osheng Hu

Myoelectric control systems—A survey

Abstract The development of an advanced human–machine interface has always been an interesting research topic in the field of rehabilitation, in which biomedical signals, such as myoelectric signals, have a key role to play. Myoelectric control is an advanced technique concerned with the detection, processing, classification, and application of myoelectric signals to control human-assisting robots or rehabilitation devices. This paper reviews recent research and development in pattern recognition- and non-pattern recognition-based myoelectric control, and presents state-of-the-art achievements in terms of their type, structure, and potential application. Directions for future research are also briefly outlined.

Human motion tracking for rehabilitation—A survey

Abstract Human motion tracking for rehabilitation has been an active research topic since the 1980s. It has been motivated by the increased number of patients who have suffered a stroke, or some other motor function disability. Rehabilitation is a dynamic process which allows patients to restore their functional capability to normal. To reach this target, a patients’ activities need to be continuously monitored, and subsequently corrected. This paper reviews recent progress in human movement detection/tracking systems in general, and existing or potential application for stroke rehabilitation in particular. Major achievements in these systems are summarised, and their merits and limitations individually presented. In addition, bottleneck problems in these tracking systems that remain open are highlighted, along with possible solutions.

Support Vector Machine-Based Classification Scheme for Myoelectric Control Applied to Upper Limb

This paper proposes and evaluates the application of support vector machine (SVM) to classify upper limb motions using myoelectric signals. It explores the optimum configuration of SVM-based myoelectric control, by suggesting an advantageous data segmentation technique, feature set, model selection approach for SVM, and postprocessing methods. This work presents a method to adjust SVM parameters before classification, and examines overlapped segmentation and majority voting as two techniques to improve controller performance. A SVM, as the core of classification in myoelectric control, is compared with two commonly used classifiers: linear discriminant analysis (LDA) and multilayer perceptron (MLP) neural networks. It demonstrates exceptional accuracy, robust performance, and low computational load. The entropy of the output of the classifier is also examined as an online index to evaluate the correctness of classification; this can be used by online training for long-term myoelectric control operations.

Head gesture recognition for hands‐free control of an intelligent wheelchair

Purpose – This paper presents a novel hands‐free control system for intelligent wheelchairs (IWs) based on visual recognition of head gestures.Design/methodology/approach – A robust head gesture‐based interface (HGI), is designed for head gesture recognition of the RoboChair user. The recognised gestures are used to generate motion control commands to the low‐level DSP motion controller so that it can control the motion of the RoboChair according to the users intention. Adaboost face detection algorithm and Camshift object tracking algorithm are combined in our system to achieve accurate face detection, tracking and gesture recognition in real time. It is intended to be used as a human‐friendly interface for elderly and disabled people to operate our intelligent wheelchair using their head gestures rather than their hands.Findings – This is an extremely useful system for the users who have restricted limb movements caused by some diseases such as Parkinsons disease and quadriplegics.Practical implicatio...

Receding horizon tracking control of wheeled mobile robots

In this paper, a receding horizon (RH) controller is developed for tracking control of a nonholonomic mobile robot. The control stability is guaranteed by adding a terminal-state penalty to the cost function and constraining the terminal state to a terminal-state region. The stability analysis in the terminal-state region is investigated, and a virtual controller is found. The analysis results show that the RH tracking control has simultaneous tracking and regulation capability. Simulation results are provided to verify the proposed control strategy. It is shown that the control strategy is feasible.

