Context-aware hybrid reasoning framework for pervasive healthcare

摘要

Pervasive computing has emerged as a viable solution capable of providing technology-driven assistive living for elderly. The pervasive healthcare system, Context-Aware Real-time Assistant (CARA), is designed to provide personalized healthcare services for elderly in a timely and appropriate manner by adapting the healthcare technology to fit in with normal activities of the elderly and working practices of the caregivers. The work in this paper introduces a personalized, flexible, and extensible hybrid reasoning framework for CARA system in a smart home environment which provides context-aware sensor data fusion as well as anomaly detection mechanisms that supports activity of daily living analysis and alert generation. We study how the incorporation of rule-based and case-based reasoning enables CARA to become more robust and to adapt to a changing environment by continuously retraining with new cases. Noteworthy about the work is the use of case-based reasoning to detect conditional anomalies for home automation, and the use of hierarchical fuzzy rule-based reasoning to deal with exceptions and to achieve query-sensitive case retrieval and case adaptation. Case study for evaluation of this hybrid reasoning framework is carried out under simulated but realistic smart home scenarios. The results indicate the feasibility of the framework for effective at-home monitoring.