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Advances in Body Area Networks I pp 319–332Cite as

Genetic-Algorithm-Based Feature-Selection Technique for Fall Detection Using Multi-placement Wearable Sensors

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Part of the book series: Internet of Things ((ITTCC))

Abstract

For a machine-learning-based fall-detection approach using wearable sensors, having a high number of features can not only cause a reduction in the detection rate because of irrelevant features, but it can also cause a high computational cost. Therefore, the number of features needs to be reduced through a feature-selection technique. However, current studies in fall-detection only consider features that can give an optimum detection rate without considering their computational cost. Having features with a high computational cost on wearable devices can cause their battery to drain fast. This paper presents a genetic-algorithm-based feature-selection technique that can search for a subset of low-computational-cost features from different sensor placements, where those features can give a relatively good detection rate in terms of F-score. The experimental results show that our technique is able to select a subset of low-computational-cost features that can achieve up to 97.7% of F-score on average. Compared to the SelectKBest and Recursive Feature Elimination (RFE) techniques (a filter and an embedded feature-selection technique, respectively), our approach is able to select features that can give a comparable F-score and significantly lower computational cost.

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Notes

  1. 1.

    The dataset can be downloaded at: http://skuld.cs.umass.edu/traces/mmsys/2015/paper-15/.

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Acknowledgements

This work was supported by an International Macquarie University Research Excellence Scholarship (iMQRES) and a Coventry University studentship, under a co-tutelle scheme. The authors would like to thank Professor Elena Gaura and Dr. James Brusey (Coventry University) for their insightful inputs.

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Authors and Affiliations

  1. School of Engineering, Macquarie University, Sydney, 2109, Australia

    I Putu Edy Suardiyana Putra & Rein Vesilo

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  1. I Putu Edy Suardiyana Putra
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  2. Rein Vesilo
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Correspondence to I Putu Edy Suardiyana Putra .

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Editors and Affiliations

  1. DIMES, Cubo 41C, Universita della Calabria, Rende, Cosenza, Italy

    Giancarlo Fortino

  2. Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, Liaoning, Liaoning, China

    Zhelong Wang

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Putra, I.P.E.S., Vesilo, R. (2019). Genetic-Algorithm-Based Feature-Selection Technique for Fall Detection Using Multi-placement Wearable Sensors. In: Fortino, G., Wang, Z. (eds) Advances in Body Area Networks I. Internet of Things. Springer, Cham. https://doi.org/10.1007/978-3-030-02819-0_24

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  • DOI: https://doi.org/10.1007/978-3-030-02819-0_24

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