TFM: Design strategies for detecting action potentials in actions based on movements
Posted on by anacp

This work is located in the studies of the brain and their signals. The puspose is to know when someone wants to make a movement. Thus, it might help to people that actually are not able to move a member of their body or more. Mainly, it is focused in the design of strategies for detection of action potentials or spikes when a movement wants to be made. This study is not looking for action potentials form, it is looking for patterns and characteristics that allow to recognize the movement. Although there are action potentials covered by the signals taken from the electrodes, but they are unavailable.

To accomplish the objective, it is used the EEG signals of a public data base. It is selected the ones related to the movement of the hands, concretely, the movement of open and close the fist. Signal sources of noise that dirty the signal are analyzed, they are called artifacts, and then, filtering stage comes, giving the signals of below for movement and no movement.

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Now, possible algorithms are checked. It is decided to use the Wavelet transform and the way in which it obtains the energy of the signal. Thanks to the calculation of Wavelet energy in 22 subjects, it is reached to the conclusion that Wavelet energy for movement is higher than for no movement. So, electrodes that comply with this condition at 100% are 4.

The final algorithm is implemented three features: correlation, a parameter that gives a relation between two signals, their energy range and their energy average. It could be said that algorithm has two parts: a training stage and a decision stage. Inside decision part, there are three algorithms: ProMove, ProMove + improve and Logic. The basic difference among ProMove and Logic is an or (||) and an and (&). The improve is based on empiric knowledge.

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Final conclusions show that the signals between subjects are very changing. Therefore, same algorithm is not useful for everybody. To some subjects, the successful probability is very high (92,86% – 1 fail), while for others is more low than what is expected (50% – 7 fail). With these test, the importance in the length of the signals is reflected, because if signals for subjects with more than 3 fails are inversely processed, the fails are reduced. The most useful algorithm for a larger number of subjects is ProMove + improve.

Detección de indicadores de fatiga basado en la obtención de imágenes en tiempo real.
Posted on by Gis

Dentro del proyecto Simbiosys buscamos la detección de fatiga mediante imágenes obtenidas por cámara, como apoyo al sistema de detección de indicadores de fatiga mediante EEG.

Este módulo del sistema multisensor consiste en una cámara de bajo coste que obtiene las imágenes del sujeto para analizar en tiempo real. Además, es necesario que pueda detectar luz infrarroja, para los casos en los que la luz sea escasa. El módulo se basa en la detección facial de la cara, para poder obtener posteriormente la detección de ambos ojos.

 

El objetivo es obtener el porcentaje de tiempo en el que el ojo se encuentra cerrado durante un minuto (AVECLOS). Por tanto, si el porcentaje es mayor que el porcentaje normal de tiempo en el que una persona presenta los ojos cerrados, se considera que el sujeto se encuentra cansado o fatigado.

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El sistema final comparará la información obtenida tanto como por el electroencefalograma como por la cámara, para obtener con mayor seguridad el estado en el que se encuentra el sujeto.

SIMBIOSYS: Simulator Biometric System plug-in
Posted on by Gis

One of the major problems facing the drivers of different vehicles is the difficulty of anticipate and react to the health-related problems that the operator may have.

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For this reason, the goal of this project is the development of a simulator system that allows to prevent and/or report states in the machine operator that can compromise the safety of the people. It will detect physical states (tachycardia, bradycardia, hypoxia, hypotension, etc.)  as well as psychics (stress, drowsiness, alertness,etc.).

To achieve this, the system will use biometrics sensors, such as breast bands or weareable bracelets to obtain the measures of heart rate or oxygen saturation. But the main sensor we are interested in is a EEG sensor that sends the raw electroencephalography. 

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The B105 Electronic Systems Lab. as a representative of Technical University of Madrid(UPM) participates with Valoriza in this innovative research project. To develop it we have the support of the Industrial Technological Center (CDTI) and the Ministry of Economy and Competitiveness.

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