Inhabitant guidance of smart environments

Authors:
Parisa Rashidi;G. Michael Youngblood;Diane J. Cook;Sajal K. Das
Affiliations:
Washington State University, Pullman, WA;University of North Carolina at Charlotte, Charlotte, NC;Washington State University, Pullman, WA;University of Texas at Arlington, Arlington, TX
Venue:
HCI'07 Proceedings of the 12th international conference on Human-computer interaction: interaction platforms and techniques
Year:
2007

Citing 7
Cited 5

Improving Home Automation by Discovering Regularly Occurring Device Usage Patterns

ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Multi-modal emotive computing in a smart house environment

Pervasive and Mobile Computing
Recognition of emergent human behaviour in a smart home: A data mining approach

Pervasive and Mobile Computing
Online Sequential Prediction via Incremental Parsing: The Active LeZi Algorithm

IEEE Intelligent Systems
Reinforcement learning with human teachers: evidence of feedback and guidance with implications for learning performance

AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Plans and planning in smart homes

Designing Smart Homes
Data Mining for Hierarchical Model Creation

IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews

Annotating smart environment sensor data for activity learning

Technology and Health Care - Smart Environments: Technology to Support Healthcare
Coping with multiple residents in a smart environment

Journal of Ambient Intelligence and Smart Environments
Recognizing multi-user activities using wearable sensors in a smart home

Pervasive and Mobile Computing
Assisted living technologies for older adults

Proceedings of the 2nd ACM SIGHIT International Health Informatics Symposium
Coping with multiple residents in a smart environment

Journal of Ambient Intelligence and Smart Environments

Quantified Score

Hi-index	0.00

Visualization

Abstract

With the convergence of technologies in artificial intelligence, human-computer interfaces, and pervasive computing, the idea of a "smart environment" is becoming a reality. While we all would like the benefits of an environment that automates many of our daily tasks, a smart environment that makes the wrong decisions can quickly becoming annoying. In this paper, we describe a simulation tool that can be used to visualize activity data in a smart home, play through proposed automation schemes, and ultimately provide guidance to automating the smart environment. We describe how automation policies can adapt to resident feedback, and demonstrate the ideas in the context of the MavHome smart home.