A Frequent Pattern Mining Approach for ADLs Recognition in Smart Environments

This paper presents an approach for recognition of Activities of Daily Living (ADLs) in smart environments. Our approach is based on the frequent pattern mining principle to extract frequent patterns in the datasets collected from different sensors disseminated in a smart environment. In contrast with existing intrusive activity recognition approaches that have been proposed in the literature, where the datasets are basically composed of audio-visual or images files recorded during experiments, our approach is fully non-intrusive and it is based on the analysis of event sequences collected from heterogenous sensors. Our approach consists of two main phases, (1) frequent pattern mining to extract frequent patterns, and (2) activity recognition using a mapping function between the extracted frequent patterns and the activity models. We show through experiments how our approach accurately recognizes tasks as well as activities and outperforms the HMM model.