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Evaluation of Deep Learning Techniques in Human Activity Recognition

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Intelligent Systems and Applications (IntelliSys 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 542))

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Abstract

The increasingly acceptance of Internet of Things (IoT) devices adversely contribute to the accumulation of massive amounts of data which challenges for the adoption of techniques capable to handle it. This paper first presents overall points of deep learning, and IoT principles. After that, this paper uses the human recognition activity scenario to evaluate two DL models: the Convolutional Neural Network (CNN), and the Recurrent Neural Network. At last, a benchmark with the state-of-the-art is presented. The main findings evidenced the suitability of the proposed model; the CNN performed a mean accuracy rate of 93%, and therefore it is likely to be embedded in an IoT device. There is room for improvements, namely, the ability to recognize additional human activities, and to include more testing scenarios.

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Notes

  1. 1.

    https://archive.ics.uci.edu/ml/datasets/human+activity+recognition+using+smartphones.

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Acknowledgment

This work is supported by Centro-01-0247-FEDER-072632-“NomaVoy - Nomad Voyager”, co-financed by the Portugal 2020 Program (PT 2020), in the framework of the Regional Operational Program of the Center (CENTRO 2020) and the European Union through the Fundo Europeu de Desenvolvimento Regional (FEDER).

Author information

Authors and Affiliations

  1. Instituto de Telecomunicações, Covilhã, Portugal

    Nuno Pombo

  2. Universidade da Beira Interior, Covilhã, Portugal

    Tiago Mendes & Nuno Pombo

Authors
  1. Tiago Mendes
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  2. Nuno Pombo
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Corresponding author

Correspondence to Nuno Pombo .

Editor information

Editors and Affiliations

  1. Saga University, Saga, Japan

    Kohei Arai

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Mendes, T., Pombo, N. (2023). Evaluation of Deep Learning Techniques in Human Activity Recognition. In: Arai, K. (eds) Intelligent Systems and Applications. IntelliSys 2022. Lecture Notes in Networks and Systems, vol 542. Springer, Cham. https://doi.org/10.1007/978-3-031-16072-1_8

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