Search Results (146)

The flexible upper-limb exoskeleton robot (exosuit) is composed of fabrics, soft actuators and compliant force-transmitting structures, which provides assistance or rehabilitation training for the shoulders, elbows, wrists and hands. By realizing human–robot collaboration, this kind of system has the advantages of comfort, light weight and portability, thus promoting motor function recovery and neural plasticity. This review establishes a classification and comparison framework for flexible upper-limb exoskeletons based on the actuation modalities and systematically summarizes the research progress under different actuation modalities. The relevant literature published from 2015 to 2025 was retrieved from the EI, IEEE Xplore, PubMed and Web of Science databases. After screening according to the preset inclusion and exclusion criteria, a total of 64 original research papers meeting the criteria were finally included for analysis. According to the actuation modalities, the flexible upper-limb exoskeleton robot is classified, and all kinds of systems are summarized and compared. Motor–cable/tendon actuation and pneumatic/hydraulic actuation have advanced substantially and are approaching technical maturity for flexible upper-limb exoskeletons. Meanwhile, designs based on passive/hybrid mechanisms (e.g., elastic energy storage elements and clutches) and new intelligent material actuations are showing a diversified development trend. In the future, the development is expected to further focus on lightweight and compliance, and by integrating multimodal sensing and feedback control, motion intention recognition and human–robot interaction theories, actuation systems will be developed towards modularization, intelligence and high-power density, in order to achieve more comfortable, lighter and more effective flexible upper-limb exoskeleton systems. Full article

The translation of knowledge and methodologies across disciplines represents a valuable source of innovation, particularly in user-centered design approaches that have become essential in medical device development. This study explores the use of the PICO (Population, Intervention, Comparison, and Outcome) framework, a cornerstone of evidence-based medicine for formulating clinical questions, as a conceptual structure to support the alignment between clinical needs and engineering design consideration in robotic rehabilitation devices, with a focus on hand exoskeletons. Through a conceptual reinterpretation and application-oriented exploration supported by illustrative case studies involving both rigid and soft robotic glove prototypes, this study shows how each PICO component can inform engineering parameters, from defining user impairments and intervention strategies to benchmarking and outcome measurements. The analysis highlights the potential of PICO in fostering a user-centered design perspective and bridging clinical and engineering domains while also identifying its structural limitations when applied to device design contexts. This study concludes that while the PICO framework offers a valuable foundational structure, it requires customization to fully address the multifactorial requirements of effective, patient-specific robotic rehabilitation device design. Full article

Stroke-induced hand motor dysfunction severely limits activities of daily living (ADL). While conventional systems face challenges in portability and sustained actuation accuracy, this work addresses these limitations through an integrated adaptive control framework and a lightweight 10-degrees-of-freedom (DoFs) tendon-driven exoskeleton. The system employs a rigid–flexible coupling design with a wearable mass under 300 g, ensuring compatibility across various finger lengths. The system is implemented via a motor imagery-based brain–computer interface (MI-BCI); by processing 64-channel electroencephalogram (EEG) signals, the system adaptively maps motor intent into three discrete grasp intensity levels (20%, 50%, and 80% maximum voluntary contraction). To reduce cognitive load and enhance system stability during rehabilitation, we propose a novel “Force–Topology Coupling” control paradigm. This paradigm functions as a synergistic filter, leveraging the correlation between intended effort level (IEL) and grasp taxonomy to map intensity levels to ADL-specific grasps (lateral, precision, and power). Validation with healthy subjects demonstrated 0° to 90° joint mobility and the successful execution of 9 ADL tasks. The results verify the efficacy of utilizing adaptive MI-BCI modulation to trigger biomechanically precise assistance, establishing a foundational computational paradigm with significant potential for clinical stroke rehabilitation. Full article

