Search Results (59)

Cerebral palsy is the leading cause of motor disability in early childhood, with no curative treatment currently available. To mitigate its effects and promote motor rehabilitation, robotic-assisted therapies have emerged as a complement to conventional physiotherapy. In particular, cable-driven exoskeletons offer notable advantages, providing patients with additional mobility and interaction with their environment while preserving motion assistance. Within this context, the Discover2Walk project introduces a modular cable-driven robotic platform designed for early-stage gait rehabilitation. This article presents a novel ankle control module capable of actuating 3 degrees of freedom: 2 translational (in the x and z directions) and 1 rotational (dorsiflexion/plantarflexion). Experimental results confirm the technical feasibility of the approach and its effectiveness in guiding motion within the targeted degrees of freedom. Full article

This article presents a series of innovative systems developed through student laboratory projects, comprising two autonomous vehicles, a quadrupedal walking robot, an active ankle-foot orthosis, a ball-on-beam balancing mechanism, a ball-on-plate system, and a manipulator arm, all actuated by pneumatic artificial muscles (PAMs). Due to their flexibility, low weight, and compliance, fluidic muscles demonstrate substantial potential for integration into various mechatronic systems, robotic platforms, and manipulators. Their capacity to generate smooth and adaptive motion is particularly advantageous in applications requiring natural and human-like movements, such as rehabilitation technologies and assistive devices. Despite the inherent challenges associated with nonlinear behavior in PAM-actuated control systems, their biologically inspired design remains promising for a wide range of future applications. Potential domains include industrial automation, the automotive and aerospace sectors, as well as sports equipment, medical assistive devices, entertainment systems, and animatronics. The integration of self-constructed laboratory systems powered by PAMs into control systems education provides a comprehensive pedagogical framework that merges theoretical instruction with practical implementation. This methodology enhances the skillset of future engineers by deepening their understanding of core technical principles and equipping them to address emerging challenges in engineering practice. Full article

This study addresses gait reference trajectory tracking control in a 13-degree-of-freedom lower-limb rehabilitation robot, where patients exhibit nonlinear perturbations in lower-limb muscle groups and gait irregularities during exoskeleton-assisted walking. We propose a novel control strategy integrating iterative learning with RBF neural network-based sliding mode control, featuring a single hidden-layer pre-feedback architecture. The RBF neural network effectively approximates uncertainties arising from lower-limb muscle perturbations, while adaptive regulation through parameter simplification ensures precise torque tracking at each joint, meeting real-time rehabilitation requirements. MATLAB 2021 simulations demonstrate the proposed algorithm’s superior trajectory tracking performance compared to conventional sliding mode control, effectively eliminating control chattering. Experimental results show maximum angular errors of 1.77° (hip flexion/extension), 1.87° (knee flexion/extension), and 0.72° (ankle dorsiflexion/plantarflexion). The integrated motion capture system enables the development of patient-specific skeletal muscle models and optimized gait trajectories, ensuring both training efficacy and safety for spasticity patients. Full article

Neurological disorders like stroke are one of the main causes of motor dysfunction and gait function disabilities in humans. These disorders impact the mobility of patients often leading to weakened and impaired ankle joints which further compromise their balance and walking abilities. Over the span of the last twenty years, there has been a rising interest in designing, developing, and using rehabilitative robots for patients suffering from various ankle joint disabilities. These robotic devices are developed by employing diverse mechanical designs, materials, and control strategies. The aim of this study is to provide a detailed overview of the recent developments in mechanical design, actuation, and control strategies of ankle rehabilitation robots. Experimental evaluation of the discussed ankle robots has also been carried out discussing their results and limitations. This article concludes by highlighting future challenges and opportunities for the advancement of ankle rehabilitation robots, stressing the need for robust and effective devices to better serve patients. Full article

