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Classification of Physiology Indicators for the Automatic Detection of Potentially Hazardous Physiological States
Zitatschlüssel Argyropoulos_2011_ASISC
Autor Damousis, Y. and Argyropoulos, Savvas and Muzet, A.
Jahr 2011
DOI 10.1155/2011/135681
Journal Applied Computational Intelligence and Soft Computing
Jahrgang 2011
Zusammenfassung 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.
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