User in the Loop: Adaptive Smart Homes Exploiting User Feedback—State of the Art and Future Directions

Due to the decrease of sensor and actuator prices and their ease of installation, smart homes and smart environments are more and more exploited in automation and health applications. In these applications, activity recognition has an important place. This article presents a general architecture tha...

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Bibliographic Details
Main Authors:	Abir B. Karami, Anthony Fleury, Jacques Boonaert, Stéphane Lecoeuche
Format:	Article
Language:	English
Published:	MDPI AG 2016-06-01
Series:	Information
Subjects:	smart homes user-centered decision making Markov decision process
Online Access:	http://www.mdpi.com/2078-2489/7/2/35

id	doaj-5f1f273c5f6c4ec18a538cd5f5632d5a
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spelling	doaj-5f1f273c5f6c4ec18a538cd5f5632d5a2020-11-24T22:37:21ZengMDPI AGInformation2078-24892016-06-01723510.3390/info7020035info7020035User in the Loop: Adaptive Smart Homes Exploiting User Feedback—State of the Art and Future DirectionsAbir B. Karami0Anthony Fleury1Jacques Boonaert2Stéphane Lecoeuche3University of Lille, F-59000 Lille and Mines Douai, URIA, F-90508 Douai, FranceUniversity of Lille, F-59000 Lille and Mines Douai, URIA, F-90508 Douai, FranceUniversity of Lille, F-59000 Lille and Mines Douai, URIA, F-90508 Douai, FranceUniversity of Lille, F-59000 Lille and Mines Douai, URIA, F-90508 Douai, FranceDue to the decrease of sensor and actuator prices and their ease of installation, smart homes and smart environments are more and more exploited in automation and health applications. In these applications, activity recognition has an important place. This article presents a general architecture that is responsible for adapting automation for the different users of the smart home while recognizing their activities. For that, semi-supervised learning algorithms and Markov-based models are used to determine the preferences of the user considering a combination of: (1) observations of the data that have been acquired since the start of the experiment and (2) feedback of the users on decisions that have been taken by the automation. We present preliminarily simulated experimental results regarding the determination of preferences for a user.http://www.mdpi.com/2078-2489/7/2/35smart homesuser-centered decision makingMarkov decision process
collection	DOAJ
language	English
format	Article
sources	DOAJ
author	Abir B. Karami Anthony Fleury Jacques Boonaert Stéphane Lecoeuche
spellingShingle	Abir B. Karami Anthony Fleury Jacques Boonaert Stéphane Lecoeuche User in the Loop: Adaptive Smart Homes Exploiting User Feedback—State of the Art and Future Directions Information smart homes user-centered decision making Markov decision process
author_facet	Abir B. Karami Anthony Fleury Jacques Boonaert Stéphane Lecoeuche
author_sort	Abir B. Karami
title	User in the Loop: Adaptive Smart Homes Exploiting User Feedback—State of the Art and Future Directions
title_short	User in the Loop: Adaptive Smart Homes Exploiting User Feedback—State of the Art and Future Directions
title_full	User in the Loop: Adaptive Smart Homes Exploiting User Feedback—State of the Art and Future Directions
title_fullStr	User in the Loop: Adaptive Smart Homes Exploiting User Feedback—State of the Art and Future Directions
title_full_unstemmed	User in the Loop: Adaptive Smart Homes Exploiting User Feedback—State of the Art and Future Directions
title_sort	user in the loop: adaptive smart homes exploiting user feedback—state of the art and future directions
publisher	MDPI AG
series	Information
issn	2078-2489
publishDate	2016-06-01
description	Due to the decrease of sensor and actuator prices and their ease of installation, smart homes and smart environments are more and more exploited in automation and health applications. In these applications, activity recognition has an important place. This article presents a general architecture that is responsible for adapting automation for the different users of the smart home while recognizing their activities. For that, semi-supervised learning algorithms and Markov-based models are used to determine the preferences of the user considering a combination of: (1) observations of the data that have been acquired since the start of the experiment and (2) feedback of the users on decisions that have been taken by the automation. We present preliminarily simulated experimental results regarding the determination of preferences for a user.
topic	smart homes user-centered decision making Markov decision process
url	http://www.mdpi.com/2078-2489/7/2/35
work_keys_str_mv	AT abirbkarami userintheloopadaptivesmarthomesexploitinguserfeedbackstateoftheartandfuturedirections AT anthonyfleury userintheloopadaptivesmarthomesexploitinguserfeedbackstateoftheartandfuturedirections AT jacquesboonaert userintheloopadaptivesmarthomesexploitinguserfeedbackstateoftheartandfuturedirections AT stephanelecoeuche userintheloopadaptivesmarthomesexploitinguserfeedbackstateoftheartandfuturedirections
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User in the Loop: Adaptive Smart Homes Exploiting User Feedback—State of the Art and Future Directions

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