A Self-Coordinating Controller with Balance-Guiding Ability for Lower-Limb Rehabilitation Exoskeleton Robot.

The restricted posture and unrestricted compliance brought by the controller during human-exoskeleton interaction (HEI) can cause patients to lose balance or even fall. In this article, a self-coordinated velocity vector (SCVV) double-layer controller with balance-guiding ability was developed for a lower-limb rehabilitation exoskeleton robot (LLRER). In the outer loop, an adaptive trajectory generator that follows the gait cycle was devised to generate a harmonious hip-knee reference trajectory on the non-time-varying (NTV) phase space. In the inner loop, velocity control was adopted. By searching the minimum L2 norm between the reference phase trajectory and the current configuration, the desired velocity vectors in which encouraged and corrected effects can be self-coordinated according to the L2 norm were obtained. In addition, the controller was simulated using an electromechanical coupling model, and relevant experiments were carried out with a self-developed exoskeleton device. Both simulations and experiments validated the effectiveness of the controller.

Improved Active Disturbance Rejection Control for Trajectory Tracking Control of Lower Limb Robotic Rehabilitation Exoskeleton.

Neurological disorders such as cerebral paralysis, spinal cord injuries, and strokes, result in the impairment of motor control and induce functional difficulties to human beings like walking, standing, etc. Physical injuries due to accidents and muscular weaknesses caused by aging affect people and can cause them to lose their ability to perform daily routine functions. In order to help people recover or improve their dysfunctional activities and quality of life after accidents or strokes, assistive devices like exoskeletons and orthoses are developed. Control strategies for control of exoskeletons are developed with the desired intention of improving the quality of treatment. Amongst recent control strategies used for rehabilitation robots, active disturbance rejection control (ADRC) strategy is a systematic way out from a robust control paradox with possibilities and promises. In this modern era, we always try to find the solution in order to have minimum resources and maximum output, and in robotics-control, to approach the same condition observer-based control strategies is an added advantage where it uses a state estimation method which reduces the requirement of sensors that is used for measuring every state. This paper introduces improved active disturbance rejection control (I-ADRC) controllers as a combination of linear extended state observer (LESO), tracking differentiator (TD), and nonlinear state error feedback (NLSEF). The proposed controllers were evaluated through simulation by investigating the sagittal plane gait trajectory tracking performance of two degrees of freedom, Lower Limb Robotic Rehabilitation Exoskeleton (LLRRE). This multiple input multiple output (MIMO) LLRRE has two joints, one at the hip and other at the knee. In the simulation study, the proposed controllers show reduced trajectory tracking error, elimination of random, constant, and harmonic disturbances, robustness against parameter variations, and under the influence of noise, with improvement in performance indices, indicates its enhanced tracking performance. These promising simulation results would be validated experimentally in the next phase of research.

PSO-SVM-Based Online Locomotion Mode Identification for Rehabilitation Robotic Exoskeletons.

Locomotion mode identification is essential for the control of a robotic rehabilitation exoskeletons. This paper proposes an online support vector machine (SVM) optimized by particle swarm optimization (PSO) to identify different locomotion modes to realize a smooth and automatic locomotion transition. A PSO algorithm is used to obtain the optimal parameters of SVM for a better overall performance. Signals measured by the foot pressure sensors integrated in the insoles of wearable shoes and the MEMS-based attitude and heading reference systems (AHRS) attached on the shoes and shanks of leg segments are fused together as the input information of SVM. Based on the chosen window whose size is 200 ms (with sampling frequency of 40 Hz), a three-layer wavelet packet analysis (WPA) is used for feature extraction, after which, the kernel principal component analysis (kPCA) is utilized to reduce the dimension of the feature set to reduce computation cost of the SVM. Since the signals are from two types of different sensors, the normalization is conducted to scale the input into the interval of [0, 1]. Five-fold cross validation is adapted to train the classifier, which prevents the classifier over-fitting. Based on the SVM model obtained offline in MATLAB, an online SVM algorithm is constructed for locomotion mode identification. Experiments are performed for different locomotion modes and experimental results show the effectiveness of the proposed algorithm with an accuracy of 96.00% ± 2.45%. To improve its accuracy, majority vote algorithm (MVA) is used for post-processing, with which the identification accuracy is better than 98.35% ± 1.65%. The proposed algorithm can be extended and employed in the field of robotic rehabilitation and assistance.

