Limb Rehabilitation Exoskeleton Research Articles

Control Method of Upper Limb Rehabilitation Exoskeleton for Better Assistance: A Comprehensive Review

Control Method of Upper Limb Rehabilitation Exoskeleton for Better Assistance: A Comprehensive Review

Lower Limb Rehabilitation Exoskeleton Robots, A review

Using a lower limb exoskeleton for rehabilitation (LLE) Lower limb exoskeleton rehabilitation robots (LER) are designed to assist patients with daily duties and help them regain their ability to walk. Even though a substantial portion of them is capable of doing both, they have not yet succeeded in conducting agile and intelligent joint movement between humans and machines, which is their ultimate goal. The typical LLE products, rapid prototyping, and cutting-edge techniques are covered in this review. Restoring a patient's athletic prowess to its pr-accident level is the aim of rehabilitation treatment. The core of research on lower limb exoskeleton rehabilitation robots is the understanding of human gait. The performance of common prototypes might be used to match wearable robot shapes to human limbs. To imitate a normal stride, robot-assisted treatment needs to be able to control the movement of the robot at each joint and move the patient's limb.

A Recurrent Deep Network for Gait Phase Identification from EMG Signals During Exoskeleton-Assisted Walking

Lower limb exoskeletons represent a relevant tool for rehabilitating gait in patients with lower limb movement disorders. Partial assistance exoskeletons adaptively provide the joint torque needed, on top of that produced by the patient, for a correct and stable gait, helping the patient to recover an autonomous gait. Thus, the device needs to identify the different phases of the gait cycle to produce precisely timed commands that drive its joint motors appropriately. In this study, EMG signals have been used for gait phase detection considering that EMG activations lead limb kinematics by at least 120 ms. We propose a deep learning model based on bidirectional LSTM to identify stance and swing gait phases from EMG data. We built a dataset of EMG signals recorded at 1500 Hz from four muscles from the dominant leg in a population of 26 healthy subjects walking overground (WO) and walking on a treadmill (WT) using a lower limb exoskeleton. The data were labeled with the corresponding stance or swing gait phase based on limb kinematics provided by inertial motion sensors. The model was studied in three different scenarios, and we explored its generalization abilities and evaluated its applicability to the online processing of EMG data. The training was always conducted on 500-sample sequences from WO recordings of 23 subjects. Testing always involved WO and WT sequences from the remaining three subjects. First, the model was trained and tested on 500 Hz EMG data, obtaining an overall accuracy on the WO and WT test datasets of 92.43% and 91.16%, respectively. The simulation of online operation required 127 ms to preprocess and classify one sequence. Second, the trained model was evaluated against a test set built on 1500 Hz EMG data. The accuracies were lower, yet the processing times were 11 ms faster. Third, we partially retrained the model on a subset of the 1500 Hz training dataset, achieving 87.17% and 89.64% accuracy on the 1500 Hz WO and WT test sets, respectively. Overall, the proposed deep learning model appears to be a valuable candidate for entering the control pipeline of a lower limb rehabilitation exoskeleton in terms of both the achieved accuracy and processing times.

Gait self-learning control based on reference trajectory generation online for an asymmetric limb rehabilitation exoskeleton

Lower limb exoskeleton (LEX) are widely used to assist stoke survivors with walking dysfunction, which is lack of a more flexible trajectory and fails to address the control challenge posed by gait variability and asymmetry in rehabilitation training. This paper introduces an asymmetric self-learning lower exoskeleton (AS-LEX) based on reference trajectory generation for the affected side. Motor intent of the unaffected limb based on thresholds was identified to classify the gait phase of stance and swing. A parameterized gait trajectory was generated online, namely a combination of circular trajectory in the stance phase and an elliptical trajectory in the swing phase. Gait self-learning control is presented to make the affected limb adaptively learn the gait parameters generated by the unaffected limb. Feasibility of the AS-LEX is demonstrated experimentally using three healthy subjects. Resuls demonstrate that overground walking in a more natural speed (with a stride length 600 mm and 700 mm) make subjects more actively learn gait of the affected side from the unaffected side. Additionally, experiments of the fatigue level of the affected limb and human-robot interaction torques were carried out, and the results indicate a more natural gait and reduced interaction forces with the AS-LEX.

