Showing papers by "Zeng-Guang Hou published in 2014"

Containment control of general linear multi-agent systems with multiple dynamic leaders: A fast sliding mode based approach

Abstract: In this paper, distributed containment control problems of general linear multi-agent systems are investigated. The objective is to make the followers in a multi-agent network converge to the convex hull spanned by some leaders whose control inputs are nonzero and not available to any followers. Sliding mode surfaces are defined for the cases of reduced order and non-reduced order, respectively. For each case, fast sliding mode controllers are designed. It is shown that all the error trajectories exponentially reach the sliding mode surfaces in a finite time if for each follower, there exists at least one of the leaders who has a directed path to the follower, and the leaders' control inputs are bounded. The control Lyapunov function for exponential finite time stability, motivated by the fast terminal sliding mode control, is used to prove reachability of the sliding mode surfaces. Simulation examples are given to illustrate the theoretical results.

Haptic interfaces for the rescue walking robots motion in the disaster areas

Abstract: In the recent years haptic interfaces became a reliable solution in order to solve problems which arise when humans interact with the environment. If in the research area of the haptic interaction between human and environment there are important researches, a innovative approach for the interaction between the robot and the environment using haptic interfaces and virtual projection method is presented in this paper. In order to control this interaction we used the Virtual Projection Method where haptic control interfaces of impedance and admittance will be embedded. The obtained results, validated by simulations assure stability, stiffness, high maneuverability and adaptability for rescue walking robots in order to move in disaster, dangerous and hazardous areas.

Feasibility of NeuCube SNN architecture for detecting motor execution and motor intention for use in BCIapplications

Abstract: The paper is a feasibility analysis of using the recently introduced by one of the authors spiking neural networks architecture NeuCube for modelling and recognition of complex EEG spatio-temporal data related to both physical and intentional (imagined) movements. The preliminary experiments reported in the paper suggest that NeuCube is much more efficient for the task than standard machine learning techniques, resulting in high recognition accuracy, a better adaptability to new data, a better interpretation of the models, leading to a better understanding of the brain data and the processes that generated it.

EEG-based classification of upper-limb ADL using SNN for active robotic rehabilitation

Abstract: Repetitive activities of daily living (ADL) and robotic active training are commonly practised in the rehabilitation of paralyzed patients, both of which have been proven rather effective to recover the locomotor function of impaired limbs. ADL classification based on electroencephalogram (EEG) is of great significance to perform active robotic rehabilitation for patients with complete spinal cord injury (SCI) who lose locomotion of affected limbs absolutely, where surface electromyography (sEMG) or active force signal can hardly be detected. It is a challenge to achieve a satisfying result in neuro-rehabilitation robotics using EEG signals due to the high randomness of the EEG data. A classification method is proposed based on spiking neural networks (SNN) to identify the upper-limb ADL of three classes with 14-channel EEG data. The continuous real-number signals are firstly encoded into spike trains through Ben's Spike Algorithm (BSA). The generated spikes are then submitted into a 3-D brain-mapped SNN reservoir called NeuCube trained by Spike Timing Dependant Plasticity (STDP). Spike trains from all neurons of the trained reservoir are finally classified using one version of dynamic evolving spiking neuron networks (deSNN) — deSNNs. Classifications are presented with and without NeuCube respectively on the same EEG data set. Results indicate that using the reservoir improves identification accuracy which turns out pretty promising despite that EEG data is highly noisy, low frequently sampled, and only from 14 channels. The classification technique reveals a great potential for the further implementation of active robotic rehabilitation to the sufferers of complete SCI.

The FES-assisted control for a lower limb rehabilitation robot: simulation and experiment

Abstract: To design a control strategy for iLeg, an exoskeleton robot developed for lower limb rehabilitation aiming at investigating the feasibility of integrating functional electrical stimulation (FES) with robot-based rehabilitation training, an FES-assisted training strategy combined with impedance control, has been proposed in this paper. Through impedance control, an active compliance of the robot is established, and the patient’s voluntary effort to accomplish the training task is inspired. During the training process, the patient’s related muscles are applied with FES which provides an extra assistance to the patient. The intensity of the FES is properly chosen in order to induce a desired active torque which is proportional to the voluntary effort extracted from the electromyography signals of the related muscles using back propagation neural networks. This kind of enhancement serves as a positive feedback which reminds the patient of the correct attempt to fulfill the desired motion. FES control is conducted by a combination of neural network-based feedforward controller and a PD feedback controller. Simulation conducted using Matlab and the experiment with a spinal cord injury subject and a healthy subject have shown satisfactory results which verify the feasibility of this control strategy.

