@inproceedings{Shahi2017-10-07Dynam-44099,
  year={2017},
  doi={10.1007/978-3-319-67274-8_3},
  title={Dynamic Real-Time Segmentation and Recognition of Activities Using a Multi-feature Windowing Approach},
  number={10526},
  isbn={978-3-319-67273-1},
  issn={0302-9743},
  publisher={Springer},
  address={Cham},
  series={Lecture notes in artificial intelligence},
  booktitle={Trends and Applications in Knowledge Discovery and Data Mining : PAKDD 2017 Workshops, MLSDA, BDM, DM-BPM, Jeju, South Korea, May 23, 2017, revised selected papers},
  pages={26--38},
  editor={Kang, U},
  author={Shahi, Ahmad and Woodford, Brendon J. and Lin, Hanhe}
}

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    <dc:contributor>Woodford, Brendon J.</dc:contributor>
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    <dc:creator>Lin, Hanhe</dc:creator>
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    <dcterms:title>Dynamic Real-Time Segmentation and Recognition of Activities Using a Multi-feature Windowing Approach</dcterms:title>
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    <dc:creator>Shahi, Ahmad</dc:creator>
    <dcterms:abstract xml:lang="eng">Segmenting sensor events for activity recognition has many key challenges due to its unsupervised nature, the real-time requirements necessary for on-line event detection, and the possibility of having to recognise overlapping activities. A further challenge is to achieve robustness of classification due to sub-optimal choice of window size. In this paper, we present a novel real-time recognition framework to address these problems. The proposed framework is divided into two phases: off-line modeling and on-line recognition. In the off-line phase a representation called Activity Features (AFs) are built from statistical information about the activities from annotated sensory data and a Naïve Bayesian (NB) classifier is modeled accordingly. In the on-line phase, a dynamic multi-feature windowing approach using AFs and the learnt NB classifier is introduced to segment unlabeled sensor data as well as predicting the related activity. How this on-line segmentation occurs, even in the presence of overlapping activities, diverges from many other studies. Experimental results demonstrate that our framework can outperform the state-of-the-art windowing-based approaches for activity recognition involving datasets acquired from multiple residents in smart home test-beds.</dcterms:abstract>
    <dc:contributor>Shahi, Ahmad</dc:contributor>
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    <dc:creator>Woodford, Brendon J.</dc:creator>
    <dc:contributor>Lin, Hanhe</dc:contributor>
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