StreamAR: Incremental and active learning with evolving sensory data for activity recognition

Zahraa Said Abdallah, Mohamed Medhat Gaber, Bala Srinivasan, Shonali Krishnaswamy

    Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

    28 Citations (Scopus)

    Abstract

    Activity recognition focuses on inferring current user activities by leveraging sensory data available on today’s sensor rich environment. Supervised learning has been applied pervasively for activity recognition. Typical activity recognition techniques process sensory data based on point-by-point approaches. In this paper, we propose a novel cluster-based classification for activity recognition Systems, termed StreamAR. The system incorporates incremental and active learning for mining user activities in data streams. The novel approach processes activities as clusters to build a robust classification framework. StreamAR integrates supervised, unsupervised and active learning and applies hybrid similarity measures technique for recognising activities. Extensive experimental results using real activity recognition datasets have evidenced that our new approach shows improved performance over other existing state of-the-art learning methods.
    Original languageEnglish
    Title of host publication2012 IEEE 24th International Conference on Tools with Artificial Intelligence, ICTAI 2012
    Subtitle of host publication7-9 November 2012, Athens, Greece, Proceedings
    EditorsThemis Panayiotopoulos, George Tsihrintzis
    Place of PublicationLos Alamitos, California
    PublisherIEEE Computer Society
    Pages1163 - 1170
    Number of pages8
    Volume1
    ISBN (Print)9780769549156
    DOIs
    Publication statusPublished - 2012
    EventInternational Conference on Tools with Artificial Intelligence 2012 - Athens, Greece
    Duration: 7 Nov 20129 Nov 2012
    Conference number: 24th
    http://dblp.org/db/conf/ictai/ictai2012.html

    Conference

    ConferenceInternational Conference on Tools with Artificial Intelligence 2012
    Abbreviated titleICTAI 2012
    CountryGreece
    CityAthens
    Period7/11/129/11/12
    Internet address

    Keywords

    • Activity recognition
    • Sensory data
    • Stream mining

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