Data-Driven End-to-End Lighting Automation Based on Human Residential Trajectory Analysis
Author:
Affiliation:
1. San Jose State University,Computer Engineering Department,San Jose,CA,USA
Funder
NSF
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx8/10577635/10577636/10577722.pdf?arnumber=10577722
Reference14 articles.
1. Machine Learning Methods in Smart Lighting Toward Achieving User Comfort: A Survey
2. Learning Setting-Generalized Activity Models for Smart Spaces
3. Recognizing multi-user activities using wearable sensors in a smart home
4. A nurse-driven method for developing artificial intelligence in “smart” homes for aging-in-place
5. Keeping the Resident in the Loop: Adapting the Smart Home to the User
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