Biological Inspiration: From Carangiform Fish to Multi-Joint Robotic Fish

This paper presents a novel approach to modelling carangiform fish-like swimming motion for multi-joint robotic fish so that they can obtain fish-like behaviours and mimic the body motion of carangiform fish. A given body motion function of fish swimming is firstly converted to a tail motion function which describes the tail motion relative to the head. Then, the tail motion function is discretized into a series of tail postures over time. Thirdly, a digital approximation method calculates the turning angles of joints in the tail to approximate each tail posture; and finally, these angles are grouped into a look-up table, or regressed to a time-dependent function, for practically controlling the tail motors in a multi-joint robotic fish. The paper made three contributions: tail motion relative to the head, an error function for digital approximation and regressing a look-up table for online optimization. To prove the feasibility of the proposed methodology, two basic swimming motion patterns, cruise straight and C-shape sharp turning, are modelled and implemented in our robotic fish. The experimental results show that the relative tail motion and the approximation error function are good choices and the proposed method is feasible.

Neural Predictive Control for a Car-like Mobile Robot

This paper presents a new path-tracking scheme for a car-like mobile robot based on neural predictive c ontrol. A multi-layer back-propagation n eural network is employed to model non-linear kinematics of the robot i nstead o f a linear r egression estimator in o rder to adapt t he robot t o a large operating range. The neural predictive c ontrol for path tracking is a model-based p redictive c ontrol based on neural network modelling, which can generate its output in term of the robot kinematics and a desired p ath. The desired p ath for the robot i s produced b y a polar polynomial with a simple c losed form. The multi-layer back-propagation n eural network is constructed b y a wavelet orthogonal decomposition to form a wavelet neural network that can o vercome the problem caused b y the local minima when training the neural network. The wavelet neural network has the a dvantage of using an explicit way to d etermine the number of the hidden nod es and initial value of weights. Simulation results for the modelling and control are provided to justify the proposed scheme.

Toward a fully decentralized architecture for multi-sensor data fusion

A fully decentralized architecture is presented for data fusion problems. This architecture takes the form of a network of sensor nodes, each with its own processing facility, which together do not require any central processor or any central communication facility. In this architecture, computation is performed locally and communication occurs between any two nodes. Such an architecture has many desirable properties, including robustness to sensors failure and flexibility to the addition or loss of one or more sensors. This architecture is appropriate for the class of extended Kalman filter (EKF)-based geometric data fusion problems. The starting point for this architecture is an algorithm which allows the complete decentralization of the multisensor EKF equations among a number of sensing nodes. This algorithm is described, and it is shown how it can be applied to a number of different data-fusion problems. An application of this algorithm to the problem of multicamera, real-time tracking of objects and people moving through a room is described.<<ETX>>

Integration of Vision and Inertial Sensors for 3D Arm Motion Tracking in Home-based Rehabilitation

The integration of visual and inertial sensors for human motion tracking has attracted significant attention recently, due to its robust performance and wide potential application. This paper introduces a real-time hybrid solution to articulated 3D arm motion tracking for home-based rehabilitation by combining visual and inertial sensors. Data fusion is a key issue in this hybrid system and two different data fusion methods are proposed. The first is a deterministic method based on arm structure and geometry information, which is suitable for simple rehabilitation motions. The second is a probabilistic method based on an Extended Kalman Filter (EKF) in which data from two sensors is fused in a predict-correct manner in order to deal with sensor noise and model inaccuracy. Experimental results are presented and compared with commercial marker-based systems, CODA and Qualysis. They show good performance for the proposed solution.

Internet‐based robotic systems for teleoperation

Today’s Internet technology provides a convenient way for us to develop an integrated network environment for the diversified applications of different robotic systems. To be successful in real‐world applications, Internet‐based robots require a high degree of autonomy and local intelligence to deal with the restricted bandwidth and arbitrary transmission delay of the Internet. This paper describes the first step toward building such an Internet‐based robotic system for teleoperation in the University of Essex. The system has a standard network protocol and an interactive human‐machine interface. Using a Web browser, a remote operator can control the mobile robot to navigate in our laboratory with visual feedback and a simulated environment map via the Internet. The employment of an intuitive user interface enables Internet users to control the mobile robot and implement useful tasks remotely. Although at its first stage, the developed system has the potential to be extended to many real‐world applications such as tele‐manufacturing, tele‐training and tele‐service.