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.
| Original language | English (US) |
|---|---|
| Title of host publication | 2016 American Control Conference, ACC 2016 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 1271-1276 |
| Number of pages | 6 |
| ISBN (Electronic) | 9781467386821 |
| DOIs | |
| State | Published - Jul 28 2016 |
| Event | 2016 American Control Conference, ACC 2016 - Boston, United States Duration: Jul 6 2016 → Jul 8 2016 |
Publication series
| Name | Proceedings of the American Control Conference |
|---|---|
| Volume | 2016-July |
| ISSN (Print) | 0743-1619 |
Other
| Other | 2016 American Control Conference, ACC 2016 |
|---|---|
| Country | United States |
| City | Boston |
| Period | 7/6/16 → 7/8/16 |
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All Science Journal Classification (ASJC) codes
- Electrical and Electronic Engineering
Cite this
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Semi-physical identification and state estimation of energy intake for interventions to manage gestational weight gain. / Guo, Penghong; Rivera, Daniel E.; Downs, Danielle S.; Savage, Jennifer S.
2016 American Control Conference, ACC 2016. Institute of Electrical and Electronics Engineers Inc., 2016. p. 1271-1276 7525092 (Proceedings of the American Control Conference; Vol. 2016-July).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - Semi-physical identification and state estimation of energy intake for interventions to manage gestational weight gain
AU - Guo, Penghong
AU - Rivera, Daniel E.
AU - Downs, Danielle S.
AU - Savage, Jennifer S.
PY - 2016/7/28
Y1 - 2016/7/28
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84992143990&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84992143990&partnerID=8YFLogxK
U2 - 10.1109/ACC.2016.7525092
DO - 10.1109/ACC.2016.7525092
M3 - Conference contribution
C2 - 27570366
AN - SCOPUS:84992143990
T3 - Proceedings of the American Control Conference
SP - 1271
EP - 1276
BT - 2016 American Control Conference, ACC 2016
PB - Institute of Electrical and Electronics Engineers Inc.
ER -