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FBG-Based Mattress for Continuous Respiratory Rate Estimation: Influence of Positioning Over and Under the Bed

Francesca De Tommasi,D. Presti,M. Caponero,M. Carassiti,E. Schena,C. Massaroni

Published 2023 in IEEE International Symposium on Medical Measurements and Applications

ABSTRACT

The respiratory rate (RR) is considered one of the most informative vital signs for preventing and diagnosing cardiovascular and sleep disorders. Among the several technologies proposed over the years to estimate RR, unobtrusive solutions are preferred in long-term applications since they allow monitoring subjects without disrupting their daily activities, such as sleeping at night. In this arena, instrumented mattresses are gaining the attention of several research groups. Solutions based on fiber Bragg grating sensors (FBGs) offer the possibility to perform multi-point measurements, a key aspect to enabling reliable RR monitoring during sleep. Our research group recently presented a smart mattress based on thirteen FBGs fully embedded polymeric materials for RR monitoring. The feasibility assessment carried out in a laboratory environment showed promising results. Here, we investigated the feasibility of the proposed mattress for estimating RR in a real-world scenario. We performed the assessment during a long acquisition time (i.e., 45 min) by placing the proposed system over or under the bed to assess the influence of these two configurations on the mattress’ response. Results evidenced better performance in RR estimation with the smart mattress over the bed mattress with a bias of -0.15 ± 2.14 breaths/min expressed as MOD±LOAs as well as higher amplitudes of the FBG signals in this condition than under the bed mattress (up to 0.027 nm).

PUBLICATION RECORD

  • Publication year

    2023

  • Venue

    IEEE International Symposium on Medical Measurements and Applications

  • Publication date

    2023-06-14

  • Fields of study

    Medicine, Computer Science, Engineering

  • Identifiers
    DOI 10.1109/MeMeA57477.2023.10171865
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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