Hierarchical generalized context inference or context-aware smart homes

Human activity is among the critical information for a context-aware smart home since knowing what activities are undertaken is important for providing appropriate services. Most of the prior works primarily focus on recognizing individual activity, thus requiring high cost to track people and performs not well when there are multiple users, which is common in a real home environment. Therefore, we propose hierarchical generalized context inference to infer multi-user contexts. By treating a multi-user context as a generalized context caused by an aggregated entity, our approach generalizes these multi-user contexts with different information granularity, and then dynamically infers and aggregates these generalized contexts. Based on the inference results of generalized contexts, a context-aware smart home can provide appropriate services as much as possible. Our experimental results demonstrate the effectiveness of the proposed approach.