Advancements in Sensor Technologies and Control Strategies for Lower-Limb Rehabilitation Exoskeletons: A Comprehensive Review.

Lower-limb rehabilitation exoskeletons offer a transformative approach to enhancing recovery in patients with movement disorders affecting the lower extremities. This comprehensive systematic review delves into the literature on sensor technologies and the control strategies integrated into these exoskeletons, evaluating their capacity to address user needs and scrutinizing their structural designs regarding sensor distribution as well as control algorithms. The review examines various sensing modalities, including electromyography (EMG), force, displacement, and other innovative sensor types, employed in these devices to facilitate accurate and responsive motion control. Furthermore, the review explores the strengths and limitations of a diverse array of lower-limb rehabilitation-exoskeleton designs, highlighting areas of improvement and potential avenues for further development. In addition, the review investigates the latest control algorithms and analysis methods that have been utilized in conjunction with these sensor systems to optimize exoskeleton performance and ensure safe and effective user interactions. By building a deeper understanding of the diverse sensor technologies and monitoring systems, this review aims to contribute to the ongoing advancement of lower-limb rehabilitation exoskeletons, ultimately improving the quality of life for patients with mobility impairments.

Adaptive neural fault-tolerant prescribed performance control of a rehabilitation exoskeleton for lower limb passive training.

This article studies the passive tracking problem of a wearable exoskeleton for lower limb rehabilitation therapy in the face of unmodeled dynamics, interactive friction, disturbance, prescribed performance constraints, and actuator faults. Adaptive neural networks and a smooth performance function are incorporated to establish a novel fault-tolerant tracking scheme, which can not only compensate for the nonlinear uncertainties and disturbance, but also handle the actuator fault with guaranteed tracking performance. A state feedback controller is presented by using the full state information and an output feedback controller is developed when the angular velocity is unavailable. The differential explosion issue of the backstepping technique is resolved by constructing a first-order filter and the unmeasurable velocity is estimated by a nonlinear observer. Semiglobal uniform boundedness stabilities of the exoskeleton system are proved via the Lyapunov direct method. The tracking performances of the designed control approaches are tested by comparative simulations.

B-FMEA-TRIZ model for scheme decision in conceptual product design: A study on upper-limb hemiplegia rehabilitation exoskeleton.

Upper-limb rehabilitation devices are essential in restoring and improving the motor function of hemiplegic patients. However, developing a product design that meets the needs of users is challenging. Current design tools and methods suffer from limitations such as a single model, poor synergy between integrated models, and subjective bias in analysing user needs and translating them into product attributes. To address these issues, this study proposes a new structural design decision-making model based on Behaviour Analysis (B), Failure Mode Effect Analysis (FMEA), and Teoriya Resheniya Izobreatatelskikh Zadatch (TRIZ theory). The model was developed and applied to design an upper-limb rehabilitation exoskeleton for hemiplegia. In this paper, an empirical investigation was conducted in several rehabilitation hospitals in Xuzhou City and used user journey mapping to identify potential failure points in the behaviour process. Then, the fault models were ranked according to the Fuzzy Risk Priority Number (FRPN) calculated by FMEA and used TRIZ theory to determine principles for resolving contradictions and generating creative design solutions for the product. By integrating B, FMEA, and TRIZ theory, it eliminated subjective bias in product design, improved the design decision-making process, and provided new methods and ideas for designing assistive rehabilitation devices and similar products. The framework of the proposed approach can be used in other contexts to develop effective and precise product designs that meet the needs of users.

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.

