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Evidential Filtering and Spatio-Temporal Gradient for Micro-movements Analysis in the Context of Bedsores Prevention

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Belief Functions: Theory and Applications (BELIEF 2022)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13506))

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Abstract

In the context of pressure ulcer prevention, this article deals with the problem of detecting and analysing micro-movements in the sacral area of bedridden patients on mattresses equipped with a network of pressure sensors. The study is based on a series of pressure measurements carried out on a cohort of patients lying on two types of mattress at the Nîmes university hospital (France). A spatio-temporal model considers first the local information of measurements from the array of sensors using an evidential filter in order to remove spatial uncertainties or measurement noise before micro-movement analysis. With a Detrended Fluctuation Analysis (DFA), the complexity level of the time series coming from the micro-movement model is finally estimated for different noise filters.

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Author information

Authors and Affiliations

  1. EuroMov Digital Health in Motion, Univ Montpellier, IMT Mines Ales, 6 Avenue de Claviéres, 30100, Alés, France

    Nicolas Sutton-Charani & Didier Delignières

  2. ISIS Engineer School, Castres, France

    Francis Faux & Marie Nourrisson

  3. CHU de Nîmes, Nîmes, France

    Willy Fagard & Arnaud Dupeyron

Authors
  1. Nicolas Sutton-Charani
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  2. Francis Faux
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  3. Didier Delignières
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  4. Willy Fagard
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  5. Arnaud Dupeyron
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  6. Marie Nourrisson
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Corresponding author

Correspondence to Nicolas Sutton-Charani .

Editor information

Editors and Affiliations

  1. University of Paris-Saclay, Gif sur Yvette, France

    Sylvie Le Hégarat-Mascle

  2. Sorbonne University, Paris, France

    Isabelle Bloch

  3. University of Paris-Saclay, Gif sur Yvette, France

    Emanuel Aldea

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Sutton-Charani, N., Faux, F., Delignières, D., Fagard, W., Dupeyron, A., Nourrisson, M. (2022). Evidential Filtering and Spatio-Temporal Gradient for Micro-movements Analysis in the Context of Bedsores Prevention. In: Le Hégarat-Mascle, S., Bloch, I., Aldea, E. (eds) Belief Functions: Theory and Applications. BELIEF 2022. Lecture Notes in Computer Science(), vol 13506. Springer, Cham. https://doi.org/10.1007/978-3-031-17801-6_28

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  • DOI: https://doi.org/10.1007/978-3-031-17801-6_28

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-17800-9

  • Online ISBN: 978-3-031-17801-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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