Search Results (696)

Background: Upper-limb impairment is a major cause of post-stroke disability, limiting participation in meaningful activities. Robotic rehabilitation may address this by delivering high-dosage, task-oriented training while reducing clinician workload. However, limited clinical translation of robotic interventions may be partly due to poor reporting in the literature. This systematic review evaluated the intervention-reporting quality (completeness and consistency) of randomized controlled trials (RCTs) comparing robotic and conventional upper-limb stroke rehabilitation. Methods: Four databases were searched for RCTs investigating robotic upper-limb interventions compared with dose-matched conventional interventions for people with stroke. Intervention reporting was assessed using the TIDieR-Rehab checklist. Trained reviewers independently extracted and evaluated data, resolving discrepancies through consensus. Completeness and consistency were analyzed descriptively. Results: Among 25 RCTs, the overall reporting completeness was low (43%). Robotic interventions were better described (50%) than conventional interventions (36%). While timing and total dose were commonly reported, critical details on provider expertise, active dose, progressive challenge, personalization, and harms were often omitted. Reporting consistency was moderate (68%), with key information dispersed across article sections. Conclusions: Inadequate reporting limits the transparency, replication, and implementation of robotic upper-limb interventions. Adopting structured reporting frameworks like TIDieR-Rehab is essential for advancing the field. Full article

The paper presents a shared control strategy that allows a human operator to physically guide the end-effector of a robotic manipulator to perform different tasks, possibly in interaction with the environment. To switch among different operational modes referring to a finite state machine algorithm, ElectroMyoGraphic (EMG) signals from the user’s arm are used to detect muscular contractions and to interact with a variable admittance control strategy. Specifically, a Support Vector Machine (SVM) classifier processes the raw EMG data to identify three classes of contractions that trigger the activation of different sets of admittance control parameters corresponding to the envisaged operational modes. The proposed architecture has been experimentally validated using a Kinova Jaco² manipulator, equipped with force/torque sensor at the end-effector, and with a limited group of users wearing Delsys Trigno Avanti EMG sensors on the dominant upper limb, demonstrating promising results. 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

Background: There are only a few available options for early rehabilitation of severely impaired individuals who must remain bedbound, as most exercise paradigms focus on out-of-bed exercises. To enable these individuals to exercise, we developed a novel hybrid rehabilitation system combining a brain–computer interface (BCI), functional electrical stimulation (FES), and a robotic device. Methods: The BCI assessed the presence of a movement-related cortical potential (MRCP) and triggered the administration of FES to produce movement of the lower limb. The exercise trajectory was supported by the robotic device. To demonstrate the system, an experiment was conducted in an out-of-lab setting by ten able-bodied participants. During exercise, the performance of the BCI was assessed, and the participants evaluated the system using the NASA Task Load Index, Intrinsic Motivation Inventory, and by answering a few subjective questions. Results: The BCI reached a true positive rate of 62.6 ± 9.2% and, on average, predicted the movement initiation 595 ± 129 ms prior to the MRCP peak negativity. All questionnaires showed favorable outcomes for the use of the system. Conclusions: The developed system was usable by all participants, but its clinical feasibility is uncertain due to the total time required for setting up the system. Full article

Background: The present study aimed to determine whether the protocolized use of pneumatic splints within neurodevelopmental therapeutic approaches produces a positive effect on sensorimotor impairments of the hemiplegic upper extremity in patients. Methods: A randomized clinical single-blind trial was conducted. Stroke patients were recruited and randomized into an experimental group, which completed a treatment protocol of splinting plus physiotherapy for 45 min per session, two sessions per week for four weeks; or a control group, which received the same type of conventional physiotherapy treatment for the same period of time. The patients were evaluated by Fugl-Meyer Assessment of the Upper Extremity (FMA-UE) and the Trunk Control Scale. Secondary variables were Mini-BEStest, the modified Ashworth scale for ankle flexors, and computerized measurements of upper limb functional parameters performed by Armeo Spring^® robotic systems and Amadeo^®. All variables were measured pre- and post-treatment. Results: Twenty stroke patients with subacute and chronic stroke completed the protocol. Mann–Whitney U tests showed statistically significant differences between groups for the FM sensation variable (Z = −2.19; p = 0.03). The rest of the variables studied in the comparison between the two study groups did not present statistically significant differences (p > 0.05). Conclusions: The use of air splints in combination with physiotherapy treatment produced improvements in exteroceptive and proprioceptive sensitivity in post-stroke adult patients in the subacute and chronic phases. Full article

