Limb Exoskeleton Research Articles

Design, Development, and Functional Validation of a 3D-Printed Passive Upper Limb Exoskeleton.

Motor disability in children is evident in diagnoses such as cerebral palsy, muscular dystrophy, multiple sclerosis, or spinal muscular atrophy, among others, due to altered movement and postural patterns. This becomes more evident as the child grows and can be treated with physical therapy. The effectiveness of early interventions in facilitating an improvement in daily life activities varies depending on the child's condition. In this context, the use of exoskeletons has emerged in recent years as a valuable resource for conducting more efficient therapy processes. This work describes the design (both structural and functional) and preliminary usability and functional validation of a 3D-printed passive upper limb exoskeleton. The goal is to provide clinicians with an efficient, low-cost device that is both easy to manufacture and assemble and, in a gamified environment, serves as an assistive device to physical therapy. The device features 5 degrees of freedom, enabling both a pro-gravity and an anti-gravity mode, controlled by a series of elastic bands. This gives rise to a dual operating mode, offering assistance or resistance to different arm, forearm, and shoulder-dependent movements. Usability validation conducted by exoskeleton users showed average results in all aspects rated above 3.8 out of 5, which implies levels of satisfaction between "quite satisfied" and "very satisfied". The analysis of metrics recorded during therapy, such as the Hand Path Ratio and Success Rate (capturing user movements using an inertial sensor in the gamified environment), as well as the range of motion, reveals quantifiable improvements which can be attributed to the use of the exoskeleton: the Hand Path Ratio tended to approach 1 throughout sessions in almost all the users, the Success Rate remained stable (as users consistently were capable of completing the assigned tasks), and the range of motion showed that all patients achieved improvements of more than 10 degrees in some of the tested movements). These functional validation processes involved the participation of 7 children with varying levels of upper limb neuro-motor impairments.

Admittance swarm-based adaptive controller for lower limb exoskeleton with gait trajectory shaping

The motivation for developing a rehabilitation lower-limb exoskeleton robot was to provide functional robot-assisted therapy for assisting physiotherapists in improving hemiplegic patients’ walking recovery. Rehabilitation tasks required robust and precise trajectory-tracking performance, mainly achieved with exoskeleton robots. This paper presents a study on the gait trajectory cycles of a rehabilitation lower-limb exoskeleton robot controlled by an Admittance Swarm Initialized Adaptive (ASIA). The aim of this paper was to develop a robust adaptive controller integrated with admittance model to overcome human–robot interaction forces generated by the wearer. The parameters of the ASIA controller were efficiently initialized using swarm beetle antenna searching. An experiment was conducted on a prototype lower limb exoskeleton with four degrees of freedom, involving a healthy human subject for gait trajectory analysis. The results demonstrated the effectiveness of the proposed method in terms of control performance, steady-state error reduction, and robustness. The statistical analysis revealed that the ASIA performed 63 %, 53 % and 48 % less in average error compared to adaptive conventional controllers used in the same exoskeleton platform. The findings ascertained the potential of the ASIA controller in improving human mobility through lower limb exoskeleton applications.

AAN Controller With Adaptive Gain for Upper Limb Exoskeleton

AAN Controller With Adaptive Gain for Upper Limb Exoskeleton

A Novel Quasi-passive Non-anthropomorphic Lower Limb Exoskeleton for Load-bearing

A Novel Quasi-passive Non-anthropomorphic Lower Limb Exoskeleton for Load-bearing

Barrier Lyapunov Function-Based Fuzzy Adaptive Admittance Control of an Upper Limb Exoskeleton Using RBFNN Compensation

Barrier Lyapunov Function-Based Fuzzy Adaptive Admittance Control of an Upper Limb Exoskeleton Using RBFNN Compensation

Real-Time Continuous Locomotion Mode Recognition and Transition Prediction for Human With Lower Limb Exoskeleton.