Dynamics modeling and identification of the human-robot interface based on a lower limb rehabilitation robot

Abstract: A lower limb rehabilitation robot, namely iLeg, has been developed recently. Since active exercises have been proven to be effective for neurorehabilitation and motor recovery, they are suggested to be implemented on iLeg. To this goal, patients' motion intention should be recognized. Therefore, a method based on the dynamic model of the human-robot interface (HRI) is designed to recognize the human motion intention. This paper is devoted to modeling and identifying the dynamics of the HRI. Firstly, the dynamic model of the HRI is designed by combining the dynamic models of the human leg and iLeg, where the human leg dynamic model (HLDM) is mainly concerned. By considering the motion trajectories during the rehabilitation exercises provided by iLeg, the human leg can be taken as a manipulator with two degrees of freedom; meanwhile, the joint angles and torques of the human leg can be measured indirectly by using the position and torque sensors mounted on the joints of iLeg. As a result, an 8-parameter HLDM can be designed by using the Lagrangian method. Then, the dynamic model of the HRI is identified by respectively and independently identifying the undetermined dynamic parameters of iLeg and the HLDM, where the dynamic parameters of the HLDM are mainly considered. Finally, the feasibility of the dynamic model of the HRI is validated by experiments.

Containment control of double-integrator multi-agent systems with aperiodic sampling: A small-gain theorem based method

Abstract: In this paper, the containment control problem of continuous-time double-integrator multi-agent systems is investigated. An aperiodic sampled-data based protocol is induced by using neighboring information with uncertainly time-varying sampling intervals. With the obtained protocol and properties of Laplacian matrix, the containment control problem of continuous-time multi-agent systems is equivalently transformed into a stability problem of discrete-time systems. By fixing a sampling length in the given range, a time-invariant discrete-time system is obtained. Then the systems with variation of sampling intervals can be considered as uncertain systems of the time-invariant discrete-time system. By using small-gain theorem, sufficient conditions are obtained to guarantee stability of uncertain discrete-time systems. The theoretical results are illustrated by some simulations.

A collision response algorithm for 3D virtual reality minimally invasive surgery simulator

Abstract: In recent years, rapid development of minimally invasive surgery has taken place. Virtual reality simulator enables the trainees to obtain the core catheter/guide wire handling skills and to decrease the error rate of operation prior to performing them on a real patient. In this paper, we present and evaluate a collision response algorithm and a force feedback computing method for simulating a catheter/guide wire in the interactive 3D virtual realty simulator based on a robotic catheter/guide wire operating system. In order to provide a real-time virtual environment, a multi-threading technology is used to accelerate the medical simulation procedure. Finally, we test the virtual catheter/guide wire with a complex and realistic 3D vascular model, which is generated from computed tomography angiography (CTA) series in DICOM datasets captured in a actual patient. The results show that the collision response algorithm in the system is effective and promising.

Polynomial trajectory tracking of networked Euler-Lagrange systems

Abstract: The coordinated tracking problem of networked Euler-Lagrange systems is studied in this paper. According to the knowledge of classical control, an integral term can eliminate the steady-state error when solving the tracking problem. A proportional-integral-derivative (PID) tracking protocol is then first proposed to solve the tracking problem of Euler-Lagrange systems where the leader has a quadratic trajectory. By properly choosing parameters, it is proved that all followers can asymptotically track the leader's quadratic trajectory if the communication topology graph has a spanning tree. Furthermore, a so-called PI m D tracking protocol is derived by adding some high-order integral terms. It is shown that this PI m D tracking protocol can solve the tracking problem of networked Euler-Lagrange systems with a leader having the higher-order polynomial trajectory. Finally, two simulation examples are presented to demonstrate the effectiveness of the proposed protocol.

