Xingang Zhao

A novel HCI based on EMG and IMU

The technology of human-computer interaction (HCI) is developing rapidly in tandem with the advancement of information and biological technologies. Many new types input device are introduced into this field; some of them are aimed to benefit special groups of people like old or disabled persons. In the meantime, Electromyography (EMG) and Inertia Measure Unit (IMU) have been readily available and extensively applied in control systems in many fields. In this paper, we propose a novel EMG-IMU based mouse controller that controls cursor movements based on IMU signals. The displacement of the cursor is determined by integrating the acceleration signal from the IMU, which moves with the operators arm. The mouse operations such as left click, right click and wheel scroll, are commanded through EMG signals. The pattern recognition algorithm, Linear Discriminant Analysis (LDA), is adopted to classify the EMG data into several clusters, which correspond to the pre-defined mouse operations. Experimental results have indicated that the proposed mouse controller can achieve an accuracy of 88%.

Design and Implement of a Rotorcraft UAV Testbed

This paper describes recent research on the design and implement of a small-scaled rotorcraft unmanned aerial vehicle (RUAV) system. This platform is going to be used as a testbed for experimentally evaluating advanced control methodologies dedicated on improving the maneuverability, reliability as well as autonomy of RUAV. Sensors and controller are implemented onboard. The full system has been tested successfully in the remote-controlled mode. A control scheme based on a simplified thrust-torque model, which is for the initial flight test, is also presented.

A State-Space EMG Model for the Estimation of Continuous Joint Movements

A state-space electromyography (EMG) model is developed for continuous motion estimation of human limb in this paper. While the general Hill-based muscle model (HMM) estimates only joint torque from EMG signals in an “open-loop” form, we integrate the forward dynamics of human joint movement into the HMM, and such an extended HMM can be used to estimate the joint motion states directly. EMG features are developed to construct measurement equations for the extended HMM to form a state-space model. With the state-space HMM, a normal closed-loop prediction-correction approach such as the Kalman-type algorithm can be used to estimate the continuous joint movement from EMG signals, where the measurement equation is used to reject model uncertainties and external disturbances. Moreover, we propose a new normalization approach for EMG signals for the purpose of rejecting the dependence of the motion estimation on varying external loads. Comprehensive experiments are conducted on the human elbow joint, and the improvements of the proposed methods are verified by the comparison of the EMG-based estimation and the inertial measurement unit measurements.

Neural Network-Based Control of Networked Trilateral Teleoperation With Geometrically Unknown Constraints

Most studies on bilateral teleoperation assume known system kinematics and only consider dynamical uncertainties. However, many practical applications involve tasks with both kinematics and dynamics uncertainties. In this paper, trilateral teleoperation systems with dual-master-single-slave framework are investigated, where a single robotic manipulator constrained by an unknown geometrical environment is controlled by dual masters. The network delay in the teleoperation system is modeled as Markov chain-based stochastic delay, then asymmetric stochastic time-varying delays, kinematics and dynamics uncertainties are all considered in the force-motion control design. First, a unified dynamical model is introduced by incorporating unknown environmental constraints. Then, by exact identification of constraint Jacobian matrix, adaptive neural network approximation method is employed, and the motion/force synchronization with time delays are achieved without persistency of excitation condition. The neural networks and parameter adaptive mechanism are combined to deal with the system uncertainties and unknown kinematics. It is shown that the system is stable with the strict linear matrix inequality-based controllers. Finally, the extensive simulation experiment studies are provided to demonstrate the performance of the proposed approach.

Missing-Data Classification With the Extended Full-Dimensional Gaussian Mixture Model: Applications to EMG-Based Motion Recognition

Missing data are a common drawback that pattern recognition techniques need to handle when solving real-life classification tasks. This paper first discusses problems in handling high-dimensional samples with missing values by the Gaussian mixture model (GMM). Since fitting the GMM by directly using high-dimensional samples as inputs is difficult due to the convergence and stability issues, a novel method is proposed to build the high-dimensional GMM by extending a reduced-dimensional GMM to the full-dimensional space. Based on the extended full-dimensional GMM, two approaches, namely, marginalization and conditional-mean imputation, are proposed to classify samples with missing data in online phase. Then, the proposed methods were employed to recognize hand motions from surface electromyography (sEMG) signals, and more than 75% of classification accuracy of motions can be obtained even if 50% of sEMG signals were missing. Comparisons with normal mean and zero imputations also demonstrate the improvements of the proposed methods. Finally, a control scheme for a myoelectric hand was designed by involving the novel methods, and online experiments confirm the ability of the proposed methods to improve the safety and stability of practical systems.

