A Dynamic Context Reasoning based on Evidential Fusion Networks in Home-Based Care

During emergency situations of the patient in home-based care, a Pervasive Healthcare Monitoring System (PHMS) (Lee et al., 2008) is significantly overloaded with pieces of information of different known reliability or unknown reliability. The pieces of the information should be processed, interpreted, and combined to recognize the situation of the patient as accurate as possible. In such a context, the information obtained from different sources such as multi-sensors and Radio Frequency Identification (RFID) devices can be imperfect due to the imperfection of the information itself or unreliability of the sources. In order to deal with different aspects of the imperfection of contextual information, we proposed an evidential fusion network based onDezert-Smarandache Theory (DSmT) (Dezert & Smarandache, 2009) as a mathematical tool in (Lee et al., 2009). However, context reasoning over time is a difficult in an emergency context, because unpredictable temporal changes in sensory information may happen (Rogova & Nimier, 2004). The (Lee et al., 2009) did not consider dynamic metrics of the context. In addition, some types of contextual information are more important than others. A high respiratory rate may be a strong indication of the emergency of the patient others may not be so important to estimate that specific situation (Padovitz et al., 2005; Wu et al., 2003). The weight of this information may change, due to the aggregation of the evidence and the variation of the value of the evidence over time. For instance, a respiratory rate (e.g., 50 Hz) at current time-indexed state (St) should have more weight compared to a respiratory rate (e.g., 21 Hz) at previous time-indexed state (St−1), because 50 Hz indicates the emergency situation of the patient strongly (Campos et al., 2009; Danninger & Stierelhagen, 2008). Thus, we propose a Dynamic Evidential Network (DEN) as a context reasoning method to estimate or infer future contextual information autonomously. The DEN deals with the relations between two consecutive time-indexed states of the information by considering dynamic metrics: temporal consistency and relation-dependency of the information using the Temporal Belief Filtering (TBF) algorithm. In particular, we deal with both relative and individual importance of evidence to obtain optimal weights of evidence. By using the proposed dynamic normalized weighting technique (Valiris et al., 2005), we fuse both intrinsic and optional context attributes. We then apply dynamic weights into the DEN in order to infer the situation of the patient based on temporal and relation dependency. Finally, A Dynamic Context Reasoning based on Evidential Fusion Networks in Home-based Care 1

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