Lower Limb Rehabilitation Robot Research Articles

Lower limb movement intention recognition for rehabilitation robot aided with projected recurrent neural network

For the lower limb rehabilitation robot, how to better realize intention recognition is the key issue in the practical application. Recognition of the patient’s movement intention is a challenging research work, which needs to be studied from the shallow to the deep. Specifically, it is necessary to ensure that the movement intention of the normal person can be accurately recognized, and then improve the model to realize the recognition of the movement intention of the patients. Therefore, before studying the patient’s movement intention, it is essential to consider the normal person first, which is also for safety considerations. In recent years, a new Hill-based muscle model has been demonstrated to be capable of directly estimating the joint angle intention in an open-loop form. On this basis, by introducing a recurrent neural network (RNN), the whole prediction process can achieve more accuracy in a closed-loop form. However, for the traditional RNN algorithms, the activation function must be convex, which brings some limitations to the solution of practical problems. Especially, when the convergence speed of the traditional RNN model is limited in the practical applications, as the error continues to decrease, the convergence performance of the traditional RNN model will be greatly affected. To this end, a projected recurrent neural network (PRNN) model is proposed, which relaxes the condition of the convex function and can be used in the saturation constraint case. In addition, the corresponding theoretical proof is given, and the PRNN method with saturation constraint has been successfully applied in the experiment of intention recognition of lower limb movement compared with the traditional RNN model.

Robust adaptive PD-like control of lower limb rehabilitation robot based on human movement data.

The combination of biomedical engineering and robotics engineering brings hope of rehabilitation to patients with lower limb movement disorders caused by diseases of the central nervous system. For the comfort during passive training, anti-interference and the convergence speed of tracking the desired trajectory, this paper analyzes human body movement mechanism and proposes a robust adaptive PD-like control of the lower limb exoskeleton robot based on healthy human gait data. In the case of bounded error perturbation, MATLAB simulation verifies that the proposed method can ensure the global stability by introducing an S-curve function to make the design robust adaptive PD-like control. This control strategy allows the lower limb rehabilitation robot to track the human gait trajectory obtained through the motion capture system more quickly, and avoids excessive initial output torque. Finally, the angle similarity function is used to objectively evaluate the human body for wearing the robot comfortably.

Human-Like Robust Adaptive PD Based Human Gait Tracking for Exoskeleton Robot

For patients with dyskinesias caused by central nervous system diseases such as stroke, in the early stage of rehabilitation training, lower limb rehabilitation robots are used to provide passive rehabilitation training. This paper proposed a human-like robust adaptive PD control strategy of the exoskeleton robot based on healthy human gait data. When the error disturbance is bounded, a human-like robust adaptive PD control strategy is designed, which not only enables the rehabilitation exoskeleton robot to quickly track the human gait trajectory obtained through the 3D NOKOV motion capture system, but also can well identify the structural parameters of the system and avoid excessively initial output torque for the robot. MATLAB simulation verifies that the proposed method has a better performance to realize tracking the experimental trajectory of human movement and anti-interference ability under the condition of ensuring global stability for a lower limb rehabilitation exoskeleton robot.

Human–Robot Cooperation Control Based on Trajectory Deformation Algorithm for a Lower Limb Rehabilitation Robot

Although many studies have certified the advantages of human–robot cooperation control with admittance model (AM), robot compliance, and movement smoothness need to be further improved. In this article, a trajectory deformation algorithm (TDA) is developed as a high-level trajectory planner, which can plan subject's desired trajectory based on interaction force during physical human–robot interaction (pHRI). A low-level proportional-derivative (PD) position controller is selected to ensure the lower limb rehabilitation robot can track the desired trajectory. Then, the validity of TDA is verified through simulation and experiment studies. The energy per unit distance (EPUD) and dimensionless squared jerk (DSJ) are chosen as indicators of robot compliance and movement smoothness, respectively. The experimental results demonstrated that both the EPUD and the DSJ values using TDA are smaller than that using the AM, indicating the TDA can improve robot compliance and movement smoothness. Therefore, it may have great potential in fields involving pHRI, such as robot-aided rehabilitation.

