# Real-Time Model Predictive Control of Human Bodyweight Based on Energy Intake
## 1 Introduction
The human body has its own feedback system to regulate body mass, meaning that an increase or decrease of energy intake does not directly relate to a change in body mass [24; 25]. Decades ago, multiple dynamic models describing changes in bodyweight based on changes in energy expenditure and energy intake were already proposed to account for this [24; 25; 26; 27; 28]. Even models applied during starvation were proposed with the aim of characterizing how the body stores and metabolizes energy in the absence of food [29; 30]. Besides, more complex models attempting to refine the description of the adaptation process of the human body during weight loss, as perturbations to the macronutrients metabolization and energy flux balances, have been proposed [2; 31; 32; 33]. The main drawbacks of these models are their high complexity and the fact that they rely on several a priori assumptions and parameter estimates based on population-based averages. These drawbacks are highlighted in [11; 34] and reduced models are proposed, although the parameter estimates still rely on population-averaged values. It is also proposed that previous models addressed the body adaptation process as an open-loop systems instead as a closed-loop one [11]. Therefore, although theoretical models provide a good insight in weight control process, there are other external and internal factors, which affect macronutrient metabolization and energy flux balances and that should be considered. For instance, new insights from the domain of chrono-nutrition indicate that, the rate of energy absorption varies in time [35; 36]. These recent insights determine the best time to consume meals and the suitable meal proportions and its impact on bodyweightstatus [37; 38]. Therefore, the main limitation of the current models is that the algorithms are developed at population-based level and cannot account for the individual time-variant characteristics of each individual person. Biological systems, such as the human body, should be considered as complex, individual, time-varying and dynamic (CITD) systems [39]. Data-based mechanistic (DBM) models can be suitable for modelling such systems. While DBM models are based on a simple model structure, they allow coping with non-linear characteristics of the process by estimating the model parameters in real-time, which are only accepted if they can be interpreted in meaningful physical/physiological terms [40]. Even though real-time monitoring of weight is still at its early stages in humans, other sectors such as the farming industry already use real-time monitoring of weight to optimize feeding and breeding activities. Data-based modelling has already proven useful in the emerging precision feeding control in livestock [41; 42]. In [41], an algorithm to control the growth trajectory of broiler chickens based on an adaptive dynamic process model is developed. In [43], this model predictive growth control is tested in a real broiler farm. In farm conditions, the mean relative error (MRE) between actual and target weight trajectories was 7.3%. Besides, the model proved to be beneficial in broiler farms, suggesting the possibility of using the model predictive control for other purposes such as for weight control in human beings [41]. Therefore, the aim of this work is to test the capabilities of DBM models to develop a model predictive control (MPC) system, which allows estimating individualized dietary energy intake needed to achieve a desired change in bodyweight. Such a system could be implemented easily in current smartphone applications for tracking weight, energy intake and physical activity in real time. Automatic, individualized and accurate dietary energy intake suggestions are expected to increase the engagement to the application and the adherence to the suggestions of the users, as well to assist dietitians in the individualized treatment of patients.