Search Results (197)

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

This work presents a pilot study to analyze the effect of aging on motor performance of young adults (YAs) and older adults (OAs) through wrist movement assessment, using an upper limb rehabilitation robot (ULRR) in passive mode coupled to a maze-solving task serious video game. The proposed approach considers the use of kinematic metrics, such as ROM, path accuracy, and movement smoothness, as quantitative biomarkers that evidence differences between YAs and OAs. An experimental protocol was conducted with 20 participants: 10 OAs and 10 YAs. Standardized wrist movements corresponding to flexion (F), extension (E), radial deviation (R), and ulnar deviation (U) were assessed at each level of the maze. The kinematic analysis was based on metrics for range of motion (ROM), path accuracy, smoothness, and root-mean-square error (RMSE) in trajectory tracking. The results revealed clear differences between the groups: the YAs achieved a greater ROM and made fewer errors on mean (2.167 errors for YAs compared to 6.000 errors for OAs), and showed a lower RMSE, while the OAs showed greater smoothness in their movements, because the YAs exhibit greater variability and disturbances in movement when correcting and controlling their movements to achieve good performance, reflecting more precise motor control and a greater capacity for error correction during movements with trajectory constraints. Full article

Upper-limb exoskeletons require precise geometric alignment between the device’s mechanical axes and the user’s anatomical joints to preserve physiological mobility and prevent functional constraints; however, many occupational exoskeleton designs oversimplify scapulothoracic mobility, potentially reducing the functional workspace and leading to kinematic misalignment during arm elevation tasks. In this context, the present study addresses this limitation by developing the design, kinematic modeling, and experimental validation of a 7-DoF passive upper-limb exoskeleton organized into dorsal, shoulder, and elbow modules, where the proposed architecture explicitly incorporates 3-DoFs in the dorsal region to accommodate scapular motion within a unified serial kinematic chain. From a modeling standpoint, the kinematic formulation is established using the Denavit–Hartenberg convention, enabling the analysis of the workspace, the properties of the Jacobian matrix, and the identification of potential singular configurations; simulation results demonstrate a continuous workspace within the evaluated functional range, with no singularities detected in the region of interest. Regarding experimental validation, two complementary approaches are implemented: a 2D video-based analysis using Kinovea compares joint trajectories with and without the exoskeleton, revealing strong kinematic agreement ($RMSE⩽$ 6.11 mm, $R^2\ge$ 0.8746), while a 3D motion-capture validation using the Qualisys system evaluates the kinematic coupling between the human arm and the exoskeleton during assisted movement, yielding high correspondence between both trajectories ($R^2$ = 0.975). Overall, the results confirm the geometric consistency of the proposed architecture and provide a solid methodological foundation for the future development of passive or hybrid upper-limb exoskeletons with integrated dorsal mobility. Full article

Background/Objectives: This study longitudinally assessed cognitive–motor control deficits in the ipsilesional upper limb following stroke and, secondarily, examined the effect of lesion laterality on these deficits. Methods: Forty-one participants (mean [SD] age: 64.6 [14.4] years; 24 with right-hemisphere lesion; 38 right-handed) were assessed using the KINARM Exoskeleton Lab at approximately 4 weeks (T1), 10 weeks (T2), and 29 weeks (T3) post-stroke. They completed the Visually Guided Reaching (VGR) and Reverse Visually Guided Reaching (RVGR; where the cursor moved in the opposite direction to the subject’s hand movement) tasks with their ipsilesional limb to assess motor control and cognitive–motor control, respectively. Global Task-scores and Z-scores for specific variables derived from normative data were used to determine the occurrence of deficits within each task. Linear mixed-effects models examined time and lesion-side effects. Results: About 88% and 56% of participants were impaired on the RVGR global Task-score, at T1 and T3, respectively. In contrast, only 12% and 9% of participants were impaired on the VGR Task-score, at T1 and T3, respectively. Performance on the RVGR task improved over time. Interestingly, deficits were significantly more severe for right-hemisphere lesions on several variables, except for the feedforward variables. Performance on the VGR task remained unchanged with no lesion-side effect. Conclusions: Stroke survivors exhibited significant impairments in cognitive–motor control of the ipsilesional upper limb, independent of pure motor deficits, persisting into the chronic stage. Right-hemisphere lesions were associated with greater impairments, indicating a potential hemispheric specialization for such cognitive–motor control task. Full article

