A control-based observer approach for estimating energy intake during pregnancy

被引:2
作者
Ranghetti, Luca [1 ]
Rivera, Daniel E. [2 ]
Guo, Penghong [2 ]
Visioli, Antonio [1 ]
Williams, Jennifer Savage [3 ]
Downs, Danielle Symons [4 ]
机构
[1] Univ Brescia, Dept Mech & Ind Engn, Brescia, Italy
[2] Arizona State Univ, Control Syst Engn Lab, Sch Engn Matter Transport & Energy, Mail Stop 876106, Tempe, AZ 85287 USA
[3] Penn State Univ, Dept Nutr Sci, University Pk, PA 16802 USA
[4] Penn State Univ, Dept Kinesiol, Exercise Psychol Lab, University Pk, PA 16802 USA
关键词
energy intake estimation; gestational weight gain; Internal Model Control; Model Predictive Control; robustness; MODEL;
D O I
10.1002/rnc.6019
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Gestational weight gain outside of Institute of Medicine guidelines poses a risk to both the mother and her unborn child. Behavioral interventions such as Healthy Mom Zone (HMZ) that aim to regulate gestational weight gain require self-monitoring of energy intake, which is often significantly under-reported by participants. This article describes the use of a control systems approach for energy intake estimation during pregnancy. It relies on an energy balance model that predicts gestational weight based on physical activity and energy intake, the latter treated as an unmeasured disturbance. Two control-based observer formulations relying on Internal Model Control and Model Predictive Control, respectively, are presented in this article, first for a hypothetical participant, then on data collected from four HMZ participants. Results demonstrate the effectiveness of the method, with generally best results obtained when estimating energy intake over a weekly time period.
引用
收藏
页码:5105 / 5127
页数:23
相关论文
共 29 条
[1]  
[Anonymous], MYFITNESSPAL
[2]  
[Anonymous], Fitbit
[3]  
[Anonymous], JAWBONE UP
[4]   Input Estimation for Nonminimum-Phase Systems With Application to Acceleration Estimation for a Maneuvering Vehicle [J].
Ansari, Ahmad ;
Bernstein, Dennis S. .
IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY, 2019, 27 (04) :1596-1607
[5]  
Bemporad A., MODEL PREDICTIVE CON
[6]  
Camacho E. F., 2013, Model predictive control, V2nd
[7]   A conceptual framework for adaptive preventive interventions [J].
Collins, LM ;
Murphy, SA ;
Bierman, KL .
PREVENTION SCIENCE, 2004, 5 (03) :185-196
[8]   Adaptive, behavioral intervention impact on weight gain, physical activity, energy intake, and motivational determinants: results of a feasibility trial in pregnant women with overweight/obesity [J].
Downs, Danielle Symons ;
Savage, Jennifer S. ;
Rivera, Daniel E. ;
Pauley, Abigail M. ;
Leonard, Krista S. ;
Hohman, Emily E. ;
Guo, Penghong ;
McNitt, Katherine M. ;
Stetter, Christy ;
Kunselman, Allen .
JOURNAL OF BEHAVIORAL MEDICINE, 2021, 44 (05) :605-621
[9]   Individually Tailored, Adaptive Intervention to Manage Gestational Weight Gain: Protocol for a Randomized Controlled Trial in Women With Overweight and Obesity [J].
Downs, Danielle Symons ;
Savage, Jennifer S. ;
Rivera, Daniel E. ;
Smyth, Joshua M. ;
Rolls, Barbara J. ;
Hohman, Emily E. ;
McNitt, Katherine M. ;
Kunselman, Allen R. ;
Stetter, Christy ;
Pauley, Abigail M. ;
Leonard, Krista S. ;
Guo, Penghong .
JMIR RESEARCH PROTOCOLS, 2018, 7 (06)
[10]   Real-Time Model Predictive Control of Human Bodyweight Based on Energy Intake [J].
Fernandez, Alberto Pena ;
Youssef, Ali ;
Heeren, Charlotte ;
Matthys, Christophe ;
Aerts, Jean-Marie .
APPLIED SCIENCES-BASEL, 2019, 9 (13)