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Energy-Aware Intelligence in Smart Spaces: A Case Study using Computer Vision and Machine Learning for User-Behavior Analysis

Energy-Aware Intelligence in Smart Spaces: A Case Study using Computer Vision and Machine Learning for User-Behavior Analysis

Sangho Park, Henry Kautz
Copyright: © 2014 |Pages: 18
ISBN13: 9781466648524|ISBN10: 146664852X|EISBN13: 9781466648531
DOI: 10.4018/978-1-4666-4852-4.ch036
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MLA

Park, Sangho, and Henry Kautz. "Energy-Aware Intelligence in Smart Spaces: A Case Study using Computer Vision and Machine Learning for User-Behavior Analysis." Sustainable Practices: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, IGI Global, 2014, pp. 640-657. https://doi.org/10.4018/978-1-4666-4852-4.ch036

APA

Park, S. & Kautz, H. (2014). Energy-Aware Intelligence in Smart Spaces: A Case Study using Computer Vision and Machine Learning for User-Behavior Analysis. In I. Management Association (Ed.), Sustainable Practices: Concepts, Methodologies, Tools, and Applications (pp. 640-657). IGI Global. https://doi.org/10.4018/978-1-4666-4852-4.ch036

Chicago

Park, Sangho, and Henry Kautz. "Energy-Aware Intelligence in Smart Spaces: A Case Study using Computer Vision and Machine Learning for User-Behavior Analysis." In Sustainable Practices: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, 640-657. Hershey, PA: IGI Global, 2014. https://doi.org/10.4018/978-1-4666-4852-4.ch036

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Abstract

The improvement of energy efficiency in our society has become an urgent issue for sustainability under global warming. The authors present research issues on sensor-based smart environments for energy-aware intelligence, and showcase a study of algorithms for monitoring human activities that provides the context awareness to the smart environments. In order to build energy-aware environments, it is desirable to embed intelligence into the environment itself so that the environment can interpret human behavior in order to adjust itself to human activities occurring in the environment. This is achievable by integrating the environment and the intelligent computing facilities. The computing facility embedded in the environment is equipped with intelligent algorithms that can monitor salient features indicative of the events and learn and recognize changes in the environment. Recent developments in sensor-based intelligent systems can provide suitable algorithms and facilities for building such energy-aware smart environments. The authors present a framework for monitoring human activities in daily living toward the energy-aware intelligence that can detect and learn inhabitants’ behavior patterns in the smart environment.

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