Control Of Rehabilitation Robot Research Articles

The control of lower limb rehabilitation robot with multi-pose based on man-machine coupling and fuzzy PID

The control of lower limb rehabilitation robot with multi-pose based on man-machine coupling and fuzzy PID

The control of lower limb rehabilitation robot with multi-pose based on man-machine coupling and fuzzy PID

The control of lower limb rehabilitation robot with multi-pose based on man-machine coupling and fuzzy PID

Adaptive Interactive Control of Rehabilitation Robot with Controllable Damping

Adaptive Interactive Control of Rehabilitation Robot with Controllable Damping

A clustering-based method for estimating pennation angle from B-mode ultrasound images.

B-mode ultrasound (US) is often used to noninvasively measure skeletal muscle architecture, which contains human intent information. Extracted features from B-mode images can help improve closed-loop human-robotic interaction control when using rehabilitation/assistive devices. The traditional manual approach to inferring the muscle structural features from US images is laborious, time-consuming, and subjective among different investigators. This paper proposes a clustering-based detection method that can mimic a well-trained human expert in identifying fascicle and aponeurosis and, therefore, compute the pennation angle. The clustering-based architecture assumes that muscle fibers have tubular characteristics. It is robust for low-frequency image streams. We compared the proposed algorithm to two mature benchmark techniques: UltraTrack and ImageJ. The performance of the proposed approach showed higher accuracy in our dataset (frame frequency is 20Hz), that is, similar to the human expert. The proposed method shows promising potential in automatic muscle fascicle orientation detection to facilitate implementations in biomechanics modeling, rehabilitation robot control design, and neuromuscular disease diagnosis with low-frequency data stream.

Variable admittance time-delay control of an upper limb rehabilitation robot based on human stiffness estimation

This paper proposes a new variable admittance time-delay control strategy based on human stiffness estimation for improving the effectiveness of robot-assisted cooperative rehabilitation training. This control strategy is developed and implemented on a planar upper limb rehabilitation robot. Given the minimum-jerk-based desired trajectories of human hand position, in the developed control strategy, a time-delay approximator is utilized to estimate the external disturbances and modeling errors without exact knowledge of dynamics parameters, a sliding mode admittance controller is applied to obtained objective admittance characteristics, and an iterative optimization algorithm is used to estimate human arm stiffness and adjust human-robot interaction compliance. The closed-loop stability of the proposed control method is demonstrated via Lyapunov function theory. Experimental investigations involving ten subjects are conducted to validate the feasibility of the proposed control scheme. The experimental results show that the interaction compliance during cooperative rehabilitation training can be accurately adjusted based on selected admittance parameters and human arm stiffness, and it contributes to satisfying the specific training requirements of patients with different weakness levels and promoting the effectiveness of the robot-assisted training.

Design and Control of a Lower Limb Rehabilitation Robot Based on Human Motion Intention Recognition with Multi-Source Sensor Information

The research on rehabilitation robots is gradually moving toward combining human intention recognition with control strategies to stimulate user involvement. In order to enhance the interactive performance between the robot and the human body, we propose a machine-learning-based human motion intention recognition algorithm using sensor information such as force, displacement and wheel speed. The proposed system uses the bi-directional long short-term memory (BILSTM) algorithm to recognize actions such as falling, walking, and turning, of which the accuracy rate has reached 99.61%. In addition, a radial basis function neural network adaptive sliding mode controller (RBFNNASMC) is proposed to track and control the patient’s behavioral intention and the gait of the lower limb exoskeleton and to adjust the weights of the RBF network using the adaptive law. This can achieve a dynamic estimation of the human–robot interaction forces and external disturbances, and it gives the exoskeleton joint motor a suitable driving torque. The stability of the controller is demonstrated using the Lyapunov stability theory. Finally, the experimental results demonstrate that the BILSTM classifier has more accurate recognition than the conventional classifier, and the real-time performance can meet the demand of the control cycle. Meanwhile, the RBFNNASMC controller has a better gait tracking effect compared with the PID controller.

A noise-suppressing neural network approach for upper limb human-machine interactive control based on sEMG signals.

The use of upper limb rehabilitation robots to assist the affected limbs for active rehabilitation training is an inevitable trend in the field of rehabilitation medicine. In particular, the active motion intention-based control of the upper limb rehabilitation robots to assist subjects in rehabilitation training is a hot research topic in human-computer interaction control. Therefore, improving the accuracy of active motion intention recognition is the premise of the human-machine interaction controller design. Furthermore, there are external disturbances (bounded/unbounded disturbances) during rehabilitation training, which seriously threaten the safety of subjects. Thereby, eliminating external disturbances (especially unbounded disturbances) is the difficulty and key to the human-machine interaction control of the upper limb rehabilitation robots. In response to these problems, based on the surface electromyogram signal of the human upper limb, this paper proposes a fuzzy neural network active motion intention recognition method to explore the internal connection between the surface electromyogram signal of the human upper limb and active motion intention, and improve the real-time and accuracy of recognition. Based on this, two types of human-machine interaction controllers, which can be called as zeroing neural network controller and noise-suppressing zeroing neural network controller are designed to establish a safe and comfortable training environment to avoid secondary damage to the affected limb. Numerical experiments verify the feasibility and effectiveness of the proposed theories and methods.

