Lizheng Pan

Robot-Aided Upper-Limb Rehabilitation Based on Motor Imagery EEG

Stroke is a leading cause of disability worldwide. In this paper, a novel robot-assisted rehabilitation system based on motor imagery electroencephalography (EEG) is developed for regular training of neurological rehabilitation for upper limb stroke patients. Firstly, three-dimensional animation was used to guide the patient image the upper limb movement and EEG signals were acquired by EEG amplifier. Secondly, eigenvectors were extracted by harmonic wavelet transform (HWT) and linear discriminant analysis (LDA) classifier was utilized to classify the pattern of the left and right upper limb motor imagery EEG signals. Finally, PC triggered the upper limb rehabilitation robot to perform motor therapy and gave the virtual feedback. Using this robot-assisted upper limb rehabilitation system, the patients EEG of upper limb movement imagination is translated to control rehabilitation robot directly. Consequently, the proposed rehabilitation system can fully explore the patients motivation and attention and directly facilitate upper limb post-stroke rehabilitation therapy. Experimental results on unimpaired participants were presented to demonstrate the feasibility of the rehabilitation system. Combining robot-assisted training with motor imagery-based BCI will make future rehabilitation therapy more effective. Clinical testing is still required for further proving this assumption.

Adaptive motion control of arm rehabilitation robot based on impedance identification

There is increasing interest in using rehabilitation robots to assist post-stroke patients during rehabilitation therapy. The motion control of the robot plays an important role in the process of functional recovery training. Due to the change of the arm impedance of the post-stroke patient in the passive recovery training, the conventional motion control based on a proportional-integral (PI) controller is difficult to produce smooth movement of the robot to track the designed trajectory set by the rehabilitation therapist. In this paper, we model the dynamics of post-stroke patient arm as an impedance model, and propose an adaptive control scheme, which consists of an adaptive PI control algorithm and an adaptive damping control algorithm, to control the rehabilitation robot moving along predefined trajectories stably and smoothly. An equivalent two-port circuit of the rehabilitation robot and human arm is built, and the passivity theory of circuits is used to analyze the stability and smoothness performance of the robot. A slide Least Mean Square with adaptive window (SLMS-AW) method is presented for on-line estimation of the parameters of the arm impedance model, which is used for adjusting the gains of the PI-damping controller. In this paper, the Barrett WAM Arm manipulator is used as the main hardware platform for the functional recovery training of the post-stroke patient. Passive recovery training has been implemented on the WAM Arm, and the experimental results demonstrate the effectiveness and potential of the proposed adaptive control strategies.

Safety Supervisory Strategy for an Upper-Limb Rehabilitation Robot Based on Impedance Control

User security is an important consideration for robots that interact with humans, especially for upper-limb rehabilitation robots, during the use of which stroke patients are often more susceptible to injury. In this paper, a novel safety supervisory control method incorporating fuzzy logic is proposed so as to guarantee the impaired limbs safety should an emergency situation occur and the robustness of the upper-limb rehabilitation robot control system. Firstly, a safety supervisory fuzzy controller (SSFC) was designed based on the impaired-limbs real-time physical state by extracting and recognizing the impaired-limbs tracking movement features. Then, the proposed SSFC was used to automatically regulate the desired force either to account for reasonable disturbance resulting from pose or position changes or to respond in adequate time to an emergency based on an evaluation of the impaired-limbs physical condition. Finally, a position-based impedance controller was implemented to achieve compliance between the robotic end-effector and the impaired limb during the robot-assisted rehabilitation training. The experimental results show the effectiveness and potential of the proposed method for achieving safety and robustness for the rehabilitation robot.

Hierarchical safety supervisory control strategy for robot-assisted rehabilitation exercise

Clinical outcomes have shown that robot-assisted rehabilitation is potential of enhancing quantification of therapeutic process for patients with stroke. During robotic rehabilitation exercise, the assistive robot must guarantee subjects safety in emergency situations, e.g., sudden spasm or twitch, abruptly severe tremor, etc. This paper presents a hierarchical control strategy, which is proposed to improve the safety and robustness of the rehabilitation system. The proposed hierarchical architecture is composed of two main components: a high-level safety supervisory controller (SSC) and low-level position-based impedance controller (PBIC). The high-level SSC is used to automatically regulate the desired force for a reasonable disturbance or timely put the emergency mode into service according to the evaluated physical state of training impaired limb (PSTIL) to achieve safety and robustness. The low-level PBIC is implemented to achieve compliance between the robotic end-effector and the impaired limb during the robot-assisted rehabilitation training. The results of preliminary experiments demonstrate the effectiveness and potentiality of the proposed method for achieving safety and robustness of the rehabilitation robot.

Adaptive Hierarchical Control for the Muscle Strength Training of Stroke Survivors in Robot-aided Upper-limb Rehabilitation

Muscle strength training for stroke patients is of vital importance for helping survivors to progressively restore muscle strength and improve the performance of their activities in daily living (ADL). An adaptive hierarchical therapy control framework which integrates the patients real biomechanical state estimation with task-performance quantitative evaluation is proposed. Firstly, a high-level progressive resistive supervisory controller is designed to determine the resistive force base for each training session based on the patients online task-performance evaluation. Then, a low-level adaptive resistive force triggered controller is presented to further regulate the interactive resistive force corresponding to the patients real-time biomechanical state – characterized by the patients bio-damping and bio-stiffness in the course of one training session, so that the patient is challenged in a moderate but engaging and motivating way. Finally, a therapeutic robot system using a Barrett WAM™ compliant manipulator is set up. We recruited eighteen inpatient and outpatient stroke participants who were randomly allocated in experimental (robot-aided) and control (conventional physical therapy) groups and enrolled for sixteen weeks of progressive resistance training. The preliminary results show that the proposed therapy control strategies can enhance the recovery of strength and motor control ability.

