Activity recognition in collaborative environments

Afsaneh Doryab, Julian Togelius

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Abstract

    We present an approach to learning to recognize concurrent activities based on multiple data streams. One example is recognition of concurrent activities in hospital operating rooms based on multiple wearable and embedded sensors. This problem differs from standard time series classification in that there is no natural single target dimension, as multiple activities are performed at the same time. Hence, most existing approaches fail. The key innovations that allow us to tackle this problem is (1) learning to recognize base activities from raw sensor data, (2) creating artificial joint activities from base activities using frequent pattern mining and (3) handling temporal dependency using virtual evidence boosting.

    Original languageEnglish (US)
    Title of host publication2012 International Joint Conference on Neural Networks, IJCNN 2012
    DOIs
    StatePublished - 2012
    Event2012 Annual International Joint Conference on Neural Networks, IJCNN 2012, Part of the 2012 IEEE World Congress on Computational Intelligence, WCCI 2012 - Brisbane, QLD, Australia
    Duration: Jun 10 2012Jun 15 2012

    Publication series

    NameProceedings of the International Joint Conference on Neural Networks

    Other

    Other2012 Annual International Joint Conference on Neural Networks, IJCNN 2012, Part of the 2012 IEEE World Congress on Computational Intelligence, WCCI 2012
    CountryAustralia
    CityBrisbane, QLD
    Period6/10/126/15/12

    ASJC Scopus subject areas

    • Software
    • Artificial Intelligence

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  • Cite this

    Doryab, A., & Togelius, J. (2012). Activity recognition in collaborative environments. In 2012 International Joint Conference on Neural Networks, IJCNN 2012 [6252608] (Proceedings of the International Joint Conference on Neural Networks). https://doi.org/10.1109/IJCNN.2012.6252608