Change search
ReferencesLink to record
Permanent link

Direct link
Smart assistants for smart homes
KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS.
2013 (English)Doctoral thesis, monograph (Other academic)
Abstract [en]

The smarter homes of tomorrow promise to increase comfort, aid elderly and disabled people, and help inhabitants save energy. Unfortunately, smart homes today are far from this vision – people who already live in such a home struggle with complicated user interfaces, inflexible home configurations, and difficult installation procedures. Under these circumstances, smart homes are not ready for mass adoption.

This dissertation addresses these issues by proposing two smart assistants for smart homes. The first assistant is a recommender system that suggests useful services (i.e actions that the home can perform for the user). The recommended services are fitted to the user’s current situation, habits, and preferences. With these recommendations it is possible to build much simpler user interfaces that highlight the most interesting choices currently available. Configuration becomes much more flexible: since the recommender system automatically learns user habits, user routines no longer have to be manually described. Evaluations with two smart home datasets show that the correct service is included in the top five recommendations in 90% of all cases.

The second assistant addresses the difficult installation procedures. The unique feature of this assistant is that it removes the need for manually describing device functionalities (such descriptions are needed by the recommender system). Instead, users can simply plug in a new device, begin using it, and let the installation assistant identify what the device is doing. The installation assistant has minimal requirements for manufacturers of smart home devices and was successfully integrated with an existing smart home. Evaluations performed with this smart home show that the assistant can quickly and reliably learn device functionalities.

Place, publisher, year, edition, pages
Stockholm: KTH Royal Institute of Technology, 2013. , xv, 141 p.
Trita-ICT-ECS AVH, ISSN 1653-6363 ; 13:16
Keyword [en]
smart homes, pervasive computing, ubiquitous computing, machine learning, HCI
National Category
Computer Science
URN: urn:nbn:se:kth:diva-129171ISBN: 978-91-7501-837-9OAI: diva2:650328
Public defence
2013-10-11, Sal E, Forum Isafjordsgatan 39, Kista, 13:00 (English)

QC 20130924

Available from: 2013-09-24 Created: 2013-09-20 Last updated: 2013-09-24Bibliographically approved

Open Access in DiVA

dissertation_krasch(1984 kB)2299 downloads
File information
File name FULLTEXT01.pdfFile size 1984 kBChecksum SHA-512
Type fulltextMimetype application/pdf

Search in DiVA

By author/editor
Rasch, Katharina
By organisation
Software and Computer systems, SCS
Computer Science

Search outside of DiVA

GoogleGoogle Scholar
Total: 2299 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

Total: 748 hits
ReferencesLink to record
Permanent link

Direct link