This paper proposes an adaptive human–robot concurrent control scheme that achieves the appropriate gait trajectory for a robotic leg prosthesis to improve the wearer’s comfort in various tasks. To accommodate different wearers, a neuromuscular-force-based impedance method was developed using muscle activation to reshape gait trajectory. To eliminate the use of sensors for torque measurement, a disturbance observer was established to estimate the interaction force between the human residual limb and the prosthetic receptacle. The cost function was combined with the interaction force and tracking errors of the joints. We aim to reduce the cost function by minimally changing the control weight of the gait trajectory generated by the Central Pattern Generator (CPG). The control scheme was primarily based on human-in-the-loop optimization to search for a suitable control weight to regenerate the appropriate gait trajectory. To handle the uncertainties and unknown coupling of the motors, an adaptive law was designed to estimate the unknown parameters of the system. Through a stability analysis, the control framework was verified by semi-globally uniformly ultimately bounded stability. Experimental results are discussed, and the effectiveness of the adaptive control framework is demonstrated. In Case 1, the mean error (MEAN) of the tracking performance was $3.6°$ and $3.3°$, respectively. And the minimized mean square errors (MSEs) of the tracking performance were $2.3°$ and $2.8°$, respectively. In Case 2, the mean error (MEAN) of the tracking performance is $2.7°$ and $3.1°$, respectively. And the minimized mean square errors (MSEs) of the tracking performance are $1.8°$ and $2.4°$, respectively. In Case 3, the mean errors (MEANs) of the tracking performance for subject1 and 2 are $2.4°$, $2.9°$, $3.4°$, and $2.2°$, $2.8°$, $3.1°$, respectively. The minimized mean square errors (MSEs) of the tracking performance for subject1 and 2 were $1.6°$, $2.3°$, $2.6°$, and $1.3°$, $1.7°$, $2.2°$, respectively. Full article

Series elastic actuators (SEAs) represent a key technological solution to enhance safety, performance, and adaptability in robotic devices for physical training. Their ability to decouple the rigid actuator’s mechanical impedance from the load, combined with passive absorption of external disturbances, makes them particularly suitable for pediatric applications. In children aged 2 to 5 years—where motor control is still developing and movements can be unpredictable or unstructured—SEAs provide a compliant mechanical response that ensures user protection and enables safe physical interaction. This study explores the role of SEAs as a central component for imparting compliance and backdrivability in robotic systems designed for upper-limb training. A dynamic model is proposed, incorporating interaction with the user’s limb, along with a computed torque control strategy featuring integral action. The system’s performance is validated through simulations and experimental tests, demonstrating stable trajectory tracking, disturbance absorption, and effective impedance decoupling. The results support the use of SEAs as a foundational technology for developing safe adaptive robotic solutions in pediatric contexts capable of responding flexibly to user variability and promoting secure interaction in early motor development environments. Full article

This work systematically addresses the dual challenges of non-inertial dynamic coupling and kinematic constraint redundancy encountered in dynamic modeling of serial–parallel–serial hybrid robotic mechanisms, and proposes an improved Newton–Euler modeling method with constraint compensation. Taking the Skiing Simulation Platform with 6-DOF as the research mechanism, the inverse kinematic model of the closed-chain mechanism is established through $G_F$ set theory, with explicit analytical expressions derived for the motion parameters of limb mass centers. Introducing a principal inertial coordinate system into the dynamics equations, a recursive algorithm incorporating force/moment coupling terms is developed. Numerical simulations reveal a 9.25% periodic deviation in joint moments using conventional methods. Through analysis of the mechanism’s intrinsic properties, it is identified that the lack of angular momentum conservation constraints on the end-effector in non-inertial frames leads to system controllability degradation. Accordingly, a constraint compensation strategy is proposed: establishing linearly independent differential algebraic equations supplemented with momentum/angular momentum balance equations for the end platform. Co-Simulation results demonstrate that the optimized model reduces the maximum relative error of actuator joint moments to 0.98%, and maintains numerical stability across the entire configuration space. The constraint compensation framework provides a universal solution for dynamics modeling of complex closed-chain mechanisms, validated through applications in flight simulators and automotive driving simulators. Full article

