Search Results (560)

Gait impairment, postural instability, and freezing of gait are major contributors to mobility limitations in Parkinson’s disease (PD). Robot-assisted gait training (RAGT) has emerged as a promising rehabilitation strategy; however, its association with phase-specific lower-limb kinetic outcomes remains insufficiently characterized. This study compared Walkbot-based RAGT and conventional rehabilitation in terms of Walkbot-derived phase-specific lower-limb torque, functional balance, self-reported freezing-related gait impairment, and health-related quality of life in individuals with PD. This retrospective comparative study included 60 individuals with idiopathic PD (Hoehn and Yahr stages II–III) who received either Walkbot-based RAGT (n = 30) or conventional rehabilitation (n = 30). Both groups completed 30 supervised sessions over 6 weeks. Biomechanical outcomes comprised Walkbot-derived lower-limb torque during the stance and swing phases for the clinically more affected and contralateral limbs. Clinical outcomes included the Berg Balance Scale (BBS), Freezing of Gait Questionnaire (FOG-Q), and Parkinson’s Disease Questionnaire-39 (PDQ-39). Between-group post-treatment differences were evaluated using analysis of covariance (ANCOVA), adjusting each outcome for its corresponding baseline value. After baseline adjustment, post-treatment phase-specific lower-limb torque was higher in the RAGT group for the more affected limb during swing (adjusted difference, 0.057 Nm/kg; 95% CI, 0.019–0.096; p = 0.004) and stance (0.145 Nm/kg; 95% CI, 0.098–0.193; p < 0.001), and for the contralateral limb during swing (0.051 Nm/kg; 95% CI, 0.006–0.096; p = 0.027) and stance (0.205 Nm/kg; 95% CI, 0.088–0.321; p = 0.001). The RAGT group also had higher baseline-adjusted BBS scores (adjusted difference, 5.78 points; 95% CI, 3.86–7.69; p < 0.001) and lower FOG-Q scores (−0.94 points; 95% CI, −1.27 to −0.61; p < 0.001). No significant between-group difference was observed for PDQ-39 (0.77 points; 95% CI, −0.16 to 1.69; p = 0.102). Walkbot-based RAGT was associated with more favorable baseline-adjusted phase-specific lower-limb torque, functional balance, and self-reported freezing-related gait impairment than conventional rehabilitation, whereas disease-specific quality of life did not differ significantly between groups. These findings highlight the potential value of integrating Walkbot-derived phase-specific kinetic assessment with clinical outcomes to provide a more quantitative characterization of rehabilitation-related locomotor changes in PD. Prospective randomized studies are needed to confirm these associations and establish the clinical utility of Walkbot-derived phase-specific lower-limb torque as an outcome measure. Full article

Wrist rehabilitation requires high precision, haptic transparency, and accurate alignment with the human joint’s physiological center of rotation. Conventional robotic systems often suffer from high moving inertia or joint misalignment. This paper presents the design and kinematic validation of a novel 3-DOF spherical parallel exoskeleton featuring base-fixed actuators. By mounting all actuators to a fixed base, the proposed architecture significantly reduces moving mass, achieving a low-inertia response critical for safe patient–robot interaction. The “virtual center” concept eliminates physical central joints, enabling a compact design completed by the user’s anatomy. To perform the kinematic and singularity analyses of the manipulator, two distinct models were used: Rotated Frame Based (RFB) and Initial Frame Based (IFB). Performance metrics, namely the manipulability index and condition number, are evaluated to assess dexterity and isotropy. Comparative kinematic analysis of Rotated (RFB) and Initial Frame Based (IFB) models confirm ideal central isotropy ($\kappa =1.0$). While RFB yields 94.14% high-dexterity ($\kappa <5.0$) and 99.78% usable ($\kappa <10.0$) workspace coverage, IFB achieves 78.80% high-dexterity ($\kappa <5.0$) and 92.18% usable ($\kappa <10.0$) workspace coverage. Analytical manipulability metrics validate singularity-free motion throughout the anatomical range. Additionally, the use of exponential rotational matrices in this paper provides systematic derivations of equations in compact form. Full article

