Search Results (220)

Against the backdrop of intensifying global population aging, lower-limb exoskeleton robots serve as core devices for rehabilitation and power assistance. Their control accuracy and motion smoothness rely on precise dynamic models. However, parameter uncertainties caused by variations in human lower limbs, assembly errors, and wear pose a critical bottleneck for accurate modeling. Aiming to achieve high-precision dynamic modeling for a two-degree-of-freedom lower-limb exoskeleton, this paper proposes a parameter identification method named Tent-GA-GWO. A dynamic model incorporating joint friction and link inertia was constructed and linearized. An excitation trajectory based on Fourier series, conforming to human physiological constraints, was designed. To enhance algorithm performance, Tent chaotic mapping was employed to optimize population initialization, a nonlinear control parameter was used to balance search behavior, and genetic algorithm operators were integrated to increase population diversity. Simulation results show that, compared to the traditional GWO algorithm, Tent-GA-GWO improved convergence efficiency by 32.1% and reduced the fitness value by 0.26%, demonstrating superior identification accuracy over algorithms such as GA and LIL-GWO. Validation on a physical prototype indicated a close agreement between the computed torque based on the identified parameters and the actual output torque, confirming the method’s effectiveness and engineering feasibility. This work provides support for precise control of exoskeletons. Full article

New wearable technology opens new possibilities for low-cost, easily accessible home-based interventions as a supplement to typical clinical rehabilitation therapy. In this pilot study, we tested a new interactive adjustable Feedback training system on 14 infants at high risk of cerebral palsy between 2 and 12 months of age to facilitate increased movements. The system consists of four wireless motion sensors placed on the infant’s limbs. Inertial sensors track the infant’s movements which control auditory and visual stimuli that act as motivational feedback. A 15 min usage of the Feedback training system four days a week for approximately six months was aimed for. None of the participants reached the recommended amount of intervention, due to time limitations. Seven of the twelve participating infants (58%) achieved at least 50% of the recommended training amount. Parents found the Feedback training system easy to use with minimal need for technical assistance. Preliminary data suggest that infants engaged more actively during training sessions where their movements actively controlled the presentation of the stimuli. The Feedback training system is promising as a user-friendly add-on to the playful and interactive stimulation of motor and cognitive development in infants with neurodevelopmental disorders. Full article

Control of elbow exoskeletons using muscular signals, although promising for the rehabilitation of millions of patients, has not yet been widely commercialized due to challenges in real-time intention estimation and management of dynamic uncertainties. From a practical perspective, millions of patients with stroke, spinal cord injury, or neuromuscular disorders annually require active rehabilitation, and elbow exoskeletons with precise and safe motion intention tracking capabilities can restore functional independence, reduce muscle atrophy, and lower treatment costs. In this research, an intelligent control framework was developed for an elbow joint exoskeleton, designed with the aim of precise and safe real-time tracking of the user’s motion intention. The proposed framework consists of two main stages: (a) real-time estimation of desired joint angle (as a proxy for movement intention) from High-Density Surface Electromyography (HD-sEMG) signals using an LSTM network and (b) implementation and comparison of three PID, impedance, and sliding mode controllers. A public EMG dataset including signals from 12 healthy individuals in four isometric tasks (flexion, extension, pronation, supination) and three effort levels (10, 30, 50 percent MVC) is utilized. After comprehensive preprocessing (Butterworth filter, 50 Hz notch, removal of faulty channels) and extraction of 13 time-domain features with 99 percent overlapping windows, the LSTM network with optimal architecture (128 units, Dropout, batch normalization) is trained. The model attained an RMSE of 0.630 Nm, R² of 0.965, and a Pearson correlation of 0.985 for the full dataset, indicating a 47% improvement in R² relative to traditional statistical approaches, where EMG is converted to desired angle via joint stiffness. An assessment of 12 motion–effort combinations reveals that the sliding mode controller consistently surpassed the alternatives, achieving the minimal tracking errors (average RMSE = 0.21 Nm, R² ≈ 0.96) and showing superior resilience across all tasks and effort levels. The impedance controller demonstrates superior performance in flexion/extension (average RMSE ≈ 0.22 Nm, R² > 0.94) but experiences moderate deterioration in pronation/supination under increased loads, while the classical PID controller shows significant errors (RMSE reaching 17.24 Nm, negative R² in multiple scenarios) and so it is inappropriate for direct myoelectric control. The proposed LSTM–sliding mode hybrid architecture shows exceptional accuracy, robustness, and transparency in real-time intention monitoring, demonstrating promising performance in offline simulation, with potential for real-time clinical applications pending hardware validation for advanced upper-limb exoskeletons in neurorehabilitation and assistive applications. Full article