In an era defined by rapid technological advancements, the intersection of artificial intelligence and industrial innovation has garnered significant attention from both academic and industry stakeholders. The emergence of Industry 4.0, characterized by the integration of cyber–physical systems, the Internet of Things, and smart manufacturing, demands the evolution of operational methodologies to ensure processes’ sustainability. One area of focus is the development of wearable systems that utilize artificial intelligence for the estimation of arm movements, which can enhance the ergonomics and efficiency of labor-intensive tasks. This study proposes a Random Forest-based regression model to estimate upper arm kinematics using only shoulder orientation data, reducing the need for multiple sensors and thereby lowering hardware complexity and energy demands. The model was trained on biomechanical data collected via a minimal three-IMU wearable configuration and demonstrated high predictive performance across all motion axes, achieving R² > 0.99 and low RMSE scores on training (1.14, 0.71, and 0.73), test (3.37, 1.97, and 2.04), and unseen datasets (2.77, 0.78, and 0.63). Statistical analysis confirmed strong biomechanical coupling between shoulder and upper arm motion, justifying the feasibility of a simplified sensor approach. The findings highlight the relevance of our method for sustainable wearable technology design and its potential applications in rehabilitation robotics, industrial exoskeletons, and human–robot collaboration systems. Full article

Extraterrestrial exploration presents unique challenges for robotic systems, as traditional rigid rovers face limitations in stowage volume, traction on unpredictable terrain, and susceptibility to damage. Soft robotics offers promising solutions through bio-inspired designs that can mimic natural locomotion mechanisms. Here, we present an optimised, spider-inspired soft jumping robot for extraterrestrial exploration that addresses key challenges in soft robotics: actuation efficiency, controllability, and deployment. Drawing inspiration from spider physiology—particularly their hydraulic extension mechanism—we develop a lightweight limb capable of multi-modal behaviour with significantly reduced energy requirements. Our 3D-printed soft actuator leverages pressure-driven collapse for efficient retraction and pressure-enhanced rapid extension, achieving a power-to-weight ratio of 249 W/kg. The integration of a non-backdriveable clutch mechanism enables the system to hold positions with zero energy expenditure—a critical feature for space applications. Experimental characterisation and a subsequent optimisation methodology across various materials, dimensions, and pressures reveal that the robot can achieve jumping heights of up to 1.86 times its body length. The collapsible nature of the soft limb enables efficient stowage during spacecraft transit, while the integrated pumping system facilitates self-deployment upon arrival. This work demonstrates how biologically inspired design principles can be effectively applied to develop versatile robotic systems optimised for the unique constraints of extraterrestrial exploration. Full article

Background: Lokomat-assisted robotic rehabilitation is increasingly used for gait restoration in patients with spinal cord injury (SCI). However, the objective evaluation of treatment effectiveness through biomechanical parameters and machine learning approaches remains underexplored. Methods: This study analyzed data from 29 SCI patients undergoing Lokomat-based rehabilitation. A dataset of 46 variables including range of motion (L-ROM), joint stiffness (L-STIFF), and muscular force (L-FORCE) was examined using statistical methods (paired t-test, ANOVA, and ordinary least squares regression), clustering techniques (k-means), dimensionality reduction (t-SNE), and anomaly detection (Isolation Forest). Predictive modeling was applied to assess the influence of age, speed, body weight, body weight support, and exercise duration on biomechanical outcomes. Results: No statistically significant asymmetries were found between left and right limb measurements, indicating functional symmetry post-treatment (p > 0.05). Clustering analysis revealed a weak structure among patient groups (Silhouette score ≈ 0.31). Isolation Forest identified minimal anomalies in stiffness data, supporting treatment consistency. Regression models showed that body weight and body weight support significantly influenced joint stiffness (p < 0.01), explaining up to 60% of the variance in outcomes. Conclusions: Lokomat-assisted robotic rehabilitation demonstrates high functional symmetry and biomechanical consistency in SCI patients. Machine learning methods provided meaningful insight into the structure and predictability of outcomes, highlighting the clinical value of weight and support parameters in tailoring recovery protocols. Full article

