Search Results (51)

Integrating neural and muscular signals into wearable robotics enables adaptive assistance during real-world tasks. This study proposes a multimodal neural interface for passive exoskeletons that combines electroencephalography (EEG) and electromyography (EMG) signals to classify motor gestures and estimate real-time cognitive and muscular effort, supported by finite-element-based biomechanical modeling. The system was implemented on the Ottobock Shoulder X passive exoskeleton© and validated using synchronous EEG–EMG acquisition via the LiveAmp platform©, a commercially available platform that was not developed specifically for this study. A hybrid CNN–LSTM architecture with deep fusion was employed to enhance robustness and responsiveness under realistic operating conditions. This study proposes a multimodal neural interface for the software-level adaptive assistance of passive upper-limb exoskeletons. While the physical device maintains a static mechanical profile, the proposed digital framework achieves adaptation by interpreting the user’s physiological and motor states. Ten healthy participants performed three functional tasks (screwing, moving the box, and lifting the box) under five assistive conditions. Finite element modeling (FEM) was used to characterize the torque–angle relationship of the passive exoskeleton and to support the interpretation of experimentally observed assistive torque profiles. The FEM model, used as an offline biomechanical analysis tool to aid in the interpretation of experimental results, has not been integrated into the real-time control loop. Results showed an average classification accuracy of 90%, an F1-score of 0.85, and inference latency below 180 ms, confirming real-time applicability. Cognitive indices such as the Cognitive Load Index (CLI) and Frontal Asymmetry Index (FAI) enabled adaptive modulation of assistance strategies without requiring active actuation, thereby preserving the device’s intrinsic passive nature. Comparative torque analysis highlighted the ergonomic benefits of passive systems in mid-range postures, while Finite Element Method (FEM) supported analysis clarified their limitations under highly dynamic loads compared to active solutions. These findings advance multimodal brain–machine interfaces for wearable robotics by integrating physiological sensing, deep learning, and biomechanical modeling, offering a safe, energy-efficient, and adaptive approach with potential rehabilitation, occupational ergonomics, and human–robot applications. Full article

Background: Technology such as telerehabilitation, virtual reality, robotics, and wearable systems are reshaping pediatric physiotherapy. While evidence remains fragmented, there is little guidance on how these approaches can be integrated into coherent, family-centered care pathways. Objective: To develop the Digital Pediatric Physiotherapy Framework (DPPF) based on a systematic review of randomized evidence on digital interventions in pediatric physiotherapy. Methods: Several databases were searched for randomized trials published after 1 January 2020, including PubMed, Web of Science Core Collection, and Google Scholar. The included studies assessed the results of physiotherapist-delivered or physiotherapist-supervised digital interventions in children and adolescents aged 18 and younger. Population, intervention, outcome, implementation, and safety data were extracted. Considering the substantial heterogeneity of the findings, they were synthesized narratively. Cochrane RoB 2 was used to assess risk of bias, and GRADE was used to evaluate certainty of evidence. Results: Twenty-nine trials involving 1196 participants were included. Most studies examined virtual reality and gaming-based interventions, with fewer evaluating telerehabilitation/tele-exercise and robotic or wearable technologies. Digital interventions were most often directed at body-function and activity-level outcomes, while participation outcomes were less frequently studied. The strongest evidence supported short-term benefits in balance, gross motor function, upper-limb activity, pain, and selected fitness outcomes, particularly in children with cerebral palsy. Evidence for telerehabilitation and robotic or wearable approaches was more limited but generally promising. Implementation, equity, cost, and long-term outcomes were rarely reported. No eligible trial directly evaluated electronic patient-reported outcome measures, digital triage, or clinical decision support as stand-alone interventions. Conclusions: Digital interventions have the potential to strengthen pediatric physiotherapy, particularly for short-term motor and functional outcomes. The proposed DPPF provides an implementation-informed structure to guide future research, pathway design, and more purposeful integration of digital health into pediatric rehabilitation practice. Full article