A critical requirement for rehabilitation robots is achieving high transparency in user interaction to minimize interference when assistance is unnecessary. Cable-driven systems are a compelling alternative to rigid-link robots due to their lighter weight and reduced inertia, enhancing transparency. However, controlling cable tension forces remains a significant challenge, as these forces directly affect the interaction between the user and the robot. Effective strategies must maintain low tension during non-assistive phases while preventing slackness. This paper introduces PACE-R (Passive Active CablE Robot), a novel lightweight actuation system for cable-driven rehabilitation devices. The PACE-R module utilizes remote actuation and an open-loop, discrete state control, where the cable is coupled to the motor only during active intervention. When not assisting, the cable is passively decoupled from the motor, and a low-stiffness spring maintains minimal tension, enabling high transparency. Benchtop tests showed that the module consistently produced force impulses proportional to motor input with delays not exceeding 15 ms. In the treadmill push-off assistance demonstration, PACE-R contributed about 20% to total ankle moment and power. Transparency analysis revealed negligible interference, with only 1% and 0.5% contributions to peak total ankle moment and power, respectively. Full article

An ankle sprain can be caused by daily activities such as running, walking, or playing sports. In many cases, the patient’s ankle suffers severe or permanent damage that requires rehabilitation to return to its initial state. Thanks to technological advances, robotics has allowed for the development of machines that generate precise, efficient, and safe movements. In addition, these machines are manipulated by a specific control depending on the rehabilitation objective. Impedance control is used in ankle rehabilitation machines for active–resistive-type rehabilitation, where the patient participates by exerting a force on the machine repeatedly. Serious games are an example of an activity where the patient can interact with a video game while rehabilitating. Currently, most machines involving impedance control and targeted at serious gaming applications are mechanically composed of one degree of freedom, so the addition of another degree is a novelty. This paper presents simulation results comparing different impedance controls reported in the literature to determine the best option for applying a 2-DOF ankle rehabilitation machine using serious games. The results obtained are presented by comparing them according to the force applied to the rehabilitation machine (emulating the behavior of a patient). From the impedance controllers analyzed for horizontal (abduction/adduction) and vertical (dorsiflexion/plantarflexion) movements in the rehabilitation machine, it was determined that the PD control, which considers some mechanical parameters, presents a better performance. With this controller, fast and smooth angular movements are generated, while the consumption of kinetic energy is kept in a low range, proportional to the applied forces, compared to the other impedance controls analyzed. Full article

Stroke, amyotrophic lateral sclerosis (ALS), and Parkinson’s disease are some of the conditions that can lead to neuromotor disabilities requiring rehabilitation. To address the socio-economic burden that is amplified by the rapidly increasing elderly population, traditional rehabilitation techniques have recently been complemented by technological advancements, particularly Robot-Assisted Therapy (RAT). RAT enhances motor learning by improving both accuracy and consistency. This study proposes an innovative rehabilitation system that combines serious gaming and augmented reality (AR) with the LegUp parallel robot, developed for the spatial rehabilitation of the hip, knee, and ankle in bed-ridden patients. The system aims to improve patient outcomes and actively involve patients in their therapy. Electro-goniometers and a HoloLens 2 device are used to provide immediate feedback about the position of the patient’s joints, forming the basis of an interactive game in which the patient moves their leg to reach various targets. Two game modes were developed, each targeting different aspects of neuromotor rehabilitation, such as coordination, strength, and flexibility. Preliminary findings suggest that combining RAT with augmented reality-based serious gaming can increase patient motivation and engagement. Furthermore, the personalized and interactive nature of the therapy holds the potential to improve rehabilitation outcomes by fostering sustained engagement and effort. Full article

The paper presents an innovative virtual reality (VR)-based environment for personalized telerehabilitation programs. This environment integrates a parallel robotic structure designed for the lower limb rehabilitation of patients with neuromotor disabilities and a virtual patient. The robotic structure is controlled via a user interface (UI) that communicates with the VR environment via the TCP/IP protocol. The robotic structure can also be operated using two controllers that communicate with a VR headset via the Bluetooth protocol. Through these two controllers, the therapist demonstrates to the patient various exercises that the robotic system can perform. With the right-hand controller, the therapist guides exercises for the hip and knee, while the left-hand controller manages ankle exercises. The therapist remotely designs a rehabilitation plan for patients at home, defining exercises, interacting with the rehabilitation robot in real-time via the VR headset and the two controllers, and initiating therapy sessions. The user interface allows monitoring of patient progress through video feedback, electromyography (EMG) sensors, and session recording. Full article

