Keeping the Resident in the Loop: Adapting the Smart Home to the User
- Resource Type
- Periodical
- Authors
- Rashidi, P.; Cook, D. J.
- Source
- IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans IEEE Trans. Syst., Man, Cybern. A Systems, Man and Cybernetics, Part A: Systems and Humans, IEEE Transactions on. 39(5):949-959 Sep, 2009
- Subject
- Signal Processing and Analysis
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Robotics and Control Systems
Power, Energy and Industry Applications
General Topics for Engineers
Smart homes
Machine learning
Artificial intelligence
Computational intelligence
Competitive intelligence
Humans
Adaptive systems
Automation
Machine learning algorithms
User centered design
machine learning
smart environments
user-centered design
- Language
- ISSN
- 1083-4427
1558-2426
Recent advancements in supporting fields have increased the likelihood that smart-home technologies will become part of our everyday environments. However, many of these technologies are brittle and do not adapt to the user's explicit or implicit wishes. Here, we introduce CASAS, an adaptive smart-home system that utilizes machine learning techniques to discover patterns in resident's daily activities and to generate automation polices that mimic these patterns. Our approach does not make any assumptions about the activity structure or other underlying model parameters but leaves it completely to our algorithms to discover the smart-home resident's patterns. Another important aspect of CASAS is that it can adapt to changes in the discovered patterns based on the resident implicit and explicit feedback and can automatically update its model to reflect the changes. In this paper, we provide a description of the CASAS technologies and the results of experiments performed on both synthetic and real-world data.