@inproceedings{275993a6a3ff4547b1560f394236ef85,
title = "Semi-physical identification and state estimation of energy intake for interventions to manage gestational weight gain",
abstract = "Excessive gestational weight gain (i.e., weight gain during pregnancy) is a significant public health concern, and has been the recent focus of novel, control systems-based interventions. This paper develops a control-oriented dynamical systems model based on a first-principles energy balance model from the literature, which is evaluated against participant data from a study targeted to obese and overweight pregnant women. The results indicate significant under-reporting of energy intake among the participant population. A series of approaches based on system identification and state estimation are developed in the paper to better understand and characterize the extent of under-reporting; these range from back-calculating energy intake from a closed-form of the energy balance model, to a constrained semi-physical identification approach that estimates the extent of systematic under-reporting in the presence of noise and possibly missing data. Additionally, we describe an adaptive algorithm based on Kalman filtering to estimate energy intake in real-time. The approaches are illustrated with data from both simulated and actual intervention participants.",
author = "Penghong Guo and Rivera, {Daniel E.} and Downs, {Danielle S.} and Savage, {Jennifer S.}",
year = "2016",
month = jul,
day = "28",
doi = "10.1109/ACC.2016.7525092",
language = "English (US)",
series = "Proceedings of the American Control Conference",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1271--1276",
booktitle = "2016 American Control Conference, ACC 2016",
address = "United States",
note = "2016 American Control Conference, ACC 2016 ; Conference date: 06-07-2016 Through 08-07-2016",
}