robotics are feasible in cervical SCI and may support reach, grasp and activities of daily living, although SCI-specific controlled evidence remains limited. Lower-limb exoskeletons and locomotor robots can support gait practice, upright mobility, exercise exposure and selected secondary health outcomes, but walking speed, energy expenditure, cost, supervision needs and community translation remain important barriers. Sensory and non-motor effects, including proprioceptive input, spasticity, pain, bowel routine, cardiometabolic conditioning, participation and psychological well-being, are clinically relevant but should be interpreted according to evidence strength. Robotics combined with functional electrical stimulation, virtual reality, brain–computer interfaces, non-invasive brain stimulation and artificial intelligence-driven adaptation is promising but not yet routine. Conclusions: Robotic rehabilitation in SCI should be prescribed through a severity-based process that considers lesion level, American Spinal Injury Association Impairment Scale grade, residual voluntary and sensory function, safety, patient priorities and measurable goals. The proposed framework supports transparent selection and prospective validation of individualized robotic rehabilitation and shifts decisions beyond device availability toward clinically meaningful and equitable implementation. Full article

Upper-limb impairments affect a substantial portion of the global population, often limiting the ability to perform daily activities. Robotic rehabilitation systems offer a promising solution by enabling high-dose, task-oriented therapy with consistent and objective feedback. However, user acceptance and perceived comfort are critical for their successful adoption. This work presents a feasibility, performance, and comfort evaluation of a 2-degree-of-freedom upper-limb rehabilitation exoskeleton capable of performing elbow flexion/extension and forearm pronation/supination. A total of 47 healthy participants were enrolled and tested across three rehabilitation modalities: passive assist, active assist, and active resist. Passive assist enabled full range-of-motion execution, active assist supported movement, and active resist provided variable resistance via a sliding bar (0–100%). Objective performance metrics, including position, current, and temperature, were recorded and analyzed, revealing trajectory-tracking errors during passive assistance of 4.82° ± 0.02° for forearm movement and 1.20° ± 0.04° for elbow movement, with actuator temperatures remaining below their rated limits throughout the study. The active assist mode did not achieve a true assist-as-needed performance, indicating a need for further refinement. Subjective evaluation included the System Usability Scale, yielding a score of 87.1 ± 9.6, indicating excellent usability, and a safety and comfort assessment averaging 4.4 ± 0.4 out of 5. Perceived effort was assessed using the Borg CR-10 scale and generally scaled appropriately across modalities, although some variability suggests the need for further investigation. Qualitative feedback identified areas for improvement, particularly in ergonomics and control behavior. Overall, the results support the feasibility, usability, and safe operation of the proposed exoskeleton and provide insights for future device refinement and evaluation with target user populations. Full article

The stabilization of public infrastructure on active volcanic slopes presents significant geotechnical challenges, particularly in coastal regions exposed to extreme hydrological stressors. This paper presents a forensic diagnosis and the structural rehabilitation of the Porto da Cruz Cemetery (Madeira Island, Portugal), which suffered severe progressive failure following localized, shallow-founded interventions in 2004. Historical inclinometer data (2015–2022) revealed continuous deep-seated creep within the volcanic colluvium (Geotechnical Zone 2–ZG2) at rates up to 0.17 mm/day, triggered by basal fluvial undercutting. To mitigate these kinematic drivers, a systemic “Toe-to-Crest” stabilization paradigm was implemented. Following the hydraulic confinement of the slope’s lower boundary, a high-capacity deep foundation network—comprising 26 m rock-socketed micropiles and 600 kN active multi-strand anchors—was executed to bypass the failure plane and encastre directly into the competent basaltic bedrock (Geotechnical Zone 1–ZG1). The structural performance was validated through rigorous load testing and a real-time robotic Structural Health Monitoring (SHM) system. Post-construction telemetry confirmed absolute kinematic stabilization, maintained continuously throughout the critical execution phases and subsequent monitoring period (2024–2025). By integrating deep bedrock anchoring, pore-pressure mitigation, and digital telemetry, this case study validates the economic and geomechanical superiority of systemic subsurface bypass over reactive surface maintenance. Ultimately, it establishes a scalable, climate-adaptive engineering blueprint for safeguarding critical coastal heritage across Macaronesia against escalating environmental multi-hazards. Full article