A novel deep learning method for motion assessment in upper limb rehabilitation grasping test

Purpose The accuracy and reliability of upper limb motion assessment have received great attention in the field of rehabilitation. Grasping test is widely carried out for motion assessment, which requires patients to grasp objects and move them to target place. The traditional assessments test the upper limb motion ability by therapists, which mainly relies on experience and lacks quantitative indicators. This paper aims to propose a deep learning method based on the vision system of our upper limb rehabilitation robot to recognize the motion trajectory of rehabilitation target objects automatically and quantitatively assess the upper limb motion in the grasping test. Design/methodology/approach To begin with, an SRF network is designed to recognize rehabilitation target objects grasped in assessment tests. Moreover, the upper limb motion trajectory is calculated through the motion of objects’ central positions. After that, a GAE network is designed to analyze the motion trajectory which reflects the motion of upper limb. Finally, based on the upper limb rehabilitation exoskeleton platform, the upper limb motion assessment tests are carried out to show the accuracy of both object recognition of SRF network and motion assessment of GAE network. The results including object recognition, trajectory calculation and deviation assessment are given with details. Findings The performance of the proposed networks is validated by experiments that are developed on the upper limb rehabilitation robot. It is implemented by recognizing rehabilitation target objects, calculating the motion trajectory and grading the upper limb motion performance. It illustrates that the networks, including both object recognition and trajectory evaluation, can grade the upper limb motion functionn accurately, where the accuracy is above 95.0% in different grasping tests. Originality/value A novel assessment method of upper limb motion is proposed and verified. According to the experimental results, the accuracy can be remarkably enhanced, and the stability of the results can be improved, which provide more quantitative indicators for further application of upper limb motion assessment.

PID Control of a Lower Limb Rehabilitation Exoskeleton

PID Control of a Lower Limb Rehabilitation Exoskeleton

SEMG data driven-based anti-disturbance control enables adaptive interaction of lower limb rehabilitation exoskeleton

The efficient human–machine interaction control is the core of human–machine integration, which can improve the tracking accuracy of the lower limb rehabilitation exoskeleton system and the compliance of human–machine cooperative movement. However, the non-ideal factors such as mechanical friction, model uncertainties, iteration errors and external interference in the training process undoubtedly bring potential dangers to the safety of rehabilitation training. Consequently, to escape unnecessary damage caused by external disturbances (especially the unbounded disturbances) during rehabilitation training, a noise-suppressing zeroing neural network human–machine interaction controller is developed in this work, which is based on the constructed human–machine coupling dynamic model and the active motion intention (active torque) of the subject identified by exploiting the deep convolutional neural network. The simulation experiments and statistical analyses verify that the present noise-suppressing zeroing neural network controller can be applied to monitor the lower limb rehabilitation exoskeleton for assisting the subjects in various task with the external disturbances,and the average root mean square error of the hip, knee and ankle joints’ angles are 0.0015rad, 0.0051rad and 0.0056rad, respectively. Furthermore, a novel model predictive control is developed and analyzed based on noise-suppressing zeroing neural network controller, which can effectively constrain the angle and angular velocity of the lower limb rehabilitation exoskeleton. The root mean square errors of hip, knee and ankle joint angle are 0.0032rad, 0.0078rad, and 0.0085rad, respectively. Finally, the platform experiment verifies that the proposed controllers can be utilized to control the lower limb rehabilitation exoskeleton to assist the subjects in rehabilitation training.

Muscle-inspired bi-planar cable routing: a novel framework for designing cable driven lower limb rehabilitation exoskeletons (C-LREX)

There is a growing interest in the research and development of Cable Driven Rehabilitation Devices (CDRDs) due to multiple inherent features attractive to clinical applications, including low inertia, lightweight, high payload-to-weight ratio, large workspace, and modular design. However, previous CDRDs have mainly focused on modifying motor impairment in the sagittal plane, despite the fact that neurological disorders, such as stroke, often involve postural control and gait impairment in multiple planes. To address this gap, this work introduces a novel framework for designing a cable-driven lower limb rehabilitation exoskeleton which can assist with bi-planar impaired posture and gait. The framework used a lower limb model to analyze different cable routings inspired by human muscle architecture and attachment schemes to identify optimal routing and associated parameters. The selected cable routings were safeguarded for non-interference with the human body while generating bi-directional joint moments. The subsequent optimal cable routing model was then implemented in simulations of tracking reference healthy trajectory with bi-planar impaired gait (both in the sagittal and frontal planes). The results showed that controlling joints independently via cables yielded better performance compared to dependent control. Routing long cables through intermediate hinges reduced the peak tensions in the cables, however, at a cost of induced additional joint forces. Overall, this study provides a systematic and quantitative in silico approach, featured with accessible graphical user interface (GUI), for designing subject-specific, safe, and effective lower limb cable-driven exoskeletons for rehabilitation with options for multi-planar personalized impairment-specific features.