Glenohumeral joint trajectory tracking for improving the shoulder compliance of the upper limb rehabilitation robot.

BACKGROUND: Exoskeletons have become an important tool to help patients with upper extremity motor dysfunction in rehabilitation training and life assistance. In the study of the upper limb exoskeleton, the human glenohumeral joint will produce accompanying movement during the movement of the shoulder joint. This phenomenon causes a positional deviation between the shoulder joint and the exoskeleton, which affects the accuracy of exoskeleton-assisted human movement and the wearing comfort. Spend. METHOD: Taking the coronal adduction and abduction of the shoulder joint as the research object, the shoulder joint angle and glenohumeral joint bony motion trajectory were fitted by bi-level X-rays, and then the Ultium Motion motion capture system was used to collect the characteristic motion of the shoulder joint surface and establish a model. A back-propagation neural network with shoulder joint motion and shoulder width as input and the coronal position of the glenohumeral joint as output, finally applied the model to the Nimbot exoskeleton upper limb rehabilitation training robot to verify the effectiveness of the algorithm. RESULTS: Real-time prediction of the glenohumeral joint motion trajectory was achieved, and the human-machine coupling compliance during the wearing of the upper limb exoskeleton was improved.

Design and kinematical performance analysis of the 7-DOF upper-limb exoskeleton toward improving human-robot interface in active and passive movement training.

BACKGROUND: Upper-limb rehabilitation robots have become an important piece of equipment in stroke rehabilitation. The design of exoskeleton mechanisms plays a key role to improve human-robot interface in the upper-limb movements under passive and active rehabilitation training. OBJECTIVE: This paper proposes a novel of the 7-DOF (RR-RR-PRR) under-actuated exoskeleton mechanism based on the characteristics of the upper-limb movements in both of active and passive training. This aim of the proposed work is to improve human-robot interface in rehabilitation training with robots. METHODS: Firstly, the characteristics of active and passive movement training are analyzed depending on the human upper-limb model. Then, a novel 7-DOF (RR-RR-PRR) exoskeleton mechanism is proposed based on the analyzed characteristics. After that, kinematical performances of the proposed exoskeleton are analyzed on the workspace, manipulability and manipulability ellipsoid by compared with the common exoskeleton configuration of the 7 DOFs (RRR-R-PRR) mechanism. In the end, the prototype is manufactured and tested by undergoing the experiments of single-joint passive movement training and multi-joint active movement training. The human-robot interface of the proposed exoskeleton is demonstrated by root mean square error, Pearson correlation coefficient, and the time-delay difference. RESULTS: The results of the kinematical performance show that the effective workspace and the flexibility of the exoskeleton with the proposed configuration are increased by 10.44% and 1.7%. In the single-joint passive movement training experiment, the root mean square errors are 6.986, 7.568, 5.846, and Pearson correlation coefficients are 0.989, 0.984, 0.988 at the shoulder joint and the elbow joint, respectively. The time-delay differences are not beyond 3.1%. In the multi-joint active movement training experiment, the root mean square errors are 9.312 and 7.677, and Pearson correlation coefficients are 0.906 and 0.968 at the shoulder joint and the elbow joint, respectively. The time-delay differences are not beyond 3.28%. CONCLUSIONS: The proposed 7 DOFs exoskeleton mechanism shows uniformity with that of the common exoskeleton on the same rehabilitation trajectory which is effective to improve human-robot interface under passive and active rehabilitation training.

Design of a control framework for lower limb exoskeleton rehabilitation robot based on predictive assessment.

