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IoT-Based Human Fall Detection Solution Using Morlet Wavelet

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Sustainable Smart Cities and Territories (SSCTIC 2021)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 253))

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Abstract

Human fall detection is a problem that needs to be addressed to decrease the significant number of elderly people being affected, disabled, or even killed by falls. While the prevention of falls is a goal harder, or impossible, to be achieved, the fast detection and aid of people are two aspects that technological solutions can help with. With the support of internet of things devices, a fall detection solution for building deployment is proposed in this paper. The classification of fall is done using the Morlet wavelet in the fog-computing layer, enabling the detection of falls near the person and near the people who can provide first aid. The proposed solution of this paper was tested using a new dataset created using a human-body model. The results are promising, proving the efficiency of the proposed solution.

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Acknowledgments

This work has received funding from FEDER Funds through COMPETE program and from National Funds through (FCT) under the project UIDB/00760/2020.

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Authors and Affiliations

  1. GECAD—Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development, Rua Dr. Antonio Bernardino de Almeida, 431, 4200-072, Porto, Portugal

    Osvaldo Ribeiro & Luis Gomes

  2. Polytechnic of Porto, Rua Dr. Antonio Bernardino de Almeida, 431, 4200-072, Porto, Portugal

    Osvaldo Ribeiro, Luis Gomes & Zita Vale

Authors
  1. Osvaldo Ribeiro
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  2. Luis Gomes
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  3. Zita Vale
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Corresponding author

Correspondence to Zita Vale .

Editor information

Editors and Affiliations

  1. Department of Computing Science, Universidad Salamanca, Salamanca, Spain

    Juan M. Corchado

  2. Texas A&M University at Qatar, Doha, Qatar

    Saber Trabelsi

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Ribeiro, O., Gomes, L., Vale, Z. (2022). IoT-Based Human Fall Detection Solution Using Morlet Wavelet. In: Corchado, J.M., Trabelsi, S. (eds) Sustainable Smart Cities and Territories. SSCTIC 2021. Lecture Notes in Networks and Systems, vol 253. Springer, Cham. https://doi.org/10.1007/978-3-030-78901-5_2

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