A Construction Method of Lower Limb Rehabilitation Robot with Remote Control System

In response to the rehabilitation needs of stroke patients who are unable to benefit from conventional rehabilitation due to the COVID-19 epidemic, this paper designs a robot that combines on-site and telerehabilitation. The objective is to assist the patient in walking. We design the electromechanical system with a gantry mechanism, body-weight support system, information feedback system, and man-machine interactive control system. The proposed rehabilitation robot remote system is based on the client/server (C/S) network framework to realize the remote control of the robot state logic and the transmission of patient training data. Based on the proposed system, doctors can set or adjust the training modes and control the parameters of the robot and guide remote patient rehabilitation training through video communication. The robotic system can further store and manage the rehabilitation data of the patient during training. Experiments show the human-computer interaction system of the lower limb rehabilitation robot has good performance, can accurately recognize the information of human motion posture, and achieve the goal of actively the following motion. Experiments confirm the feasibility of the proposed design, the information management of stroke patients, and the efficiency of rehabilitation training. The proposed system can reduce the workload of the doctors in practical training.

Tracking control for lower limb rehabilitation robots based on polynomial nonlinear uncertain models

AbstractThis paper aims to propose a novel idea for the modeling and trajectory tracking control of a lower limb rehabilitation robot. A polynomial nonlinear uncertain model is established to deal with the difficulty of accurate modeling for the lower limb rehabilitation robot with complex dynamic characteristics. A high‐order nonlinear disturbance observer (HONDO) is utilized to estimate the lumped disturbance with fast time‐varying and the corresponding observer gain matrix is obtained by H∞ performance. Then an HONDO‐based composite tracking controller is designed to achieve good tracking performance of the lower limb rehabilitation robot. In addition, all solvability conditions can be directly handled by sum of squares programming, effectively avoiding the calculation problem. Finally, numerical simulations are conducted to verify the validity of the proposed method.

Control Strategy and Experimental Research of Cable-Driven Lower Limb Rehabilitation Robot

The passive training with fixed trajectory is more suitable for the initial rehabilitation training of patients with no muscle strength in the affected limb. In order to meet the needs of patients' rehabilitation training in different rehabilitation stages and improve the active participation and rehabilitation training effect of patients, a fuzzy sliding mode variable admittance (FSMVA) controller based on safety evaluation and supervision is proposed for the cable-driven lower limb rehabilitation robot (CDLR) in this paper. The FSMVA controller consists of an inner loop fuzzy sliding mode controller, an outer loop variable admittance controller, and a safety evaluation and supervision module. Through the safety evaluation index of the CDLR system and the comprehensive evaluation of patients' rehabilitation effect given by rehabilitation physiotherapists, a real-time adjustment algorithm of variable admittance controller parameters is designed, which can realize the real-time switching of training modes and adjustment of variable admittance controller parameters in active training mode and passive training mode. The trajectory tracking experiments with no admittance, fixed admittance, and variable admittance based on safety evaluation and supervision were carried out on the experimental platform. The experimental results show that the proposed FSMVA control strategy has high position control accuracy in the active training mode. In addition, the training intensity and the active participation of patients can be increased according to the patient's exercise ability. In the passive training mode, according to the training needs of patients, the amount of the trajectory adjustment can be increased to improve the comfort of the training process and the safety of patients. It lays a foundation for the further study of the evaluation system of the rehabilitation training process and the experiment of human-machine interaction compliance.

Individualized Gait Generation for Rehabilitation Robots Based on Recurrent Neural Networks.

Individualized reference gait patterns for lower limb rehabilitation robots can greatly improve the effectiveness of rehabilitation. However, previous methods can only generate customized gait patterns at several fixed discrete walking speeds and generating gaits at continuously varying speeds and stride lengths remains unsolved. This work proposes an individualized gait pattern generation method based on a recurrent neural network (RNN), which is proficient in series modeling. We collected the largest gait data set of this kind, which consists of 4,425 gait patterns from 137 subjects. Using this data set, we trained an RNN to create a function mapping from body parameters and gait parameters to a gait pattern. The experimental results indicate that our model is able to generate gait patterns at continuously varying walking speeds and stride lengths while also reducing the errors in the ankle, knee, and hip measurements by 12.83%, 20.95%, and 28.25%, respectively, compared to previous state-of-the-art method.

Safety Evaluation and Experimental Study of a New Bionic Muscle Cable-Driven Lower Limb Rehabilitation Robot.