Upper-limb motor impairment is a major consequence of stroke and neuromuscular disorders, imposing a sustained clinical and socioeconomic burden worldwide. Quantitative assessment of limb positioning and motion accuracy is fundamental to rehabilitation, guiding therapy evaluation and robotic assistance. The evolution of upper-limb positioning systems has progressed from optical motion capture to wearable inertial measurement units (IMUs) and, more recently, to data-driven estimators integrated with rehabilitation robots. Each generation has aimed to balance spatial accuracy, portability, latency, and metrological reliability under ecological conditions. This review presents a systematic synthesis of the state of measurement uncertainty, calibration, and traceability in upper-limb rehabilitation robotics. Studies are categorised across four layers, i.e., sensing, fusion, cognitive, and metrological, according to their role in data acquisition, estimation, adaptation, and verification. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol was followed to ensure transparent identification, screening, and inclusion of relevant works. Comparative evaluation highlights how modern sensor-fusion and learning-based pipelines achieve near-optical angular accuracy while maintaining clinical usability. Persistent challenges include non-standard calibration procedures, magnetometer vulnerability, limited uncertainty propagation, and absence of unified traceability frameworks. The synthesis indicates a gradual transition toward cognitive and uncertainty-aware rehabilitation robotics in which metrology, artificial intelligence, and control co-evolve. Traceable measurement chains, explainable estimators, and energy-efficient embedded deployment emerge as essential prerequisites for regulatory and clinical translation. The review concludes that future upper-limb systems must integrate calibration transparency, quantified uncertainty, and interpretable learning to enable reproducible, patient-centred rehabilitation by 2030. Full article

With an aging population and the high incidence of neurological diseases, rehabilitative lower limb exoskeleton robots, as a wearable assistance device, present important application prospects in gait training and human function recovery. As the core of human–computer interaction, control strategy directly determines the exoskeleton’s ability to perceive and respond to human movement intentions. This paper focuses on the control strategies of rehabilitative lower limb exoskeleton robots. Based on the typical hierarchical control architecture of “perception–decision–execution,” it systematically reviews recent research progress centered around four typical control tasks: trajectory reproduction, motion following, Assist-As-Needed (AAN), and motion intention prediction. It emphasizes analyzing the core mechanisms, applicable scenarios, and technical characteristics of different control strategies. Furthermore, from the perspectives of drive system and control coupling, multi-source perception, and the universality and individual adaptability of control algorithms, it summarizes the key challenges and common technical constraints currently faced by control strategies. This article innovatively separates the end-effector control strategy from the hardware implementation to provide support for a universal control framework for exoskeletons. Full article

Background: Immersive virtual reality (VR) has emerged as a promising tool to enhance neuroplasticity, motivation, and engagement during post-stroke motor rehabilitation. However, evidence on its feasibility and data-driven integration into clinical practice remains limited. Objective: This pilot study aimed to evaluate the feasibility, usability, and short-term motor outcomes of an immersive VR-assisted rehabilitation program using the Travee-VR system. Methods: Fourteen adults with post-stroke upper-limb paresis completed a 10-day hybrid rehabilitation program combining conventional therapy with immersive VR sessions. Feasibility and tolerability were assessed through adherence, adverse events, the System Usability Scale (SUS), and the Simulator Sickness Questionnaire (SSQ). Motor outcomes included active and passive range of motion (AROM, PROM) and a derived GAP index (PROM–AROM). Correlations between clinical changes and in-game performance metrics were explored to identify potential digital performance metrics of recovery. Results: All participants completed the program without adverse events. Usability was rated as high (mean SUS = 79 ± 11.3), and cybersickness remained mild (SSQ < 40). Significant improvements were observed in shoulder abduction (+7.3°, p < 0.01) and elbow flexion (+5.8°, p < 0.05), with moderate-to-large effect sizes. Performance gains in the Fire and Fruits games correlated with clinical improvement in shoulder AROM (ρ = 0.45, p = 0.041). Cluster analysis identified distinct responder profiles, reflecting individual variability in neuroplastic adaptation. Conclusions: The Travee-VR system proved feasible, well tolerated, and associated with measurable short-term improvements in upper-limb function. By linking clinical outcomes with real-time kinematic data, this study supports the role of immersive, feedback-driven VR as a catalyst for data-informed neuroplastic recovery. These results lay the groundwork for adaptive, clinic-to-home rehabilitation models integrating clinical and exploratory digital performance metrics. Full article

