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Multiwindow Fusion for Wearable Activity Recognition

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Advances in Computational Intelligence (IWANN 2015)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9095))

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Abstract

The recognition of human activity has been extensively investigated in the last decades. Typically, wearable sensors are used to register body motion signals that are analyzed by following a set of signal processing and machine learning steps to recognize the activity performed by the user. One of the most important steps refers to the signal segmentation, which is mainly performed through windowing approaches. In fact, it has been proved that the choice of window size directly conditions the performance of the recognition system. Thus, instead of limiting to a specific window configuration, this work proposes the use of multiple recognition systems operating on multiple window sizes. The suggested model employs a weighted decision fusion mechanism to fairly leverage the potential yielded by each recognition system based on the target activity set. This novel technique is benchmarked on a well-known activity recognition dataset. The obtained results show a significant improvement in terms of performance with respect to common systems operating on a single window size.

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Author information

Authors and Affiliations

  1. Department of Computer Engineering, Kyung Hee University, Yongin-si, Korea

    Oresti Banos, Choong Seon Hong & Sungyoung Lee

  2. Department of Computer Architecture and Computer Technology, University of Granada, Granada, Spain

    Juan-Manuel Galvez, Miguel Damas, Alberto Guillen, Luis-Javier Herrera, Hector Pomares, Ignacio Rojas & Claudia Villalonga

Authors
  1. Oresti Banos
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  2. Juan-Manuel Galvez
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  3. Miguel Damas
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  4. Alberto Guillen
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  5. Luis-Javier Herrera
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  6. Hector Pomares
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  7. Ignacio Rojas
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  8. Claudia Villalonga
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  9. Choong Seon Hong
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  10. Sungyoung Lee
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Corresponding author

Correspondence to Oresti Banos .

Editor information

Editors and Affiliations

  1. University of Granada, Granada, Spain

    Ignacio Rojas

  2. Department of Electronics Technology, University of Malaga, Malaga, Spain

    Gonzalo Joya

  3. Polytechnic University of Catalonia, Vilanova i la Geltrú, Spain

    Andreu Catala

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© 2015 Springer International Publishing Switzerland

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Banos, O. et al. (2015). Multiwindow Fusion for Wearable Activity Recognition. In: Rojas, I., Joya, G., Catala, A. (eds) Advances in Computational Intelligence. IWANN 2015. Lecture Notes in Computer Science(), vol 9095. Springer, Cham. https://doi.org/10.1007/978-3-319-19222-2_24

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  • DOI: https://doi.org/10.1007/978-3-319-19222-2_24

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-19221-5

  • Online ISBN: 978-3-319-19222-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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