The decline in neuromusculoskeletal capabilities of older adults can affect motor control, independence, and locomotion. Because the elderly population is increasing worldwide, assisting independent mobility and improving rehabilitation therapies has become a priority. The combination of rehabilitation robotic devices and virtual reality (VR) tools can be used in gait training to improve clinical outcomes, motivation, and treatment adherence. Nevertheless, VR tools may be associated with cybersickness and changes in gait kinematics. This paper analyzes the gait parameters of fourteen elderly participants across three experimental tasks: free walking (FW), smart walker-assisted gait (AW), and smart walker-assisted gait combined with VR assistance (VRAW). The kinematic parameters of both lower limbs were captured by a 3D wearable motion capture system. This research aims at assessing the kinematic adaptations when using a smart walker and how the integration between this robotic device and the VR tool can influence such adaptations. Additionally, cybersickness symptoms were investigated using a questionnaire for virtual rehabilitation systems after the VRAW task. The experimental data indicate significant differences between FW and both AW and VRAW. Specifically, there was an overall reduction in sagittal motion of 16%, 25%, and 38% in the hip, knee, and ankle, respectively, for both AW and VRAW compared to FW. However, no significant differences between the AW and VRAW kinematic parameters and no adverse symptoms related to VR were identified. These results indicate that VR technology can be used in walker-assisted gait rehabilitation without compromising kinematic performance and presenting potential benefits related to motivation and treatment adherence. Full article

Cerebral Palsy refers to a group of incurable motor disorders affecting 0.22% of the global population. Symptoms are managed by physiotherapists, often using rehabilitation robotics. Exoskeletons, offering advantages over conventional therapies, are evolving to be more wearable and biomimetic, requiring new flexible actuators that mimic human tissue. The main objective behind this article is the design of a flexible exosuit based on shape-memory-alloy-based artificial muscles for pediatric patients that replicate the walking cycle pattern in the ankle joint. Thus, four shape-memory-alloy-based actuators were sewn to an exosuit at the desired actuation points and controlled by a two-level controller. The loop is closed through six inertial sensors that estimate the real angular position of both ankles. Different frequencies of actuation have been tested, along with the response of the actuators to different walking cycle patterns. These tests have been performed over long periods of time, comparing the reference created by a reference generator based on pediatric walking patterns and the response measured by the inertial sensors. The results provide important measurements concerning errors, working frequencies and cooling times, proving that this technology could be used in this and similar applications and highlighting its limitations. Full article

Enhancing human–robot interaction has been a primary focus in robotic gait assistance, with a thorough understanding of human motion being crucial for personalizing gait assistance. Traditional gait trajectory references from Clinical Gait Analysis (CGA) face limitations due to their inability to account for individual variability. Recent advancements in gait pattern generators, integrating regression models and Artificial Neural Network (ANN) techniques, have aimed at providing more personalized and dynamically adaptable solutions. This article introduces a novel approach that expands regression and ANN applications beyond mere angular estimations to include three-dimensional spatial predictions. Unlike previous methods, our approach provides comprehensive spatial trajectories for hip, knee and ankle tailored to individual kinematics, significantly enhancing end-effector rehabilitation robotic devices. Our models achieve state-of-the-art accuracy: overall RMSE of 13.40 mm and a correlation coefficient of 0.92 for the regression model, and RMSE of 12.57 mm and a correlation of 0.99 for the Long Short-Term Memory (LSTM) model. These advancements underscore the potential of these models to offer more personalized gait trajectory assistance, improving human–robot interactions. Full article

The purpose of this article is to establish a prediction model of joint movements and realize the prediction of joint movemenst, and the research results are of reference value for the development of the rehabilitation equipment. This will be carried out by analyzing the impact of surface electromyography (sEMG) on ankle movements and using the Hill model as a framework for calculating ankle joint torque. The table and scheme used in the experiments were based on physiological parameters obtained through the model. Data analysis was performed on ankle joint angle signal, movement signal, and sEMG data from nine subjects during dorsiflexion/flexion, varus, and internal/external rotation. The Hill model was employed to determine 16 physiological parameters which were optimized using a genetic algorithm. Three experiments were carried out to identify the optimal model to calculate torque and root mean square error. The optimized model precisely calculated torque and had a root mean square error of under 1.4 in comparison to the measured torque. Ankle movement models predict torque patterns with accuracy, thereby providing a solid theoretical basis for ankle rehabilitation control. The optimized model provides a theoretical foundation for precise ankle torque forecasts, thereby improving the efficacy of rehabilitation robots for the ankle. Full article