Real-time continuous locomotion mode recognition and seamless timely transition detection is critical for the exoskeleton robot. This study aims to present a comprehensive and innovative framework for locomotion mode recognition and transition prediction, exclusively utilizing inertial measurement unit (IMU) signals from the exoskeleton. In this framework, a CNN-BiLSTM model was developed and trained to be the classifier and a novel majority filter was designed to reduce the transition misjudgment rate. Moreover, a comprehensive evaluation system encompassing eight dimensions for the classifier, incorporating evaluation metrics specifically for transition misjudgment, was proposed. We collected locomotion motion data from six subjects wearing a rigid exoskeleton robot using six IMU sensors on the exoskeleton. The proposed method achieves a high level of recognition accuracy, with an overall average of 99.58% for the five steady locomotion modes (level ground walking (LG), stair ascent/descent (SA/SD), and ramp ascent/descent (RA/RD)) across six subjects following the transition decision. All transitions are recognizable, and the majority can be predicted in advance, with an average prediction time of 353ms. Furthermore, the implementation of majority filter resulted in an average 87.04% reduction in the transition misjudgment rate among six subjects, thereby decreasing the average transition misjudgment rate to 4.82%. Finally, the model was tested on a Jetson Nano to verify its real-time performance. The results presented above were obtained under the condition where either leg could function as the first transition leg and revealed that the developed system was capable of achieving precise locomotion mode recognition and timely transition prediction, with high real-time performance.

An Upper Limb Exoskeleton Motion Generation Algorithm Based on Separating Shoulder and Arm Motion.

Many rehabilitation exoskeletons have been used in the field of stroke rehabilitation. Generating human-like motion is necessary for exoskeletons to help patients perform activities of daily living (ADL) while maintaining interaction quality and ergonomics. However, most of the current motion generation algorithms utilize inverse kinematics (IK) to solve the final configuration before generation, and do not consider the movement of shoulder girdle. Separately considering the shoulder girdle motion and arm motion, this paper proposes an algorithm integrated IK to generate human-like motion. The arm moves towards the target with a bell-shaped velocity in the absence of the final configuration, and the shoulder girdle maintain natural passive motion. Moreover, the generated motion can be mapped to the configuration space of exoskeletons. Compared with the experimental data collected using a motion capture system, the values of RMSE and HPDI of the generated wrist trajectory in the task space are within 0.2 and 0.17, respectively, while those of RMSE in the joint space are within 15 deg, which demonstrates the human-like nature of the generated motion.

Human–Robot Interaction Force Control of Series Elastic Actuator-Driven Upper Limb Exoskeleton Robot

Human–Robot Interaction Force Control of Series Elastic Actuator-Driven Upper Limb Exoskeleton Robot

Gait-Generation Strategy for Lower Limb Exoskeleton Based on Central Pattern Generator

Gait-Generation Strategy for Lower Limb Exoskeleton Based on Central Pattern Generator

Implementation of a Long-Lasting, Untethered, Lightweight, Upper Limb Exoskeleton

Implementation of a Long-Lasting, Untethered, Lightweight, Upper Limb Exoskeleton

A Modified Dynamic Movement Primitive Algorithm for Adaptive Gait Control of a Lower Limb Exoskeleton

A Modified Dynamic Movement Primitive Algorithm for Adaptive Gait Control of a Lower Limb Exoskeleton