BACKGROUND: Patients suffering from lower limb dyskinesia, especially in early stages of rehabilitation, have weak residual muscle strength in affected limb and require passive training by the lower limb rehabilitation robot. Anatomy indicates that the biceps femoris short head muscle has a strong influence on knee motion at the swing phase of walking. We sought to explore how it would influence on gait cycle in optimization framework. However, the training trajectory of conventional rehabilitation robots performing passive training usually follows gait planning based on general human gait data, which cannot simultaneously ensure both effective rehabilitation of affected limbs with varying severity pathological gait and comfort of the wearer within a safe motion trajectory. METHODS: To elucidate the effects of weakness and contracture, we systematically introduced isolated defects into the musculoskeletal model and generated walking simulations to predict the gait adaptation due to these defects. An impedance control model of the rehabilitation robot is developed. Knee joint parameters optimized by predictive forward dynamics simulation are adopted as the expected values for the robot controller to achieve customized adjustment of the robot motion trajectory. FINDINGS: Severe muscle contracture leads to severe knee flexion; severe muscle weakness induces a significant posterior tilt of the upper trunk, which hinders walking speed. INTERPRETATION: Our simulation results attempt to reveal pathological gait features, which may help to reproduce the simulation of pathological gait. Furthermore, the robot simulation results show that the robot system achieves a speedy tracking by setting a larger stiffness value. The model also allows the implementation of different levels of damping or elasticity effects. TRIAL REGISTRATION: The method proposed in this paper is an initial basic study that did not reach clinical trials and therefore retains retrospectively registered.

Anti-Disturbance Sliding Mode Control of a Novel Variable Stiffness Actuator for the Rehabilitation of Neurologically Disabled Patients.

Lower limb exoskeletons are widely used for rehabilitation training of patients suffering from neurological disorders. To improve the human-robot interaction performance, series elastic actuators (SEAs) with low output impedance have been developed. However, the adaptability and control performance are limited by the constant spring stiffness used in current SEAs. In this study, a novel load-adaptive variable stiffness actuator (LaVSA) is used to design an ankle exoskeleton. To overcome the problems of the LaVSA with a larger mechanical gap and more complex dynamic model, a sliding mode controller based on a disturbance observer is proposed. During the interaction process, due to the passive joints at the load side of the ankle exoskeleton, the dynamic parameters on the load side of the ankle exoskeleton will change continuously. To avoid this problem, the designed controller treats it and the model error as a disturbance and observes it with the disturbance observer (DOB) in real time. The first-order derivative of the disturbance set is treated as a bounded value. Subsequently, the parameter adaptive law is used to find the upper bound of the observation error and make corresponding compensation in the control law. On these bases, a sliding mode controller based on a disturbance observer is designed, and Lyapunov stability analysis is given. Finally, simulation and experimental verification are performed. The wearing experiment shows that the resistance torque suffered by humans under human-robot interaction is lower than 120 Nmm, which confirms that the controller can realize zero-impedance control of the designed ankle exoskeleton.

Adaptive interaction torque-based AAN control for lower limb rehabilitation exoskeleton.

In this paper, an adaptive interaction torque-based assist-as-needed (AITAAN) control method for the lower limb rehabilitation exoskeleton is proposed. Firstly, a desired input torque for the wearer's lower limb is designed based on computed torque control (CTC). A nonlinear disturbance observer (NDO) is used to assess the lower limb muscle torque. Subtract the estimated muscle torque from the desired input torque, the exoskeleton only provides the remaining torque through interaction torque. Then, the interaction torque tracking problem can be converted to the exoskeleton trajectory tracking problem by using the spring-damper like dynamics model of the interaction force. A flexible boundary prescribed performance controller (PPC) is designed for the exoskeleton to achieve fast and accurate trajectory tracking. The coupled wearer-exoskeleton system is established in SolidWorks and imported to MATLAB/Simulink with SimMechanics. The AITAAN controller's effectiveness and superiority were then verified through co-simulations.

Designing a robust controller for a lower limb exoskeleton to treat an individual with crouch gait pattern in the presence of actuator saturation.

