Evolving human activity classifier from sensor streams

Human activity recognition in intelligent environments is a very important task for many applications such as assisted living or surveillance. In order to make those environments sensitive to people, it is necessary to recognize and track the activities that they perform as part of their daily routines. Most of the current approaches for recognizing human activities do not consider the changes in how a human performs a specific activity. Those approaches rely on predefined activities which are represented as static models over time. In this paper, we propose an automated approach to track and recognize daily activities from sensor streams. Any activity is represented in this research as a sequence of raw sensors data. These sequences are treated using statistical methods in order to discover activity patterns. However, these patterns change due to the dynamic nature of human activities. Therefore, as the way to perform an activity is usually not fixed but it changes and evolves, we propose a human activity recognition method based on Evolving Systems.