While soft hand exoskeleton robots have approached human-level dexterity in terms of degrees of freedom, precise control methods for fine motor movements remain a significant challenge. Surface electromyography (sEMG) is widely employed in gesture recognition to enable patients to independently control a soft hand exoskeleton. However, individual finger control remains challenging through sEMG-based control due to the complexity of decoupling synergistic muscle activities. In this study, we propose a muscle fiber-based ultrasound perception strategy for fine hand motion recognition and soft hand exoskeleton control. Ultrasound imaging enables non-invasive visualization of forearm muscle morphology and provides information associated with underlying muscle-fiber activity. By reconstructing muscle morphology from ultrasound images, biologically relevant muscle-fiber features are extracted and fused to characterize fine hand movements. A lightweight Random Forest classifier is subsequently employed to map these biologically informed features to discrete hand actions, providing a computationally efficient recognition module for real-time control. To the best of our knowledge, publicly available ultrasound image datasets specifically designed for fine hand gesture recognition in rehabilitation applications remain limited. In the experiments, a dataset containing 21 hand gestures based on muscle ultrasound images was constructed to evaluate the proposed method. All data were collected from healthy participants as a preliminary proof-of-concept investigation. The results show that the proposed approach achieves an average recognition accuracy of 95.24% across three subjects in finger motion recognition. This preliminary study demonstrates the potential of machine learning-based ultrasound perception for improving fine hand gesture recognition and providing an intuitive control interface for soft hand exoskeletons, thereby enhancing their applicability in hand rehabilitation scenarios. Full article

Soft and compliant rehabilitation devices may provide improved anatomical adaptability, reduced distal mass, and more flexible human–robot interaction compared with conventional rigid exoskeletons. This scoping review examines elbow rehabilitation technologies across three interconnected domains: (1) the broader landscape of soft and compliant actuation, (2) the structural, material, and operating architectures of pneumatic soft actuators, and (3) the sensing, intention-detection, and closed-loop control methods used in pneumatic systems. The review included 109 peer-reviewed reports. Four main actuation families were identified: pneumatic soft actuators, motor-driven cable and tendon systems, series-elastic or variable-stiffness actuators, and shape-memory-alloy-based devices. Pneumatic architectures were primarily organized around linear artificial muscles and chamber-based bending or rotary actuators, complemented by rigid–soft integrated, cable-transmitted, modular, antagonistic, self-sensing, and variable-stiffness configurations. Feedback most relied on pressure, kinematic, force, and electromyographic signals. Control architectures combined position, force, torque, impedance, and pressure regulation, while nonlinearities and uncertainties were addressed using model-based, adaptive, sliding-mode, fuzzy, neural, and hybrid methods. Full article

Wearable robotics has expanded beyond conventional exoskeletons and prosthetic devices toward compact systems that attach to or move along the human body. Within this field, on-body mobile robots have emerged as a promising platform for healthcare monitoring, haptic interaction, assistance with daily activities, rehabilitation support, and dynamic wearable interfaces. However, on-body locomotion remains challenging because local curvature changes continuously, limb diameters vary, contact is compliant, and the system must preserve user comfort while ensuring safe physical human–robot interaction. A recent study introduced an open on-limb locomotion mechanism based on spherical rollers and passive diameter adaptation. That work also revealed the need to integrate sensing, control electronics, and actuation more tightly within the wearable platform. These limitations motivate the present study, which advances a validated locomotion concept into an embedded wearable mechatronic system evaluated under more anthropomorphic conditions. To overcome these limitations, this paper presents an integrated, second-generation open on-limb robot that incorporates active steering for real-time locomotion and contact alignment. Moving beyond previous passive compliance and rigid 2D bilateral symmetry constraints, we introduce a generalized differential kinematic framework based on a complete Jacobian of the roller centers and coordinate-independent circumradius estimation. This mathematical foundation enables the system to actively leverage the geometric alignment variable as feedback for closed-loop steering corrections. Experimental results on variable-diameter surfaces demonstrate the platform’s ability to maintain longitudinal locomotion, handle non-symmetric link deflections, and perform online diameter estimation. To support reproducibility, all 3D-printable components are made openly available. 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 review examined cumulative findings from recent meta-analyses to identify current challenges and possible suggestions for improving the effects of BCI systems on stroke motor recovery. Consistent with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, a systematic literature search was conducted using PubMed, Web of Science, and the Cochrane Library on 30 June 2026. A total of 17 systematic reviews and meta-analyses were included. Among three motor intent-induced (i.e., motor attempt, motor observation, and motor imagery) modalities, motor attempt was the modality most consistently associated with significant therapeutic effects across meta-analyses. Electrical stimulation was a consistently effective feedback modality, whereas robot-assisted and visual feedback showed heterogeneous effects. Higher weekly session frequencies and moderate session durations (approximately 20–60 min) showed consistent motor recovery. Stroke type, age, intervention period, total sessions, total training time, and long-term effect durability were inconsistent across the included evidence. These findings suggest that applying BCI-based training is an effective rehabilitation program for the functional recovery of upper extremities in patients with stroke who have moderate to severe motor impairments, potentially achieving greater therapeutic efficacy when combining motor attempts with electrical stimulation. Full article

