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Dynamic Real-Time Segmentation and Recognition of Activities Using a Multi-feature Windowing Approach

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2017

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Shahi, Ahmad
Woodford, Brendon J.

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KANG, U, ed. and others. 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. Cham: Springer, 2017, pp. 26-38. Lecture notes in artificial intelligence. 10526. ISSN 0302-9743. eISSN 1611-3349. ISBN 978-3-319-67273-1. Available under: doi: 10.1007/978-3-319-67274-8_3

Zusammenfassung

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.

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004 Informatik

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Human activity recognition, On-line stream mining, Real-time, Machine learning, Classification

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PAKDD 2017 Workshops, MLSDA, BDM, DM-BPM, 23. Mai 2017, Jeju, South Korea
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ISO 690SHAHI, Ahmad, Brendon J. WOODFORD, Hanhe LIN, 2017. Dynamic Real-Time Segmentation and Recognition of Activities Using a Multi-feature Windowing Approach. PAKDD 2017 Workshops, MLSDA, BDM, DM-BPM. Jeju, South Korea, 23. Mai 2017. In: KANG, U, ed. and others. 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. Cham: Springer, 2017, pp. 26-38. Lecture notes in artificial intelligence. 10526. ISSN 0302-9743. eISSN 1611-3349. ISBN 978-3-319-67273-1. Available under: doi: 10.1007/978-3-319-67274-8_3
BibTex
@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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