Hand exoskeletons are used in rehabilitation together with serious games to enhance patient experience and, possibly, therapy outcomes. To achieve good engagement, a realistic virtual representation of hand motion is needed; however, the relationship between exoskeleton joint motion and anatomical finger kinematics is rarely obtained using low-cost procedures. This work introduces a mechanical redesign and modeling pipeline that utilizes temporary sensors to identify the exoskeleton–finger mapping, enabling qualitatively realistic virtual hand motion driven solely by the existing on-board sensor. A recently developed hand exoskeleton prototype was redesigned to host two temporary rotary encoders aligned with the MetaCarpoPhalangeal (MCP) and Proximal InterPhalangeal (PIP) joints, in addition to the actuation encoder. Healthy subjects wore the modified device and performed full flexion–extension cycles. Encoder trajectories were processed; then each cycle was approximated by a third-order polynomial in the normalized actuation angle, and a group-level model was obtained by averaging coefficients across valid cycles. Finally, the encoder-based reconstructions of MCP and PIP motion were evaluated against measurements from a gold-standard optical motion capture system. Results indicate that the proposed polynomial model enables joint-angle estimation with sufficient accuracy for interactive rehabilitation scenarios, supporting its use to drive smooth virtual hand motion from the on-board exoskeleton encoder alone. Full article

3D-printed hand exoskeletons are important because they enable the creation of affordable, lightweight, and highly customizable assistive and rehabilitation devices tailored to individual patient needs. Their rapid production and design flexibility accelerate innovation, improve access to therapies, and accelerate functional recovery for people with hand impairments. This article discusses the development of a hand exoskeleton using advanced additive manufacturing. It highlights how Industry 4.0 principles such as digital design, automation, and smart manufacturing enable precise prototyping and efficient use of materials. Moving on to Industry 5.0, the study highlights the role of human–machine collaboration, where customization and ergonomics are prioritized to ensure user comfort and rehabilitation effectiveness. The integration of AI-based generative design and digital twins (DTs) is explored as a path to Industry 6.0, where adaptive and self-optimizing systems support continuous improvement. The perspective of personal experience provides insight into practical challenges, including material selection, printing accuracy, and wearability. The results show how technological optimization can be used to reduce costs, improves efficiency and sustainability, and accelerates the personalization of medical devices. The article shows how evolving industrial paradigms are driving the design, manufacture, and refinement of 3D-printed hand exoskeletons, combining technological innovation with human-centered outcomes. Full article

Hand rehabilitation in neurologic conditions such as stroke and cerebral palsy traditionally emphasizes repetitive task practice with visually observable feedback, despite motor impairment arising largely from abnormal neuromuscular activation. We present a platform that leverages noninvasive measurements of brain and muscle activity for neurofeedback-guided movement training. Trainees first learn to control EEG during movement preparation, followed by reciprocal control of finger muscle EMG during exoskeleton-assisted movement. We describe the platform design and two feasibility studies. Five neurotypical individuals learned to use EEG and EMG to drive an exoskeleton to grasp and release a virtual ball in a single session. They achieved a mean success rate of 65%, demonstrating improved movement latency (9%) and task completion time (6%) across the session. One individual post-stroke trained with the platform across eight sessions and exhibited improvements on the Box and Blocks Test, the Action Research Arm Test, and the Wolf Motor Function Test. These results demonstrate the feasibility of multi-level, neurofeedback training that targets neural activation throughout movement, rather than movement outcome alone. By explicitly engaging both cortical and muscular control signals, this paradigm offers a promising new direction for hand rehabilitation following neurologic injury. Full article