Safety is a significant evaluation index of rehabilitation medical devices and a significant precondition for practical application. However, the safety evaluation of cable-driven rehabilitation robots has not been reported, so this work aims to study the safety evaluation methods and evaluation index of cable-driven rehabilitation robots. A bionic muscle cable (BM cable) is proposed to construct a bionic muscle cable-driven lower limb rehabilitation robot (BM-CDLR). The working principle of the BM-CDLR is introduced. The safety performance factors are defined based on the mechanical analysis of the BM-CDLR. The structural safety evaluation index and the use safety evaluation index of the BM-CDLR are given by comprehensively considering the safety performance factors and a proposed speed influence function. The effect of the structural parameters of the elastic elements in the BM cable on the safety performance factors and safety of the BM-CDLR is analyzed and verified by numerical simulations and experimental studies. The results provide the basis for further study of the compliance control strategy and experiments of the human-machine interaction of the BM-CDLR.

A New Projected Active Set Conjugate Gradient Approach for Taylor-Type Model Predictive Control: Application to Lower Limb Rehabilitation Robots With Passive and Active Rehabilitation.

In this paper, a three-order Taylor-type numerical differentiation formula is firstly utilized to linearize and discretize constrained conditions of model predictive control (MPC), which can be generalized from lower limb rehabilitation robots. Meanwhile, a new numerical approach that projected an active set conjugate gradient approach is proposed, analyzed, and investigated to solve MPC. This numerical approach not only incorporates both the active set and conjugate gradient approach but also utilizes a projective operator, which can guarantee that the equality constraints are always satisfied. Furthermore, rigorous proof of feasibility and global convergence also shows that the proposed approach can effectively solve MPC with equality and bound constraints. Finally, an echo state network (ESN) is established in simulations to realize intention recognition for human–machine interactive control and active rehabilitation training of lower-limb rehabilitation robots; simulation results are also reported and analyzed to substantiate that ESN can accurately identify motion intention, and the projected active set conjugate gradient approach is feasible and effective for lower-limb rehabilitation robot of MPC with passive and active rehabilitation training. This approach also ensures computational when disturbed by uncertainties in system.

MECHANICAL CHARACTERISTICS ANALYSIS OF A BIONIC MUSCLE CABLE-DRIVEN LOWER LIMB REHABILITATION ROBOT

Cable-driven parallel robots (CDPR) have been well used in the rehabilitation field. However, the cables can provide the tension in a single direction, there is a pseudo-drag phenomenon of the cables in the CDPR, which will have a great impact on the safety of patients. Therefore, the novelty of this work is that a bionic muscle cable is used to replace the ordinary cable in the CDPR, which can solve the pseudo-drag phenomenon of the cables in the CDPR and improve the safety performance of the rehabilitation robot. The cable-driven lower limb rehabilitation robot with bionic muscle cables is called as the bionic muscle cable-driven lower limb rehabilitation robot (BMCDLR). The motion planning of the rigid branch chain of the BMCDLR is studied, and the dynamics and system stiffness of the BMCDLR are analyzed based on the man–machine model in this paper. The influence of the parameters of the elastic elements in the bionic muscle cables on the mechanical characteristics of the BMCDLR system was analyzed by using simulation experiments. The research results can provide a reference basis for research on the safety evaluation and control methods of the BMCDLR system.

Robotic mirror therapy system for lower limb rehabilitation

PurposeThis paper aims to develop a robotic mirror therapy system for lower limb rehabilitation, which is applicable for different patients with individual movement disability levels.Design/methodology/approachThis paper puts forward a novel system that includes a four-degree-of-freedom sitting/lying lower limb rehabilitation robot and a wireless motion data acquisition system based on mirror therapy principle. The magnetorheological (MR) actuators are designed and manufactured, whose characteristics are detected theoretically and experimentally. The passive training control strategy is proposed, and the trajectory tracking experiments verify its feasibility. Also, the active training controller that is adapt to the human motor ability is designed and evaluated by the comparison experiments.FindingsThe MR actuators produce continuously variable and compliant torque for robotic joints by adjusting excitation current. The reference limb joint position data collected by the wireless motion data acquisition system can be used as the motion trajectory of the robot to drive the affected limb. The passive training strategy based on proportional-integral control proves to have great trajectory tracking performance through experiments. In the active training mode, by comparing the real-time parameters adjustment in two phases, it is certified that the proposed fuzzy-based regulated impedance controller can adjust assistance torque according to the motor ability of the affected limb.Originality/valueThe system developed in this paper fulfills the needs of robot-assisted mirror therapy for hemiplegic patients.

