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Hidden Markov Models: Discrete Feature Selection in Activity Recognition

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Hidden Markov Models and Applications

Part of the book series: Unsupervised and Semi-Supervised Learning ((UNSESUL))

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Abstract

Hidden Markov models represent one of the pillars of time series and sequential modelling. Its application is wide-reaching. In this chapter, we focus on its investigation for the recognition of indoor activities. The challenging nature of the problem is further enforced by the binary nature of the benchmark dataset that we have considered for this task and its relatively low number of features. We further experiment with the filter- and wrapper-based feature selection techniques. This application is of significant importance, especially in the arena of smart city and Internet of Things applications. Finally, we present our experimental results and draw relevant conclusions as well as potential future works.

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Notes

  1. 1.

    https://archive.ics.uci.edu/ml/datasets/opportunity+activity+recognition.

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Acknowledgements

This work was funded and supported by Ericsson—Global Artificial Intelligence Accelerator in Montreal and a Mitacs Accelerate fellowship.

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Authors and Affiliations

  1. Concordia University, Montreal, QC, Canada

    Samr Ali

  2. Global Artificial Intelligence Accelerator (GAIA), Ericsson, Montreal, QC, Canada

    Samr Ali

  3. Concordia Institute for Information Systems Engineering, Concordia University, Montreal, QC, Canada

    Nizar Bouguila

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  1. Samr Ali
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  2. Nizar Bouguila
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Correspondence to Nizar Bouguila .

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Editors and Affiliations

  1. Concordia Institute for Information Systems Engineering, Concordia University, Montreal, QC, Canada

    Nizar Bouguila

  2. Department of Computer Science and Technology, Huaqiao University, Xiamen, China

    Wentao Fan

  3. Grenoble Institute of Technology, Grenoble, France

    Manar Amayri

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Ali, S., Bouguila, N. (2022). Hidden Markov Models: Discrete Feature Selection in Activity Recognition. In: Bouguila, N., Fan, W., Amayri, M. (eds) Hidden Markov Models and Applications. Unsupervised and Semi-Supervised Learning. Springer, Cham. https://doi.org/10.1007/978-3-030-99142-5_5

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  • DOI: https://doi.org/10.1007/978-3-030-99142-5_5

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