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Routine as resource for the design of learning systems

Published: 26 September 2010 Publication History

Abstract

Even though the coordination of kids' activities is largely successful, the modern dual income family still regularly experiences breakdowns in their practices. Families often rely on routines to help them coordinate when plans prove less effective. Routines, however, are rarely documented, challenging to express in detail, and frequently evolving, making them cumbersome to manually describe and so largely unavailable to computational systems as input. This work proposes that this disconnect can be overcome, and argues that unsupervised models of family routine can be learned using a single, lightweight sensor. This way, the successful but tacit knowledge of the routine might be captured and exploited by learning systems, providing a new kind of information for families and computational systems alike. A method is proposed to develop a Bayesian Network to reason about the state of family coordination. This model relies on learned routines of pickup and drop-off at kids' activities.

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Cited By

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  • (2018)Living preference modeling of smart homes for different target groupsJournal of Ambient Intelligence and Smart Environments10.3233/AIS-18048410:2(103-125)Online publication date: 1-Jan-2018
  • (2016)Shared Language and the Design of Home Healthcare TechnologyProceedings of the 2016 CHI Conference on Human Factors in Computing Systems10.1145/2858036.2858496(3584-3594)Online publication date: 7-May-2016
  • (2012)Considerations for technology that support physical activity by older adultsProceedings of the 14th international ACM SIGACCESS conference on Computers and accessibility10.1145/2384916.2384923(33-40)Online publication date: 22-Oct-2012

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    cover image ACM Conferences
    UbiComp '10 Adjunct: Proceedings of the 12th ACM international conference adjunct papers on Ubiquitous computing - Adjunct
    September 2010
    203 pages
    ISBN:9781450302838
    DOI:10.1145/1864431
    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: 26 September 2010

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    Author Tags

    1. data mining
    2. family coordination
    3. machine learning
    4. statistical modeling
    5. unsupervised learning

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    Ubicomp '10
    Ubicomp '10: The 2010 ACM Conference on Ubiquitous Computing
    September 26 - 29, 2010
    Copenhagen, Denmark

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    Overall Acceptance Rate 764 of 2,912 submissions, 26%

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    Cited By

    View all
    • (2018)Living preference modeling of smart homes for different target groupsJournal of Ambient Intelligence and Smart Environments10.3233/AIS-18048410:2(103-125)Online publication date: 1-Jan-2018
    • (2016)Shared Language and the Design of Home Healthcare TechnologyProceedings of the 2016 CHI Conference on Human Factors in Computing Systems10.1145/2858036.2858496(3584-3594)Online publication date: 7-May-2016
    • (2012)Considerations for technology that support physical activity by older adultsProceedings of the 14th international ACM SIGACCESS conference on Computers and accessibility10.1145/2384916.2384923(33-40)Online publication date: 22-Oct-2012

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