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Estimating Change Intensity and Duration in Human Activity Recognition Using Martingales

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Machine Learning, Optimization, and Data Science (LOD 2021)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 13163))

Abstract

The subject of physical activity is becoming prominent in the healthcare system for the improvement and monitoring of movement disabilities. Existing algorithms are effective in detecting changes in data streams but most of these approaches are not focused on measuring the change intensity and duration. In this paper, we improve on the geometric moving average martingale method by optimising the parameters in the weighted average using a genetic algorithm. The proposed approach enables us to estimate the intensity and duration of transitions that happen in human activity recognition scenarios. Results show that the proposed method makes some improvement over previous martingale techniques.

Supported by Ulster University.

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Acknowledgements

This work was supported by a University of Ulster Vice-Chancellor’s Research Studentship. The authors would like to thank anonymous reviewers for their constructive suggestions.

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Authors and Affiliations

  1. Ulster University, Shore Rd, Newtownabbey, Jordanstown, BT37 0QB, UK

    Jonathan Etumusei, Jorge Martinez Carracedo & Sally McClean

Authors
  1. Jonathan Etumusei
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  2. Jorge Martinez Carracedo
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  3. Sally McClean
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Corresponding author

Correspondence to Jonathan Etumusei .

Editor information

Editors and Affiliations

  1. University of Catania, Catania, Italy

    Giuseppe Nicosia

  2. Department of Computer Science, University of Reading, Reading, UK

    Varun Ojha

  3. Department of Computer Science, University of Oxford, Oxford, UK

    Emanuele La Malfa

  4. Cambridge Judge Business School, University of Cambridge, Cambridge, UK

    Gabriele La Malfa

  5. Department of Biochemistry, University of Cambridge, Cambridge, UK

    Giorgio Jansen

  6. Department of Industrial and Systems Engineering, University of Florida, Gainesville, FL, USA

    Panos M. Pardalos

  7. University of Catania, Catania, Italy

    Giovanni Giuffrida

  8. Department of Informatics, Dana-Farber Cancer Institute, Boston, MA, USA

    Renato Umeton

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Etumusei, J., Carracedo, J.M., McClean, S. (2022). Estimating Change Intensity and Duration in Human Activity Recognition Using Martingales. In: Nicosia, G., et al. Machine Learning, Optimization, and Data Science. LOD 2021. Lecture Notes in Computer Science(), vol 13163. Springer, Cham. https://doi.org/10.1007/978-3-030-95467-3_40

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  • DOI: https://doi.org/10.1007/978-3-030-95467-3_40

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-95466-6

  • Online ISBN: 978-3-030-95467-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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