Design and motion control of exoskeleton robot for paralyzed lower limb rehabilitation.

Patients suffering from limb movement disorders require more complete rehabilitation treatment, and there is a huge demand for rehabilitation exoskeleton robots. Flexible and reliable motion control of exoskeleton robots is very important for patient rehabilitation. This paper proposes a novel exoskeleton robotic system for lower limb rehabilitation. The designed lower limb rehabilitation exoskeleton robot mechanism is mainly composed of the hip joint mechanism, the knee joint mechanism and the ankle joint mechanism. The forces and motion of the exoskeleton robot were analyzed in detail to determine its design parameters. The robot control system was developed to implement closed-loop position control and trajectory planning control of each joint mechanism. Multiple experiments and tests were carried out to verify robot's performance and practicality. In the robot angular response experiments, the joint mechanism could quickly adjust to different desired angles, including 15°, 30°, 45°, and 60°. In the trajectory tracking experiments, the exoskeleton robot could complete tracking movements of typical actions such as walking, standing up, sitting down, go upstairs and go downstairs, with a maximum tracking error of ±5°. Robotic wearing tests on normal people were performed to verify the assistive effects of the lower limb rehabilitation exoskeleton at different stages. The experimental results indicated that the exoskeleton robot has excellent reliability and practicality. The application of this exoskeleton robotic system will help paralyzed patients perform some daily movements and sports.

Design of lower limb rehabilitation exoskeleton with self-adaptive knee optimal force

With the increasingly serious trend of population aging in China, various diseases of the elderly are emerging in an endless stream, including hemiplegia, half limb disorder, limb numbness, hemianopia, and aphasia, with a high disability rate. The available auxiliary device cannot make a clear assistance for the patient, so that this design plans to make an exoskeleton for the patients who need effective rehabilitation alternatives and improve the rehabilitation efficiency. In this design, we would create Solid works model with some joint to fit human knees and then select sensors which are suitable to collect signs to depict kinematics features. The model, which has self-adaptation for receiving force signs from calf and knee joint, depends on the different force points of the calf lift affecting the stress state of the knee joint. Because the different stress points of the calf lift directly affect the stress state of the knee, so to find the best stress point of the calf is the research direction of this paper. In order to find the best and the most suitable force point of the human lower limb, we two methods, step-by-step process and dichotomy process, were discussed and compared in this study. Reasonable material may decide the robotic features.

Study on the safety of elbow joint with simple upper limb rehabilitation exoskeleton

Due to the great demand for treating physicians for patients with disabilities caused by stroke, in order to reduce the workload of doctors, exoskeleton robots have been effectively applied and promoted in post-illness health care. Because the human rehabilitation movement needs certain accuracy and low error tolerance rate, the rehabilitation exoskeleton robot requires high precision. Since the exoskeleton robot needs to be worn on the human body, there are certain requirements for the size and wearing comfort of the machine, and the functional structure of all aspects of the upper limb exoskeleton rehabilitation robot still needs to be further improved. In the medical rehabilitation exoskeleton, for the elbow exoskeleton of the upper limb, an elbow exoskeleton with binding structure is proposed in this paper. The elastic structure connects the human body with the binding structure, and on this basis, the wearing comfort is improved through the improvement of the structure and the selection of materials. At the same time, the passive joint between the binding structure and the exoskeleton is designed. In case of motion error when the motor is actively driven, the passive joint reduces the harmful force and torque, so as to ensure safety. Finally, the feasibility of this method is demonstrated through simulation and analysis of the movement of exoskeleton machine. It is proved that the design has certain practicability and reference value for practical treatment.

Active Neural Network Control for a Wearable Upper Limb Rehabilitation Exoskeleton Robot Driven by Pneumatic Artificial Muscles.

