Affiliations: Department of Computer Science and Engineering, University of North Texas, Denton, TX, USA | School of Electrical Engineering and Computer Science, Washington State University, Pullman, WA, USA
Abstract: The pervasive sensing technologies found in smart environments offer unprecedented opportunities for monitoring and assisting the individuals who live and work in these spaces. As aspect of daily life that is often overlooked in maintaining a healthy lifestyle is the air quality of the environment. In this paper we investigate the use of machine learning technologies to predict CO2 levels as an indicator of air quality in smart environments. We introduce techniques for collecting and analyzing sensor information in smart environments and analyze the correlation between resident activities and air quality levels. The effectiveness of our techniques is evaluated using three physical smart environment testbeds.
Keywords: Data mining, machine learning, smart environments, activity recognition, air quality