Stochastic consensus of linear multi-agent systems: Communication noises and Markovian switching topologies

Abstract: This paper studies the mean square and almost sure consensus of discrete-time linear multi-agent systems with communication noises under Markovian switching topologies. By a sophisticated stochastic-approximation type protocol, the closed-loop dynamics of this linear multi-agent system can be transformed into a discrete-time first-order integral multi-agent system. It is proved that if all roots of a polynomial, whose coefficients are the parameters in the gain vector of the proposed protocol, are in the unit circle, there is certain equivalence between the consensus of original linear multi-agent system and the consensus of transformed first-order integral multi-agent system. Then some sufficient conditions on the mean square/almost sure consensus of linear multi-agent systems can be obtained accordingly. Finally, theoretical analysis is verified by simulation examples.

sEMG-Based Single-Joint Active Training with iLeg—A Horizontal Exoskeleton for Lower Limb Rehabilitation

Abstract: In this paper, surface electromyography (sEMG) from muscles of the lower limb is acquired and processed to estimate the single-joint voluntary motion intention, based on which, two single-joint active training strategies are proposed with iLeg, a horizontal exoskeleton for lower limb rehabilitation newly developed at our laboratory. In damping active training, the joint angular velocity is proportionally controlled by the voluntary effort derived from sEMG, performing as an ideal damper, while spring active training aims to create a spring-like environment where the joint angular displacement from the constant reference is proportionally controlled by the voluntary effort. Experiments are conducted with iLeg and one healthy male subject to validate the feasibility of the two single-joint active training strategies.

FEM-based guide wire simulation and interaction for a minimally invasive vascular surgery training system

Abstract: Minimally invasive vascular surgery is an important surgical procedure which requires specialized skills to manipulate surgical instruments. However, the insufficient conventional training paradigm of “see one, do one, teach one” cannot meet the urgent need for training surgeons. In this paper we present a novel computer-based training system which aims at helping the trainees with acquiring manipulation skills and gaining simulated surgical experience. This system integrates a physics model of guide wire, a 3D vasculature segmented from real patients and a self-designed haptic device. We use the finite element method (FEM) to model the guide wire based on its specific physical parameters. Trainees can advance and rotate the simulated instrument by the haptic device to interact with the human vasculature. The experimental results of delivering the guide wire to the entrance of the coronary in real time validated that the guide wire simulation is effective.

Metric learning for event-related potential component classification in EEG signals

Abstract: In this paper, we introduce a metric learning approach for the classification process in the recognition procedure for P300 waves in electroencephalographic (EEG) signals. We show that the accuracy of support machine vector (SVM) classification is significantly improved by learning a similarity metric from the training data instead of using the default Euclidean metric. The effectiveness of the algorithm is validated through experiments on the dataset II of the brain-computer interface (BCI) Competition III(P300 speller). Index Terms – Metric learning, SVM, P300

Centerlines extraction for lumen model of human vasculature for computer-aided simulation of intravascular procedures

Abstract: The computer-aided surgical simulation aims to provide an economic tool of effectiveness and convenience for the training process. In building this simulation system, the construction of the virtual anatomic environment is one of the major tasks. It provides the virtual tools with the scenario in which they are manipulated by the trainee. In intravascular surgery simulation, the surface model of the blood vessels is the most important part of the virtual environment. In order to achieve better performances in the simulation of path planning and navigation, the surface model based on real patient's CTA data needs further process. We proposed in this paper an approach to extract the centerlines of each segment of the image-based surface model of the blood vessels. The surface model is firstly processed to check the connectivity of the consisting polygons in order to extract the largest connected region within the surface. Next, the resulting surface is smoothed by a windowed sinc function kernel with proper parameters. After the normal vectors of the smoothed surface are computed, the surface is subdivided and the centerlines of the surface model are computed by using the power crust algorithm. The experimental results show that the approach is capable of extracting the centerlines of the vessel model.