Development, Research, Optimization and Experiment of Exoskeleton Robot for Hand Rehabilitation Training

As one of the most influential symptoms of daily life after stroke, hand dysfunction has seriously affected the quality of life of patients and families. At present, the commonly used rehabilitation method is to carry out continuous passive training on the patient’s fingers with the help of physical therapists, so as to promote the rehabilitation of the hands. However, this kind of therapist-assisted rehabilitation greatly increases the cost of rehabilitation treatment and is not conducive to the promotion of household use. Many exoskeleton hand rehabilitation robots still lack in overall weight and control, resulting in the application potential of exoskeleton hand rehabilitation robots not being fully developed, and the commercial and clinical success cases are limited. The research of this paper focuses on the structural design and electronic control design of the exoskeleton hand rehabilitation robot. Through the design of an exoskeleton hand rehabilitation robot suitable for human hands, the kinematics parameters are obtained by kinematics simulation, and the lightweight design of the hand rehabilitation robot is completed by using topology optimization. At the same time, this paper shows the development technology of the rehabilitation robot control system. We hope that through the subsequent product development, the exoskeleton hand rehabilitation robot studied in this paper can be applied in the future.

SEMG-Based Gain-Tuned Compliance Control for the Lower Limb Rehabilitation Robot during Passive Training.

The lower limb rehabilitation robot is a typical man-machine coupling system. Aiming at the problems of insufficient physiological information and unsatisfactory safety performance in the compliance control strategy for the lower limb rehabilitation robot during passive training, this study developed a surface electromyography-based gain-tuned compliance control (EGCC) strategy for the lower limb rehabilitation robot. First, the mapping function relationship between the normalized surface electromyography (sEMG) signal and the gain parameter was established and an overall EGCC strategy proposed. Next, the EGCC strategy without sEMG information was simulated and analyzed. The effects of the impedance control parameters on the position correction amount were studied, and the change rules of the robot end trajectory, man-machine contact force, and position correction amount analyzed in different training modes. Then, the sEMG signal acquisition and feature analysis of target muscle groups under different training modes were carried out. Finally, based on the lower limb rehabilitation robot control system, the influence of normalized sEMG threshold on the robot end trajectory and gain parameters under different training modes was experimentally studied. The simulation and experimental results show that the adoption of the EGCC strategy can significantly enhance the compliance of the robot end-effector by detecting the sEMG signal and improve the safety of the robot in different training modes, indicating the EGCC strategy has good application prospects in the rehabilitation robot field.

Impedance Control of a Wrist Rehabilitation Robot Based on Autodidact Stiffness Learning

Dynamic control of an intrinsically compliant robot is paramount to ensuring safe and synergistic assistance to the patient. This paper presents an impedance controller for the rehabilitation of stroke patients with compromised wrist motor functions. The control design employs a Koopman operator-based autodidactic system identification model to predict the anatomical stiffness of the wrist joint during its various degrees of rotational motion. The proposed impedance controller, perceiving the level of the subjects’ participation from their joint stiffness, can modify the applied force. The end-effector robot has a parallel structure that uses four biomimetic muscle actuators as parallel links between the end-effector and the base platform. The controller performance is corroborated by testing the end-effector robot with three healthy subjects.

Motion Rehabilitation Robot Control Based on Human Posture Information

In order to meet the needs of postoperative rehabilitation training of lower limbs, a motion rehabilitation robot control system based on human posture information is proposed in this paper. The functions of active/passive training mode control, movement posture and EMG signal acquisition, WiFi communication, safety protection, etc. of the lower limb rehabilitation robot are realized. The recognition and analysis of the training process are realized by using random forest machine learning algorithm and linear regression algorithm. The experimental results show that in the first row of the confusion matrix of the random forest algorithm, 7316 data are correctly identified as speed a and only one data is incorrectly identified as speed B , which is superior to other algorithms. In conclusion, the developed control and monitoring system of lower limb rehabilitation robot can be portable controlled by Android and can realize intelligent analysis of the training process through the monitoring signals in the training process. At the same time, the random forest algorithm has more advantages than the linear regression algorithm in motion recognition, which is of positive significance to the automatic monitoring and intelligent control of the training process.

