publication . Doctoral thesis . 2013

Smart assistants for smart homes

Rasch, Katharina;
Open Access English
  • Published: 01 Jan 2013
  • Publisher: KTH, Programvaruteknik och Datorsystem, SCS
  • Country: Sweden
Abstract
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 ...
Subjects
free text keywords: smart homes, pervasive computing, ubiquitous computing, machine learning, HCI, Computer Sciences, Datavetenskap (datalogi)
Related Organizations
Funded by
EC| SM4ALL
Project
SM4ALL
SMART HOMES FOR ALL. AN EMBEDDED MIDDLEWARE PLATFORM FOR PERVASIVE AND IMMERSIVE ENVIRONMENTS FOR-ALL
  • Funder: European Commission (EC)
  • Project Code: 224332
  • Funding stream: FP7 | SP1 | ICT
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1 Introduction 1 1.1 Visions for the home of the future . . . . . . . . . . . . . . . . . . . . . . 1 1.2 Actual user experiences . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 1.3 Smart assistants for smart homes . . . . . . . . . . . . . . . . . . . . . . 3 1.3.1 A recommender system for smart homes . . . . . . . . . . . . . . 3 1.3.2 An installation assistant for smart homes . . . . . . . . . . . . . 4 1.4 Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 1.5 Source material . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 1.6 Dissertation structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

2 Background 11 2.1 Ubiquitous computing . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 2.2 Smart homes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 2.3 Contributing technologies . . . . . . . . . . . . . . . . . . . . . . . . . . 13 2.3.1 Sensing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 2.3.2 Reasoning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 2.3.3 Acting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 2.3.4 Human-Computer Interaction . . . . . . . . . . . . . . . . . . . . 17 2.4 User expectations and experiences . . . . . . . . . . . . . . . . . . . . . 18 2.4.1 Studies of potential smart home users . . . . . . . . . . . . . . . 18 2.4.2 Studies of actual smart home users and smart home test labs . . . 19 2.4.3 Study results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 2.4.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 2.5 Ethical considerations . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 2.5.1 Privacy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 2.5.2 Addressing important human needs . . . . . . . . . . . . . . . . . 27 2.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

4 A recommender system for smart homes 53 4.1 Example scenario . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53 4.2 Utilizing the recommendation results . . . . . . . . . . . . . . . . . . . . 55 4.2.1 Active context-awareness . . . . . . . . . . . . . . . . . . . . . . 55 4.2.2 Passive context-awareness . . . . . . . . . . . . . . . . . . . . . 55 4.2.3 Mixed strategy . . . . . . . . . . . . . . . . . . . . . . . . . . . 56 4.3 Performance requirements . . . . . . . . . . . . . . . . . . . . . . . . . . 57 4.4 Overview of the recommender system . . . . . . . . . . . . . . . . . . . 58 4.5 Filtering unavailable services . . . . . . . . . . . . . . . . . . . . . . . . 59 4.5.1 An algorithm for service filtering . . . . . . . . . . . . . . . . . 60 4.5.2 Reuse of previous filtering results . . . . . . . . . . . . . . . . . 60 4.5.3 Updated algorithm with reuse of previous results . . . . . . . . . . 61 4.5.4 Runtime analysis . . . . . . . . . . . . . . . . . . . . . . . . . . 62 4.6 Evaluation of service filtering . . . . . . . . . . . . . . . . . . . . . . . . 62 4.6.1 Experimental setup . . . . . . . . . . . . . . . . . . . . . . . . . 62 4.6.2 Inspecting the rate of affected services in smart home datasets . . 63 4.6.3 Runtime of the filtering algorithms on smart home datasets . . . . 63 4.6.4 Scalability of the filtering algorithms . . . . . . . . . . . . . . . 64 4.7 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66

8 Related work 119 8.1 Formal context model . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119 8.2 Smart home recommender system . . . . . . . . . . . . . . . . . . . . . . 121 8.3 Behavior prediction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122 8.4 Installation assistant . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 124

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