Pneumatic artificial muscle (PAM) has been widely used in rehabilitation and other fields as a flexible and safe actuator. In this paper, a PAM-actuated wearable exoskeleton robot is developed for upper limb rehabilitation. However, accurate modeling and control of the PAM are difficult due to complex hysteresis. To solve this problem, this paper proposes an active neural network method for hysteresis compensation, where a neural network (NN) is utilized as the hysteresis compensator and unscented Kalman filtering is used to estimate the weights and approximation error of the NN in real time. Compared with other inversion-based methods, the NN is directly used as the hysteresis compensator without needing inversion. Additionally, the proposed method does not require pre-training of the NN since the weights can be dynamically updated. To verify the effectiveness and robustness of the proposed method, a series of experiments have been conducted on the self-built exoskeleton robot. Compared with other popular control methods, the proposed method can track the desired trajectory faster, and tracking accuracy is gradually improved through iterative learning and updating.

A Human-centered Kinematics Design Optimization of Upper Limb Rehabilitation Exoskeleton Based on Configuration Manifold

A Human-centered Kinematics Design Optimization of Upper Limb Rehabilitation Exoskeleton Based on Configuration Manifold

Design and Control of Lower Limb Rehabilitation Exoskeleton Based on Simulink

Design and Control of Lower Limb Rehabilitation Exoskeleton Based on Simulink

Online gait learning with Assist-As-Needed control strategy for post-stroke rehabilitation exoskeletons

AbstractLower limb exoskeletons (LLEs) have demonstrated their potential in delivering quantified repetitive gait training for individuals afflicted with gait impairments. A critical concern in robotic gait training pertains to fostering active patient engagement, and a viable solution entails harnessing the patient’s intrinsic effort to govern the control of LLEs. To address these challenges, this study presents an innovative online gait learning approach with an appropriate control strategy for rehabilitation exoskeletons based on dynamic movement primitives (DMP) and an Assist-As-Needed (AAN) control strategy, denoted as DMP-AAN. Specifically tailored for post-stroke patients, this approach aims to acquire the gait trajectory from the unaffected leg and subsequently generate the reference gait trajectory for the affected leg, leveraging the acquired model and the patient’s personal exertion. Compared to conventional AAN methodologies, the proposed DMP-AAN approach exhibits adaptability to diverse scenarios encompassing varying gait patterns. Experimental validation has been performed using the lower limb rehabilitation exoskeleton HemiGo. The findings highlight the ability to generate suitable control efforts for LLEs with reduced human-robot interactive force, thereby enabling highly patient-controlled gait training sessions to be achieved.

Lower limb rehabilitation exoskeleton using brain–computer interface based on multiband filtering with classifier fusion

AbstractIn this study, we propose two adaptive frameworks based on evolutionary algorithms for improving the accuracy of motor imagery (MI) task classification. In an MI task, each participant has a different reaction time delay. In this study, the reaction delay was used as a parameter. Specifically, the time point after the reaction delay was defined as the starting point (SP) when MI began. Various classification and optimization algorithms were employed. The methods include the use of a fixed and dynamic SP for each individual, a genetic algorithm, particle swarm optimization (PSO), spatial filter tuning, the use of common spatial patterns, linear discriminant analysis, bagged decision trees, convolutional neural network (CNN), and long short‐term memory (LSTM). Moreover, a dynamic optimization method for multiband filtering (MBF) method for the subbands of EEG signals was developed that improved accuracy by 5.4%. Combining MBF with bagged decision trees with PSO‐optimized parameters achieved the highest accuracy of 82.3%. Moreover, after optimization by PSO, the CNN‐LSTM method achieved a classification accuracy of 81.4% for 1‐s signals, shorter than those in other studies. The method enables the use of EEG signals to control an exoskeleton for patient rehabilitation.

