Transparency Issues in a Hybrid Reasoning Architecture for Assistive Healthcare

Abstract

When pervasive computing is used to provide technology-driven assistive healthcare, there is a need for the system to be as sophisticated and adaptable as possible, while also being as transparent as possible for both subject and caregivers. A personalized, extensible hybrid reasoning framework has been implemented for the CARA (Context Aware Real-time Assistant) system which aims to satisfy these design goals. It provides context-aware sensor data fusion (including medical and environmental sensors) and incorporates anomaly detection mechanisms that support Activity of Daily Living (ADL) analysis and alert generation. The hybrid reasoning architecture incorporates both rule-based and case-based reasoning; this enables CARA to be more robust and to adapt to a changing environment by continuously retraining with new cases. The rules used for anomaly detection in a smart-home situation are given in a structured natural language, allowing subject or caregiver to inspect and, if appropriate, modify these rules; this supports the goal of transparency. For the case-based reasoning part, attention is drawn to the transparency issues that arise in the evaluation criteria used and interpretation of results.