Model Predictive Control Strategies for Optimized mHealth Interventions for Physical Activity

Mohamed El Mistiri, Daniel E. Rivera, Predrag Klasnja, Junghwan Park, Eric Hekler

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations

Abstract

Many individuals fail to engage in sufficient physical activity (PA), despite its well-known health benefits. This paper examines Model Predictive Control (MPC) as a means to deliver optimized, personalized behavioral interventions to improve PA, as reflected by the number of steps walked per day. Using a health behavior fluid analogy model representing Social Cognitive Theory, a series of diverse strategies are evaluated in simulated scenarios that provide insights into the most effective means for implementing MPC in PA behavioral interventions. The interplay of measurement, information, and decision is explored, with the results illustrating MPC's potential to deliver feasible, personalized, and user-friendly behavioral interventions, even under circumstances involving limited measurements. Our analysis demonstrates the effectiveness of sensibly formulated constrained MPC controllers for optimizing PA interventions, which is a preliminary though essential step to experimental evaluation of constrained MPC control strategies under real-life conditions.

Original languageEnglish (US)
Title of host publication2022 American Control Conference, ACC 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1392-1397
Number of pages6
ISBN (Electronic)9781665451963
DOIs
StatePublished - 2022
Event2022 American Control Conference, ACC 2022 - Atlanta, United States
Duration: Jun 8 2022Jun 10 2022

Publication series

NameProceedings of the American Control Conference
Volume2022-June
ISSN (Print)0743-1619

Conference

Conference2022 American Control Conference, ACC 2022
Country/TerritoryUnited States
CityAtlanta
Period6/8/226/10/22

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

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