Background: Sitting and standing with conventional hip–knee–ankle–foot (HKAF) prostheses are demanding tasks for hip disarticulation (HD) amputees due to the passive nature of current prosthetic hip joints that cannot assist with moment generation. This study developed a sitting and standing control strategy for a motorized hip joint and evaluated whether providing active assistance reduces the intact side demand of these activities. Methods: A dedicated control strategy was developed and implemented for a motorized hip prosthesis (Power Hip) compatible with existing prosthetic knees, feet, and sockets. One HD participant was trained to perform sitting and standing tasks using the Power Hip. Its performance was compared with the participant’s prescribed passive HKAF prosthesis through measurements of ground reaction forces (GRFs), joint moments, and activity durations. GRFs were collected using force plates, kinematics were captured via Theia3D markerless motion capture, and joint moments were computed in Visual3D. Results: The Power Hip enabled more symmetric limb loading and faster stand-to-sit transitions (1.22 ± 0.08 s vs. 2.62 ± 0.41 s), while slightly prolonging sit-to-stand (1.69 ± 0.49 s vs. 1.22 ± 0.40 s) compared to the passive HKAF. The participant exhibited reduced intact-side loading impulses during stand-to-sit (4.97 ± 0.78 N∙s/kg vs. 15.06 ± 2.90 N∙s/kg) and decreased reliance on upper-limb support. Hip moment asymmetries between the intact and prosthetic sides were also reduced during both sit-to-stand (−0.18 ± 0.09 N/kg vs. −0.69 ± 0.67 N/kg) and stand-to-sit transitions (0.77 ± 0.20 N/kg vs. 2.03 ± 0.58 N/kg). Conclusions: The prototype and control strategy demonstrated promising improvements in sitting and standing performance compared to conventional passive prostheses, reducing the physical demand on the intact limb and upper body. Full article

Background and Clinical Significance: Hemiplegia following a cerebrovascular accident (CVA) disrupts gait symmetry and efficiency, compromising functional independence. The integration of surface electromyography (sEMG) and inertial measurement units (IMU) enables quantitative assessment of muscle activation and segmental dynamics, providing objective data for therapeutic planning. Case presentation: A 57-year-old male with chronic right hemiplegia, eight years post-ischemic stroke of the left middle cerebral artery. The patient ambulated independently without assistive devices, exhibiting right lower-limb circumduction. Clinical assessment revealed the following scores: Barthel Index 85/100, Tinetti Performance-Oriented Mobility Assessment (POMA) 16/28, Timed Up and Go (TUG) test 13 s, and Modified Ashworth Scale (MAS) scores of 1 (upper limb) and 1+ (lower limb). Methods: Multichannel sEMG (Miotool 800^®, 8 channels) was recorded form the lumbar erectors, gluteus medius and maximus, vastus medialis, vastus intermedius, vastus lateralis, biceps femoris, tibialis anterior, medial gastrocnemius, and lateral gastrocnemius. Ag/AgCI electrodes were positioned according to SENIAM recommendations: sampling rate: 1000 Hz; band-pass filter: 20–500 Hz; notch filter: 60 Hz; normalization to %MVC. Simultaneously, IMU signals (Xsens DOT^®, 60 Hz) were collected from both ankles during slow, medium and fast walking (20 s each) and compared with a healthy control subject. Results: The patient exhibited reduced sEMG amplitude and increased peak irregularity on the affected side, particularly in the gluteus medius, tibialis anterior, and gastrocnemius, along with agonist desynchronication. IMU data revealed decreased range of motion and angular pattern irregularity, with inconsistent acceleration peaks in the right ankle compared to the control, confirming neuromuscular and kinematic asymmetry. Conclusions: The combined sEMG-IMU analysis identified deficits in selective motor control and propulsion on the affected hemibody, providing essential information to guide physiotherapeutic interventions targeting pelvic stability, dorsiflexion, and propulsive phase training, enabling objective follow-up beyond specialized laboratory settings. Full article

