Context Reasoning Under Uncertainty Based On Evidential Fusion Networks In Home-based Care

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Title: Context Reasoning Under Uncertainty Based On Evidential Fusion Networks In Home-based Care
Author: Lee, Hyun
Abstract: Pervasive computing technologies use embedded intelligent systems to enable various real -time applications . Some of these applications are : continuous healthcare monitoring , autonomous diagnosis and treatment , and remote disease management without spatial -temporal limitations . Additional healthcare applications include home -based care , disaster relief management , medical facility management , and sports health management . Issues related to the pervasive healthcare are generally classified into five categories : Hardware , Software , Regulations , Standardization and Organization . Our focus in this dissertation is on software issues . We propose new methods to generate a reliable context in a pervasive information system that has high rates of new measurements over time using data aggregation and data fusion . Different aggregation and fusion techniques can be applied depending on the types of sensed data and autonomous processing within the fusion step .The goal of this research is to produce a high confidence level in the generated context for remote monitoring of patients . Reliable contextual information of remotely monitored patients can prevent hazardous situations by recognizing emergency situations in home -based care . However , it is difficult to achieve a high confidence level of contextual information for several reasons . First , the pieces of information obtained from multi -sensors have different degrees of uncertainty . Second , generated contexts can be conflicting even though they are acquired by simultaneous operations . And last , context reasoning over time is difficult because of unpredictable temporal changes in sensory information . In particular , some types of contextual information are more important than others in home -based care . The weight of this information may change , due to the aggregation of the various sensors (evidence ) and the variation of the values of the various sensors (evidence ) over time . This causes difficulty in defining the absolute weight of the evidence in order to obtain the correct decision making .In this dissertation , we propose an evidential fusion process as a context reasoning method based on the defined context classification and state -space based context modeling . First , the context reasoning method processes sensed data with an evidential form based on Dezert -Smarandache Theory (DSmT ) . The DSmT approach reduces ambiguous or conflicting contextual information in multi -sensor networks . Second , we deal with dynamic metrics such as preference , temporal consistency , and relation -dependency of the context using Autonomous Learning Process (ALP ) and Temporal Belief Filtering (TBF ) in order to improve the confidence level of contextual information that makes a correct decision about the situation of the patient . And last , we deal with both relative and individual importance of the evidence to obtain an optimal weight of the evidence . We then apply dynamic weights of the evidence into Dynamic Evidential Network (DEN ) in order to improve the confidence level of the context and to understand the emergency progress of the patient in home -based care .Finally , we compare the Evidential Fusion Process on DSmT with traditional fusion processes such as Bayesian Networks (BNs ) , Dempster -Shafer Theory (DST ) , and Dynamic Bayesian Networks (DBNs ) . This comparison makes us understand the uncertainty analysis in decision -making by distinguishing sensor reading errors (i .e . , false alarm ) from new sensor activations or deactivations , and shows the improvement of our proposed method compared to the others .The main contributions of the proposed context reasoning method under uncertainty based on evidential fusion networks are : 1 ) Reducing the conflicting mass in uncertainty level and improving the confidence level by adapting the DSmT , 2 ) Distinguishing the sensor reading error from new sensor activations or deactivations by considering the ALP and the TBF algorithm , and 3 ) Representing optimal weights of the evidence by applying the normalized weighting technique into related context attributes . These advantages help to make correct decisions about the situation of the patient in home -based care .
URI: http : / /hdl .handle .net /10106 /5538
Date: 2011-03-03


Context Reasoning Under Uncertainty Based On Evidential Fusion Networks In Home-based Care. Available electronically from http : / /hdl .handle .net /10106 /5538 .

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