sEMG Analysis for Recognition of Rehabilitation Actions

Abstract: Surface electromyography (sEMG), a measurement of biomedical electronic signals from the muscle surface using electrodes, shows the motor status of the nerve–muscle system and motor instruction information. Active motion intention and the motor status of impaired stroke patients can be acquired by sEMG. Currently, sEMG is widely used in prosthetic arm control, rehabilitation robot control, exoskeletal power assist robot control, tele-operated robots, virtual reality, and so on. The application of sEMG to a rehabilitation robot is studied in this chapter. sEMG is used to build an information channel between the patient and the robot to obtain biological feedback control during rehabilitation training. It is of great significance for the improvement of patientsʼ consciousness of active participation. It will also help to improve the evaluation and efficiency of rehabilitation. It establishes a general scheme for other applications related to the human–machine interface.

Dynamic behavior analysis on SISO multi-agent systems in a noisy environment

Abstract: This paper studies the output consensus problem of single-input single-output (SISO) multi-agent systems. It is assumed that the multi-agent system works in a noisy environment (state noises, measurement noises and communication noises). A dynamic output-feedback based protocol is proposed to solve the stochastic output consensus problem in this setting. It is proved that the mathematical expectations of relative outputs between agents are convergent to zero, and the second-order moments of relative outputs between agents are uniformly bounded. Finally, some simulation examples are presented to demonstrate this phenomenon.

Segmentation and visualization of the heart region for 3-D simulation of coronary intervention procedures

Abstract: In fighting the coronary heart diseases, percutaneous coronary intervention is proved to be a powerful and reliable clinical procedure in the modern catheterization labs all over the world. Due to its minimally invasive characteristics, the procedure must be performed in the image-guided way, which makes this important skill very difficult to learn. To make the learning more accessible, a computer-aided surgical simulator is planned to be implemented in our lab. For now, the prototyping model is completed and the further validation is being conducted. In implementing the virtual anatomic environment, we aim to provide the trainee an intuitive visual effect so that the models of the organs bear resemblance to their counterpart of the human's. Besides the blood vessels per se, the surrounding organs seen in the real surgery also need to be visualized during the simulation. The heart is undoubtedly the most critical one among them. The segmentation of the heart is a challenging task because of the noisy and indistinct boundaries of the heart in the images due to the natural heart beating during the image acquisition. In this paper, an approach based on the active contours method is developed to fulfill this job. The experimental results demonstrate the effectiveness of our approach.

Tree structures segmentation of coronary arteries using CURVES method for interventional surgical simulation

Abstract: Visualization model of the coronary vasculature is of utmost importance for the diagnosis of the coronary heart diseases, as well as the planning and navigation of the intravascular surgery. To protect the cardiologists and operation staff against the ionizing radiation, surgical robots are designed and come to assist the practitioners during the interventional procedure. Robotic surgical simulation aims to provide effective, economic and convenient support for the learners with the surgical details as real as possible. In building this system, the geometric model of the blood vessels especially the coronary arteries are the key part of the virtual anatomic scenario. Because of the complex topologies, the segmentation of the coronary arteries is full of challenges. We developed in this paper a semi-automatic approach for this challenging work. The approach is based on an improved geodesic active contours model called CURVES. Firstly, the region that contains the whole heart in the original images are completely extracted. Secondly, the extracted volumetric data is smoothed and thresholded in order to remove noises and irrelevant details. Next the image features are generated by calculating the gradients pixel-wisely, while the initial contours are generated by a modified fast marching method. Then the contour evolution is provoked to segment the boundaries of the coronary arteries. Finally the surface model is visualized after information is organized by using the marching cubes method. Experimental results showed the capability of the proposed approach in the segmentation of the coronary arteries tree.

An 3D interactive virtual reality software toolkit for minimally invasive vascular surgery

Abstract: Recently, much more attention has been paid to the development of minimally invasive vascular surgery. Simulating behaviors of a catheter/guide wire in a realistic 3D vascular model for minimally invasive vascular surgery is a challenging subject. One of the main problems for an 3D computer-based virtual reality simulator is how to design a software toolkit primarily targeted to medical simulation. In this paper, we describe an 3D interactive virtual reality software toolkit and present an example of application to a virtual minimally invasive vascular surgery procedure. Finally, experimental results are given to show that the 3D interactive virtual reality software toolkit is effective and promising.

