Discovering varying patterns of Normal and interleaved ADLs in smart homes

People may do the same activity in many different ways hence, modeling and recognizing that activity based on data gathered through simple sensors like motion sensor is a complex task. In this paper, we propose an approach for activity mining and activity tracking which identifies frequent normal and interleaved activities that individuals perform. With this capability, we can track the occurrence of regular activities to monitor users and detect changes in an individual’s behavioral pattern and lifestyle. We have tested the proposed method using the datasets of Washington State University CASAS and the Massachusetts Institute of Technology (MIT) smart home projects. The obtained results show considerable improvements compared with existing methods.