Robotics serves as a key enabler in disability support by offering new pathways to augment social work practice and enhance autonomy, safety, and inclusion for persons with disabilities (PWDs). In this study, an interdisciplinary team executed a socio-technical investigation that integrated robotics engineering, artificial intelligence, rehabilitation sciences, and social work. It was examined how assistive and socially interactive robots, including mobility robots, cognitive-assistive systems, exoskeletons, telepresence units, and socially assistive humanoids, must be embedded within disability services to improve functional independence, strengthen care continuity, and address increasing workforce demands. A comprehensive research design was adopted by combining a systematic literature review and technical benchmarking of robot capabilities with qualitative inputs gathered from co-codesign workshops involving PWDs, caregivers, and social workers. To test these applications, the team evaluated three pilot domains: home-based independent living support, community-based rehabilitation, and social-work-led remote engagement utilizing telepresence robotics. The results demonstrate that these robotic interventions improved independent task completion by 22–41% and reduced caregiver burden by 18–34%. Furthermore, the data revealed significant gains in communication and emotional engagement for individuals with cognitive or speech impairments. While robotics cannot replace social workers, these technologies meaningfully complement care delivery when practitioners develop them through ethical, participatory, and contextually sensitive frameworks. Ultimately, this paper highlights clear pathways toward scalable, inclusive robotic support systems that align engineering innovation with person-centered social work values. Full article

As a result of population ageing, rehabilitation assistive products for older adults increasingly need to meet long-term user requirements in terms of usability, comfort, and interactive experience. Although existing hand rehabilitation robots (HRRs) are capable of supporting hand training tasks, they still exhibit deficiencies in experience-oriented design, which in turn affect user acceptability and continued use. This study aimed to identify and prioritize user requirements for HRRs and, based on their relative importance, formulate emotional design strategies to inform future UX-oriented development. User interviews, the Kano questionnaire, and an adapted quality function deployment (QFD) requirement-prioritization framework were employed for the systematic investigation. The results revealed a clear priority structure. Personalized and adaptive training (H8) ranked first, followed by function–form integration (H3), continuous functional updates (H15), ergonomic wearing comfort (H4), and voice guidance (H9). Clear and readable screen content (H5), timely feedback (H11), positive emotional motivation (H17), and mobile connectivity (H13) also received relatively high priorities. These findings provide a quantifiable basis for design decision-making and subsequent prototype development. The study identifies user priorities and proposes design strategies; it does not experimentally demonstrate improvements in UX, adherence, or rehabilitation outcomes. Full article