MODEL PREDICTIVE CONTROL OF EXOSKELETON JOINT ANGLES

Lower limb exoskeletons are used for rehabilitation purposes, so their precise control is an important issue. Classical controllers are often unable to provide high accuracy and speed, which can lead to errors in exoskeleton operation. Complexities associated with lower limb exoskeleton control systems arise from the dynamic and nonlinear nature of human locomotion. Solving these complexities involves designing modern control systems. One of the modern approaches is Model Predictive Control, which, predicting future deviations of system state variables, performs optimization calculations and selects such input signals, in which case the error will tend to the minimum value. This controller is widely used for controlling nonlinear dynamic systems with constraints. Considering this fact, it was proposed to develop a controller with a predictive model for controlling the angles of the exoskeleton joints. Control with a predictive model assumes that the dynamic equations of the control object are known, so a nonlinear mathematical model of the lower limb exoskeleton was firstly designed, taking into account the mechanical and electromechanical components. An examination of the exoskeleton state variables and control input constraints, which were taken into account during the design of the controller, was performed, and a reduction function was defined, according to which the controller with a predictive model should perform an optimization calculation, thereby enabling the reduction of errors or inaccuracies. Simulations performed in the MATLAB/Simulink environment validate the effectiveness and accuracy of the developed controller, showing small deviations between the desired and actual joint angles during dynamic motions. Future efforts will focus on improving control quality and validating results through empirical studies, thereby strengthening the role of model predictive controllers for the design of exoskeleton control systems.

Feedforward Control of Lower Limb Exoskeletons: Which Torque Profile Should We Use?

Feedforward Control of Lower Limb Exoskeletons: Which Torque Profile Should We Use?

Passive Lower Limb Exoskeleton for Kneeling and Postural Transition Assistance With Expanded Support Polygon

Passive Lower Limb Exoskeleton for Kneeling and Postural Transition Assistance With Expanded Support Polygon

A Knowledge Transfer-based Personalized Human–Robot Interaction Control Method for Lower Limb Exoskeletons

A Knowledge Transfer-based Personalized Human–Robot Interaction Control Method for Lower Limb Exoskeletons

Human-Robot Joint Misalignment, Physical Interaction, and Gait Kinematic Assessment in Ankle-Foot Orthoses.

Lower limb exoskeletons and orthoses have been increasingly used to assist the user during gait rehabilitation through torque transmission and motor stability. However, the physical human-robot interface (HRi) has not been properly addressed. Current orthoses lead to spurious forces at the HRi that cause adverse effects and high abandonment rates. This study aims to assess and compare, in a holistic approach, human-robot joint misalignment and gait kinematics in three fixation designs of ankle-foot orthoses (AFOs). These are AFOs with a frontal shin guard (F-AFO), lateral shin guard (L-AFO), and the ankle modulus of the H2 exoskeleton (H2-AFO). An experimental protocol was implemented to assess misalignment, fixation displacement, pressure interactions, user-perceived comfort, and gait kinematics during walking with the three AFOs. The F-AFO showed reduced vertical misalignment (peak of 1.37 ± 0.90 cm, p-value < 0.05), interactions (median pressures of 0.39-3.12 kPa), and higher user-perceived comfort (p-value < 0.05) when compared to H2-AFO (peak misalignment of 2.95 ± 0.64 and pressures ranging from 3.19 to 19.78 kPa). F-AFO also improves the L-AFO in pressure (median pressures ranging from 8.64 to 10.83 kPa) and comfort (p-value < 0.05). All AFOs significantly modified hip joint angle regarding control gait (p-value < 0.01), while the H2-AFO also affected knee joint angle (p-value < 0.01) and gait spatiotemporal parameters (p-value < 0.05). Overall, findings indicate that an AFO with a frontal shin guard and a sports shoe is effective at reducing misalignment and pressure at the HRI, increasing comfort with slight changes in gait kinematics.

Model‐free based adaptive finite time control with multilayer perceptron neural network estimation for a 10 DOF lower limb exoskeleton

SummaryThis article presents a Model‐Free Adaptive Nonsingular Fast Terminal Sliding Mode Controller with Super Twisting and Multi‐Layer Perceptron (MLP) neural network for motion control of a 10 DOFs lower limb exoskeleton used in rehabilitation. The proposed controller employs a second‐order ultra‐local model to replace the complex dynamics of the exoskeleton and uses an MLP neural network to estimate the lumped disturbance of the ultra‐local model. To ensure accurate tracking of the desired trajectory and address the estimation errors of the MLP, an Adaptive Nonsingular Fast Terminal Sliding Mode Controller is introduced. Moreover, a Super Twisting approach is employed to eliminate the chattering phenomenon. The system's stability is analyzed using Lyapunov theory, and the desired trajectories are obtained from surface electromyography (EMG) signal measurements. The effectiveness of the proposed controller is validated through co‐simulation experiments using SolidWorks, Simscape Multibody, and MATLAB/Robotics Toolbox. Results demonstrate significant improvements in stability and precision compared to existing model‐free controllers.

