Model Predictive Control approach to adaptive messaging intervention for physical activity

TL;DR

This paper presents a probabilistic model predictive control framework for designing adaptive physical activity interventions tailored to individual behavioral data, aiming to maximize goal achievement.

Contribution

It introduces a novel data-driven control approach using mixed-integer programming for personalized, adaptive physical activity interventions.

Findings

Effective control of physical activity behavior using the proposed MPC framework.

Personalized interventions improve the likelihood of achieving activity goals.

The approach is computationally tractable for real-time application.

Abstract

In this work, we have developed a framework for synthesizing data driven controllers for a class of uncertain switched systems arising in an application to physical activity interventions. In particular, we present an application of probabilistic model predictive control to design an efficient, tractable, and adaptive intervention using behavioral data sets i.e. physical activity behavior. The models of physical activity for each individual are provided for the design of controllers that maximize the probability of achieving a desired physical activity goal subject to intervention specifications. We have tailored the mixed-integer programming-based approach for evaluating the Model Predictive Control decision at each time step.