Semi-physical identification and state estimation of energy intake for interventions to manage gestational weight gain

Penghong Guo, Daniel E. Rivera, Danielle Symons Downs, Jennifer Savage Williams

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Citations (Scopus)

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 languageEnglish (US)
Title of host publication2016 American Control Conference, ACC 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1271-1276
Number of pages6
ISBN (Electronic)9781467386821
DOIs
StatePublished - Jul 28 2016
Event2016 American Control Conference, ACC 2016 - Boston, United States
Duration: Jul 6 2016Jul 8 2016

Publication series

NameProceedings of the American Control Conference
Volume2016-July
ISSN (Print)0743-1619

Other

Other2016 American Control Conference, ACC 2016
CountryUnited States
CityBoston
Period7/6/167/8/16

Fingerprint

State estimation
Identification (control systems)
Energy balance
Public health
Adaptive algorithms
Dynamical systems
Control systems
Energy Intake

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering

Cite this

Guo, P., Rivera, D. E., Downs, D. S., & Williams, J. S. (2016). Semi-physical identification and state estimation of energy intake for interventions to manage gestational weight gain. In 2016 American Control Conference, ACC 2016 (pp. 1271-1276). [7525092] (Proceedings of the American Control Conference; Vol. 2016-July). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ACC.2016.7525092
Guo, Penghong ; Rivera, Daniel E. ; Downs, Danielle Symons ; Williams, Jennifer Savage. / Semi-physical identification and state estimation of energy intake for interventions to manage gestational weight gain. 2016 American Control Conference, ACC 2016. Institute of Electrical and Electronics Engineers Inc., 2016. pp. 1271-1276 (Proceedings of the American Control Conference).
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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.",
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Guo, P, Rivera, DE, Downs, DS & Williams, JS 2016, Semi-physical identification and state estimation of energy intake for interventions to manage gestational weight gain. in 2016 American Control Conference, ACC 2016., 7525092, Proceedings of the American Control Conference, vol. 2016-July, Institute of Electrical and Electronics Engineers Inc., pp. 1271-1276, 2016 American Control Conference, ACC 2016, Boston, United States, 7/6/16. https://doi.org/10.1109/ACC.2016.7525092

Semi-physical identification and state estimation of energy intake for interventions to manage gestational weight gain. / Guo, Penghong; Rivera, Daniel E.; Downs, Danielle Symons; Williams, Jennifer Savage.

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 proceedingConference contribution

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Guo P, Rivera DE, Downs DS, Williams JS. Semi-physical identification and state estimation of energy intake for interventions to manage gestational weight gain. In 2016 American Control Conference, ACC 2016. Institute of Electrical and Electronics Engineers Inc. 2016. p. 1271-1276. 7525092. (Proceedings of the American Control Conference). https://doi.org/10.1109/ACC.2016.7525092