Guozheng Xu

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.

Integration of semantic and visual hashing for image retrieval

Semantic hashing codes are learned by leveraging the similarity between textual structure and visual structure.Maximum entropy principle is exploited to achieve compact binary codes.Function decay principle is introduced to remove noisy visual attributes. With the rapid proliferation of large-scale web images, recent years have witnessed more and more images labeled with user-provided tags, which leads to considerable effort made on hashing based image retrieval in huge databases. Current research efforts focus mostly on learning semantic hashing functions which design compact binary codes to map semantically similar images into similar codes; however the visual similarity is not well explored for constructing semantic hashing functions. Here a novel approach is proposed to learn hashing functions that preserve semantic and visual similarity between images. Specifically, semantic hashing codes are first learned by leveraging the similarity between textual structure and visual structure; then, the maximum entropy principle is exploited to achieve compact binary codes; finally, the function decay principle is introduced to remove noisy visual attributes. Experimental results conducted on a widely-used image dataset demonstrate the superior performance of the proposed method over the examined state-of-the-art techniques.

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.

Tracklet association for object tracking

This paper proposes a novel multi-target tracking framework, where two different association strategies are utilized to obtain local and global tracking trajectories. Specifically, a scene self-adaptive model is first utilized to generate local trajectories by constructing the association between detection responses and tracking tracklets; then, a novel incremental linear discriminative appearance model is utilized to generate global trajectories by constructing the association between local trajectories; finally, a non-linear motion model is utilized to fill the vacancies between global trajectories to obtain continuous and smooth tracking trajectories. Experimental results conducted on PETS 2009/2010 and TUD-Stadtmitte database demonstrate the proposed framework can achieve continuous and smooth tracking trajectories under the case of significant deformation, appearance change, similar appearance, motion direction change, and long-time occlusion.

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.

Optical flow and spatio-temporal gradient based abnormal behavior detection

To improve the accuracy of the detection of local abnormal behavior, a novel method is here proposed. The main idea of the proposed method is described as follows: firstly, a video sequence is divided into spatio-temporal blobs; then, a statistical method based on the semi-parametric model is adopted to detect these blobs where abnormal behaviors most likely to appear; finally, maximum optical flow energy and local nearest descriptor are utilized to determinate whether these suspicious blobs really contain abnormal behaviors. The experimental results conducted on UCSD dataset demonstrate the effectiveness of the proposed method.

Fusing Canny operator with vibe algorithm for target detection

Due to the complexity of human motion, the target detection results by using traditional ViBe algorithm are not so satisfactory. Therefore, this paper proposes a method to deal with the target detection issue by fusing an improved Canny operator with Vibe algorithm. Specifically, the ViBe algorithm is utilized to achieve the initial foreground region of a moving object; then, the improved Canny operator is applied to extract the edge information of a moving object; finally, the extracted foreground region and edge information are fused to obtain more accurate foreground region. The experimental results performed on KTH human behavior database demonstrate the effectiveness of the proposed scheme.

Robotic neurorehabilitation system design for stroke patients

In this article, a neurorehabilitation system combining robot-aided rehabilitation with motor imagery–based brain–computer interface is presented. Feature extraction and classification algorithm for the motor imagery electroencephalography is implemented under our brain–computer interface research platform. The main hardware platform for functional recovery therapy is the Barrett Whole-Arm Manipulator. The mental imagination of upper limb movements is translated to trigger the Barrett Whole-Arm Manipulator Arm to stretch the affected upper limb to move along the predefined trajectory. A fuzzy proportional–derivative position controller is proposed to control the Whole-Arm Manipulator Arm to perform passive rehabilitation training effectively. A preliminary experiment aimed at testing the proposed system and gaining insight into the potential of motor imagery electroencephalography-triggered robotic therapy is reported.