Therapist shortages make home-based rehabilitation an essential component of post-stroke care, yet patients often exhibit reduced adherence when functional gains are difficult to quantify and interpret. This study presents a cloud-enabled assessment framework centered on a dynamic reaching task for upper-limb rehabilitation in individuals with mild stroke. The proposed system combines wearable sensing and Internet of Things (IoT) connectivity to stream kinematic data to the cloud for near real-time analysis, and integrates a force-feedback rehabilitation robot to deliver motion guidance during training. The pipeline proceeds in three stages. First, smoothness-related kinematic descriptors are extracted and fed into a deep multi-class classifier to discriminate the affected side (left, right, or healthy). Second, movement quality is modeled using a Gaussian Mixture Model (GMM) trained on IoT-acquired trajectories to quantify performance via probabilistic similarity. Third, a calibrated scoring function transforms GMM log-likelihood into a normalized 0–1 quality index, producing visual reports that support interpretable feedback for patients and therapists. The framework is validated using motion data collected from stroke patients at Taipei Veterans General Hospital. Experimental results demonstrate that the neural network multi-classifier achieved an F1-score of 0.95. Incorporating robot-derived interaction signals further improved classification performance by approximately 5%. For movement quality assessment, the derived scores showed a significant positive correlation (Pearson correlation = 0.632, p = 0.02) with therapist-defined gold reference standards for right-affected patients. Additionally, integrating robot force-feedback signals and AIoT-enabled dynamic streams improved score accuracy by 8% and score responsiveness by 10%. These quantitative outcomes substantiate the efficacy of combining IoT-driven sensing and robot-assisted training for objective, interpretable, and remotely deployable motor assessment. Full article

Rehabilitation robotics faces the challenges of aligning engineering design with patient-specific needs. Most existing controllers in rehabilitation robots often constrain motion to fixed paths or provide only passive guidance, limiting user engagement and adaptability. This study proposes a novel active-assistive mode controller that integrates a virtual tunnel-based force generation mechanism with a dynamic moving-window technique for tracking activities of daily living (ADL) trajectories. Unlike conventional impedance controllers, the proposed method dynamically adjusts the virtual tunnel in real time, permitting voluntary upper-limb movement within a safe operational range while preventing excessive deviation. The system was implemented on a wearable two-degree-of-freedom (DOF) upper-limb exoskeleton equipped with drive and integrated sensor units. Experimental results demonstrated that decreasing the guidance force ($F_{gf}$) increased tracking errors, from $1°$ at 100% $F_{gf}$ to $5°$ at 30% $F_{gf}$, indicating greater voluntary participant motion. Peak actuator torques correspondingly decreased from 14.75 to 13.43 Nm (elbow) and from 4.14 to 2.48 Nm (wrist), confirming the controller’s capability to modulate robotic assistance according to user effort. Tests with 30 healthy participants demonstrated the effectiveness of guidance along predefined ADL trajectories, validating the controller’s potential for patient-centered rehabilitation. Full article

Upper limb rehabilitation exoskeletons form a spatial closed kinematic chain with the human arm, where inevitable joint-center and axis misalignment can generate hyperstatic interaction forces and torques. Passive degrees of freedom (DOF) are widely introduced to improve kinematic compatibility, yet different compatible configurations may exhibit distinct wearable performance. This study experimentally compares three compatible four-degree-of-freedom exoskeleton configurations derived from the synthesis of Li et al. using a single reconfigurable rehabilitation robot. The platform is assembled into each configuration through modular passive units and instrumented with two six-axis force–torque sensors at the upper-arm and forearm interfaces. Interaction forces and torques are measured in passive training mode during eating and combing trajectories. For each configuration, tests are performed with passive joints released and with passive joints locked to quantify the effect of passive motion accommodation. Directional and resultant metrics are computed using mean and peak values over movement cycles. Results show that releasing passive joints consistently reduces interaction loading, and Category 2 achieves the lowest forces and torques with the strongest peak suppression, indicating the best practical compatibility. Full article

The development of upper-limb prostheses is often hindered by limited dexterity, a restricted workspace, and bulky designs, primarily due to performance limitations in proximal joints like the shoulder and elbow, which contribute to high user abandonment rates. To overcome these challenges, this paper presents a novel, bioinspired, and integrated prosthetic system as an advancement in bionic technology. The design incorporates a shoulder joint based on an asymmetric 3-RRR spherical parallel mechanism (SPM) with actuators embedded within the moving platform, and an elbow joint actuated by low-voltage Shape Memory Alloy (SMA) springs. The inverse kinematics of the shoulder mechanism was established, revealing the existence of up to eight configurations. We employed Multi-Objective Particle Swarm Optimization (MOPSO) to simultaneously maximize workspace coverage, enhance dexterity, and minimize joint torque. The optimized design achieves remarkable performance: (1) 85% coverage of the natural shoulder’s workspace; (2) a maximum von Mises stress of merely 3.4 MPa under a 40 N load, ensuring structural integrity; and (3) a sub-0.2 s response time for the SMA-driven elbow under low-voltage conditions (6 V) at a motion velocity of 6°/s. Both motion simulation and prototype testing validated smooth and anthropomorphic motion trajectories. This work provides a comprehensive framework for developing lightweight, high-performance prosthetic limbs, establishing a solid foundation for next-generation wearable robotics and bionic devices. Future research will focus on the integration of neural interfaces for intuitive control. 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

