Classification of Physiology Indicators for the Automatic Detection of Potentially Hazardous Physiological States. (3rd November 2011)

• Record Type: Journal Article
• Authors: Damousis, I. G.; Argyropoulos, S.; Muzet, A.
• Other Names: Melgani Farid Academic Editor.
• Is Part Of: Applied computational intelligence and soft computing. Volume 2011(2011)
• Publication Date: 2011-11-03
• Subjects: Computational intelligence -- Periodicals; Soft computing -- Periodicals; 006.305
• DOI: 10.1155/2011/135681
• Languages: English
• ISSNs: 1687-9724

Abstract

Abstract : 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.