Indoor companion, rehabilitation, logistics, laboratory transport, and service robot scenarios require mobile platforms that can follow a human operator safely and flexibly under lighting changes, occlusion, texture-poor corridors, and dynamic pedestrian environments. Vision-, LiDAR-, and UWB-based following systems can provide high perception capability, but their deployment cost, environmental dependence, and sensing complexity remain limiting factors for low-perception-dependence applications. This paper presents a passive following system for a Mecanum-wheel mobile platform based on gimbal posture perception and orthogonal odometry fusion. A rope-tensioned two-axis gimbal is mounted above a 300 mm × 300 mm × 150 mm omnidirectional chassis, and a six-axis inertial sensor installed at the top of the gimbal detects pitch and roll changes induced by user traction. A piecewise posture-to-velocity mapping model with a dead zone, saturation, low-pass filtering, and acceleration limiting converts the user’s traction intention into planar velocity commands in the vehicle coordinate frame. To reduce pose errors caused by Mecanum-wheel slip and discontinuous roller-ground contact, two orthogonal passive odometry wheels and inertial attitude estimation are fused to provide planar position feedback for closed-loop following. A prototype was implemented using an Infineon TRAVEO CYT4BB77 controller, TI DRV8701E motor drivers, six-axis IMUs, magnetic encoders, and an embedded display interface. Experiments evaluated attitude estimation accuracy, planar localization accuracy, passive following performance, gyroscope compensation, and open-loop/closed-loop following. The compensated attitude module achieved a static yaw drift of 0.45 deg/h and a dynamic attitude RMSE below 0.56 deg. Orthogonal odometry fusion produced an average positioning error of 3.8 mm over a 3000 mm linear displacement, reducing error by approximately 84.6% compared with pure Mecanum-wheel drive odometry. In a 5000 mm forward traction task, closed-loop following reduced the average distance error from 38.6 mm to 11.5 mm compared with open-loop attitude mapping. The results indicate that the proposed gimbal-orthogonal odometry architecture provides a compact, intuitive, and environment-robust solution for passive following on omnidirectional mobile platforms. Full article

Brain–computer interface (BCI) technology has shown potential for future rehabilitation-related and assistive control applications. Nevertheless, single-modality electroencephalography-based motor imagery (EEG-MI) signals are susceptible to interference, whereas existing algorithmic models suffer from limited classification accuracy and insufficient actionable control commands for interactive devices, thereby impeding their practical deployment. To tackle these limitations, this study presents a multimodal human–computer interaction control scheme that integrates eye-movement command encoding with EEG motor imagery decoding. Self-collected EEG-MI and eye-movement datasets were established to support the proposed multimodal control framework. In this framework, eye movements are not used merely as auxiliary inputs, but are encoded as discrete commands for start, stop, grasp, and release, thereby reducing the command burden of EEG-MI decoding. The EEG-TransNet model is enhanced by integrating a time–frequency feature branch and replacing the original convolutional encoder with an adaptive multi-branch EEG feature gating module, strengthening the representation and fusion of multi-domain features. The model yields average classification accuracies of 86.96% and 88.73% on the BCI IV-2a dataset and the self-collected EEG dataset, respectively. Four independent SVM binary classifiers are adopted to identify four eye movement patterns. The EEG and eye movement classification results are binary-encoded to generate hardware-compatible control commands. Robotic-arm grasping experiments with healthy trained participants showed an average task completion time of 17 s, and the repeated grasping success-rate results further provide preliminary evidence for the real-time feasibility of the multimodal control framework under controlled laboratory conditions. Full article

This paper proposes a safe and effective human–robot physical interaction control framework for exoskeleton robots that enhances system compliance and safety while enabling the robot to adapt to human motion. The framework is designed around two primary objectives: first, a model-free adaptive control method is employed for reference trajectory estimation to achieve real-time estimation of human motion intention; second, the Forgetting Factor Recursive Least Squares (FFRLS) method is utilized for online estimation and the learning of human impedance parameters, considering their time-varying nature. In addition, a model-free adaptive trajectory tracking control strategy is proposed to optimize control performance during human–robot physical interaction. Simulation results demonstrate that the proposed control framework outperforms conventional methods significantly in terms of safety and compliance. Full article

