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Real-Time Model Predictive Control of Human Bodyweight Based on Energy Intake

1
Department of Biosystems, Division Animal and Human Health Engineering, M3-BIORES: Measure, Model & Manage of Bioresponses Laboratory, KU Leuven, Kasteelpark Arenberg 30, 3001 Heverlee, Belgium
2
Nutrition & Obesity, Clinical and Experimental Endocrinology, Department of Chronic Diseases, Metabolism and Aging, KU Leuven, UZ Herestraat 49, 3000 Leuven, Belgium
3
Clinical Nutrition Unit, Department of Endocrinology, University Hospitals Leuven, 3000 Leuven, Belgium
*
Author to whom correspondence should be addressed.
Appl. Sci. 2019, 9(13), 2609; https://doi.org/10.3390/app9132609
Received: 3 June 2019 / Revised: 22 June 2019 / Accepted: 25 June 2019 / Published: 27 June 2019
(This article belongs to the Special Issue Human Health Engineering)
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Abstract

The number of overweight people reached 1.9 billion in 2016. Lifespan decrease and many diseases have been linked to obesity. Efficient ways to monitor and control body weight are needed. The objective of this work is to explore the use of a model predictive control approach to manage bodyweight in response to energy intake. The analysis is performed based on data obtained during the Minnesota starvation experiment, with weekly measurements on body weight and energy intake for 32 male participants over the course of 27 weeks. A first order dynamic auto-regression with exogenous variables model exhibits the best prediction, with an average mean relative prediction error value of 1.01 ± 0.02% for 1 week-ahead predictions. Then, the performance of a model predictive control algorithm, following a predefined bodyweight trajectory, is tested. Root mean square errors of 0.30 ± 0.06 kg and 9 ± 3 kcal day−1 are found between the desired target and simulated bodyweights, and between the measured energy intake and advised by the controller energy intake, respectively. The model predictive control approach for bodyweight allows calculating the needed energy intake in order to follow a predefined target bodyweight reference trajectory. This study shows a first possible step towards real-time active control of human bodyweight. View Full-Text
Keywords: weight loss; mHealth; dynamic model; adaptive control weight loss; mHealth; dynamic model; adaptive control
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Peña Fernández, A.; Youssef, A.; Heeren, C.; Matthys, C.; Aerts, J.-M. Real-Time Model Predictive Control of Human Bodyweight Based on Energy Intake. Appl. Sci. 2019, 9, 2609.

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