Clinical experimental research on adaptive robot-aided therapy control methods for upper-limb rehabilitation

This study presents novel robotic therapy control algorithms for upper-limb rehabilitation, using newly developed passive and progressive resistance therapy modes. A fuzzy-logic based proportional-integral-derivative (PID) position control strategy, integrating a patients biomechanical feedback into the control loop, is proposed for passive movements. This allows the robot to smoothly stretch the impaired limb through increasingly rigorous training trajectories. A fuzzy adaptive impedance force controller is addressed in the progressive resistance muscle strength training and the adaptive resistive force is generated according to the impaired limbs muscle strength recovery level, characterized by the online estimated impaired limbs bio-damping and bio-stiffness. The proposed methods are verified with a custom constructed therapeutic robot system featuring a Barrett WAM™ compliant manipulator. Twenty-four recruited stroke subjects were randomly allocated in experimental and control groups and enrolled in a 20-week rehabilitation training program. Preliminary results show that the proposed therapy control strategies can not only improve the impaired limbs joint range of motion but also enhance its muscle strength.

Impedance Identification and Adaptive Control of Rehabilitation Robot for Upper-Limb Passive Training

Rehabilitation robot can assist post-stroke patients during rehabilitation therapy. The movement control of the robot plays an important role in the process of functional recovery training. Owing to the change of the arm impedance of the post-stroke patient in the passive recovery training, the conventional movement control based on PI controller is difficult to produce smooth movement to track the designed trajectory set by the rehabilitation therapist. In this paper, we model the dynamics of post-stroke patient arm as an impedance model, and an adaptive control scheme which consists of an adaptive PI control algorithm and a damp control algorithm is proposed to control the rehabilitation robot moving along predefined trajectories stably and smoothly. An equivalent 2-port circuit of the rehabilitation robot and human arm is built, and passivity theory of circuit is used to analyze the stability and smoothness performance of the robot. A slide least mean square with adaptive window (SLMS-AW) method is presented to online estimate the parameters of the arm impedance model, which is used for adjusting the gains of PI-damp controller. In this paper, the Barrett WAM Arm manipulator is used as the main hardware platform for the functional recovery training of the post-stroke patient. Passive recovery training has been implemented on the WAM Arm. Experimental results demonstrate the effectiveness and potential of the proposed adaptive control strategies.

Patient-Centered Robot-Aided Passive Neurorehabilitation Exercise Based on Safety-Motion Decision-Making Mechanism.

Safety is one of the crucial issues for robot-aided neurorehabilitation exercise. When it comes to the passive rehabilitation training for stroke patients, the existing control strategies are usually just based on position control to carry out the training, and the patient is out of the controller. However, to some extent, the patient should be taken as a “cooperator” of the training activity, and the movement speed and range of the training movement should be dynamically regulated according to the internal or external state of the subject, just as what the therapist does in clinical therapy. This research presents a novel motion control strategy for patient-centered robot-aided passive neurorehabilitation exercise from the point of the safety. The safety-motion decision-making mechanism is developed to online observe and assess the physical state of training impaired-limb and motion performances and regulate the training parameters (motion speed and training rage), ensuring the safety of the supplied rehabilitation exercise. Meanwhile, position-based impedance control is employed to realize the trajectory tracking motion with interactive compliance. Functional experiments and clinical experiments are investigated with a healthy adult and four recruited stroke patients, respectively. The two types of experimental results demonstrate that the suggested control strategy not only serves with safety-motion training but also presents rehabilitation efficacy.

Design and evaluation of a motor imagery electroencephalogram-controlled robot system

Brain–computer interface provides a new communication channel to control external device by directly translating the brain activity into commands. In this article, as the foundation of electroencephalogram-based robot-assisted upper limb rehabilitation therapy, we report on designing a brain–computer interface–based online robot control system which is made up of electroencephalogram amplifier, acquisition and experimental platform, feature extraction algorithm based on discrete wavelet transform and autoregressive model, linear discriminant analysis classifier, robot control board, and Rhino XR-1 robot. The performance of the system has been tested by 30 participants, and satisfactory results are achieved with an average error rate of 8.5%. Moreover, the advantage of the feature extraction method was further validated by the Graz data set for brain–computer interface competition 2003, and an error rate of 10.0% was obtained. This method provides a useful way for the research of brain–computer interface system and lays a foundation for brain–computer interface–based robotic upper extremity rehabilitation therapy.

Real-time Safety Control of Upper-limb Rehabilitation Robot

A real-time online safety supervisory-control strategy based on fuzzy logic is presented to improve the safety and stability for the upper-limb rehabilitation robot in clinic application.During the robot-aided impaired limb rehabilitation exercise,the impaired limb condition impacts the control performance.An intelligent safety supervisory fuzzy controller (SSFC) is designed to enhance the movement stability and safety in emergent condition.Firstly,the movement features are extracted to evaluate the stability of the impaired limb,subsequently the proposed safety supervisory fuzzy controller intelligently adapts the desired control force to a reasonable disturbance or responds to a sudden event in time.Secondly,a position-based impedance control strategy is adopted to achieve the compliance between the impaired limb and the robotic end-effector.Experimental results show the effectiveness of the proposed method for achieving the safety and stability of the rehabilitation robot.