System Identification of Just Walk: Using Matchable-Observable Linear Parametrizations

• Published in: IEEE transactions on control systems technology Vol. 28; no. 1; pp. 264 - 275
• Main Authors: Lopes dos Santos, Paulo, Freigoun, Mohammad T., Martin, Cesar A., Rivera, Daniel E., Hekler, Eric B., Romano, Rodrigo Alvite, Azevedo Perdicoulis, Teresa P.
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.01.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptation models; Algorithms; Behavioral interventions; behavioral sciences; Data models; Design of experiments; Estimation; Identification; Intervention; parameter estimation; Predictive models; Sociology; Statistics; System identification; systems modeling
• ISSN: 1063-6536; 1558-0865; 2374-0159; 1558-0865
• DOI: 10.1109/TCST.2018.2884833

System identification approaches have been used to design an experiment, generate data, and estimate dynamical system models for Just Walk, a behavioral intervention intended to increase physical activity in sedentary adults. The estimated models serve a number of important purposes, such as understanding the factors that influence behavior and as the basis for using control systems as decision algorithms in optimized interventions. A class of identification algorithms known as matchable-observable linear identification has been reformulated and adapted to estimate linear time-invariant models from data obtained from this intervention. The experimental design, estimation algorithms, and validation procedures are described, with the best models estimated from data corresponding to an individual intervention participant. The results provide insights into the individual and the intervention, which can be used to improve the design of future studies.