The design of active pneumatic upper limb exoskeletons is complicated by the challenge of reliably determining a kinematically safe workspace. Existing analytical kinematic methods are not sufficient to predict geometric collisions between elements of closed kinematic chains, which poses risks of mechanical damage and threats to user safety during exoskeleton operation. This paper proposes a hybrid algorithm for verifying the workspace of a pneumatic exoskeleton, combining analytical modelling in MATLAB R2020b based on the Product of Exponentials (PoE) method with high-performance static simulation in the Unity environment. At the initial stage, a discrete set comprising 758 million positions of the upper exoskeleton manipulator was generated. Subsequently, a multithreaded two-stage filtering process was implemented: analytical verification of rod stroke limits and angular constraints, followed by the detection of physical intersections of solid-state meshes using the PhysX engine. The results indicate that while the analytical model filters out 99.6% of invalid configurations. Yet, among the remaining positions—formally correct from a mathematical standpoint—up to 50% lead to critical geometric collisions or breaks in the kinematic chain. The computational efficiency of the proposed architecture enabled full static workspace verification in under 20 min. A reachable zone topology was established, revealing pronounced asymmetry and the presence of a “manoeuvrability core” in the user’s anterior hemisphere. The developed algorithm generates a verified set of kinematically safe exoskeleton states, providing a foundation for the kinematic safety layer of a hierarchical control system. These findings demonstrate the necessity of complementing analytical kinematics with physical collision detection when designing hybrid kinematic mechanisms, and the approach can be applied to verify collision-free movement trajectories in various robotic systems. The approach can be applied to verify collision-free movement trajectories in simulation, with physical validation deferred to future work. Full article

The growing integration of rehabilitation robotics and artificial intelligence has created new opportunities for developing control strategies that better support clinicians during patient therapy. This study investigates machine learning and deep learning approaches for classifying upper limb motion using encoder-based biomechanical data, with the goal of identifying a model suitable for implementation in a rehabilitation exoskeleton. Several classical algorithms such as k-Nearest Neighbors, Random Forest, multiclass logistic regression, XGBoost, and an SVM classifier were evaluated alongside three deep learning architectures: convolutional layers, GRU and LSTM units. Models were trained and tested on two types of datasets using both standard cross-validation and leave-one-subject-out validation. The analysis included assessments of class separability, signal features’ importance, and comparative performance based on F1-score, accuracy, and confusion matrices. Results showed notable differences between validation strategies, with LOSO evaluation revealing limitations of the available dataset and emphasising the need for broader data collection. Overall, the findings indicate that, in the LOSO evaluation of the five-class multi-subject dataset—the most clinically realistic validation scenario—the LSTM-based model achieved the highest generalisation performance (accuracy 92.8%, macro-F1 0.927), supporting its suitability for integration into exoskeleton control systems aimed at detecting and mitigating compensatory movements. Full article

Performance of fine motor tasks during the initial phase of space missions is often compromised by the adaptation to microgravity. Since traditional Earth-based training methods are limited and struggle to replicate these conditions without strict time constraints, we propose the training of fine motor tasks with simulated microgravity on earth using an upper limb active exoskeleton. With a model-based control approach, we create a state of microgravity for both arms. To enable realistic microgravity simulation, a suitable model of the human arm is needed. We developed a method to identify the parameters of an arm model by leveraging the computational graph of the inverse dynamics algorithm and utilizing gradient descent to minimize the discrepancy between model and reality. Preliminary data from parabolic flights show that subjects trained with our exoskeleton achieved higher accuracy in a fine motor task during their first exposure to real microgravity compared to untrained subjects. Full article

Neurological disorders often cause severe upper limb motor impairments that restrict independence and quality of life. Robot-assisted rehabilitation enables high-intensity, task-oriented, and quantifiable training. One key feature, gravity compensation (GC), reduces the muscular effort needed to lift the limb and supports voluntary movement by offsetting the weight of the arm. This systematic review aimed to identify the types of GC strategies used in upper limb rehabilitation robots and assess clinical evidence on their effectiveness for improving motor outcomes. A search of PubMed, Scopus, Web of Science, and IEEE Xplore (January 2005–May 2025) identified 60 eligible studies: 23 describing GC implementation and 40 reporting clinical results. GC was implemented into exoskeletons, end-effectors, and sling-suspension systems through passive mechanical designs or active, model-based, and adaptive control algorithms. However, few studies reported key technical parameters such as controller algorithms, loop frequency, or tuning procedures, and only one addressed the control system stability. Clinically, GC-assisted training improved arm movement and range of motion, with greater effects in participants with higher impairment levels. However, the functional gains were modest and not superior to conventional or other robotic therapies. Substantial heterogeneity in training protocols and participants’ demographics further limits direct comparison among GC strategies. Overall, the relative effectiveness of robot-assisted GC across devices remains unclear. Standardized reporting and more clinical trials are needed to compare GC strategies within and between different types of robots. Full article

Aiming to reduce muscle fatigue and prevent occupational injuries caused by prolonged lifting in insulator replacement operations, this study presents the design of an upper-limb exoskeleton. Firstly, this study performs kinematic analysis and phase segmentation of the lifting motion in the insulator replacement operation. Based on the analysis, in terms of mechanical structure, the proposed upper-limb exoskeleton adopts a unilateral three-degree-of-freedom shoulder mechanism that biomimics the human glenohumeral joint, which reduces the misalignment between the exoskeleton and the human body. Meanwhile, a waist–back support structure is integrated into the exoskeleton to realize a more reasonable torque transmission path. In terms of the control strategy, based on the operation’s phase segmentation and dynamic modeling of the human upper limb, this study develops a neural network-based assistive control algorithm for insulator replacement operations, enabling the exoskeleton to provide phase-specific torque output. Experimental results demonstrate that, under a simulated insulator replacement operation with a 20 kg load, the exoskeleton significantly reduces the subject’s sEMG activity of the biceps brachii and triceps brachii, effectively alleviating muscle fatigue. Full article