Hand exoskeletons are increasingly used to support post-stroke reach-to-grasp, yet most intention-detection strategies trigger assistance from local hand events without considering the synergy between proximal arm transport and distal hand shaping. We evaluated whether proximal arm kinematics, alone or fused with EMG, can predict flexor and extensor digitorum activity for synergy-aligned hand assistance. We trained nine models per participant: linear regression (LINEAR), feedforward neural network (NONLINEAR), and LSTM, each under EMG-only, kinematics-only (KIN), and EMG+KIN inputs. Performance was assessed by RMSE on test trials and by a synergy-retention analysis, comparing synergy weights from original EMG versus a hybrid EMG in which extensor and flexor digitorum measure signals were replaced by model predictions. Results have shown that kinematic information can predict muscle activity even with a simple linear model (average RMSE around 30% of signal amplitude peak during go-to-grasp contractions), and synergy analysis indicated high cosine similarity between original and hybrid synergy weights (on average 0.87 for the LINEAR model). Furthermore, the LINEAR model with kinematics input has been tested in a real-time go-to-grasp motion, developing a high-level control strategy for a hand exoskeleton, to better simulate post-stroke rehabilitation scenarios. These results suggest the intrinsic synergistic motion of go-to-grasp actions, offering a practical path, in hand rehabilitation contexts, for timing hand assistance in synergy with arm transport and with minimal setup burden. Full article

Osteoarthritis (OA) is a progressive musculoskeletal disorder that affects not only older adults but also younger populations, often leading to chronic pain, joint stiffness, functional impairment, and a decline in quality of life. Non-invasive physical rehabilitation plays a critical role in slowing disease progression, alleviating symptoms, and maintaining joint mobility. However, rehabilitation tools such as compression gloves and manual exercise aids are typically passive and provide minimal real-time feedback to patients or clinicians. Others, such as exoskeletons and soft-actuated devices, can be costly or complex to use. This study presents the design and development of an electrically actuated glove integrated with force and flex sensors, intended to assist individuals diagnosed with Stage 2 OA in performing guided finger exercises. The system integrates a digital front-end application that offers real-time feedback and data visualization, enabling more personalized and trackable therapy sessions for both patients and healthcare providers. Preliminary results from an initial human trial with healthy participants demonstrate that the glove enables naturalistic movement without imposing excessive restriction or augmentation of motion. These findings support the glove’s potential in preserving hand coordination and dexterity, key objectives in early-stage OA intervention, and suggest its suitability for integration into home-based or clinical rehabilitation protocols. Full article

This study presents a soft exoskeleton system designed to enhance the safety of electrical maintenance personnel during tower climbing by augmenting the hand grip and providing fall prevention assistance. Inspired by biological principles, a compact, stroke-amplified, and fast-response actuator based on a spring energy storage–release mechanism was developed and evaluated through tensile and speed tests, demonstrating sufficient locking force and a fast response time of 37.5 ms. A dual-sensing module integrating pressure and flexible bending sensors was designed to detect grasping states in real time. System effectiveness was further validated through functional electrical stimulation (FES) and simulated climbing experiments. FES tests confirmed the system’s ability to maintain grasp posture under involuntary hand extension, while climbing experiments verified consistent and reliable transitions between locking and unlocking during movement. Although preliminary, these results suggest that integrating soft exoskeletons with rapid-response actuators offers a promising solution for improving grip stability and operational safety in high-risk vertical environments. Full article

Hand rehabilitation exoskeletons play a critical role in restoring motor function in patients with stroke or hand injuries. However, most existing designs rely on fixed-axis assumptions, neglecting the rolling–sliding coupling of finger joints that causes instantaneous center of rotation (ICOR) drift, leading to kinematic misalignment and localized pressure concentrations. This study proposes the Instant Radius Method (IRM) based on medical imaging to continuously model ICOR trajectories of the MCP, PIP, and DIP joints, followed by the construction of an equivalent ICOR through curve fitting. Crossing-type biomimetic kinematic pairs were designed according to the equivalent ICOR and integrated into a three-loop ten-linkage exoskeleton capable of dual DOFs per finger (flexion–extension and abduction–adduction, 10 DOFs in total). Kinematic validation was performed using IMU sensors (Delsys) to capture joint angles, and interface pressure distribution at MCP and PIP was measured using thin-film pressure sensors. Experimental results demonstrated that with biomimetic kinematic pairs, the exoskeleton’s fingertip trajectories matched physiological trajectories more closely, with significantly reduced RMSE. Pressure measurements showed a reduction of approximately 15–25% in mean pressure and 20–30% in peak pressure at MCP and PIP, with more uniform distributions. The integrated framework of IRM-based modeling–equivalent ICOR–biomimetic kinematic pairs–multi-DOF exoskeleton design effectively enhanced kinematic alignment and human–machine compatibility. This work highlights the importance and feasibility of ICOR alignment in rehabilitation robotics and provides a promising pathway toward personalized rehabilitation and clinical translation. Full article

