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Unsupervised discovery of spatial relationships between objects for activity recognition inside smart home

Published: 05 September 2012 Publication History

Abstract

Data mining techniques have been vastly exploited recently to overcome complex problems that humans struggle to solve. Particularly, the recognition of the activity of daily living of a smart home's resident is a challenging issue that requires advanced algorithms using extensive plans' library. In this paper, we propose a novel unsupervised learning technique for the discovery of sequential pattern related to spatial relationships of objects inside a smart home. We concretely use this approach to automatically construct a library of plans. Finally, we demonstrate the efficiency with a practical activity recognition algorithm by comparing learned knowledge over expert's defined library in a real smart home.

References

[1]
Rashidi, P., Cook, D. J., Holder, L. B. and Schmitter-Edgecombe, M. Discovering Activities to Recognize and Track in a Smart Environment. IEEE Trans. on Knowl. and Data Eng., 23, 4 2011), 527--539.
[2]
Augusto, J. C. and Nugent, C. D. Smart homes can be smarter. The Role of Artificial Intelligence 2006, Springer-Verlag Berlin, 1--15.
[3]
Srikant, R. and Agrawal, R. Mining Sequential Patterns: Generalizations and Performance Improvements. In Proc EDBT 1996, Springer-Verlag, 3--17.
[4]
Bouchard, K., Bouchard, B. and Bouzouane, A. A new qualitative spatial recognition model based on Egenhofer topological approach using C4.5 algorithm: experiment and results. In Proc. ANT 2011, Elsevier, 497--504.
[5]
Cohn, A. G., Bennett, B., Gooday, J. and Gotts, N. M. Qualitative Spatial Representation and Reasoning with the Region Connection Calculus. Geoinformatica, 1, 3 (1997), 275--316.

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  • (2019)Home Worlds: Situating Domestic Computing in Everyday Life Through a Study of DIY Home RepairProceedings of the ACM on Human-Computer Interaction10.1145/33592633:CSCW(1-22)Online publication date: 7-Nov-2019

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  1. Unsupervised discovery of spatial relationships between objects for activity recognition inside smart home

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    cover image ACM Conferences
    UbiComp '12: Proceedings of the 2012 ACM Conference on Ubiquitous Computing
    September 2012
    1268 pages
    ISBN:9781450312240
    DOI:10.1145/2370216
    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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    New York, NY, United States

    Publication History

    Published: 05 September 2012

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    Author Tags

    1. GSP
    2. activity recognition
    3. data mining
    4. patterns discovery
    5. smart home

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    Ubicomp '12
    Ubicomp '12: The 2012 ACM Conference on Ubiquitous Computing
    September 5 - 8, 2012
    Pennsylvania, Pittsburgh

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    UbiComp '12 Paper Acceptance Rate 58 of 301 submissions, 19%;
    Overall Acceptance Rate 764 of 2,912 submissions, 26%

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    • (2019)Home Worlds: Situating Domestic Computing in Everyday Life Through a Study of DIY Home RepairProceedings of the ACM on Human-Computer Interaction10.1145/33592633:CSCW(1-22)Online publication date: 7-Nov-2019

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