Wearable robotic devices for rehabilitation and assistive applications face a critical challenge: discomfort induced by prolonged pressure at the human–robot interface. Conventional attachment systems with static straps or rigid cuffs frequently exceed pain tolerance thresholds, limiting clinical acceptance and patient adherence. This study presents a novel dynamic pressure modulation system using thermally activated Twisted and Coiled Artificial Muscles (TCAMs). The system integrates a lightweight lattice structure (0.1 kg) with biocompatible silicone coating incorporating two TCAMs fabricated from silver-coated nylon 6,6 fibers (Shieldex 235/36 × 4 HCB). Electrothermal activation via 2 A constant current induces axial contraction, dynamically regulating circumferential pressure from 0.05 kgf/cm² to 0.50 kgf/cm² within physiological comfort ranges. Experimental validation on a wrist-worn prototype demonstrates precise pressure control, rapid response (5–10 s), and thermal safety through 8 mm Ecoflex insulation. The system enables on-demand interface stiffening during robotic actuation and controlled pressure release during rest periods, significantly enhancing comfort and device tolerability. This approach represents a promising solution for clinically viable wearable robotic devices supporting upper limb rehabilitation and activities of daily living. Full article

Upper limb disabilities resulting from stroke affect millions worldwide, yet current rehabilitation systems face limitations in portability, cost-effectiveness, and multi-joint integration. This study presents a cable-driven parallel exoskeleton integrating elbow, wrist, and finger assistance into a single portable device. The design strategically separates actuation components, housing all motors in a backpack unit, while limb-mounted modules serve as cable routing guides, achieving seven degrees of freedom within practical constraints of portability (1.2–1.5 kg) and cost-effectiveness (3D-printed components). The device incorporates seven servo motors controlled via Arduino with IMU feedback and PID algorithms. Kinematic and dynamic analyses informed mechanical design, while ARMAX system identification enabled controller optimization achieving 87.96% model fit. Experimental validation with eight healthy participants performing four upper limb exercises demonstrated consistent trends toward reduced activation in four monitored agonist muscles with exoskeleton assistance (21.3% average reduction, p = 0.087), with moderate effect sizes for proximal muscles (Cohen’s d = 0.70–0.79) and significant reductions in brachioradialis during radial/ulnar deviation (23.4%, p = 0.045). These findings provide preliminary evidence of the device’s potential to reduce muscular effort during assisted movements, warranting further clinical validation with patient populations. Full article

Conventional upper limb rehabilitation methods often encounter significant obstacles, including high costs, limited accessibility, and reduced patient adherence. Emerging technological solutions, such as telerehabilitation, virtual reality (VR), and wearable sensor-based systems, address some of these challenges but still face issues concerning supervision quality, affordability, and usability. To overcome these limitations, this study presents an innovative and cost-effective rehabilitation system based on advanced computer vision techniques and artificial intelligence (AI). Developed using Python (3.11.5), the proposed system utilizes a standard webcam in conjunction with robust pose estimation algorithms to provide real-time analysis of patient movements during guided upper limb exercises. Instructional exercise videos featuring an NAO robot facilitate patient engagement and consistency in practice. The system generates instant quantitative feedback on movement precision, repetition accuracy, and exercise phase completion. The core advantages of the proposed approach include minimal equipment requirements, affordability, ease of setup, and enhanced interactive guidance compared to traditional telerehabilitation methods. By reducing the complexity and expense associated with many VR and wearable-sensor solutions, while acknowledging that some lower-cost and haptic-enabled VR options exist, this single-webcam approach aims to broaden access to guided home rehabilitation without specialized hardware. Full article

This study presents the design, development, and experimental validation of a novel cable-driven shoulder exosuit (CDSE) for upper limb rehabilitation and assistance. Unlike existing exoskeletons, which are often bulky, limited in degrees of freedom (DOFs), or impractical for home use, the proposed DSE offers a lightweight (≈2 kg), portable, and wearable solution capable of supporting three shoulder movements: abduction, flexion, and horizontal adduction. The system employs a bioinspired tendon-driven mechanism using Bowden cables, transferring actuation forces from a backpack to the arm, thereby reducing user load and improving comfort. Mathematical models and inverse kinematics were derived to determine cable length variations for targeted motions, while control strategies were implemented using a PID-based approach in MATLAB Simscape-Multibody simulations. The prototype was fabricated in three iterations using PLA, aluminum, and carbon fiber—culminating in a durable and ergonomic final version. Experimental evaluations on a healthy subject demonstrated high accuracy in position tracking (<5% error) and torque profiles consistent with simulation outcomes, validating system robustness. The CDSE successfully supported loads up to 4 kg during rehabilitation tasks, highlighting its potential for clinical and at-home applications. This research contributes to advancing wearable robotics by addressing portability, biomechanical alignment, and multi-DOF functionality in upper limb exosuits. Full article

