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

Luca Ranghetti, Daniel E. Rivera, Penghong Guo, Antonio Visioli, Jennifer Savage Williams, Danielle Symons Downs

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish (US)
JournalInternational Journal of Robust and Nonlinear Control
DOIs
StateAccepted/In press - 2022
Externally publishedYes

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Chemical Engineering(all)
  • Biomedical Engineering
  • Aerospace Engineering
  • Mechanical Engineering
  • Industrial and Manufacturing Engineering
  • Electrical and Electronic Engineering

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