A control-based observer approach for estimating energy intake during pregnancy

Gestational weight gain outside of Institute of Medicine guidelines poses a risk to both the mother and her unborn child. Behavioral interventions such as Healthy Mom Zone (HMZ) that aim to regulate gestational weight gain require self-monitoring of energy intake, which is often significantly under-reported by participants. This article describes the use of a control systems approach for energy intake estimation during pregnancy. It relies on an energy balance model that predicts gestational weight based on physical activity and energy intake, the latter treated as an unmeasured disturbance. Two control-based observer formulations relying on Internal Model Control and Model Predictive Control, respectively, are presented in this article, first for a hypothetical participant, then on data collected from four HMZ participants. Results demonstrate the effectiveness of the method, with generally best results obtained when estimating energy intake over a weekly time period.

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Work Title A control-based observer approach for estimating energy intake during pregnancy
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Open Access
Creators
  1. Luca Ranghetti
  2. Daniel E. Rivera
  3. Penghong Guo
  4. Antonio Visioli
  5. Jennifer Savage Williams
  6. Danielle Symons Downs
Keyword
  1. Energy intake estimation
  2. Gestational weight gain
  3. Internal Model Control
  4. Model Predictive Control
  5. Robustness
License In Copyright (Rights Reserved)
Work Type Article
Publisher
  1. International Journal of Robust and Nonlinear Control
Publication Date January 30, 2022
Publisher Identifier (DOI)
  1. https://doi.org/10.1002/rnc.6019
Deposited February 26, 2024

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Version 1
published

  • Created
  • Added Intl_J_Robust___Nonlinear_-_2022_-_Ranghetti_-_A_control_based_observer_approach_for_estimating_energy_intake_during.pdf
  • Added Creator Luca Ranghetti
  • Added Creator Daniel E. Rivera
  • Added Creator Penghong Guo
  • Added Creator Antonio Visioli
  • Added Creator A Visioi
  • Added Creator D Symons Downs
  • Added Creator J S Savage
  • Added Creator Jennifer Savage Williams
  • Added Creator Danielle Symons Downs
  • Published
  • Updated Keyword, Description, Publication Date Show Changes
    Keyword
    • Energy intake estimation, Gestational weight gain, Internal Model Control, Model Predictive Control, Robustness
    Description
    • <p>Gestational weight gain outside of Institute of Medicine guidelines poses a risk to both the mother and her unborn child. Behavioral interventions such as Healthy Mom Zone (HMZ) that aim to regulate gestational weight gain require self-monitoring of energy intake, which is often significantly under-reported by participants. This article describes the use of a control systems approach for energy intake estimation during pregnancy. It relies on an energy balance model that predicts gestational weight based on physical activity and energy intake, the latter treated as an unmeasured disturbance. Two control-based observer formulations relying on Internal Model Control and Model Predictive Control, respectively, are presented in this article, first for a hypothetical participant, then on data collected from four HMZ participants. Results demonstrate the effectiveness of the method, with generally best results obtained when estimating energy intake over a weekly time period.</p>
    • <p>Gestational weight gain outside of Institute of Medicine guidelines poses a risk to both the mother and her unborn child. Behavioral interventions such as _Healthy Mom Zone_ (HMZ) that aim to regulate gestational weight gain require self-monitoring of energy intake, which is often significantly under-reported by participants. This article describes the use of a control systems approach for energy intake estimation during pregnancy. It relies on an energy balance model that predicts gestational weight based on physical activity and energy intake, the latter treated as an unmeasured disturbance. Two control-based observer formulations relying on Internal Model Control and Model Predictive Control, respectively, are presented in this article, first for a hypothetical participant, then on data collected from four HMZ participants. Results demonstrate the effectiveness of the method, with generally best results obtained when estimating energy intake over a weekly time period.</p>
    Publication Date
    • 2022-01-01
    • 2022-01-30
  • Deleted Creator A Visioi
  • Deleted Creator D Symons Downs
  • Deleted Creator J S Savage
  • Updated Creator Jennifer Savage Williams
  • Updated Creator Danielle Symons Downs
  • Updated