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Applied Computational Intelligence and Soft Computing
Volume 2011, Article ID 135681, 8 pages
Research Article

Classification of Physiology Indicators for the Automatic Detection of Potentially Hazardous Physiological States

1Centre for Research and Technology Hellas, Informatics and Telematics Institute, 57001 Thessaloniki, Greece
2FORENAP, Pharma, 68250 Rouffach, France

Received 25 May 2011; Revised 18 July 2011; Accepted 20 July 2011

Academic Editor: Farid Melgani

Copyright © 2011 I. G. Damousis et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


In EU-funded project HUMABIO, physiological signals are used as biometrics for security purposes. Data are collected via electrode sensors that are attached to the body of the subject and are obtrusive to some degree. In order to maximize the obtained information and the benefits from the use of obtrusive, physiological sensors, the collected data are processed to also detect abnormal physiology states that may endanger the subjects and those around them during critical operations. Three abnormal states are studied: drug and alcohol consumption and sleep deprivation. For the classification of the physiology, four state-of-the-art techniques were compared, support vector machines, fuzzy expert systems, neural networks, and Gaussian mixture models. The results reveal that there is significant potential on the automatic detection of potentially hazardous physiology states without the need for a human supervisor and that such a system could be included at installations such as nuclear factories to enhance safety by reducing the possibility of human operator related accidents.