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Recognition of Activities in Resource Constrained Environments; Reducing the Computational Complexity

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Book cover Ubiquitous Computing and Ambient Intelligence (IWAAL 2016, AmIHEALTH 2016, UCAmI 2016)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10070))

Abstract

In our current work we propose a strategy to reduce the vast amounts of data produced within smart environments for sensor-based activity recognition through usage of the nearest neighbor (NN) approach. This approach has a number of disadvantages when deployed in resource constrained environments due to its high storage requirements and computational complexity. These requirements are closely related to the size of the data used as input to NN. A wide range of prototype generation (PG) algorithms, which are designed for use with the NN approach, have been proposed in the literature to reduce the size of the data set. In this work, we investigate the use of PG algorithms and their effect on binary sensor-based activity recognition when using a NN approach. To identify the most suitable PG algorithm four datasets were used consisting of binary sensor data and their associated class activities. The results obtained demonstrated the potential of three PG algorithms for sensor-based activity recognition that reduced the computational complexity by up to 95 % with an overall accuracy higher than 90 %.

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Notes

  1. 1.

    ODI1 and ODI2 datasets were generated by Ulster University thanks to the Open Data Initiative (ODI) [33] for Activity Recognition consortium that aims to create a structured approach to provide annotated datasets in an accessible format.

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Acknowledgments

This contribution has been supported by research projects: TIN2015-66524-P and UJAEN/2014/06/14. Invest Northern Ireland is acknowledged for partially supporting this project under the Competence Centre Program Grant RD0513853 - Connected Health Innovation Centre.

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Authors and Affiliations

  1. Computer Sciences Department, University of Jaén, Campus Las Lagunillas S/n, 23071, Jaén, Spain

    M. Espinilla, A. Rivera, M. D. Pérez-Godoy, J. Medina & L. Martínez

  2. School of Computing and Mathematics, University of Ulster, Jordanstown, BT37 0QB, UK

    C. Nugent

Authors
  1. M. Espinilla
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  2. A. Rivera
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  3. M. D. Pérez-Godoy
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  4. J. Medina
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  5. L. Martínez
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  6. C. Nugent
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Corresponding author

Correspondence to M. Espinilla .

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Editors and Affiliations

  1. University of Las Palmas de Gran Canaria, Las Palmas, Spain

    Carmelo R. García

  2. Departamento de Estadistica, Universidad La Laguna, La Laguna, Spain

    Pino Caballero-Gil

  3. Florida State University, Tallahassee, Florida, USA

    Mike Burmester

  4. University of Las Palmas de Gran Canaria, Las Palmas, Spain

    Alexis Quesada-Arencibia

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Espinilla, M., Rivera, A., Pérez-Godoy, M.D., Medina, J., Martínez, L., Nugent, C. (2016). Recognition of Activities in Resource Constrained Environments; Reducing the Computational Complexity. In: García, C., Caballero-Gil, P., Burmester, M., Quesada-Arencibia, A. (eds) Ubiquitous Computing and Ambient Intelligence. IWAAL AmIHEALTH UCAmI 2016 2016 2016. Lecture Notes in Computer Science(), vol 10070. Springer, Cham. https://doi.org/10.1007/978-3-319-48799-1_8

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  • DOI: https://doi.org/10.1007/978-3-319-48799-1_8

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