Compound lower limb vibration training rehabilitation robot

SummaryThis paper uses a bionic lower limb rehabilitation mechanism combined with a running frame structure to simulate human gait movement for lower limb rehabilitation training. The vibration excited by the vertical vibration module causes the muscle to oscillate, and the mechanical vibration excites the neuromuscular system to obtain corresponding rehabilitation functions. The robot system is modeled in three dimensions, and the static analysis and modal analysis of the running frame are carried out. The mechanism and application of vertical vibration in the key technology are clarified, and the vibration element in the vertical vibration device is analyzed. The vibration theory is deduced, and the vibration displacement figure is drawn using simulation software. The related cooperative vibration spring has also been analyzed with single‐degree‐of‐freedom damping vibration, and the spring's center of mass movement, momentum, and position changes over time are illustrated. The design of the robot system solves the current situation of single movement of the lower limb rehabilitation robot and unsatisfactory rehabilitation effect, laying a foundation for the practical application of the subsequent lower limb rehabilitation robot system.

Control of a New Cycling Rehabilitation Robot Based on Fuzzy PID

For completing the standardized rehabilitation training of the affected limb based on the lower limb rehabilitation training robot system, it is designed that a lower limb rehabilitation robot control system based on fuzzy PID control method to realize stable and accurate control of the rehabilitation training process. First of all, this paper analyzes the ranges of motion of human hip and knees, and a single degree of freedom lower limb rehabilitation training robot system based on the belt transmission principle is designed. Then the fuzzy PID control is analyzed. And the Matlab is used to simulate the passive rehabilitation mode of the lower limb rehabilitation training robot system under different control strategies. The designed rehabilitation training test shows that the control system of the lower limb rehabilitation robot based on the fuzzy PID control method has a small speed overshoot, high control accuracy and smoother operation.

Control strategy and experimental research of a cable-driven lower limb rehabilitation robot

This paper aims to solve the problems of the existing limbs rehabilitation robots in terms of configuration limitations, human-machine compatibility, multimodal rehabilitation training. In addition, the control method of the cable tension of cable drive unit (CDU) loading system is studied to improve loading accuracy of cable tension and safety of the rehabilitation training robot. The novelty of this work is to propose a compound correction controller that can not only ensure the tracking accuracy of the cable-driven lower limb rehabilitation robot (CDLR) but also effectively improve the force loading accuracy of the cable tension force. Hence, this paper proposes a CDLR that can realize the active training mode, passive training mode, and assistive training mode. Firstly, the structure and working principle of CDLR is introduced. The dynamic model of the CDU loading system is established and the frequency characteristic of the CDU loading system is analyzed. In order to improve the loading accuracy and response speed of the CDU loading system, a compound correction controller is designed based on the frequency characteristics of the CDU loading system. Finally, the active force servo control experiment and the passive force servo control experiment of the CDU loading system are carried out on the experimental platform. The experimental results show that the compound correction control strategy can meet the requirements of lower limb rehabilitation training in the active force servo control experiment; the compound correction control strategy can significantly improve the loading precision and dynamic performance of the system in the passive force servo control experiment. That is, the compound correction control strategy can meet the requirements of lower limb rehabilitation training. The results provide a basis for the whole robot experiment and human-machine experiments and improve the stability of the CDLR system and patient safety.

Design and control of a lower limb rehabilitation robot considering undesirable torques of the patient's limb.

This research introduces a new exoskeleton-type rehabilitation robot, which can be used in lower limb rehabilitation therapy for post-stroke patients. A novel design of a typical knee and ankle rehabilitation robot is proposed. The kinematic and dynamic models of the knee and ankle rehabilitation robot are derived. Furthermore, a super-twisting nonsingular terminal sliding mode control is developed to achieve the desired training missions and its results are compared with those of an adaptive sliding mode control. To reduce undesired interaction torques between knee and ankle rehabilitation robot and patient, an admittance control algorithm is added to the controller to guarantee a safe therapy session. The admittance super-twisting nonsingular terminal sliding mode control structure is considered as the novelty of this article. Taking into account the dynamic uncertainties, external disturbances, and the interaction torques, the validity of the admittance super-twisting nonsingular terminal sliding mode control controller is approved by various numerical simulations over the admittance adaptive sliding mode control.

Design and Validation of a Lower-Limb Haptic Rehabilitation Robot.