Disturbance rejection model predictive control of lower limb rehabilitation exoskeleton

Nowadays, exoskeleton is broadly used in the rehabilitation training of many postoperative patients. However, the uncertainty and disturbances caused by different patients and system itself may lead to incompletely rehabilitation training as planned, or even unsafety. This paper addresses the control problem of a lower limb exoskeleton, in the spirit of the recent progress on model predictive control (MPC) and extended state observer (ESO). More precisely, our approach is based on the strategy that designing an ESO to estimate the total disturbance of the dynamics model and compensating it in the design of the MPC process. To accomplish this, we introduce the virtual control quantity to decouple the dynamics model of the system and summarize the human disturbances, unmeasured states and system non-linearity as the total disturbance of the model. By doing so, the uncertainty can be estimated by our designed ESO. Based on the moving horizontal optimization and feedback mechanism of MPC, the output prediction of the system can be more accurate since the uncertainty are effectively compensated. The virtual experiment results demonstrate that proposed controller significantly improves the control accuracy on lower limb rehabilitation exoskeleton with disturbances (improved by over 34%), comparing with conventional MPC and fuzzy PID. As a result, our achievements will make contributions to better rehabilitation training for patients using rehabilitation exoskeletons.

Event-triggered critic learning impedance control of lower limb exoskeleton robots in interactive environments

In this paper, we present an event-triggered critic learning impedance control algorithm for a lower limb rehabilitation exoskeleton robot in an interactive environment, where the control objective is specified by a desired impedance model. In comparison to many other traditional impedance controller design algorithms, in this paper, the impedance control problem is transformed into an optimal control problem. Firstly, the interactive environment accounts for the interaction between the exoskeleton, the human, and the environment, and is modeled by a linear time-invariant exogenous system. Secondly, in contrast to time-triggered control design mechanisms, the event-triggered controller is updated only when the system states deviate from prescribed threshold values. To obtain the event-triggered optimal controller, a critic neural network is developed through the framework of reinforcement learning. A modified gradient descent method is introduced to update the weights of the critic network with an additional stable term employed to eliminate the need for an initial admissible control. Meanwhile, with the simultaneous application of historical and transient state data to the critic neural network, the persistent excitation conditions are relaxed. The Lyapunov method is used to rigorously demonstrate the stability of the overall system. Finally, the effectiveness of the proposed algorithm is demonstrated via simulation.

A Human-like Inverse Kinematics Algorithm of an Upper Limb Rehabilitation Exoskeleton

Powered exoskeleton rehabilitation is an effective way to help stroke patients recover their motor abilities. Bionic structures and human-like control strategies can be used to enhance both the safety and efficacy of exoskeletons. However, the motion characteristics of the shoulder complex are not sufficiently considered. In this paper, we designed a 7-degrees-of-freedom (DOF) upper limb rehabilitation exoskeleton, FREE (functional rehabilitation exoskeleton). The mechanical structures of the shoulder and forearm of FREE are in accordance with human anatomy, and can be used to perform a wide range of synergistic motion of multiple joints while keeping a safe distance from the patient’s head. A multiple-input-multiple-output (MIMO) shoulder girdle motion prediction model was developed to satisfy the synergy between humans and exoskeletons. Moreover, a constrained task priority and projected gradient-based inverse kinematics algorithm (CTPPG-IK) was proposed to achieve assistance with scapulohumeral rhythm. A motion capture system was used to collect different activities of daily life (ADL) motion data to validate the proposed algorithm. The experimental results show that the accuracy of the prediction model is higher than that of existing models, and the inverse kinematics algorithm can handle the end-effector task and joint space with a maximum angle error of 3.04×10−3 rad.

Trajectory Learning by Therapists' Demonstrations for an Upper Limb Rehabilitation Exoskeleton

In this work, we propose a method for trajectory implementation based on the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Learning by Demonstrations</i> approach to deal with trajectory planning issues in upper-limb rehabilitation exoskeletons. Currently applied path-planning methods use mathematical trajectories or <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Teach-and-play</i> approaches. The former do not propose human-like movements to patients, which is crucial to induce correct motor relearning. Moreover, they often differ from therapists' expectations of how movements should be executed, reducing the acceptability and use of exoskeletons in hospitals. The latter, using a single filtered trajectory demonstration, better meet therapists' expectations but lack consistency and optimization. In our approach, we employed <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Hidden Markov Models</i> , still never used for rehabilitation robotics, to study a set of demonstrations and we optimized the results to respect physiological muscular activation patterns. Recorded few repetitions of a movement from the interaction of a therapist with an exoskeleton, our machine-learning-based algorithm returns a ready-to-use trajectory representing the therapist's desires. We tested our method on a 4 degrees-of-freedom exoskeleton to record 5 exercises, interacting with 5 therapists. Comparing our trajectories with those obtained with literature methods, we see that our approach produces better kinematic and human-likeness results, and is better according to the global opinion expressed by the therapists.