Achieving energy-efficient, human-like gait remains a major challenge in bipedal humanoid robotics, as traditional serial actuation architectures often lead to high instantaneous power peaks and uneven load distribution. This study addresses the lack of research on how mechanical symmetry, achieved through parallel actuation, can improve power management in lower-limb joints. We developed a 14-degree-of-freedom (DOF) hip-sized bipedal robot model and conducted simulations comparing a conventional serial configuration—using single-DOF rotary actuators—with a novel parallel configuration that employs paired linear actuators at the hip pitch, hip roll, ankle pitch, and ankle roll joints. Simulation results over a standardized walking cycle show that the parallel configuration reduces peak hip-pitch power by 80.4% and peak ankle-pitch power by 53.5%. These findings demonstrate that incorporating actuator symmetry through parallel joint design significantly reduces actuator stress, improves load sharing, and enhances overall energy efficiency in bipedal locomotion. Full article

This review provides a comprehensive analysis of recent advancements in lower limb exoskeleton systems, focusing on applications, control strategies, hardware architecture, sensing modalities, human-robot interaction, evaluation methods, and technical innovations. The study spans systems developed for gait rehabilitation, mobility assistance, terrain adaptation, pediatric use, and industrial support. Applications range from sit-to-stand transitions and post-stroke therapy to balance support and real-world navigation. Control approaches vary from traditional impedance and fuzzy logic models to advanced data-driven frameworks, including reinforcement learning, recurrent neural networks, and digital twin-based optimization. These controllers support personalized and adaptive interaction, enabling real-time intent recognition, torque modulation, and gait phase synchronization across different users and tasks. Hardware platforms include powered multi-degree-of-freedom exoskeletons, passive assistive devices, compliant joint systems, and pediatric-specific configurations. Innovations in actuator design, modular architecture, and lightweight materials support increased usability and energy efficiency. Sensor systems integrate EMG, EEG, IMU, vision, and force feedback, supporting multimodal perception for motion prediction, terrain classification, and user monitoring. Human–robot interaction strategies emphasize safe, intuitive, and cooperative engagement. Controllers are increasingly user-specific, leveraging biosignals and gait metrics to tailor assistance. Evaluation methodologies include simulation, phantom testing, and human–subject trials across clinical and real-world environments, with performance measured through joint tracking accuracy, stability indices, and functional mobility scores. Overall, the review highlights the field’s evolution toward intelligent, adaptable, and user-centered systems, offering promising solutions for rehabilitation, mobility enhancement, and assistive autonomy in diverse populations. Following a detailed review of current developments, strategic recommendations are made to enhance and evolve existing exoskeleton technologies. 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

Robotic-assisted therapy is an increasingly vital approach for upper-limb rehabilitation, offering consistent, high-intensity training critical to neuroplastic recovery. However, current control strategies often lack robustness against uncertainties and external disturbances, limiting their efficacy in dynamic, real-world settings. Addressing this gap, this study proposes a novel control framework for the iTbot—a 2-DoF end-effector rehabilitation robot—by integrating differential flatness theory with a derivative-free Kalman filter (DFK). The objective is to achieve accurate and adaptive trajectory tracking in the presence of unmeasured dynamics and human–robot interaction forces. The control design reformulates the nonlinear joint-space dynamics into a 0-flat canonical form, enabling real-time computation of feedforward control laws based solely on flat outputs and their derivatives. Simultaneously, the DFK-based observer estimates external perturbations and unmeasured states without requiring derivative calculations, allowing for online disturbance compensation. Extensive simulations across nominal and disturbed conditions demonstrate that the proposed controller significantly outperforms conventional flatness-based control in tracking accuracy and robustness, as measured by reduced mean absolute error and standard deviation. Experimental validation under both simple and repetitive physiotherapy tasks confirms the system’s ability to maintain sub-millimeter Cartesian accuracy and sub-degree joint errors even amid dynamic perturbations. These results underscore the controller’s effectiveness in enabling compliant, safe, and disturbance-resilient rehabilitation, paving the way for broader deployment of robotic therapy in clinical and home-based environments. Full article