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.
authors: Control Systems Engineering Laboratory (CSEL), School for Engineering of Matter, Transport, and Energy, Arizona State University, Tempe, AZ, USA.
authors: Penghong
authors: P
authors: Guo
authors: Control Systems Engineering Laboratory (CSEL), School for Engineering of Matter, Transport, and Energy, Arizona State University, Tempe, AZ, USA.
authors: Daniel E
authors: DE
authors: Rivera
authors: Exercise Psychology Laboratory, Department of Kinesiology, Penn State University, University Park, PA, USA.
authors: Danielle S
authors: DS
authors: Downs
authors: Center for Childhood Obesity Research and the Department of Nutritional Sciences, Penn State University, University Park, PA, USA.
authors: Jennifer S
authors: JS
authors: Savage