Semi-physical Identification and State Estimation of Energy Intake for Interventions to Manage Gestational Weight Gain.
Semi-physical Identification and State Estimation of Energy Intake for Interventions to Manage Gestational Weight Gain.
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DOI：
10.1109/acc.2016.7525092
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发表时间：
2016-07
期刊：
影响因子：
--
通讯作者：
Savage JS
中科院分区：
文献类型：
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作者：
Guo P;Rivera DE;Downs DS;Savage JS
妊娠期体重过度增加（即孕期体重增加）是一个重大的公共卫生问题，也是最近基于控制系统的新型干预措施的重点。本文基于文献中的第一性原理能量平衡模型，建立了一个面向控制的动力系统模型，并对一项针对肥胖和超重孕妇的研究的参与者数据进行了评估。结果表明，在参与者人群中，能量摄入明显少报。本文提出了一系列基于系统识别和状态估计的方法，以更好地理解和表征低报的程度；这些方法包括从封闭形式的能量平衡模型中反向计算能量摄入，到在存在噪声和可能缺失数据的情况下估计系统低报程度的受限半物理识别方法。此外，我们还描述了一种基于卡尔曼滤波的自适应算法来实时估计能量摄入。这些方法用模拟和实际干预参与者的数据来说明。
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.