Mesh optimization of vessel surface model for computer-aided simulation of percutaneous coronary intervention

Abstract: Percutaneous coronary intervention is the gold standard to coronary diseases in the past decades due to much less trauma and quick recovery. However, due to the traits of minimal invasiveness, clinicians have to defeat the difficulties in eye-hand coordination during the procedure, which also makes it a non-trivial task in the catheterization lab. The computer-aided surgical simulation is designed to provide a reliable tool for the early stage of the training of the procedure. In this simulation system, the surface model of the vessels contribute the major part in the virtual anatomic environment. On the other hand, heavy interactions between the virtual surgical tools and the model surface occur during the training. In order to achieve acceptable performances, the patient-specific vessel surface model needs further process to adapt to this situation. We proposed in this paper an approach to optimize the meshes that consist the surface model with its application in consideration. The connectivity of the surface model is firstly checked. Next a smooth processing is applied without modifying the geometry of the largest-connected surface. Then the quantities of the polygons consisting the model surface are eliminated both dramatically and appropriately. The resultant surface model is applied in the validation test interacting with the virtual guidewire.

Haptic rendering of guide wire manipulation in vascular surgery simulation

Abstract: Haptic technology is highlighted in minimally invasive vascular surgery simulation. During the virtual reality-based surgery training, realistic haptic feedback can be provided by control devices to help enhance the surgeons' surgical performance. In this paper, we present a haptic rendering scheme for the vascular surgery simulation. The scheme consists of a self-designed haptic device, a physics-based resistance model and a virtual coupling network. The resistance model considers not only the contact forces between the vasculature and the beam element model of the guide wire but also the blood flow resistance. The virtual coupling network is applied to guarantee safety of the manipulation and stability of the simulation. Experimental results verified that the haptic force is close to reality and of good usability.

Lumen segmentation and visualization of abdominal aorta using geodesic active contours for intravascular surgical simulation

Abstract: Percutaneous transluminal coronary angioplasty (PTCA) has been proved to be a standard solution to most cardiovascular diseases (CVDs). The surgical simulator provides the trainees a new vehicle to learn this skill much more conveniently and effectively. The blood vessel model is at the core of the virtual environment. In this paper, a robust and semi-automatic approach to segment the abdominal aorta from the computed tomography angiography (CTA) is developed. The proposed approach employs the geodesic active contours method as the main component. The edge potential map is generated by applying nonlinear mapping function. The initial contours are evolved by applyging the fast marching method. The surface information representing the vessel is extracted by the marching cubes method. This approach has been proved successful for the construction of 3-D surface model of the aorta based on the CTA series.

A comparison for probabilistic spiking neuron model and spiking integrated and fired neuron model

Abstract: As the third generation of artificial neural networks (ANNs), spiking neural networks (SNNs) have many advantages over the traditional ones. Selecting proper spiking neuron models for the design of SNNs is important. In this paper, schematic and training algorithm of spiking integrated and fired (IAF) neuron model and probability spiking neuron model (pSNM) are introduced. By comparing the classification results for mobile robots' corridor-scene-classifier based on IAF model and pSNM, and the control results of mobile robots' wall-following controller based on spiking IAF model and pSNM, the similarities and differences between the two models are discussed. The similar and different features of the two spiking neuron models are obtained. IAF model is more suitable for the design of mobile robots controller than that of pSNM. While pSNM has better noise robust than IAF model. Spiking IAF model and pSNM are suitable for different situations.

3D interactive virtual environments for minimally invasive vascular surgery

Abstract: Recently, the development of minimally invasive surgery has led to the emergence of the new research area: minimally invasive vascular surgery simulation. The goal of the surgery simulation is to provide a new way to enable the trainees to obtain the core skills of the techniques. One of the key component of the simulation for core skills training in minimally invasive vascular surgery is how to develop 3D real-time computer-based virtual environments. In this paper, we present the virtual environments targeting to simulate the motion of a catheter/guide wire with complex 3D vascular models. Experimental results show that the 3D virtual environments are effective and promising.