A Parallel Compliant Leg for energy efficient walking of exoskeleton–walker systems

Lower limb exoskeletons have garnered significant attention for their effectiveness in gait training for paraplegic patients. For patients with insufficient trunk or upper limb strength to maintain balance, employing an unpowered robotic walker with an exoskeleton for gait training is an effective approach. The energy efficiency of the actuation system is a pivotal consideration in the design of the exoskeleton–walker system due to its significant influence on the system’s durability and service efficiency. The main contribution of this paper is the development of a Parallel Compliant Leg (PCL) for the exoskeleton–walker system. The PCL consists of both powered legs of the exoskeleton and a passive flexible mechanism within the walker. This integration allows for the storage and release of energy during cyclic walking, resulting in reduced system energy consumption. To enhance energy efficiency, the support force optimization of the flexible mechanism is established. Based on this optimization, a design scheme and parameter optimization for the flexible mechanism are proposed. The effectiveness of the proposed flexible mechanism is verified through simulations on a robot simulation platform. Experimental results demonstrate a remarkable 67.6% reduction in system energy consumption achieved by the optimized mechanism.

Human motor augmentation with an extra robotic arm without functional interference.

Extra robotic arms (XRAs) are gaining interest in neuroscience and robotics, offering potential tools for daily activities. However, this compelling opportunity poses new challenges for sensorimotor control strategies and human-machine interfaces (HMIs). A key unsolved challenge is allowing users to proficiently control XRAs without hindering their existing functions. To address this, we propose a pipeline to identify suitable HMIs given a defined task to accomplish with the XRA. Following such a scheme, we assessed a multimodal motor HMI based on gaze detection and diaphragmatic respiration in a purposely designed modular neurorobotic platform integrating virtual reality and a bilateral upper limb exoskeleton. Our results show that the proposed HMI does not interfere with speaking or visual exploration and that it can be used to control an extra virtual arm independently from the biological ones or in coordination with them. Participants showed significant improvements in performance with daily training and retention of learning, with no further improvements when artificial haptic feedback was provided. As a final proof of concept, naïve and experienced participants used a simplified version of the HMI to control a wearable XRA. Our analysis indicates how the presented HMI can be effectively used to control XRAs. The observation that experienced users achieved a success rate 22.2% higher than that of naïve users, combined with the result that naïve users showed average success rates of 74% when they first engaged with the system, endorses the viability of both the virtual reality-based testing and training and the proposed pipeline.

A Real-Time Control Method for Upper Limb Exoskeleton Based on Active Torque Prediction Model.

Exoskeleton rehabilitation robots have been widely used in the rehabilitation treatment of stroke patients. Clinical studies confirmed that rehabilitation training with active movement intentions could improve the effectiveness of rehabilitation treatment significantly. This research proposes a real-time control method for an upper limb exoskeleton based on the active torque prediction model. To fulfill the goal of individualized and precise rehabilitation, this method has an adjustable parameter assist ratio that can change the strength of the assist torque under the same conditions. In this study, upper limb muscles' EMG signals and elbow angle were chosen as the sources of control signals. The active torque prediction model was then trained using a BP neural network after appropriately extracting features. The model exhibited good accuracy on PC and embedded systems, according to the experimental results. In the embedded system, the RMSE of this model was 0.1956 N·m and 94.98%. In addition, the proposed real-time control system also had an extremely low delay of only 40 ms, which would significantly increase the adaptability of human-computer interactions.