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Fusion-Based Activity Recognition

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Self-Powered Internet of Things

Part of the book series: Green Energy and Technology ((GREEN))

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Abstract

In the previous chapter, we have discussed that SEH can be employed as source of context information and energy simultaneously. However, it may face problems during low light conditions such as at night to harvest sufficient energy to power a sensor node. Therefore, in order to enhance the harvested energy and context recognition performance, a fused signal which employs both solar and kinetic energy harvesting signals can be explored.

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Notes

  1. 1.

    Ethical approval has been granted from CSIRO [106/19] for carrying out this experiment.

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Author information

Authors and Affiliations

  1. Australian e-Health Research Centre (AEHRC), Commonwealth Scientific and Industrial Research Organisation (CSIRO), Herston, QLD, Australia

    Muhammad Moid Sandhu

  2. Commonwealth Scientific and Industrial Research Organisation (CSIRO), Data61, Pullenvale, QLD, Australia

    Sara Khalifa

  3. School of Information Technology and Electrical Engineering, University of Queensland, St. Lucia, QLD, Australia

    Marius Portmann

  4. School of Computer Science, Queensland University of Technology, Brisbane, QLD, Australia

    Raja Jurdak

Authors
  1. Muhammad Moid Sandhu
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  2. Sara Khalifa
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  3. Marius Portmann
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  4. Raja Jurdak
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Corresponding author

Correspondence to Muhammad Moid Sandhu .

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Sandhu, M.M., Khalifa, S., Portmann, M., Jurdak, R. (2023). Fusion-Based Activity Recognition. In: Self-Powered Internet of Things. Green Energy and Technology. Springer, Cham. https://doi.org/10.1007/978-3-031-27685-9_7

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  • DOI: https://doi.org/10.1007/978-3-031-27685-9_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-27684-2

  • Online ISBN: 978-3-031-27685-9

  • eBook Packages: EnergyEnergy (R0)

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