Adaptive mobile activity recognition system with evolving data streams

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

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    59 Citations (Scopus)

    Abstract

    Mobile activity recognition focuses on inferring current user activities by leveraging sensory data available on today's sensor rich mobile phones. Supervised learning with static models has been applied pervasively for mobile activity recognition. In this paper, we propose a novel phone-based dynamic recognition framework with evolving data streams for activity recognition. The novel framework incorporates incremental and active learning for real-time recognition and adaptation in streaming settings. While stream evolves, we refine, enhance and personalise the learning model in order to accommodate the natural drift in a given datastream. Extensive experimental results using real activity recognition data have evidenced that the novel dynamic approach shows improved performance of recognising activities especially across different users.
    Original languageEnglish
    Pages (from-to)304 - 317
    Number of pages14
    JournalNeurocomputing
    Volume150
    Issue numberPart A
    DOIs
    Publication statusPublished - 2015

    Keywords

    • Ubiquitous computing
    • Mobile application
    • Activity recognition
    • Stream mining
    • Incremental learning
    • Active learning

    Cite this

    Abdallah, Z. S., Gaber, M. M., Srinivasan, B., & Krishnaswamy, S. (2015). Adaptive mobile activity recognition system with evolving data streams. Neurocomputing, 150(Part A), 304 - 317. https://doi.org/10.1016/j.neucom.2014.09.074