Exoskeletons are widely utilized across various domains, including biomedical and rehabilitative engineering. In clinical applications, precise joint torque evaluation is critical to ensuring exoskeleton efficiency, especially when assisting patients with impaired mobility. This work presents a straightforward inverse-dynamics framework to compute human joint torques using motion capture and force plate data. Estimating these torques is a key requirement for exoskeleton systems to deliver appropriate and individualized assistive support. A key innovation of the proposed model is the explicit integration of a three-link chain—comprising the thigh, shank, and foot—treated as a cohesive multi-segment limb. By formally incorporating the foot segment, the model enables a more rigorous representation of ground reaction forces (GRF) and the dynamic migration of the center of pressure (COP). The proposed framework was validated against OpenSim 4.0 using benchmark datasets involving walking, squatting, and drop-jump maneuvers. The results demonstrated strong agreement with OpenSim, yielding normalized root mean square errors of approximately 10% across major lower-limb joints during walking. In contrast, the squatting posture provided a significant magnitude offset, despite maintaining close temporal phase alignment. Beyond torque estimation, the results provide insight into the sensitive interplay among COP trajectories, foot geometry, and GRF orientation. The proposed framework offers a computationally efficient tool for biomechanical analysis and provides a practical foundation for future lower-limb exoskeleton and assistive robotic applications. Full article

Background: The study aimed to investigate the effects of non-suspended robot-assisted ambulatory training with approximately 80–100% weight-bearing during the stance phase on one leg and 0% weight-bearing support during the swing phase on the other leg on hemiplegic stroke patients who were unable to ambulate at baseline. Traditional robot-assisted gait training commonly provided substantial body-weight support (approximately 0–20% weight-bearing during the stance phase on one leg), whereas the present system enables near-normal weight-bearing during gait training. Methods: Pre- and post-assessments of Brunnstrom stage, standing balance, and the Barthel Index of Activities of Daily Living (ADL) were performed in sixty hemiplegic stroke patients (30 right-sided and 30 left-sided hemiplegia) with stroke onset less than 6 months and without ambulatory ability. A retrospective controlled study was performed using a non-suspended robot-assisted ambulatory training machine (RAATM) that provides approximately 80–100% weight-bearing during the stance phase on one leg for more than 150 min a month (>4500 guided steps), combined with a 4-week conventional rehabilitation program (RAATM group, n = 30). Outcomes were compared with those of an age-, affected side-, and baseline walking-ability-matched control group (control group, n = 30) that received only a 4-week conventional rehabilitation program. Results: The average accumulated intervention duration in the RAATM group was 246 ± 74 min, which received intervention of RAATM with 80–100% weight-bearing during the stance phase on one leg and 0% weight-bearing during the swing phase on the other leg. The pre-to-post changes in the Brunnstrom stage of the lower extremities, static standing balance score, dynamic standing balance score, mobility on level surfaces, stairs, and total Barthel Index score were significantly higher in the RAATM group than in the control group. Conclusions: Functions of lower extremities, standing balance, and mobility ability can be improved after intervention of non-suspended RAATM within a month. Non-suspended robot-assisted ambulatory training appeared to be an effective therapeutic approach for hemiplegic stroke patients pre-assessed without ambulatory ability. Full article

This article discusses advanced artificial intelligence (AI)-based strategies for the design and personalization of three-dimensionally (3D) fabricated hand exoskeletons, with a focus on adaptive, data-driven methodologies. It highlights the crucial role of intelligent personalization in improving user comfort, functional performance, and rehabilitation outcomes, particularly in medical and care settings. The proposed approach integrates biomechanical modeling, high-resolution 3D scanning, and machine learning (ML) algorithms to create exoskeleton systems tailored to the unique anatomical and motor characteristics of individual users. This article presents both a theoretical framework and practical implementation of AI-based adaptation, addressing key challenges such as precise anatomical fit, ergonomic optimization, and real-time responsiveness. Specific emphasis is placed on AI-based feedback mechanisms that enable continuous, dynamic adjustment of control parameters during device operation. Case studies illustrate the effectiveness of these techniques in improving performance and rehabilitation progress for individual users. By combining intelligent modeling, adaptive control, and additive manufacturing, this research advances the field of wearable robotics and points the way to more accessible, efficient, and fully personalized assistive technologies. Full article

