Baoguo Xu

FEATURE EXTRACTION OF MOTOR IMAGERY EEG BASED ON WAVELET TRANSFORM AND HIGHER-ORDER STATISTICS

Feature extraction plays an important role in brain-computer interface (BCI) systems. In order to characterize the motor imagery related rhythm and higher-order statistics information contained within the EEG signals, a novel feature extraction method based on harmonic wavelet transform and bispectrum is developed and applied to the recognition of right and left motor imageries for developing EEG-based BCI systems. The experimental results on the Graz BCI data set have shown that the separability of the two classes features extracted by the proposed method is notable. Its performance was evaluated by a linear discriminant analysis (LDA) classifier. The recognition accuracy of 90% was obtained. The recognition results have demonstrated the effectiveness of the proposed method. This method provides an effective way for EEG feature extraction in BCI system.

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.

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.

A Novel Human-Robot Cooperative Method for Upper Extremity Rehabilitation

There are a certain number of arm dysfunction patients whose legs could move. Considering the neuronal coupling between arms and legs during locomotion, this paper proposes a novel human-robot cooperative method for upper extremity rehabilitation. Legs motion is considered at the passive rehabilitation training of disabled arm, and its traversed trajectory is represented by the patient trunk motion. A Kinect based vision module, two computers and a WAM robot construct the human-robot cooperative upper extremity rehabilitation system. The vision module is employed to track the position of the subject trunk in horizontal; the WAM robot is used to guide the arm of post-stroke patient to do passive training with the predefined trajectory, and meanwhile the robot follows the patient trunk movement which is tracked by Kinect in real-time. A hierarchical fuzzy control strategy is proposed to improve the position tracking performance and stability of the system, which consists of an external fuzzy dynamic interpolation strategy and an internal fuzzy PD position controller. Four experiments were conducted to test the proposed method and strategy. The experimental results show that the patient felt more natural and comfortable when the human-robot cooperative method was applied; the subject could walk as he/she wished in the visual range of Kinect. The hierarchical fuzzy control strategy performed well in the experiments. This indicates the high potential of the proposed human-robot cooperative method for upper extremity rehabilitation.

Measuring Tape-Like Sampling Arm and Drill for Sampling Lunar Regolith

Abstract For the task of shallow lunar regolith sampling, we have designed a novel Measuring Tape-like Sampling Arm and Drill (MTS) which is a kind of flexible robot. The advantages of the sampling arm are that it has a small shrink volume, long working distance, light weight, low power consumption and good adaptivity, which is driven by one motor only. Furthermore, it has a large reverse torque with a short force arm, so the pushing force is large enough for the end-effector to penetrate into the lunar regolith. The sampling head which includes two opposite rotating parts has no-reaction screw force. The vibrator located in the sampling head can vibrate at frequencies ranging from 1 to 42Hz and can result in resonance of the sampling head. Therefore, it can improve the efficiency of thrusting, sampling and casting throw. We have carried out theoretical analysis, simulations and experimental studies and it is has been proved that the sampler can sample quantitatively at a of depth of 10 cm in simulated lunar regolith (model CAS-1), cement and sand.

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.

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.

Robotic arm control using hybrid brain-machine interface and augmented reality feedback

Brain-machine interface (BMI) can be used to control robotic arm to assist paralysis people improving their quality of life. However process control of objects grasping is still a complex task for BMI users. High efficiency and accuracy is hard to achieve in objects grasping process even after extensive training. An important reason is lack of sufficient feedback information for performing the closed-loop control. In this study, we describe a method of augmented reality (AR) guiding assistance to provide extra feedback information to the user for closed-loop control. A hybrid BMI based system with AR feedback is proposed to evaluate the performance of our method in objects grasping task using robotic arm. Reaching and releasing tasks are completed by the robotic arm automatically. For the grasping task controlled by the user, AR is used to enrich the normal visual information during the grasping process to provide the BMI user augmented feedback information about the gripper status in real time. The feasibility of the proposed system both in open-loop (visual inspection) and closed-loop (AR feedback) are compared. According to our experimental results obtained from 5 subjects, the time used for controlling the robotic arm to grasp objects with AR feedback reduces more than 5s and the error rate of the gripper aperture decreases approximately 20% compared to those of grasping with normal visual inspection only. The results reveal that the BMI user can benefit from the information provided by AR interface in the grasping task.