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A multi-modal approach for activity classification and fall detection

Author

Listed:
  • José Carlos Castillo
  • Davide Carneiro
  • Juan Serrano-Cuerda
  • Paulo Novais
  • Antonio Fernández-Caballero
  • José Neves

Abstract

The society is changing towards a new paradigm in which an increasing number of old adults live alone. In parallel, the incidence of conditions that affect mobility and independence is also rising as a consequence of a longer life expectancy. In this paper, the specific problem of falls of old adults is addressed by devising a technological solution for monitoring these users. Video cameras, accelerometers and GPS sensors are combined in a multi-modal approach to monitor humans inside and outside the domestic environment. Machine learning techniques are used to detect falls and classify activities from accelerometer data. Video feeds and GPS are used to provide location inside and outside the domestic environment. It results in a monitoring solution that does not imply the confinement of the users to a closed environment.

Suggested Citation

  • José Carlos Castillo & Davide Carneiro & Juan Serrano-Cuerda & Paulo Novais & Antonio Fernández-Caballero & José Neves, 2014. "A multi-modal approach for activity classification and fall detection," International Journal of Systems Science, Taylor & Francis Journals, vol. 45(4), pages 810-824, April.
    • Handle: RePEc:taf:tsysxx:v:45:y:2014:i:4:p:810-824
    DOI: 10.1080/00207721.2013.784372
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    References listed on IDEAS

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    Cited by:

    1. Nirmalya Thakur & Chia Y. Han, 2021. "Country-Specific Interests towards Fall Detection from 2004–2021: An Open Access Dataset and Research Questions," Data, MDPI, vol. 6(8), pages 1-21, August.

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