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

机译：半物理识别和能量摄入状态估计，以管理妊娠体重增加的干预措施

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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.
机译：妊娠体重增加过多（即怀孕期间体重增加）是对公共卫生的重大关注，并且已成为基于控制系统的新型干预措施的近期焦点。本文基于文献中的第一性原理能量平衡模型，开发了一个面向控制的动力学系统模型，该模型是根据针对肥胖和超重孕妇的一项研究的参与者数据进行评估的。结果表明参与人群中能量摄入的报告不足。本文开发了一系列基于系统识别和状态估计的方法，以更好地理解和描述漏报的程度。这些范围包括从能量平衡模型的封闭形式中反算能量摄入量，到一种受约束的半物理识别方法，该方法可以估计在存在噪声和可能缺少数据的情况下系统报告不足的程度。另外，我们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。用来自模拟和实际干预参与者的数据说明了这些方法。

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