Sensors & Behavior (ARPA-E) > Technology Projects

Smart Automation

While encouraging user's proactive involvement in reducing their energy use is an important goal, a complementary strategy looks at how automation can improve efficiency with minimal user input.

Investigators: Hamid Aghajan, Amir Hossein Khalili, Chen Wu, Louis Chen

Nest Labs' thermostat—sensor-driven, Wi-Fi-enabled, self-learning and programmable—is one example. This approach circumvents persistence issues in user behavior. In order for these techniques to be effective, however, they need to consider behavior. The vast majority of current home automation systems operate without such consideration, using one of two insufficient paradigms. Either the systems operate using fixed rules that fail to account for individual differences, or they require that users specify the operation rules themselves which is time consuming, unintuitive and may be easily ignored.

This project built an automated light and TV control implementation using a network of wireless switches based on detecting the location of a user and their pose with a number of cameras. Investigators also developed a web-based user interface to capture the user's input about the automation setting and build a context-aware user profile, which was used to adapt the setting according to the user's preferences.

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Publications and Presentations

Towards adaptive and user-centric smart home applications
In Behavior Monitoring and Interpretation, part of the "Ambient Intelligence and Smart Environments" book series (IOS Press)
Khalili, A.H., Wu, C., Aghajan, H. (2011)

Learning Human Behaviour Patterns in Work Environments
Presented at IEEE's Workshop on CVPR4BH, in conjunction with CVPR 2011
Chen, C., Aztiria, A., Aghajan, H. (2011)

Discovering Social Interactions in Real Work Environments
Presented at IEEE's International Workshop on Social Behavior Analysis, in conjunction with FG 2011.
Chen, C., Cilla, R., Wu, C., Aghajan, H. (2011)

Multiview Social Behavior Analysis in Work Environments
Presented at IEEE's International Conference on Distributed Smart Cameras
Chen, C., Aghajan, H. (2011).

Hierarchical Preference Learning for Light Control from user Feedback
Presented at IEEE's Computer Vision & Pattern Recognition
Khalili, A., Wu, C., Aghajan, H. (2010)

Future work

On further development effort, algorithms can be created to detect complex but unobvious schedule regularities and automatically control devices in a home area network. The behavior of the network can change to adapt to underlying changes in the users' behavior or in preferences or with seasonal changes. Other algorithms can be developed to track actual and expected energy use of appliances and suggest when appliances or electronics should be repaired or replaced. On commercialization efforts, researchers can share their findings with commercial entities involved in home automation services and collaborate with them on developing test cases for actual user deployment settings.