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Towards user identification in the home from appliance usage patterns

Published: 08 September 2013 Publication History

Abstract

We explore the feasibility of identifying users from the unique patterns they exhibit when interacting with an individual electrical appliance in the home. We evaluate the effectiveness of a supervised learning based approach for user identification from a dataset of appliance usage collected across five users and three kitchen appliances over a period of eight weeks. Our results show that using appliance usage information alone provides a moderate average accuracy of 32% for group sizes of up to five users in the home. However augmenting usage information with hints about user presence can improve accuracy by 15-20%.

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Y. Kim, T. Schmid, Z. M. Charbiwala, and M. B. Srivastava. ViridiScope: Design and Implementation of a Fine Grained Power Monitoring System for Homes. In Ubicomp, pages 245--254, 2009.
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S. N. Patel, T. Robertson, J. A. Kientz, M. S. Reynolds, and G. D. Abowd. At the flick of a switch: detecting and classifying unique electrical events on the residential power line. In Ubicomp, pages 271--288, 2007.
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  • (2017)Challenges and Opportunities in Designing Smart SpacesInternet of Everything10.1007/978-981-10-5861-5_6(131-152)Online publication date: 17-Oct-2017

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    cover image ACM Conferences
    UbiComp '13 Adjunct: Proceedings of the 2013 ACM conference on Pervasive and ubiquitous computing adjunct publication
    September 2013
    1608 pages
    ISBN:9781450322157
    DOI:10.1145/2494091
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Publication History

    Published: 08 September 2013

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    Author Tags

    1. energy meter
    2. energy meters
    3. smart home
    4. smart home user identification
    5. user identification

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    UbiComp '13 Adjunct Paper Acceptance Rate 254 of 399 submissions, 64%;
    Overall Acceptance Rate 764 of 2,912 submissions, 26%

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    • (2017)Challenges and Opportunities in Designing Smart SpacesInternet of Everything10.1007/978-981-10-5861-5_6(131-152)Online publication date: 17-Oct-2017

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