Smart Environments: Technology, Protocols and Applications (Wiley Series on Parallel and Distributed Computing)
Inferring Activities from Interactions with Objects
IEEE Pervasive Computing
Activity Recognition and Monitoring Using Multiple Sensors on Different Body Positions
BSN '06 Proceedings of the International Workshop on Wearable and Implantable Body Sensor Networks
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Findings from a participatory evaluation of a smart home application for older adults
Technology and Health Care
Activity Recognition for the Smart Hospital
IEEE Intelligent Systems
Location-based activity recognition using relational Markov networks
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Keeping the resident in the loop: adapting the smart home to the user
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Detecting individual activities from video in a smart home
KES'07/WIRN'07 Proceedings of the 11th international conference, KES 2007 and XVII Italian workshop on neural networks conference on Knowledge-based intelligent information and engineering systems: Part I
Toward scalable activity recognition for sensor networks
LoCA'06 Proceedings of the Second international conference on Location- and Context-Awareness
Data Mining for Hierarchical Model Creation
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Health-status monitoring through analysis of behavioral patterns
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Socially-Competent Computing Implementing Social Sensor Design
International Journal of Web-Based Learning and Teaching Technologies
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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.