Please use this identifier to cite or link to this item: http://repositorio.inesctec.pt/handle/123456789/4782
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dc.contributor.authorOliveira,CCen
dc.contributor.authorJosé Machado da Silvaen
dc.date.accessioned2017-12-22T17:13:33Z-
dc.date.available2017-12-22T17:13:33Z-
dc.date.issued2015en
dc.identifier.urihttp://repositorio.inesctec.pt/handle/123456789/4782-
dc.identifier.urihttp://dx.doi.org/10.1109/ims3tw.2015.7177874en
dc.description.abstractA new methodology for fault detection on wearable medical devices is proposed. The main strategy relies on correctly classifying the captured physiological signals, in order to distinguish whether the actual cause is a wearer health abnormality or a system functional flaw. Data fusion techniques, namely fuzzy logic, are employed to process the captured data, like the electrocardiogram and blood pressure, to increase the trust levels with which diagnostics are made. Concerning the wearer condition, additional information is provided after classifying the set of signals into normal or abnormal (e.g. arrhythmia, chest angina, and stroke). As for the monitoring system, once an abnormal situation is detected in its operation or in the sensors, a set of tests is run to check if actually the wearer shows a degradation of his health condition or if the system is reporting erroneous values.en
dc.languageengen
dc.relation1600en
dc.rightsinfo:eu-repo/semantics/openAccessen
dc.titleA fuzzy logic approach for highly dependable medical wearable systemsen
dc.typeconferenceObjecten
dc.typePublicationen
Appears in Collections:CTM - Articles in International Conferences

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