Bilateral teleoperation combines the agility of robotic manipulators with the ability to perform complex contact tasks guided by human expertise, thereby fulfilling a pivotal function in environments beyond human access. However, due to the limited workspace of existing master robots necessitating frequent mapping mode switches, coupled with the pronounced heterogeneity and asymmetry between the workspaces of the master and slave systems, achieving teleoperation of the mobile manipulator remains challenging. In this study, we innovatively introduced a 7 DOFs upper limb exoskeleton as the master control device, rigorously designed to align with the motion coordination of the human arm. Regarding teleoperation mapping, a hybrid heterogeneous teleoperation control framework with a variable mapping scheme, designed for an aerial manipulator performing physical operations, is proposed. The system incorporates mode switching driven by the operator’s hand gestures, seamlessly and intuitively integrating the advantages of position control and rate control modalities to enable adaptive transitions adaptable to diverse task requirements. Comparative teleoperation experiments were conducted using a fully actuated aerial equipped with a compliant 3D end-effector performing physical aerial writing tasks. The mode-switching algorithm was effectively validated in experiments, demonstrating no instability during transitions and achieving a position tracking RMSE of 7.7% and 5.2% in the X,Y-axis, respectively. This approach holds significant potential for future applications in UAM inspection and physical operational scenarios. Full article

This study assessed the effects of passive arm-support exoskeletons (ASEs) on postural stability during repetitive arm-reaching tasks. In a 3 × 3 × 2 within-subject design, twenty-four healthy right-handed men completed left-, front-, and right-facing arm-reaching tasks at two working distances (65.5 and 68.9 cm) under three intervention conditions (Without, VEX, Airframe). Postural stability was assessed using center of pressure (CoP) data recorded from a force plate. Both ASEs clearly reduced the mean amplitude of CoP in the mediolateral (ML) direction (i.e., the absolute value of MEAN ML and ML APDF10), although neither yielded improvements in anteroposterior (AP) stability. Task direction significantly influenced all CoP measures: left-facing tasks produced the greatest leftward bias, whereas front-facing tasks yielded the smallest AP CoP amplitude. Increasing the working distance by <4 cm modestly heightened AP bias, as reflected in larger AP bias metrics (i.e., MEAN AP, ML APDF50, and ML APDF90). Overall, passive ASEs selectively enhanced lateral postural control, while their effect on AP stability was negligible or even slightly adverse. These findings indicate that the practical utility of passive ASEs depends on the directional demands of specific occupational tasks. Full article

Soft–rigid interfaces in exoskeletons are key to balancing flexibility and structural support, providing both comfort and function. In our experience, combining Bioflex material with a rigid filament improves mechanical properties while allowing the exoskeleton to adapt to complex hand movements. Flexible components provide adaptability, reducing pressure points and discomfort during prolonged use. At the same time, rigid components provide the stability and force transfer necessary to support weakened grip strength. A key challenge in this integration is achieving a smooth transition between materials to prevent stress concentrations that can lead to material failure. Techniques for providing adhesion and mechanical locking are essential to ensure the durability and longevity of soft and rigid interfaces. One issue we have observed is that rigid filaments can restrict movement if not strategically placed, potentially leading to unnatural hand movement. On the other hand, excessive softness can reduce the force output needed for effective rehabilitation or assistance. Optimizing the interface design requires iterative testing to find the perfect balance between flexibility and mechanical support. In some prototypes, material fatigue in soft sections led to early failure, requiring reinforced hybrid structures. Addressing these issues through better material bonding and geometric optimization can significantly improve the performance and comfort of hand exoskeletons. The aim of this study was to investigate the transition between rigid and soft materials for exoskeletons. Full article

