State Estimation Under Correlated Partial Measurement Losses: Implications for Weight Control Interventions

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

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

The growing prevalence of obesity and related health problems warrants immediate need for effective weight control interventions. Quantitative energy balance models serve as powerful tools to assist in these interventions, as a result of their ability to accurately predict individual weight change based on reliable measurements of energy intake and energy expenditure. However, the data collected in most existing weight interventions is self-monitored; these measurements often have significant noise or experience losses resulting from participant non-adherence, which in turn, limits accurate model estimation. To address this issue, we develop a Kalman filter-based estimation algorithm for a practical scenario where on-line state estimation for weight, or energy intake/expenditure is still possible despite correlated partial data losses. To account for non-linearities in the models, an algorithm based on extended Kalman filtering is also developed for sequential state estimation in the presence of missing data. Simulation studies are presented to illustrate the performance of the algorithms and the potential benefits of these techniques in real-life interventions.

Original languageEnglish (US)
Pages (from-to)13532-13537
Number of pages6
JournalIFAC-PapersOnLine
Volume50
Issue number1
DOIs
StatePublished - Jul 1 2017

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Weight control
State estimation
Medical problems
Energy balance
Kalman filters
Energy Intake

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering

Cite this

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title = "State Estimation Under Correlated Partial Measurement Losses: Implications for Weight Control Interventions",
abstract = "The growing prevalence of obesity and related health problems warrants immediate need for effective weight control interventions. Quantitative energy balance models serve as powerful tools to assist in these interventions, as a result of their ability to accurately predict individual weight change based on reliable measurements of energy intake and energy expenditure. However, the data collected in most existing weight interventions is self-monitored; these measurements often have significant noise or experience losses resulting from participant non-adherence, which in turn, limits accurate model estimation. To address this issue, we develop a Kalman filter-based estimation algorithm for a practical scenario where on-line state estimation for weight, or energy intake/expenditure is still possible despite correlated partial data losses. To account for non-linearities in the models, an algorithm based on extended Kalman filtering is also developed for sequential state estimation in the presence of missing data. Simulation studies are presented to illustrate the performance of the algorithms and the potential benefits of these techniques in real-life interventions.",
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State Estimation Under Correlated Partial Measurement Losses : Implications for Weight Control Interventions. / Guo, Penghong; Rivera, Daniel E.; Williams, Jennifer Savage; Downs, Danielle Symons.

In: IFAC-PapersOnLine, Vol. 50, No. 1, 01.07.2017, p. 13532-13537.

Research output: Contribution to journalArticle

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