Kui Yuan

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...

EMG-based hands-free wheelchair control with EOG attention shift detection

This paper presents a novel hands-free control system for an electric-powered wheelchair, which is based on EMG (Electromyography) signals recorded from eyebrow muscle activity. By using a simple CyberLink device, one-dimensional continuous EMG signals are obtained, analysed, and then translated into multi-directional control commands (forward, left, right, etc.) for the wheelchair that supports multi-directional control. At the same time, EOG (Electrooculography) signals detected from eye movements are used to adjust wheelchair speed. The system also allows a user to choose either control state or non-control state so that any non-intended muscle activity can be ignored during the non-control state. The system is reliable, easy to set up, and easy to use.

An improved Canny edge detector and its realization on FPGA

Canny edge detector treats edge detection as a signal processing problem to design an optimal edge detector and has been widely used for edge detection. However, the traditional Canny edge detector has two shortcomings. First, the threshold of the algorithm needs to be set by manual. Secondly, the algorithm is very time consuming and can not be implemented in real time. A new self-adapt threshold Canny algorithm is proposed in this paper to solve the first problem. A pipelined implementation on FPGA for this new algorithm is also designed to solve the second problem. Experiment results are also given to show the efficiency of the proposed method.

Use of forehead bio-signals for controlling an Intelligent Wheelchair

This paper presents a novel method to classify human facial movement based on multi-channel forehead bio-signals. Five face movements form three face regions: forehead, eye and jaw are selected and classified in back propagation artificial neural networks (BPANN) by using a combination of transient and steady features from EMG and EOG waveforms. The identified face movements are subsequently employed to generate five control commands for controlling a simulated Intelligent Wheelchair. A human-machine interface (HMI) is designed to map movement patterns into corresponding control commands via a logic control table. The simulation result shows the feasibility and performance of the proposed system, which can be extended into real-world applications as a control interface for disabled and elderly users.

An Embedded Control System for Intelligent Wheelchair

Due to recent advancement of AI and robotics technology, the research of intelligent wheelchair (iWheelChair) begins to draw attention from both scientific community and industry. WheelChair is a kind of home welfare tools and can help the handicapped and elderly people to gain mobility and lead to independent life. This paper describes a newly developed intelligent wheelchair. The controller of the iWheelChair adopts the advanced DSP technology, and plays the role of data acquisition and processing of joystick and ultrasonic sensors. 8 ultrasonic sensors are mounted on WheelChair and can detect the environment changes for safe operation. Experiments are presented to show that iWheelChair is able to avoid obstacles safely while controlled by its user via the joystick

MR code for indoor robot self-localization

A novel 2D code which can be used as an efficient artificial landmark system for indoor mobile robot and is thus called MR (mobile robot) code, is described in this paper. A real time detection-recognition algorithm for this MR code is also proposed. Experiments in cluttered indoor environments under various viewing angles and illumination conditions are carried out to show the effectiveness and robustness of the MR code detection-recognition algorithm proposed. How to use this new MR code for indoor mobile robot self-localization and objects recognition is also discussed and experiments are also carried out to show the feasibility and reliability.

Monte Carlo multi-robot localization based on grid cells and characteristic particles

In this paper, a Monte Carlo method based approach for multi-robot localization is described. In this approach, grid cells are used to describe the whole particle set which is used in MCL method to estimate the pose of robot. Then, the sizes of the grid cells are adjusted to capture the characteristic particles that can represent the property of all particles. The characteristic particles can be used to estimate the robots position in its operation space. Because the number of the characteristic particles is much less than that of the total particles, this approach can reduce the computing time greatly. Simulation results are also given to show that this approach can obtain good localization performance in multi-robot system

Real-time elliptical head contour detection under arbitrary pose and wide distance range

In this paper, we propose a real time elliptical head contour detection method based on quadrant arcs, which is efficient and robust to arbitrary head pose and wide distance range. First, the moving object area is detected according to background model which is built on three color channels. Then, all the valid elliptical arcs are extracted out from connected edges, and classified into four kinds of quadrant arc sets according to their gradient information. Finally, the arcs lying in different sets are combined to fit out the elliptical head contour based on the least square method. Experimental results confirm the robustness and the accuracy of this method under arbitrary head pose and wider distance range, as well as the real time property, strong robustness to long or short hair and with or without hat.

Visual odometry based on locally planar ground assumption

A method for estimating ego-motion with vehicle mounted single camera is presented in this paper. Simultaneous translation and rotation are accurately measured by detecting and tracking features in image sequences. This approach is based on locally planar ground assumption and runs in real-time. It has been verified in a variety of real-world environments and on real mobile robot platforms.

Dynamic and tip-over stability analysis of a planetary wheeled stair-climbing wheelchair

In this paper, the design of the mechanism and the control system of a planetary wheeled stair-climbing wheelchair is introduced. The dynamic model of the planetary wheel clusters is established based on Lagrange equation, and the angle acceleration curves are studied with various given equivalent torques. Stability margin of the wheelchair is analyzed in detail during a single step climbing procedure. According to the simulation results, the control law of wheelchairs angle is derived from the projection of the wheelchairs CG on the condition that the wheelchairs stability is always maintained. The wheelchair can be easily operated by an assistant accordance with the control law.