Accurate estimation of upper-limb joint angles is essential in biomechanics, rehabilitation, and wearable robotics. While inertial measurement units (IMUs) offer portability and flexibility, systems requiring multiple inertial sensors can be intrusive and complex to deploy. In contrast, optical motion capture (MoCap) systems provide precise tracking but are constrained to controlled laboratory environments. This study presents a deep learning-based approach for estimating shoulder and elbow joint angles using only three IMU sensors positioned on the chest and both wrists, validated against reference angles obtained from a MoCap system. The input data includes Euler angles, accelerometer, and gyroscope data, synchronized and segmented into sliding windows. Two recurrent neural network architectures, Convolutional Neural Network with Long-short Term Memory (CNN-LSTM) and Bidirectional LSTM (BLSTM), were trained and evaluated using identical conditions. The CNN component enabled the LSTM to extract spatial features that enhance sequential pattern learning, improving angle reconstruction. Both models achieved accurate estimation performance: CNN-LSTM yielded lower Mean Absolute Error (MAE) in smooth trajectories, while BLSTM provided smoother predictions but underestimated some peak movements, especially in the primary axes of rotation. These findings support the development of scalable, deep learning-based wearable systems and contribute to future applications in clinical assessment, sports performance analysis, and human motion research. Full article

The development of wearable robotic exoskeletons has seen rapid progress in recent years, driven by the growing need for technologies that support motor rehabilitation, assist individuals with physical impairments, and enhance human capabilities in both clinical and everyday contexts. Within this field, elbow exoskeletons have emerged as a key focus due to the joint’s essential role in upper limb functionality and its frequent impairment following neurological injuries such as stroke. With increasing research activity, there is a strong interest in evaluating these systems not only from a technical perspective but also in terms of user comfort, adaptability, and clinical relevance. This review investigates recent advancements in elbow exoskeleton technology, evaluating their effectiveness and identifying key design challenges and limitations. Devices are categorized based on three main criteria: mechanical structure (rigid, soft, or hybrid), actuation method, and sensing technologies. Additionally, the review classifies systems by their supported range of motion, flexion–extension, supination–pronation, or both. Through a systematic analysis of these features, the paper highlights current design trends, common trade-offs, and research gaps, aiming to guide the development of more practical, effective, and accessible elbow exoskeletons. Full article

Shape Memory Alloys (SMAs) have emerged as a promising actuation technology for wearable rehabilitation robots due to their unique properties, including the shape memory effect, high actuation stress, pseudoelasticity, and three-dimensional actuation. With a significantly higher Young’s modulus than biological tissues, SMAs enable efficient and responsive interaction with the human body, making them well suited for musculoskeletal rehabilitation applications. This paper provides a comprehensive review of SMA-based wearable devices for both upper- and lower-limb rehabilitation. It explores their configurations, actuation mechanisms, associated challenges, and optimization strategies to enhance performance. By discussing recent advancements, this review aims to inform researchers and engineers on the development of sustainable, effective, and patient-centric wearable rehabilitation robots. Full article

Upper limb exoskeleton robots, as highly integrated wearable devices with the human body structure, hold significant potential in rehabilitation medicine, human performance enhancement, and occupational safety and health. The rapid advancement of high-precision, low-noise acquisition devices and intelligent motion intention recognition algorithms has led to a growing demand for more rational and reliable control strategies. Consequently, the control systems and strategies of exoskeleton robots are becoming increasingly prominent. This paper innovatively takes the hierarchical control system of exoskeleton robots as the entry point and comprehensively compares the current control strategies and intelligent technologies for upper limb exoskeleton robots, analyzing their applicable scenarios and limitations. The current research still faces challenges such as the insufficient real-time performance of algorithms and limited individualized adaptation capabilities. It is recognized that no single traditional control algorithm can fully meet the intelligent interaction requirements between exoskeletons and the human body. The integration of many advanced artificial intelligence algorithms into intelligent control systems remains restricted. Meanwhile, the quality of control is closely related to the perception and decision-making system. Therefore, the combination of multi-source information fusion and cooperative control methods is expected to enhance efficient human–robot interaction and personalized rehabilitation. Transfer learning and edge computing technologies are expected to enable lightweight deployment, ultimately improving the work efficiency and quality of life of end-users. Full article