Abstract
Excessive gestational weight gain (GWG) represents a major public health issue. In this paper, we pursue a control engineering approach to the problem by applying model predictive control (MPC) algorithms to act as decision policies in the intervention for assigning optimal intervention dosages. The intervention components consist of education, behavioral modification and active learning. The categorical nature of the intervention dosage assignment problem dictates the need for hybrid model predictive control (HMPC) schemes, ultimately leading to improved outcomes. The goal is to design a controller that generates an intervention dosage sequence which improves a participant's healthy eating behavior and physical activity to better control GWG. An improved formulation of self-regulation is also presented through the use of Internal Model Control (IMC), allowing greater flexibility in describing self-regulatory behavior. Simulation results illustrate the basic workings of the model and demonstrate the benefits of hybrid predictive control for optimized GWG adaptive interventions.
| Original language | English (US) |
|---|---|
| Title of host publication | 2013 American Control Conference, ACC 2013 |
| Pages | 1970-1975 |
| Number of pages | 6 |
| State | Published - Sep 11 2013 |
| Event | 2013 1st American Control Conference, ACC 2013 - Washington, DC, United States Duration: Jun 17 2013 → Jun 19 2013 |
Publication series
| Name | Proceedings of the American Control Conference |
|---|---|
| ISSN (Print) | 0743-1619 |
Other
| Other | 2013 1st American Control Conference, ACC 2013 |
|---|---|
| Country | United States |
| City | Washington, DC |
| Period | 6/17/13 → 6/19/13 |
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All Science Journal Classification (ASJC) codes
- Electrical and Electronic Engineering
Cite this
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Hybrid model predictive control for optimizing gestational weight gain behavioral interventions. / Dong, Yuwen; Rivera, Daniel E.; Downs, Danielle Symons; Williams, Jennifer Savage; Thomas, Diana M.; Collins, Linda Marie.
2013 American Control Conference, ACC 2013. 2013. p. 1970-1975 6580124 (Proceedings of the American Control Conference).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - Hybrid model predictive control for optimizing gestational weight gain behavioral interventions
AU - Dong, Yuwen
AU - Rivera, Daniel E.
AU - Downs, Danielle Symons
AU - Williams, Jennifer Savage
AU - Thomas, Diana M.
AU - Collins, Linda Marie
PY - 2013/9/11
Y1 - 2013/9/11
N2 - Excessive gestational weight gain (GWG) represents a major public health issue. In this paper, we pursue a control engineering approach to the problem by applying model predictive control (MPC) algorithms to act as decision policies in the intervention for assigning optimal intervention dosages. The intervention components consist of education, behavioral modification and active learning. The categorical nature of the intervention dosage assignment problem dictates the need for hybrid model predictive control (HMPC) schemes, ultimately leading to improved outcomes. The goal is to design a controller that generates an intervention dosage sequence which improves a participant's healthy eating behavior and physical activity to better control GWG. An improved formulation of self-regulation is also presented through the use of Internal Model Control (IMC), allowing greater flexibility in describing self-regulatory behavior. Simulation results illustrate the basic workings of the model and demonstrate the benefits of hybrid predictive control for optimized GWG adaptive interventions.
AB - Excessive gestational weight gain (GWG) represents a major public health issue. In this paper, we pursue a control engineering approach to the problem by applying model predictive control (MPC) algorithms to act as decision policies in the intervention for assigning optimal intervention dosages. The intervention components consist of education, behavioral modification and active learning. The categorical nature of the intervention dosage assignment problem dictates the need for hybrid model predictive control (HMPC) schemes, ultimately leading to improved outcomes. The goal is to design a controller that generates an intervention dosage sequence which improves a participant's healthy eating behavior and physical activity to better control GWG. An improved formulation of self-regulation is also presented through the use of Internal Model Control (IMC), allowing greater flexibility in describing self-regulatory behavior. Simulation results illustrate the basic workings of the model and demonstrate the benefits of hybrid predictive control for optimized GWG adaptive interventions.
UR - http://www.scopus.com/inward/record.url?scp=84883521778&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84883521778&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:84883521778
SN - 9781479901777
T3 - Proceedings of the American Control Conference
SP - 1970
EP - 1975
BT - 2013 American Control Conference, ACC 2013
ER -