MULTIPLE-CRITERIA DECISION-MAKING BASED ON PROBABILISTIC ESTIMATION WITH CONTEXTUAL INFORMATION FOR PHYSIOLOGICAL SIGNAL MONITORING

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We propose a multiple-criteria decision-making (MCDM) method based on Maximum A Posteriori (MAP) estimation to analyze users' physiological status either normal or abnormal. The decision-making problem is formulated using MAP estimation and is turned out to be MCDM problem given the assumption that all probability density functions (pdfs) follow exponential forms, especially Gaussian. It indicates that this MCDM equation is decomposed into direct sum of group's physiological status distribution. Group distribution is estimated by probabilistic approach using population from the same age or same sex. For verification, we applied the proposed method to public heart rate database. According to experimental results, the proposed method considering group context reduced overall classification errors by 20.42% compared to typical decision-making (TDM) method. This method is applicable to various personalized health monitoring applications, which estimates user's physiological status by referring other group distribution without prior knowledge about previous health records.
Publisher
WORLD SCIENTIFIC PUBL CO PTE LTD
Issue Date
2011-01
Language
English
Article Type
Article
Keywords

HEART-RATE; SUPPORT; SYSTEM; FIELD; MODEL

Citation

INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGY DECISION MAKING, v.10, no.1, pp.109 - 120

ISSN
0219-6220
DOI
10.1142/S0219622011004245
URI
http://hdl.handle.net/10203/104157
Appears in Collection
GCT-Journal Papers(저널논문)
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