Abstract
The emerging research on automatic identification of user’s contexts from the cross-domain environment in ubiquitous and pervasive computing systems has proved to be successful. Monitoring the diversified user’s contexts and behaviors can help in controlling lifestyle associated to chronic diseases using context-aware applications. However, availability of cross-domain heterogeneous contexts provides a challenging opportunity for their fusion to obtain abstract information for further analysis. This work demonstrates extension of our previous work from a single domain (i.e., physical activity) to multiple domains (physical activity, nutrition and clinical) for context-awareness. We propose multi-level Context-aware Framework (mlCAF), which fuses the multi-level cross-domain contexts in order to arbitrate richer behavioral contexts. This work explicitly focuses on key challenges linked to multi-level context modeling, reasoning and fusioning based on the mlCAF open-source ontology. More specifically, it addresses the interpretation of contexts from three different domains, their fusioning conforming to richer contextual information. This paper contributes in terms of ontology evolution with additional domains, context definitions, rules and inclusion of semantic queries. For the framework evaluation, multi-level cross-domain contexts collected from 20 users were used to ascertain abstract contexts, which served as basis for behavior modeling and lifestyle identification. The experimental results indicate a context recognition average accuracy of around 92.65% for the collected cross-domain contexts.
Highlights
Context-awareness (CA) is considered to be an essential element in the ubiquitous and pervasive computing systems [1] and is widely recognized by the research community
This representation gives intuition how vertical fusion helps in reducing huge volume of low-level contexts to PA-High-level Context (HLC) and Nutrition High-Level Context (N-HLC)
This paper has presented the ontology-based modeling and cross-domain context fusioning, associated implementation using multi-level Context-aware Framework mlCAF, methods for transforming
Summary
Context-awareness (CA) is considered to be an essential element in the ubiquitous and pervasive computing systems [1] and is widely recognized by the research community. Keeping in mind the above-mentioned facts, our motivation was to design and develop an efficient framework to fuse the multi-levels cross-domain contexts for providing a richer contextual information. To achieve this goal, this study was undertaken with the following objectives: (1) extend the state-of-the-art context-awareness approaches from a single domain (i.e., physical activity) to cross-domains (i.e., physical activity, nutrition, and clinical) (see Section 3.2), (2) infer a more abstract representation of cross-domain contexts (see Section 5.1), (3) separate the ontology model (T-Box) and the application data (A-Box) (see Section 6.2), and (4) provide an effective contextual state of the users in a real-time manner to boost health and wellness services (see Section 6).
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