Crouch gait is a gait anomaly observed in youngsters with cerebral palsy (CP). Rehabilitation robots are useful for treating individuals with crouch gait. Multiple factors have impact on crouch, including contracture, spasticity, weak motor control, and muscle feebleness, which make the designing and controlling of these exoskeletons for this population a challenging job. A harsh kinematic trajectory enforced by an exoskeleton control strategy may place individuals with spasticity at a high risk of muscle tissue injury. Therefore, in this article, a multi-input multi-output (MIMO) control method is proposed to reduce this risk and improve crouch gait pattern. A constrained control law is used in the model since high power demands may threaten the wearer. In addition, the controller needs to be robust enough against external disturbances and uncertainties. Thus, a nonlinear disturbance observer (NDO) is presented to compute the wearer-generated muscular torque and the uncertainties in the modeling. In addition, a robust constrained MIMO backstepping sliding controller (CMBSC) based on NDO is used to deal with the effect of actuator saturation and uncertainties. A simulation test was used to validate the proposed model and controller. The results of Simulation confirmed the efficiency of the proposed control method when applied to crouch gait with subject specific gait reference. Then, some experimental tests were undertaken to validate the efficiency of the proposed controller.

Reinforcement Learning and Control of a Lower Extremity Exoskeleton for Squat Assistance.

A significant challenge for the control of a robotic lower extremity rehabilitation exoskeleton is to ensure stability and robustness during programmed tasks or motions, which is crucial for the safety of the mobility-impaired user. Due to various levels of the user's disability, the human-exoskeleton interaction forces and external perturbations are unpredictable and could vary substantially and cause conventional motion controllers to behave unreliably or the robot to fall down. In this work, we propose a new, reinforcement learning-based, motion controller for a lower extremity rehabilitation exoskeleton, aiming to perform collaborative squatting exercises with efficiency, stability, and strong robustness. Unlike most existing rehabilitation exoskeletons, our exoskeleton has ankle actuation on both sagittal and front planes and is equipped with multiple foot force sensors to estimate center of pressure (CoP), an important indicator of system balance. This proposed motion controller takes advantage of the CoP information by incorporating it in the state input of the control policy network and adding it to the reward during the learning to maintain a well balanced system state during motions. In addition, we use dynamics randomization and adversary force perturbations including large human interaction forces during the training to further improve control robustness. To evaluate the effectiveness of the learning controller, we conduct numerical experiments with different settings to demonstrate its remarkable ability on controlling the exoskeleton to repetitively perform well balanced and robust squatting motions under strong perturbations and realistic human interaction forces.

Musculoskeletal modeling and humanoid control of robots based on human gait data.

The emergence of exoskeleton rehabilitation training has brought good news to patients with limb dysfunction. Rehabilitation robots are used to assist patients with limb rehabilitation training and play an essential role in promoting the patient's sports function with limb disease restoring to daily life. In order to improve the rehabilitation treatment, various studies based on human dynamics and motion mechanisms are still being conducted to create more effective rehabilitation training. In this paper, considering the human biological musculoskeletal dynamics model, a humanoid control of robots based on human gait data collected from normal human gait movements with OpenSim is investigated. First, the establishment of the musculoskeletal model in OpenSim, inverse kinematics, and inverse dynamics are introduced. Second, accurate human-like motion analysis on the three-dimensional motion data obtained in these processes is discussed. Finally, a classic PD control method combined with the characteristics of the human motion mechanism is proposed. The method takes the angle values calculated by the inverse kinematics of the musculoskeletal model as a benchmark, then uses MATLAB to verify the simulation of the lower extremity exoskeleton robot. The simulation results show that the flexibility and followability of the method improves the safety and effectiveness of the lower limb rehabilitation exoskeleton robot for rehabilitation training. The value of this paper is also to provide theoretical and data support for the anthropomorphic control of the rehabilitation exoskeleton robot in the future.

A Multi-Information Fusion Method for Gait Phase Classification in Lower Limb Rehabilitation Exoskeleton.