Work-related shoulder disorders during overhead assembly represent a persistent occupational challenge. We evaluated a quasi-direct-drive (QDD) active upper-limb exoskeleton during simulated overhead work, providing simultaneous metabolic, electromyographic, and kinematic assessment of QDD actuation under static and dynamic conditions. Seven healthy males completed within-subject comparisons of without-exoskeleton (WO) and with-exoskeleton (WE) conditions during dynamic screwing (5 min) and static holding (2.5 min, 3 kg). During static holding, the exoskeleton achieved substantial shoulder offloading (Upper Trapezius: −68.2%, 6/6 participants, p = 0.031, d = 3.61; Anterior Deltoid: −43.6%) and improved postural stability (32–41% variability reduction). However, metabolic cost increased during both static (+57.2%) and dynamic (+30.6%) tasks, while movement smoothness degraded. These findings extend prior task-dependent exoskeleton observations to QDD actuation, revealing that intrinsic backdrivability does not eliminate whole-body energy penalties from device mass. The exoskeleton exhibits task-dependent effectiveness: potentially suitable for prolonged static overhead holding but not currently recommended for dynamic assembly without mass reduction and control refinement. 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

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

Upper limb motor dysfunction resulting from stroke requires effective rehabilitation solutions; however, current exoskeletons are limited by single-input control, inadequate adaptation to various rehabilitation stages, and restriction to one limb. This study presents the development of a three-degree-of-freedom upper limb rehabilitation exoskeleton with three core innovations: (1) a neuro-fuzzy adaptive admittance control architecture that integrates human–robot interaction force and joint angular velocity as dual inputs for real-time damping adjustment, enabling accurate capture of dynamic movement intentions; (2) a Brunnstrom stage-specific fuzzy rule base that directly links clinical rehabilitation needs to adaptive control parameters; (3) a bilateral adaptable mechanical structure, allowing dual-upper limb training to enhance practical application. By combining radial basis function (RBF) neural network-based adaptive proportional–integral–derivative (PID) control with fuzzy variable-parameter admittance control, the system achieves a maximum trajectory tracking error of less than 1.2° and a root mean square (RMS) error of ≤0.13°. Trajectory tracing experiments confirm an RMS error of 2.99 mm for a circular trajectory at Bd = 2. The proposed strategy, validated through position tracking, admittance interaction, and trajectory tracing experiments, effectively balances tracking accuracy and human–machine compliance, providing valuable technical support for robot-assisted upper limb rehabilitation. Full article

Background: Post-stroke pain (PSP), particularly shoulder pain, is frequent and often underdiagnosed, limiting rehabilitation adherence and functional recovery. Current pharmacological and physical treatments offer only partial relief. Robotic-assisted therapy (RAT), such as the gravity-supporting Armeo^® Spring exoskeleton, delivers intensive, task-specific training with visual 2D feedback that may also alleviate PSP while enhancing motor outcomes. This study investigates whether RAT performed with the Armeo^® Spring reduces upper-limb PSP in chronic stroke patients versus conventional therapy and evaluates its effects on motor function and functional independence. Methods: In this retrospective parallel group study, 32 chronic post-stroke patients (8 females and 24 males with a mean age of 57 ± 11.74) were allocated to two groups: 16 received upper-limb RAT with the Armeo^® Spring, a gravity-supporting exoskeleton, (RAT group) and 16 underwent conventional rehabilitation (CR). The RAT group completed one-hour sessions 6 days/week for 8 weeks, performing 2D/3D gamified tasks targeting shoulder, elbow and forearm movements. The CR group received an equivalent amount of standard therapy, including passive/active-assisted mobilization, Bobath-based neuromuscular facilitation and reaching exercises. Results: Both the Armeo^® Spring and conventional therapy groups showed significant reductions in post-stroke pain (RAT p < 0.001 and conventional rehabilitation p = 0.004) and improvements in upper-limb motor function and functional independence (both p ≤ 0.002). Spasticity in the impaired limb decreased modestly in the RAT group (p = 0.031), with no significant between-group differences in pain or spasticity change (p = 0.437; p > 0.05, respectively). Conclusions: Gravity-support exoskeleton training reduced upper-limb spasticity, and no statistically significant between-group differences were observed compared with conventional physiotherapy for pain, mobility, and functional independence. Although clinical outcomes improved, health-related quality-of-life domains showed heterogeneous trajectories, underscoring the complexity of perceived health changes during chronic stroke rehabilitation. Larger randomized controlled trials incorporating neurophysiological and kinematic endpoints and longer follow-up are warranted to confirm effectiveness, particularly in chronic stroke and durability. 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