Soft exoskeletons have attracted increasing attention as wearable robotic devices for lower limb rehabilitation and assistive mobility. This study presents an integrated modelling, control, and mechanical design framework for a cable-driven soft exoskeleton operating in the sagittal plane, targeting elderly users with reduced mobility. Lower limb swing-phase dynamics were derived using the Euler–Lagrange formulation and subsequently recast via a Legendre transformation into a Hamiltonian representation of the coupled hip–knee system under tendon-driven actuation. Building upon this model, a hierarchical control architecture combining Quasi-Sliding Mode Control (QSMC) for angular regulation with Sliding Mode Control (SMC) for conjugate-momentum dynamics is developed. A Lyapunov-based stability analysis formally establishes the asymptotic stability of the closed-loop system and derives explicit gain conditions for robust tracking in the presence of bounded disturbance. In parallel, a compact winch-based actuation module was designed and geometrically optimized using a genetic algorithm to minimize the distal mass while preserving mechanical robustness and ergonomic wearability. The framework was validated through numerical simulations in MATLAB–Simulink^® and physics-based simulations in a MuJoCo–ROS2 environment, in which gravity, contact interactions, and cable compliance were considered. A modular mechanical prototype was developed and worn by users with different anthropometric characteristics for a static qualitative assessment of its fit and structural feasibility. These results establish a rigorous foundation for the future integration of embedded sensing and actuation hardware into experimental rehabilitation assessment. Full article

Objective: Modern high-intensity warfare, such as the Russo-Ukrainian conflict, generates a high incidence of multisystem polytrauma, blast-induced traumatic brain injury, and limb amputations often complicated by post-traumatic stress disorder and chronic pain. Traditional specialised rehabilitation protocols are structurally inadequate for these co-occurring symptoms and rely heavily on medical treatments, increasing the risk of opioid dependency. The primary aim of this review is to conceptualise a multidimensional integrative framework for war-related polytrauma; a secondary aim is to evaluate and summarise the evidence underpinning its key rehabilitation strategies for limited-resource, post-conflict environments. Methods: This narrative review synthesises evidence identified through targeted database searches and purposive selection guided by clinical relevance, methodological quality, and applicability to war-related polytrauma across physical, technological, and non-pharmacological rehabilitation domains. Evidence was evaluated using a four-tier (A–D) hierarchy, comprising Tier A (systematic reviews and RCTs), Tier B (prospective cohort studies and non-randomised controlled trials), Tier C (descriptive, case-series, and retrospective studies), and Tier D (exploratory, mechanism-based, or expert opinion evidence). No systematic inclusion/exclusion protocol was applied, and no claims of comprehensive search coverage are made. Results: Concurrent physical and trauma-focused psychological rehabilitation, evidenced by reduced pain and psychological symptom burden alongside improved functional independence, is supported by high-quality evidence (Tier A) for managing comorbid polytrauma. Robotic exoskeletons and AI-supported tele-rehabilitation demonstrate dose-dependent motor benefits in selected rehabilitation populations (Tier A–B), but their scalability in wartime settings is contingent on electricity supply, connectivity, equipment maintenance, and trained personnel. Acupuncture shows Tier A evidence for neuropathic pain and Tier B cost-effectiveness; evidence for phantom limb pain and PTSD is preliminary (Tier C–D) and requires dedicated RCTs in veteran populations. Ayurvedic herbal interventions are exploratory (Tier C–D) with no war-specific clinical trials; mechanistic plausibility is discussed as hypothesis-generating only. Conclusions: Addressing the war-related rehabilitation gap requires integration of biomedical care, technology-assisted rehabilitation, and evidence-graded non-pharmacological adjuncts within a structured biopsychosocial framework. Future priorities include pragmatic, adapted trial designs (e.g., stepped-wedge or cohort-embedded designs, which are more feasible than classical RCTs under wartime conditions) for acupuncture in phantom limb pain, feasibility trials for robotic rehabilitation in frontline-adjacent facilities, and health-economic modelling adapted to the Ukrainian context. 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

Wearable technologies are reshaping gait rehabilitation by shifting assessment and therapy from intermittent, clinic-based observation to continuous, data-driven, and adaptive care. This narrative review synthesizes the biomechanical basis of gait instability, the architecture and classification of wearable rehabilitation systems, and the clinical evidence for motion sensors, smart insoles, biofeedback devices, robotic and orthotic wearables, neuromodulatory systems, immersive platforms, and artificial intelligence (AI)-enabled closed-loop interventions. Its main contribution is an integrated framework that links AI, digital biomarkers, device components, adaptive control, and translational implementation, rather than treating wearable rehabilitation as a device-only or disease-specific topic. Current evidence indicates that these technologies can improve gait speed, symmetry, balance, endurance, fall-risk monitoring, and dual-task performance in neurological, musculoskeletal, frailty-related, and aging populations. However, the field is still limited by heterogeneous protocols, small samples, limited longitudinal validation, insufficient device standardization, usability barriers, cybersecurity concerns, uncertain reimbursement, and restricted interoperability with healthcare systems. Future progress will depend on multimodal sensor fusion, explainable and federated AI, digital twins, adaptive wearable robotics, tele-rehabilitation pathways, and large-scale pragmatic trials that validate effectiveness in real-world rehabilitation settings. Full article

