Dynamic Real-Time Segmentation and Recognition of Activities Using a Multi-feature Windowing Approach
Dynamic Real-Time Segmentation and Recognition of Activities Using a Multi-feature Windowing Approach
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2017
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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 / Kang, U et al. (Hrsg.). - Cham : Springer, 2017. - (Lecture notes in artificial intelligence ; 10526). - S. 26-38. - ISSN 0302-9743. - eISSN 1611-3349. - ISBN 978-3-319-67273-1
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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SHAHI, 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, pp. 26-38. ISSN 0302-9743. eISSN 1611-3349. ISBN 978-3-319-67273-1. Available under: doi: 10.1007/978-3-319-67274-8_3BibTex
@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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