A novel EMG-driven state space model for the estimation of continuous joint movements

Electromyography (EMG) has been widely used as control commands for prosthesis, powered exoskeletons and rehabilitative robots. In this paper, an EMG-driven state space model is developed to estimate continuous joint angular displacement and velocity, demonstrated by elbow flexion/ extension. The model combines the Hill-based muscle model with the forward dynamics of joint movement, in which kinematic variables are expressed as a function of neural activation levels. EMG features including integral of absolute value and waveform length are then extracted, and two quadratic equations which associate the kinematic variables with EMG features are constructed to represent the measurement equation. The proposed model are verified by extensively experiments, where the angular movements of human elbow joint are estimated only using the EMG signals, and the estimations are compared with the IMU measurements to validate the accuracy. As a demonstration, a robotic arm is commanded to follow the human elbow movement estimated by the proposed model, which shows the possibility of EMG-based robotic assisted rehabilitation.

UKF-based rotorcraft UAV Fault adaptive control for actuator failure

A new fault adaptive control methodology against the actuator failure is proposed in this paper. The actuator failure modeling are introduced to denote the actuator healthy level (AHL) and the Unscented Kalman Filter (UKF) is employed for on-line estimation of both the flight states and the AHL parameters of rotorcraft UAV (RUAV). The functionality of the approach has been illustrated through experiments on the Shenyang Institute of Automation RUAV platform SIA-Heli-90 and the results show that the proposed method is an effective tool for actuator fault adaptive control of RUAVs.

An Adaptive Threshold Neural-Network Scheme for Rotorcraft UAV Sensor Failure Diagnosis

This paper presents an adaptive threshold neural-network scheme for Rotorcraft Unmanned Aerial Vehicle (RUAV) sensor failure diagnosis. The approach based on adaptive threshold has the advantages of better detection and identification ability compared with traditional neural-network-based scheme. In this paper, the proposed scheme is demonstrated using the model of a RUAV and the results show that the adaptive threshold neural-network method is an effective tool for sensor fault detection of a RUAV.

Adaptive UKF and Its Application in Fault Tolerant Control of Rotorcraft UAV

Fault tolerant control (FTC) is essential for rotorcraft UAV (RUAV). Due to the inherently unstable dynamics, either flight test or real application of a RUAV is in high risk while a minutial failure may lead to the whole system collapse. In this paper, a novel adaptive unscented Kalman filter (AUKF) is proposed for onboard failure coefficient estimation and a new FTC method is designed against the actuator failure of RUAV. In the AUKF, the error between the covariance matrices of innovation and their corresponding estimations/predictions in normal UKF is utilized as a cost function. Based on the MIT rule, an adaptive algorithm is developed to update the covariance of process noise by minimizing the cost function. The updated covariance is then fed back into the normal UKF. Such an adaptive mechanism intends to release the dependence of UKF on a prior knowledge of the noise environment and improve the convergence speed and estimation accuracy of normal UKF. By introducing the actuator health coefficients (AHCs) into the dynamics equation of a RUAV, the proposed AUKF is utilized to online estimate both the flight states and the AHCs. A fault adaptive control is further designed based on the estimated states and AHCs. Simulations are conducted on the dynamics of a model helicopter developed in Shenyang Institute of Automation. The comparisons between the adaptive- UKF-based FAC and the normal-UKF-based one show the effectiveness and improvements of the proposed method.

SSVEP-Based Brain–Computer Interface Controlled Functional Electrical Stimulation System for Upper Extremity Rehabilitation

Traditional rehabilitation techniques have limited effects on the recovery of patients with tetraplegia. A brain-computer interface (BCI) provides an interactive channel that does not depend on the normal output of peripheral nerves and muscles. In this paper, an integrated framework of a noninvasive electroencephalogram (EEG)-based BCI with a noninvasive functional electrical stimulation (FES) is established, which can potentially enable the upper limbs to achieve more effective motor rehabilitation. The EEG signals based on steady-state visual evoked potential are used in the BCI. Their frequency domain characteristics identified by the pattern recognition method are utilized to recognize intentions of five subjects with average accuracy of 73.9%. Furthermore the movement intentions are transformed into instructions to trigger FES, which is controlled with iterative learning control method, to stimulate the relevant muscles of upper limbs tracking desired velocity and position. It is a useful technology with potential to restore, reinforce or replace lost motor function of patients with neurological injuries. Experiments with five healthy subjects demonstrate the feasibility of BCI integrated with upper extremity FES toward improved function restoration for an individual with upper limb disabilities, especially for patients with tetraplegia.