Background/Objectives: Perioperative androgen-deprivation therapy (ADT) and androgen-receptor pathway inhibitors (ARPIs) are increasingly used in high-risk prostate cancer, yet patient-centred functional-recovery data after radical prostatectomy are scarce and rarely standardized. We assessed whether a smartphone-based digital monitoring pathway could characterize social-continence recovery and capture differences associated with a perioperative hormonal therapy (PHT) strategy after robot-assisted radical prostatectomy with pelvic lymph-node dissection (RARP + LND). Methods: In a two-centre consecutive cohort (n = 98) of men with high-risk, non-metastatic prostate cancer aligned with SUGAR and PROTEUS criteria, we compared upfront surgery (standard of care [SOC], n = 72) with a planned PHT strategy (ADT or ARPI, n = 26); all patients were urinary-continent preoperatively. Clinical data were prospective; patient-reported and patient-experience measures were captured through a routine-care digital platform (Betty Coaching application) delivering an identical prehabilitation/rehabilitation pathway to both groups, with pre- and postoperative pelvic-floor muscle training performed by all patients. The primary endpoint was 6-month social continence (0–1 pad/day), assessed by clinician and patient reports as co-primary sources. Differences are reported as risk differences with 95% confidence intervals (CIs). Results: At 6 weeks, no statistically clear difference was observed (clinician 66.2% vs. 56.5%; risk difference 9.7 percentage points, 95% CI −12 to 32). At 6 months, social continence was lower with PHT (clinician 87.1% vs. 65.0%, difference 22.1 points, 95% CI 2.6 to 44.7; patient 81.3% vs. 58.3%, 22.9 points, 95% CI 2.4 to 43.9), converging by 12 months (96.9% vs. 95.0%). Analgesic-free rates were lower with PHT on postoperative days (POD) 7 and 10. Conclusions: Digital monitoring captured granular recovery trajectories and identified a mid-term social-continence signal associated with PHT, compatible with a transient delay in recovery. These hypothesis-generating findings support embedding standardized digital functional-outcome monitoring in perioperative trials. Full article

Background: Recent advancements in enabling technologies, including artificial intelligence and telemedicine, alongside robust clinical study outcomes, have led to significant progress in upper limb and hand exoskeletons utilised for post-stroke rehabilitation. Objectives: This review aims to synthesize the recent scientific literature (2010–2025) on post-stroke upper-limb and hand exoskeletons, with particular attention to the sensing architectures—sensing modalities, signal processing, sensor fusion, and sensor-driven control—that integrate technical and biomedical domains to examine device architecture, clinical context, and outcome selection. Methods: A search of PubMed and Scopus was conducted on 10 November 2025, cross-checked against IEEE Xplore, Web of Science, Embase, and ACM Digital Library. We included studies evaluating wearable exoskeletons or robotic orthoses for the upper limb/hand in post-stroke rehabilitation. Two independent reviewers screened records and extracted data, with disagreements resolved by consensus. Data were synthesised using a predefined label-based taxonomy. The review protocol was not registered. Results: From 1889 identified records, 219 studies met the inclusion criteria. The synthesis reveals a transition from rigid, laboratory-centered systems to lighter, soft, and home-oriented solutions. Available evidence suggests potential impairment-level benefits, particularly for proximal motor control, but certainty remains limited due to heterogeneity, small samples, blinding limitations, inconsistent dosing, and limited long-term follow-up; gains in hand/finger dexterity appear even more variable. Discussion: While exoskeleton-assisted therapy appears associated with impairment-level gains, transfer to activities of daily living (ADLs) and real-world function remains inconsistently documented and insufficiently powered to support firm conclusions. Full article