Luciano Boquete

System for assisted mobility using eye movements based on electrooculography

Describes an eye-control method based on electrooculography (EOG) to develop a system for assisted mobility. One of its most important features is its modularity, making it adaptable to the particular needs of each user according to the type and degree of handicap involved. An eye model based on electrooculographic signal is proposed and its validity is studied. Several human-machine interfaces (HMI) based on EOG are commented, focusing our study on guiding and controlling a wheelchair for disabled people, where the control is actually effected by eye movements within the socket. Different techniques and guidance strategies are then shown with comments on the advantages and disadvantages of each one. The system consists of a standard electric wheelchair with an on-board computer, sensors and a graphic user interface run by the computer. On the other hand, this eye-control method can be applied to handle graphical interfaces, where the eye is used as a mouse computer. Results obtained show that this control technique could be useful in multiple applications, such as mobility and communication aid for handicapped persons.

Wheelchair Guidance Strategies Using EOG

This paper describes an eye-control method, based on electrooculography (EOG), for guiding and controlling a wheelchair for disabled people; the control is actually effected by eye movements within the socket. An eye model based on an electrooculographic signal is proposed and its validity is studied. Different techniques and guidance strategies are then shown with comments on the advantages and disadvantages of each one. The system consists of a standard electric wheelchair with an on-board computer, sensors and a graphic user interface run by the computer. This control technique could be useful in multiple applications, such as mobility and communication aid for handicapped persons.

A Color Vision-Based Lane Tracking System for Autonomous Driving on Unmarked Roads

This work describes a color Vision-based System intended to perform stable autonomous driving on unmarked roads. Accordingly, this implies the development of an accurate road surface detection system that ensures vehicle stability. Although this topic has already been documented in the technical literature by different research groups, the vast majority of the already existing Intelligent Transportation Systems are devoted to assisted driving of vehicles on marked extra urban roads and highways. The complete system was tested on the BABIECA prototype vehicle, which was autonomously driven for hundred of kilometers accomplishing different navigation missions on a private circuit that emulates an urban quarter. During the tests, the navigation system demonstrated its robustness with regard to shadows, road texture, and weather and changing illumination conditions.

Unsupervised and adaptive Gaussian skin-color model

Abstract In this article a segmentation method is described for the face skin of people of any race in real time, in an adaptive and unsupervised way, based on a Gaussian model of the skin color (that will be referred to as Unsupervised and Adaptive Gaussian Skin-Color Model, UAGM). It is initialized by clustering and it is not required that the user introduces any initial parameters. It works with complex color images, with random backgrounds and it is robust to lighting and background changes. The clustering method used, based on the Vector Quantization (VQ) algorithm, is compared to other optimum model selection methods, based on the EM algorithm, using synthetic data. Finally, real results of the proposed method and conclusions are shown.

EOG guidance of a wheelchair using neural networks

Presents a method to control and guide mobile robots. In this case, to send different commands we have used electrooculography (EOG) techniques, so that, control is made by means of the ocular position (eye displacement into its orbit). A neural network is used to identify the inverse eye model, therefore the saccadic eye movements can be detected and where the user is looking can be determined. This control technique can be useful in multiple applications, but in this work it is used to guide an autonomous robot (wheelchair) as a system to help to people with severe disabilities. The system consists of a standard electric wheelchair with an on-board computer, sensors and graphical user interface running on a computer.

EOG-based eye movements codification for human computer interaction

Highlights? We present a new eye-control method for eye-based computer interaction using EOG. ? An electrooculographic eye model based on wavelet and neural networks is proposed. ? Results demonstrate the systems reliability in detecting eye movements. ? This model minimizes the problems associated with progressive user tiredness. ? Any HCI can be controlled using eye movements detected by EOG. This paper describes a new eye-control method for eye-based computer interaction using EOG. This work aims to resolve some of the problems encountered in current systems when used for long periods of time and users become tired. For this purpose, a new electrooculographic eye model based on wavelet transform and neural networks is proposed. The results obtained demonstrate the systems reliability in detecting eye movements and show an error of less than 2? during long periods of use. The system proposed may be used to control any graphical interface using eye movements detected by electrooculography.

Electro-Oculographic Guidance of a Wheelchair Using Eye Movements Codification

In this paper we present a new method to guide mobile robots. An eye-control device based on electro-oculography (EOG) is designed to develop a system for assisted mobility. Control is made by means eye movements detected using electro-oculographic potential. Using an inverse eye model, the saccadic eye movements can be detected and know where the user is looking. This control technique can be useful in multiple applications, but in this work it is used to guide a wheelchair for helping people with severe disabilities. The system consists of a standard electric wheelchair, an on-board computer, sensors and a graphical user interface. Finally, we comment on some experimental results and conclusions about electro-oculographic guidance using ocular commands.

Neural Control of the Movements of a Wheelchair

This paper studies the problem of controlling the movements of a handicapped persons motorized wheelchair from a practical point of view. The control system implemented has been divided into two levels: the “low level”, consisting of an electronic system which directly controls the drivers of the chairs motors, with a classic PID (proportional-integral-derivative) control loop. The aim of this level is to ensure that the speeds of each one of the wheels is similar to the input speed of these control boards. The second control level (“high level”), implemented by means of neural techniques, ensures that the linear and angular speeds of the wheelchair are those indicated by a trajectory generator. A new recurrent model is used as the neural network, for which the stability conditions of the complete control system are obtained and various practical tests are carried out, which show the correct performance of the actual system implemented.

Sensory System for Implementing a Human—Computer Interface Based on Electrooculography

This paper describes a sensory system for implementing a human–computer interface based on electrooculography. An acquisition system captures electrooculograms and transmits them via the ZigBee protocol. The data acquired are analysed in real time using a microcontroller-based platform running the Linux operating system. The continuous wavelet transform and neural network are used to process and analyse the signals to obtain highly reliable results in real time. To enhance system usability, the graphical interface is projected onto special eyewear, which is also used to position the signal-capturing electrodes.

Automated Detection of Breast Cancer in Thermal Infrared Images, Based on Independent Component Analysis

Breast cancer, among women, is the second-most common cancer and the leading cause of cancer death. It has become a major health issue in the world over the past decades and its incidence has increased in recent years mostly due to increased awareness of the importance of screening and population ageing. Early detection is crucial in the effective treatment of breast cancer. Current mammogram screening may turn up many tiny abnormalities that are either not cancerous or are slow-growing cancers that would never progress to the point of killing a woman and might never even become known to her. Ideally a better screening method should find a way of distinguishing the dangerous, aggressive tumors that need to be excised from the more languorous ones that do not. This paper therefore proposes a new method of thermographic image analysis for automated detection of high tumor risk areas, based on independent component analysis (ICA) and on post-processing of the images resulting from this algorithm. Tests carried out on a database enable tumor areas of 4 × 4 pixels on an original thermographic image to be detected. The proposed method has shown that the appearance of a heat anomaly indicating a potentially cancerous zone is reflected as an independent source by ICA analysis of the YCrCb components; the set of available images in our small series is giving us a sensitivity of 100% and a specificity of 94.7%.