Present robots for investigating lower-limb motor control and rehabilitation focus on gait training. An alternative approach is to focus on restoring precursor abilities such as motor adaptation and volitional movement, as is common in upper-limb robotic therapy. Here we describe NOTTABIKE, a one degree-of-freedom rehabilitation robot designed to probe and promote these underlying capabilities. A recumbent exercise cycle platform is powered with a servomotor and instrumented with angular encoders, force-torque sensing pedals, and a wireless EMG system. Virtual environments ranging from spring-mass-damper systems to novel foot-to-crank mechanical laws present variants of leg-reaching and pedaling tasks that challenge perception, cognition, motion planning, and motor control systems. This paper characterizes the dynamic performance and haptic rendering accuracy of NOTTABIKE and presents an example motor adaptation task to illustrate its use. Torque and velocity mode controllers showed near unity magnitude ratio and phase loss less than 60 degrees up to 10 Hz. Spring rendering demonstrated 1% mean error in stiffness, and damper rendering performed comparably at 2.5%. Virtual mass rendering was less accurate but successful in varying perceived mass. NOTTABIKE will be used to study lower-limb motor adaptation in intact and impaired persons and to develop rehabilitation protocols that promote volitional movement recovery.

A 4-DOF Workspace Lower Limb Rehabilitation Robot: Mechanism Design, Human Joint Analysis and Trajectory Planning

Most of currently rehabilitation robots cannot achieve the adduction/abduction (A/A) training of the hip joint and lack the consideration of the patient handling. This paper presents a four degrees of freedom (DOF) spatial workspace lower limb rehabilitation robot, and it could provide flexion/extension (F/E) training to three lower limb joints and A/A training to the hip joint. The training method is conducting the patient’s foot to complete the rehabilitation movement, and the patient could directly take training on the wheelchair and avoid frequent patient handling between the wheelchair and the rehabilitation device. Because patients own different joint range of motions (ROM), an analysis method for obtaining human joint motions is proposed to guarantee the patient’s joint safety in this training method. The analysis method is based on a five-bar linkage kinematic model, which includes the human lower limb. The human-robot hybrid kinematic model is analyzed according to the Denavit-Hartenberg (D-H) method, and a variable human-robot workspace based on the user is proposed. Two kinds of trajectory planning methods are introduced. The trajectory planning method and the human joint analysis method are validated through the trajectory tracking experiment of the prototype.

Design, comprehensive evaluation, and experimental study of a cable-driven parallel robot for lower limb rehabilitation

The stability of the rehabilitation robot system is an important prerequisite for the safety of patients’ rehabilitation training, and there are few studies on the evaluation method of the stability of the cable-driven rehabilitation robot. Hence, this paper aims to study the evaluation method of the dynamical stability of a cable-driven lower limb rehabilitation robot (CDLR) through comprehensive consideration of the position, cable tension, system stiffness, and velocity of the traction point. The structure and working principle of the CDLR is introduced. The position performance factor, the cable tension performance factor, and the system stiffness performance factor are defined based on the kinematics, dynamics, and system stiffness model of the CDLR. The evaluation index of the static stability of the CDLR is presented by comprehensively considering three performance factors. Considering the safety and comfort of the patients’ training, a velocity influence function is given. Combined with the evaluation index of the static stability and the velocity influence function, the evaluation index of the dynamical stability of the CDLR is proposed. Finally, the stability distribution laws of the CDLR are presented by simulation and experimental study. The rationality and correctness of the stability evaluation index are verified by experiments. The experiment results provide an important reference for structural design, rehabilitation training task planning, and control strategy of the CDLR.

Adaptive robust control for lower limb rehabilitation robot with uncertainty based on Udwadia–Kalaba approach

ABSTRACT This paper aims to address the trajectory control of a lower limb rehabilitation robot which works under passive mode. Udwadia–Kalaba approach is a novel and concise method to address the control of constrained systems with holonomic and nonholonomic constraints. According to Udwadia–Kalaba approach, the fundamental equation of lower limb rehabilitation robot considering the resistance of human muscles is first obtained, then a closed-form expression of the joint control torques is obtained. In practice, it is quite difficult to meet the control target due to various uncertainties such as the initial condition deviation from the constraints, the uncertainty of model, and the external disturbance. To deal with this condition, an adaptive control on the basis of Udwadia–Kalaba approach is presented. The uniform boundedness and the uniform ultimate boundedness of the proposed control are verified by the Lyapunov method. Experiments and simulations are performed to show the simplicity, efficiency and accuracy of the proposed control. Specifically, the tracking trajectory of the robot is designed based on the data collected from the experiments. The simulation results show that the proposed control can be used in the lower limb rehabilitation robot.