Adaptive Robust Variable Impedance Controller for Lower Limb Rehabilitation Robot with Augmented Type-2 Fuzzy System

Adaptive Robust Variable Impedance Controller for Lower Limb Rehabilitation Robot with Augmented Type-2 Fuzzy System

Hardware Development and Safety Control Strategy Design for a Mobile Rehabilitation Robot

The use of bodyweight unloading force control on a treadmill with therapist manual assistance for gait training imposes constraints on natural walking. It influences the patient’s training effect for a full range of natural walks. This study presents a prototype and a safety controller for a mobile rehabilitation robot (MRR). The prototype integrates an autonomous mobile bodyweight support system (AMBSS) with a lower-limb exoskeleton system (LES) to simultaneously achieve natural over-ground gait training and motion relearning. Human-centered rehabilitation robots must guarantee the safety of patients in the presence of significant tracking errors. It is difficult for traditional stiff controllers to ensure safety and excellent tracking accuracy concurrently, because they cannot explicitly guarantee smooth, safe, and overdamped motions without overshoot. This paper integrated a linear extended state observer (LESO) into proxy-based sliding mode control (ILESO-PSMC) to overcome this problem. The LESO was used to observe the system’s unknown states and total disturbance simultaneously, ensuring that the “proxy” tracks the reference target accurately and avoids the unsafe control of the MRR. Based on the Lyapunov theorem to prove the closed-loop system stability, the proposed safety control strategy has three advantages: (1) it provides an accurate and safe control without worsening tracking performance during regular operation, (2) it guarantees safe recoveries and overdamped properties after abnormal events, and (3) it need not identify the system model and measure unknown system states as well as external disturbance, which is quite difficult for human–robot interaction (HRI) systems. The results demonstrate the feasibility of the proposed ILESO-PSMC for MRR. The experimental comparison also indicates better safety performance for the ILESO-PSMC than for the conventional proportional–integral–derivative (PID) control.

Current State of Robotics in Hand Rehabilitation after Stroke: A Systematic Review

Among the methods of hand function rehabilitation after stroke, robot-assisted rehabilitation is widely used, and the use of hand rehabilitation robots can provide functional training of the hand or assist the paralyzed hand with activities of daily living. However, patients with hand disorders consistently report that the needs of some users are not being met. The purpose of this review is to understand the reasons why these user needs are not being adequately addressed, to explore research on hand rehabilitation robots, to review their current state of research in recent years, and to summarize future trends in the hope that it will be useful to researchers in this research area. This review summarizes the techniques in this paper in a systematic way. We first provide a comprehensive review of research institutions, commercial products, and literature. Thus, the state of the art and deficiencies of functional hand rehabilitation robots are sought and guide the development of subsequent hand rehabilitation robots. This review focuses specifically on the actuation and control of hand functional rehabilitation robots, as user needs are primarily focused on actuation and control strategies. We also review hand detection technologies and compare them with patient needs. The results show that the trends in recent years are more inclined to pursue new lightweight materials to improve hand adaptability, investigating intelligent control methods for human-robot interaction in hand functional rehabilitation robots to improve control robustness and accuracy, and VR virtual task positioning to improve the effectiveness of active rehabilitation training.

EMG-driven fatigue-based self-adapting admittance control of a hand rehabilitation robot

Upper-limb rehabilitation therapy sessions for post-stroke people generally contain rhythmic hand movements in a tiresome manner to rebuild the injured neural circuits. Fatigue formation causes breaks in the training and limits the therapy duration. Therefore, it is essential to establish a correlation between the patient’s muscle condition and the rehabilitation exercises to improve the physiotherapy sessions. A self-adapting control method based on online fatigue detection in rhythmic arm movements is presented. Experimental tests were performed on twenty healthy subjects to validate the method’s feasibility. Electromyography (EMG) and force signals considered the interfaces between users and the robot. In the first stage of the experiment, utilizing the frequency features from EMG signals, a neural network for fatigue detection trained; however, in the end, it substituted with a simple function as a refinement in the time-consuming aspect for the online employment. The initiation of the fatigue process is followed by reducing the admittance controller damping term based on the EMG signal processing. Trajectory tracking with the robot employs the self-adapting admittance controller (SAAC) method and the non-adapting admittance controller (NAAC). Movement accuracy and smoothness were measured and showed a better performance of the SAAC method related to the NAAC. Simulations with two different stiffness levels were performed on an upper-limb OpenSim model to study a stroke-injured arm and evaluate the proposed method’s proficiency. The metabolic cost indicated the movement’s superiority in a fatigue situation for reduced environment stiffness.