Background: Virtual reality (VR) has been increasingly used in motor rehabilitation over the past two decades, but the overall research landscape of this field has not been fully mapped from a bibliometric perspective. Objective: This study aimed to conduct a bibliometric analysis to determine the development of research on VR for motor rehabilitation, focusing on its knowledge structure, major research topics, and temporal changes in the field. Methods: A topic-based search combining VR- and motor rehabilitation-related terms was conducted in the Web of Science Core Collection for the period from 2005 to 2025, yielding 1232 publications. VOSviewer, CiteSpace, R, and Scimago Graphica were used to analyze publication trends, country and institutional contributions, author collaboration, journal and reference co-citation, keyword co-occurrence, citation bursts, and thematic evolution. Results: Publications increased in three stages: slow exploration, steady growth, and rapid expansion. The United States, Italy, China, and Canada were the leading contributors, with McGill University as the most productive institution. Research hotspots included gait and neurological rehabilitation, post-stroke upper-limb recovery, robotics- and neuroscience-integrated rehabilitation, and the rise of immersive VR technology. Conclusions: This study provides a bibliometric overview of research progress in the application of virtual reality technology to motor rehabilitation, offering systematic insights into the field’s knowledge structure, core research themes, evolutionary trajectory, and future research directions. Full article

Background: Drop finger may occur in patients with C7 and/or C8 cervical radiculopathy caused by cervical spondylosis. Although surgical decompression of the affected nerve roots is performed in patients with drop finger refractory to conservative treatment, postoperative recovery of drop finger is often unsatisfactory. Furthermore, no effective rehabilitation strategy for improving drop finger has yet been established. Methods: Here, we report a patient with drop finger who underwent a novel postoperative rehabilitation program. A 64-year-old man presented with drop finger of the left hand caused by left C7 and C8 radiculopathy and underwent cervical foraminotomy. For postoperative rehabilitation, we applied the single-joint Hybrid Assistive Limb (HAL), a wearable robotic suit. The patient underwent a total of 21 sessions of metacarpophalangeal HAL training, which assisted voluntary flexion and extension movements of the metacarpophalangeal joints, and 6 sessions of wrist abduction HAL training, which assisted ulnar-direction wrist abduction movements. Results: As a result, improvement in the left-sided drop finger was achieved. In this case, the use of HAL enabled voluntary motor training within the normal range of motion of the fingers and wrist even during the early postoperative phase, when sufficient neurological recovery had not yet been achieved. Conclusions: This successful motor experience may have facilitated the reacquisition of normal movement patterns, thereby contributing to improvement in drop finger. Full article

The objective of this feasibility study was to demonstrate and evaluate a remotely tongue-controlled wheelchair mounted assistive robotic manipulator (ARM), for the first time with end users: three individuals with cervical spinal cord injury. For three days, they remotely tongue-controlled the wheelchair and the ARM (WMARM) to complete two activities of daily living (ADL): Driving the wheelchair and ARM to a remote setting to (1) pick up a bottle and (2) pick up a ball. The participants controlled the system using full manual control by tongue and through semi-automation. Finally, the participants answered a NASA Task Load Index (TLX) questionnaire and a semi-structured interview. Results: All participants were able to remotely control the WMARM by tongue. Semi-automation resulted in shorter task completion time, gripping time and fewer commands as compared with manual control. Semi-automation decreased the measured mental load in the NASA TLX questionnaire by an average of 57%. The participants rated high satisfaction with the system. Conclusion: It was possible for the users with tetraplegia to control the wheelchair with the ARM using their tongue to perform ADL in Wi-Fi-based remote setting. This proposed system has the potential to increase independence and social interaction of individuals with tetraplegia. Full article