TY - JOUR
T1 - System Identification of Just Walk
T2 - Using Matchable-Observable Linear Parametrizations
AU - Lopes dos Santos, Paulo
AU - Freigoun, Mohammad T.
AU - Martin, Cesar A.
AU - Rivera, Daniel
AU - Hekler, Eric B.
AU - Romano, Rodrigo Alvite
AU - Azevedo Perdicoulis, Teresa P.
N1 - Publisher Copyright:
© 1993-2012 IEEE.
PY - 2020/1
Y1 - 2020/1
N2 - 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.
AB - 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.
KW - Behavioral interventions
KW - behavioral sciences
KW - design of experiments
KW - parameter estimation
KW - system identification
KW - systems modeling
UR - http://www.scopus.com/inward/record.url?scp=85058987091&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85058987091&partnerID=8YFLogxK
U2 - 10.1109/TCST.2018.2884833
DO - 10.1109/TCST.2018.2884833
M3 - Article
AN - SCOPUS:85058987091
SN - 1063-6536
VL - 28
SP - 264
EP - 275
JO - IEEE Transactions on Control Systems Technology
JF - IEEE Transactions on Control Systems Technology
IS - 1
M1 - 8587117
ER -