Background/Objectives: Marker-based motion capture remains widely used for lower limb kinematics due to its high precision, although its application is often constrained by elevated operational costs and the requirement for controlled laboratory environments. Markerless methods, such as MediaPipe offer a promising alternative for extending biomechanical analyses beyond traditional laboratory settings, but evidence supporting their validity in controlled tasks is still limited. This study aimed to validate a pixel-based markerless pipeline for two-dimensional kinematic analysis of hip and knee motion during squatting. Methods: Ten healthy volunteers performed three squats with a maximum depth of 90°. Kinematic data were collected simultaneously using marker-based and markerless systems. For the marker-based method, hip and knee joint angles were calculated from marker trajectories within a fixed coordinate system. For the markerless approach, a custom pixel-based pipeline was developed in MediaPipe 0.10.26 to compute bidimensional joint angles from screen coordinates. A paired t-test was conducted using Statistical Parametric Mapping, and maximum flexion values were compared between systems with Bland–Altman analysis. Total range of motion was also analyzed. Results: The markerless pipeline provided valid estimates of hip and knee motion, despite a systematic tendency to overestimate joint angles compared to the marker-based system, with a mean bias of −17.49° for the right hip (95% LoA: −51.89° to 16.91°). Conclusions: These findings support the use of markerless tools in clinical contexts where cost and accessibility are priorities, provided that systematic biases are taken into account during interpretation. Overall, despite the systematic differences, the 2D MediaPipe-based markerless system demonstrated sufficient consistency to assist clinical decision-making in settings where traditional motion capture is not available. Full article

Dental caries is one of the most widespread chronic infectious diseases for humans. It results in localized destruction of dental hard tissues and has negative impacts on systemic health. Aims: This study aims to design, model, and control a novel 4-DOF dental robotic manipulator, Dentatron, specifically tailored for dental applications. The objectives were to (1) develop a compact robotic arm optimized for dental workspace constraints, (2) implement and compare three controllers—Computed Torque Control (CTC), Fuzzy Logic Control (FLC), and Neural Network Adaptive Control (NNAC), (3) evaluate tracking accuracy, transient response, and robustness in step and trajectory tasks, and (4) assess the potential of adaptive neural controllers for future clinical integration. Materials and Methods: The Dentatron system integrates a custom-designed robotic manipulator with adaptive controllers. The methodology consists of five main stages: robot modeling, control design, neural network adaptation, training, and evaluation. Simulations were performed to evaluate performance across joint tracking and Cartesian trajectory tasks using MATLAB 2022. Human-inspired trajectory design is fundamental to the Dentatron control and simulation framework to emulate the continuous curvature and minimum jerk characteristics of human upper-limb motion. The desired end-effector paths were formulated using fifth-degree polynomial trajectories that produce bell-shaped velocity profiles with gradual acceleration changes. Results: The study revealed that the Neural Network Adaptive Controller (NNAC) achieved the fastest convergence and lowest tracking error (<3 mm RMSE), consistently outperforming Fuzzy Logic Control (FLC) and Computed Torque Control (CTC). NNAC consistently provided precise joint tracking with minimal overshoot, while FLC ensured smoother but slower responses, and CTC exhibited large overshoot and persistent oscillations, requiring precise modeling to remain competitive. Conclusion: NNAC demonstrated the most robust and accurate control performance, highlighting its promise for safe, precise, and clinically adaptable robotic assistance in dentistry. Dentatron represents a step toward the development of compact dental robots capable of enhancing the precision and efficiency of future dental procedures. Full article

Table ASSIST-EW is a lightweight, portable, and ergonomic exoskeletal device that is designed to support upper limb rehabilitation and to facilitate regular exercise in elderly users. Targeting the elbow and wrist joints, the device delivers smooth controlled assistance through a cable-driven actuation system that mimics natural muscle–tendon action. The system works with a scalable modular control architecture that enables the regulation of joint motion across a range of user needs and therapeutic contexts. The control design integrates force and motion feedback to implement assist-as-needed strategies, ensuring both safety and adaptability. Built on a bioinspired mechanical framework with revolute joint alignment and a soft inner interface for enhanced comfort, the device accommodates varied arm geometries and motion patterns. Simulation of key parameters—torque, stress, and energy demands—informed component selection and controller tuning. Experimental validation results confirm consistent performance across passive, active–assistive, and resistive control modes. Full article

Objective: Exercise is widely recognized as an effective non-pharmacological intervention to maintain health in older adults. With advances in artificial intelligence (AI), AI-assisted exercise has emerged as a novel rehabilitation approach, yet its comparative effectiveness against traditional and software-assisted programs remains unclear. This study aimed to evaluate and rank the relative effectiveness of these interventions on multiple physical and psychological outcomes using a network meta-analysis (NMA). Methods: Following the PRISMA-NMA guidelines, we systematically searched PubMed, Embase, Cochrane Library, Web of Science, and Scopus up to June 2025. Eligible studies were randomized controlled trials (RCTs) involving adults ≥ 60 years comparing AI-assisted, software-assisted, and conventional upper/lower limb rehabilitation. Six outcomes were analyzed: gait, balance, range of motion (ROM), muscle strength, cognitive function, and quality of life (QOL). Stata 17.0 was used to conduct the NMA, calculating the standardized mean differences (SMDs) and SUCRA rankings, with assessments of heterogeneity and risk of bias. Results: Seventy RCTs with 808 participants were included. All active interventions outperformed the placebo. AI-assisted programs showed the strongest effects on gait (SMD = 1.33) and balance (SMD = 0.76), while software-assisted interventions ranked highest for ROM (SMD = 0.69) and QOL (SMD = 1.06). Both AI and software interventions improved cognition and muscle strength. Heterogeneity was low (I² ≤ 38.5%). Subgroup analysis indicated that AI-based methods were superior to traditional rehabilitation, although differences among novel interventions were not statistically significant. Conclusions: AI-assisted exercise is highly effective for gait and balance, while software-assisted approaches excel in ROM and QOL. These interventions hold promise for community and home-based rehabilitation. Future studies should investigate integrated “AI + traditional” models and incorporate biomechanical and neurophysiological indicators to optimize personalized care. Full article