Gait phase classification is important for rehabilitation training in patients with lower extremity motor dysfunction. Classification accuracy of the gait phase also directly affects the effect and rehabilitation training cycle. In this article, a multiple information (multi-information) fusion method for gait phase classification in lower limb rehabilitation exoskeleton is proposed to improve the classification accuracy. The advantage of this method is that a multi-information acquisition system is constructed, and a variety of information directly related to gait movement is synchronously collected. Multi-information includes the surface electromyography (sEMG) signals of the human lower limb during the gait movement, the angle information of the knee joints, and the plantar pressure information. The acquired multi-information is processed and input into a modified convolutional neural network (CNN) model to classify the gait phase. The experiment of gait phase classification with multi-information is carried out under different speed conditions, and the experiment is analyzed to obtain higher accuracy. At the same time, the gait phase classification results of multi-information and single information are compared. The experimental results verify the effectiveness of the multi-information fusion method. In addition, the delay time of each sensor and model classification time is measured, which shows that the system has tremendous real-time performance.

A Real-Time Stability Control Method Through sEMG Interface for Lower Extremity Rehabilitation Exoskeletons.

Herein, we propose a real-time stable control gait switching method for the exoskeleton rehabilitation robot. Exoskeleton rehabilitation robots have been extensively developed during the past decade and are able to offer valuable motor ability to paraplegics. However, achieving stable states of the human-exoskeleton system while conserving wearer strength remains challenging. The constant switching of gaits during walking may affect the center of gravity, resulting in imbalance of human-exoskeleton system. In this study, it was determined that forming an equilateral triangle with two crutch-supporting points and a supporting leg has a positive impact on walking stability and ergonomic interaction. First, the gaits planning and stability analysis based on human kinematics model and zero moment point method for the lower limb exoskeleton are demonstrated. Second, a neural interface based on surface electromyography (sEMG), which realizes the intention recognition and muscle fatigue estimation, is constructed. Third, the stability of human-exoskeleton system and ergonomic effects are tested through different gaits with planned and unplanned gait switching strategy on the SIAT lower limb rehabilitation exoskeleton. The intention recognition based on long short-term memory (LSTM) model can achieve an accuracy of nearly 99%. The experimental results verified the feasibility and efficiency of the proposed gait switching method for enhancing stability and ergonomic effects of lower limb rehabilitation exoskeleton.

Inverse kinematic analysis and trajectory planning of a modular upper limb rehabilitation exoskeleton.

BACKGROUND: Stroke is the most prevalent neurological disease and often leads to disability. Stroke can affect a person's daily life, for example, its typical feature is the decline in the patient's upper limbs. In order to reduce the sports injury of stroke patients, the best method is to carry out certain rehabilitation training. OBJECTIVE: In this paper, inverse kinematic analysis and trajectory planning of a modular upper limb rehabilitation exoskeleton are proposed. METHODS: The reverse coordinate system method is applied to solve inverse kinematics of the exoskeleton with a non-spherical joint in the wrist. For verifying the effectiveness of the algorithms, the smooth round-trip trajectory movement in joint place is designed and simulated. RESULTS: The reverse coordinate system method can simplify the calculation process compared with the normal coordinate system. Smooth round-trip trajectory planning is simulated to generate a smooth trajectory curve. CONCLUSIONS: The developed inverse kinematics algorithm and trajectory planning method are effective.

Active disturbance rejection control based human gait tracking for lower extremity rehabilitation exoskeleton.

This paper presents an active disturbance rejection control (ADRC) based strategy, which is applied to track the human gait trajectory for a lower limb rehabilitation exoskeleton. The desired human gait trajectory is derived from the Clinical Gait Analysis (CGA). In ADRC, the total external disturbance can be estimated by the extended state observer (ESO) and canceled by the designed control law. The observer bandwidth and the controller bandwidth are determined by the practical principles. We simulated the proposed methodology in MATLAB. The numerical simulation shows the tracking error comparison and the estimated errors of the extended state observer. Two experimental tests were carried out to prove the performance of the algorithm presented in this paper. The experiment results show that the proposed ADRC behaves a better performance than the regular proportional integral derivative (PID) controller. With the proposed ADRC, the rehabilitation system is capable of tracking the target gait more accurately.