System Identification Approaches for Energy Intake Estimation: Enhancing Interventions for Managing Gestational Weight Gain

Penghong Guo, Daniel E. Rivera, Jennifer S. Savage, Emily E. Hohman, Abigail M. Pauley, Krista S. Leonard, Danielle Symons Downs

Research output: Contribution to journalArticle

Abstract

Excessive maternal weight gain during pregnancy represents a major public health concern that calls for novel and effective gestational weight management interventions. In Healthy Mom Zone (HMZ), an on-going intervention study, energy intake (EI) underreporting has been found to be an important consideration that interferes with accurate weight control assessment and the effective use of energy balance (EB) models in an intervention setting. In this paper, a series of estimation approaches that addresses measurement noise and measurement losses are developed to better understand the extent of EI underreporting. These include back-calculating EI from an EB model developed for gestational weight gain prediction, a Kalman filtering-based approach to recursively estimate EI from intermittent measurements in real time, and an approach based on semiphysical identification principles which features the capability of adjusting future self-reported EI by parameterizing the extent of underreporting. The three approaches are illustrated by evaluating with participant data obtained through the HMZ intervention study, with the results demonstrating the potential of these methods to promote the success of weight control. The pros and cons of the presented approaches are discussed to generate insights for users in the future applications.

Original languageEnglish (US)
Article number8490737
Pages (from-to)63-78
Number of pages16
JournalIEEE Transactions on Control Systems Technology
Volume28
Issue number1
DOIs
StatePublished - Jan 2020

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All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Electrical and Electronic Engineering

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