Online activity recognition using evolving classifiers

This paper describes the use of evolving classifiers for activity recognition from sensor readings in ambient assisted living environments. Recognizing the activities an elderly person who lives alone performs, and identifying potential problems from the detected activities is a very active topic of research. However, current approaches do not take into account the fact that the way an activity is performed by a person evolves over time and therefore activities are identified by mapping them to a static model. In this work we describe and evaluate an approach for online classifying based on Evolving Fuzzy Systems (EFS): activities are described by a model that evolves over time, according to the changes observed in the way an activity is performed. These classifiers have been evaluated on three datasets obtained from real home settings, achieving a good recognition performance, at a confidence interval of 95%, compared with well know probabilistic models in terms of F-Measure, but improving their performance in terms of online capabilities and ability to adapt to the evolving ways in which activities are carried out.

► We apply two evolving classifiers to recognize ADLs from sensor readings.
► Each ADL is defined by a model which evolves according to the changes observed in how that ADL is performed.
► ADL classification is considered, treated and modeled as a dynamic and evolving phenomenon.
► The approach is evaluated on three datasets coming from real home settings.
► Evolving classifiers perform as well as established probabilistic methods and present important advantages as they are one pass and recursive.