This paper addresses the problem of recognising and classifying hand movements to control an upper-limb exoskeleton. To solve this problem, a multisensory system based on the fusion of data from electromyography (EMG) sensors, inertial measurement units (IMUs), and virtual reality (VR) trackers is proposed, which provides highly accurate detection of users’ movements. Signal preprocessing (noise filtering, segmentation, normalisation) and feature extraction were performed to generate input data for regression and classification models. Various machine learning algorithms are used to recognise motor activity, ranging from classical algorithms (logistic regression, k-nearest neighbors, decision trees) and ensemble methods (random forest, AdaBoost, eXtreme Gradient Boosting, stacking, voting) to deep neural networks, including convolutional neural networks (CNNs), gated recurrent units (GRUs), and transformers. The algorithm for integrating machine learning models into the exoskeleton control system is considered. In experiments aimed at abandoning proprietary tracking systems (VR trackers), absolute position regression was performed using data from IMU sensors with 14 regression algorithms: The random forest ensemble provided the best accuracy (mean absolute error = 0.0022 metres). The task of classifying activity categories out of nine types is considered below. Ablation analysis showed that IMU and VR trackers produce a sufficient informative minimum, while adding EMG also introduces noise, which degrades the performance of simpler models but is successfully compensated for by deep networks. In the classification task using all signals, the maximum result (99.2%) was obtained on Transformer; the fully connected neural network generated slightly worse results (98.4%). When using only IMU data, fully connected neural network, Transformer, and CNN–GRU networks provide 100% accuracy. Experimental results confirm the effectiveness of the proposed architectures for motor activity classification, as well as the use of a multi-sensor approach that allows one to compensate for the limitations of individual types of sensors. The obtained results make it possible to continue research in this direction towards the creation of control systems for upper exoskeletons, including those used in rehabilitation and virtual simulation systems. Full article

Purpose: Many individuals with stroke partake in rehabilitation to improve their movements. Rehabilitation operates on the assumption that individuals with stroke can use visual feedback from their movements or visual cues from a therapist to improve their movements through practice. However, this type of visuomotor learning can be impaired after stroke. It is unclear whether and how learning impairments relate to impairments in movement. Here, we examined the relationship between learning and movement impairments after stroke. Methods: We recruited adults with first-time unilateral stroke and controls matched for overall age and sex. The participants performed a visuomotor learning task in a Kinarm exoskeleton robot. The task assessed how they adapted their reaching movements to a systematic visual disturbance that altered the relationship between the observed and actual motion of their hand. Learning was quantified as the extent to which the participants adapted their movements to the visual disturbance. A separate visually-guided reaching task was used to assess the straightness, direction, smoothness, and duration of their movements. The relationships between visuomotor adaptation and movement were analyzed using Spearman’s correlations. Control data were used to identify impairments in visuomotor adaptation and movement. The independence of these impairments was examined using Fisher’s exact tests. Results: Impairments in visuomotor adaptation (46.3%) and movement (73.2%) were common in participants with stroke (n = 41). We observed weak–moderate correlations between continuous measures of adaptation and movement performance (rho range: −0.44–0.58). Adaptation and movement impairments, identified using the range of performance in the control participants, were statistically independent (all p > 0.05). Conclusions: Movement impairments accounted for 34% of the variance in visuomotor adaptation at best. Our findings suggest that factors other than movement impairments may influence visuomotor adaptation after stroke. Full article