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Human Activity Recognition System Using Artificial Neural Networks

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XXVII Brazilian Congress on Biomedical Engineering (CBEB 2020)

Part of the book series: IFMBE Proceedings ((IFMBE,volume 83))

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Abstract

Population aging and the increasing costs of health care, especially for the elderly affected by chronic diseases, requires new medical assistance strategies that makes it possible to monitor these people remotely and provide reliable information on their routines. In this context, human activity recognition (HAR) systems are an important element to overcoming the problem. Therefore, this paper proposes a HAR system prototype containing a multilayer perceptron (MLP) as a classifier. The model hyperparameters were selected using a publicly available dataset. Then, data was collected from accelerometers and gyroscopes embedded in wearable devices of 15 subjects while performing six basic activities (walking, sitting, lying down, standing, walking upstairs and walking downstairs). The system reached an average accuracy of 90.74% and weighted F-measure of 90.03% based on leave-one-subject-out cross-validation.

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Author information

Authors and Affiliations

  1. Department of Electrical Engineering, Federal Institute of Espírito Santo, Vitória Avenue, 1729, Vitória, Brazil

    Vinícius Ferreira De Almeida & Rodrigo Varejão Andreão

Authors
  1. Vinícius Ferreira De Almeida
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  2. Rodrigo Varejão Andreão
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Corresponding author

Correspondence to Vinícius Ferreira De Almeida .

Editor information

Editors and Affiliations

  1. Department of Electrical Engineering, Universidade Federal do Espírito Santo, Vitória, Brazil

    Teodiano Freire Bastos-Filho

  2. Department of Electrical Engineering, Universidade Federal do Espírito Santo, Vitória, Brazil

    Eliete Maria de Oliveira Caldeira

  3. Department of Electrical Engineering, Universidade Federal do Espírito Santo, Vitória, Brazil

    Anselmo Frizera-Neto

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De Almeida, V.F., Andreão, R.V. (2022). Human Activity Recognition System Using Artificial Neural Networks. In: Bastos-Filho, T.F., de Oliveira Caldeira, E.M., Frizera-Neto, A. (eds) XXVII Brazilian Congress on Biomedical Engineering. CBEB 2020. IFMBE Proceedings, vol 83. Springer, Cham. https://doi.org/10.1007/978-3-030-70601-2_192

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  • DOI: https://doi.org/10.1007/978-3-030-70601-2_192

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-70600-5

  • Online ISBN: 978-3-030-70601-2

  • eBook Packages: EngineeringEngineering (R0)

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