LAUSR

Recent research in ambient intelligence allows wireless sensor networks to perceive environmental states and their changes in smart environments. An intelligent living environment could not only provide better interactions with its ambiance, inside electrical devices and everyday objects, but also offer smart services, even smart assistance to disabled or elderly people when necessary. This paper proposes a new inference engine based on the formal concept analysis to achieve activity prediction and recognition, even abnormal behavioral pattern detection for ambient-assisted living. According to occupants’ historical data, we explore useful frequent patterns to guide future prediction, recognition and detection tasks. Like the way of human reasoning, the engine could incrementally infer the most probable activity according to successive observations. Furthermore, we propose a hierarchical clustering approach to merge activities according to their semantic similarities. As an optimized knowledge discovery approach in hierarchical ambient intelligence environments, it could optimize the prediction accuracies at the earliest stages when only a few observations are available.