Design of Adaptive PID Controller for Lower Limb Rehabilitation Robot Based on Particle Swarm Optimization Algorithm.

The proportional-integral-derivative (PID) is still the most common controller and stabilizer used in industry due to its simplicity and ease of implementation. However, in most of the real applications, the controlled system has parameters that slowly vary or are uncertain. Thus, PID gains must be adapted to cope with such changes.
 In this research, an Adaptive Proportional-Integral-derivative controller (APID) is proposed to control the 2-DOF lower limb rehabilitation robot system. The parameters gains of the proposed controller are optimized using the Particle Swarm Optimization algorithm (PSO). The simulation results show no overshoot and zero steady-state error, but large settling time (ts=3.654 sec. for link1 and ts=2.844 sec. for link2) for linear path, and the actual path tracks the desired path with a large error for the nonlinear path. The results illustrate that the robot's performance is inefficient for linear and nonlinear paths when using the APID controller to control the lower limb rehabilitation robot. Therefore, the controller needs to modify for controlling the robot efficiently.

A Computationally Efficient Musculoskeletal Model of the Lower Limb for the Control of Rehabilitation Robots: Assumptions and Validation

We present and validate a computationally efficient lower limb musculoskeletal model for the control of a rehabilitation robot. It is a parametric model that allows the customization of joint kinematics, and it is able to operate in real time. Methods: Since the rehabilitation exercises corresponds to low-speed movements, a quasi-static model can be assumed, and then muscle force coefficients are position dependent. This enables their calculation in an offline stage. In addition, the concept of a single functional degree of freedom is used to minimize drastically the workspace of the stored coefficients. Finally, we have developed a force calculation process based on Lagrange multipliers that provides a closed-form solution; in this way, the problem of dynamic indeterminacy is solved without the need to use an iterative process. Results: The model has been validated by comparing muscle forces estimated by the model with the corresponding electromyography (EMG) values using squat exercise, in which the Spearman’s correlation coefficient is higher than 0.93. Its computational time is lower than 2.5 ms in a conventional computer using MATLAB. Conclusions: This procedure presents a good agreement with the experimental values of the forces, and it can be integrated into real time control systems.

Design of Modified Adaptive PID Controller for Lower Limb Rehabilitation Robot based on Grey Wolf Optimization Algorithm

The traditional Proportional-Integral-Derivative (PID) controller is a feedback control loop that is commonly used in industrial control systems with fixed parameters, whereas the adaptive PID (APID) controller is based on the analysis of traditional PID controllers. It utilizes an online parameter adjustment method built on the state of the system resulting in better system adaptability. In this paper, the APID controller that is suggested by (Ebel, 2011) is used firstly to control a 2-degree of freedom (DOF) lower limb rehabilitation robot. The structure of this controller is then modified to perform a Modified Adaptive PID (MAPID) control in order to improve the efficiency of APID controller and hence improve the performance of the rehabilitation robot. The parameters of APID and the suggested MAPID controllers are optimized by using Grey Wolf Optimization (GWO) algorithm. Linear and non-linear desired trajectories are used to test the performance of the controlled rehabilitation robot. Simulation results show that the obtained performance of the rehabilitation robot is more efficient with the MAPID than with the APID having no overshoot and very small steady state error. The controller has settling time of (0.463) and (0.851) seconds, and rise time of (0.485) and (0.752) seconds respectively for link1 and link2.

Development and Control of an upper Limb Rehabilitation Robot via Ant Colony Optimization -PID and Fuzzy-PID Controllers

The control of movement rehabilitation robots is necessary for the recovery of physically disabled patients and is an interesting open problem. This paper presents a mathematical model of the upper limb rehabilitation robot using Euler-Lagrange approach. Since the PID controller is one of the most popular feedback controllers in the control strategy due to its simplicity, we proposed an ACO-PID controller for an upper limb rehabilitation robot. The main part of designing the PID controller is determining the gains of the controller. For this purpose, we used Ant Colony Optimization Algorithm (ACO) to tune the coefficients. To evaluate the validity of the proposed controller, we have compared it to Fuzzy-PID controller and the PID controller adjusted with the Ziegler-Nichols method (ZN-PID). The results showed that the performance of the ACO-PID controller is better than the others. Also, the adaptive PID controllers (ACO-PID and Fuzzy-PID) ensure accurate tracking, finite-time convergence, and stability. The results showed that the mean absolute error and normalized root mean square (NRMS) of tracking error using the ACO-PID are less than that using the Fuzzy-PID and ZN-PID controller.

Human-machine Coupling Dynamics Modeling and Active Compliance Control of Lower Limb Rehabilitation Robot

Human-machine Coupling Dynamics Modeling and Active Compliance Control of Lower Limb Rehabilitation Robot