Continuous estimation of joint angles based on surface electromyography (sEMG) signals is a promising method for naturally controlling prosthetic limbs and assistive devices. However, conventional methods based on neural networks have limitations such as long training times and calibration burdens. This study investigates the use of an echo state network (ESN), which enables fast training, to estimate wrist joint angles from sEMG. Five participants mimicked the motion of a 1-degree-of-freedom robotic arm by flexing and extending their wrist, while sEMG signals from the wrist flexor and extensor muscles and the robotic arm’s angle were recorded. The ESN was trained to take two sEMG channels as input and the robotic joint angle as output. High-accuracy estimation with a median coefficient of determination R² = 0.835 was achieved for representative ESN parameters. Additionally, the effects of the reservoir size, spectral radius, and time constant on estimation accuracy were evaluated using data from a single participant. Furthermore, online estimation of joint angles based on sEMG signals enabled successful control of the robotic arm. These results suggest that sEMG-based ESN estimation offers fast, accurate joint control and could be useful for prosthetics and fundamental studies on body perception. Full article

The pediatric period is a crucial window for motor function learning and growth. Individuals with central nervous system injuries like cerebral palsy commonly display severe crouch gait in the lower limbs. Hyperflexion of the knee joints promotes the forward trunk and increases reliance on the handle frame of a walker for support. In this study, we developed a quantitative partial body weight training system integrated with a soft pneumatic exosuit to assist the knee extension during the stance phase of the gait cycle. In the preliminary results for five pediatric cerebral palsy subjects, compared to the baseline condition, excessive knee flexion ameliorated with the assistance of the soft pneumatic exosuit. The peak knee extension and range of motion increased by 19.72° (±3.47°) and 15.46° (±5.06°), respectively. With exosuit assistance, the subjects demonstrated improved gait retraining compared to baseline. They were able to bear significantly more body weight on their affected limb, as evidenced by a 33.3% increase in the fraction of body weight measured by the force plate. Additionally, they relied less on the handrail for support during walking. With more extended knee joints to bear the load over gravity, the pediatric subjects transferred the reliance from external support and upper limbs back to the lower limbs as a more independent status during the loading response to terminal stance. Full article

Chronic lower-extremity wounds in patients undergoing exoskeleton-assisted rehabilitation require materials that can both protect tissue and enable real-time physiological monitoring. Conventional dressings lack dynamic sensing capability, while current conductive hydrogels often compromise either adhesion or electronic performance. Here, we present a biointegrated hydrogel (CPSD) composed of carboxymethyl chitosan (CMCS) and poly(3,4-ethylenedioxythiophene):poly(styrenesulfonate) (PEDOT:PSS) forming the conductive backbone, integrated with dopamine-functionalized sodium alginate (SD); the network is assembled via electrostatic complexation and carbodiimide (EDC/NHS)-mediated covalent crosslinking. The resulting hydrogel exhibits a dense, tissue-conformal porous network with tunable swelling, stable mechanical integrity, and high photothermal conversion efficiency. In vitro assays confirmed potent antioxidant activity, strong antibacterial performance (>90% under near-infrared), and excellent cytocompatibility and hemocompatibility. CPSD shows bulk conductivity ~1.6 S·m⁻¹, compressive modulus ~15 kPa, lap-shear adhesion on porcine skin ~9.5 kPa, and WVTR ~75 g·m⁻²·h⁻¹, supporting stable biointerfaces for motion/sEMG sensing. Integrated into a lower-limb exoskeleton, CPSD hydrogels adhered securely during motion and reliably captured electromyographic and strain signals, enabling movement-intent detection. These results highlight CPSD hydrogel as a multifunctional interface material for next-generation closed-loop rehabilitation systems and mobile health monitoring. Full article