Most lower limb rehabilitation robots are limited to specific training postures to adapt to stroke patients in multiple stages of recovery. In addition, there is a lack of attention to the switching functions of the training side, including left, right, and bilateral, which enables patients with hemiplegia to rehabilitate with a single device. This article presents an exoskeleton robot named the multistage hemiplegic lower-limb rehabilitation robot, which has been designed to do rehabilitation in multiple training postures and training sides. The mechanism consisting of the thigh, calf, and foot is introduced. Additionally, the design of the multi-mode limit of the hip, knee, and ankle joints supports delivering therapy in any posture and training sides to aid patients with hemiplegia in all stages of recovery. The gait trajectory is planned by extracting the gait motion trajectory model collected by the motion capture device. In addition, a control system for the training module based on adaptive iterative learning has been simulated, and its high-precision tracking performance has been verified. The gait trajectory experiment is carried out, and the results verify that the trajectory tracking performance of the robot has good performance. Full article

The global aging population faces significant health challenges, including an increasing vulnerability to disability due to natural aging processes. Wearable lower limb exoskeletons (LLEs) have emerged as a promising solution to enhance physical function in older individuals. This systematic review synthesizes the use of LLEs in alignment with the WHO’s healthy aging vision, examining their impact on intrinsic capacities and functional abilities. We conducted a comprehensive literature search in six databases, yielding 36 relevant articles covering older adults (65+) with various health conditions, including sarcopenia, stroke, Parkinson’s Disease, osteoarthritis, and more. The interventions, spanning one to forty sessions, utilized a range of LLE technologies such as Ekso^®, HAL^®, Stride Management Assist^®, Honda Walking Assist^®, Lokomat^®, Walkbot^®, Healbot^®, Keeogo Rehab^®, EX1^®, overground wearable exoskeletons, Eksoband^®, powered ankle–foot orthoses, HAL^® lumbar type, Human Body Posturizer^®, Gait Enhancing and Motivation System^®, soft robotic suits, and active pelvis orthoses. The findings revealed substantial positive outcomes across diverse health conditions. LLE training led to improvements in key performance indicators, such as the 10 Meter Walk Test, Five Times Sit-to-Stand test, Timed Up and Go test, and more. Additionally, enhancements were observed in gait quality, joint mobility, muscle strength, and balance. These improvements were accompanied by reductions in sedentary behavior, pain perception, muscle exertion, and metabolic cost while walking. While longer intervention durations can aid in the rehabilitation of intrinsic capacities, even the instantaneous augmentation of functional abilities can be observed in a single session. In summary, this review demonstrates consistent and significant enhancements in critical parameters across a broad spectrum of health conditions following LLE interventions in older adults. These findings underscore the potential of LLE in promoting healthy aging and enhancing the well-being of older adults. Full article

Patients who are suffering from neuromuscular disorders or injuries that impair motor control need to undergo rehabilitation to regain mobility. Gait training is commonly prescribed to patients to regain muscle memory. Automated-walking training devices were created to aid this process; while these devices establish accurate ankle-path trajectories, the knee and hip movements are inaccurate. In this work, a redesign of the leg assembly in a multi-purpose rehabilitation robotic device (RoboREHAB) was explored to improve hip- and knee-movement accuracy by adding an extra link and rollers to the assembly. Motion analysis was employed to test feasibility, reinforcement learning was utilized to train the new leg assembly to walk, and the joint motions achieved with the redesign were compared to those achieved by motion-capture (mocap) data. As a key result, the motion analysis showed an improvement in the knee- and hip-path trajectories due to the added roller/joint segment. The redesigned leg assembly, under the reinforcement-learning policy, showed a 5% deviation from the motion-capture joint trajectories with a maximum deviation of 51.177 mm but maintained a similar profile to the mocap trajectory data. This is an improvement over the original two-segment design, which achieved a maximum deviation of 72.084 mm. These results in the knee- and hip-joint movements more closely reflect the mocap and motion-analysis results, validating the redesign and opening it up to further experimentation and technical improvement. Full article