System Identification of Just Walk: Using Matchable-Observable Linear Parametrizations Freigoun,MT en Rivera,DE en Martin,CA en Paulo Santos en Azevedo Perdicoulis,TPA en Romano,RA en Hekler,EB en
dc.contributor.other 7594 en 2020-07-06T17:01:35Z 2020-07-06T17:01:35Z 2020 en
dc.description.abstract 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. IEEE en
dc.identifier.uri en
dc.language eng en
dc.rights info:eu-repo/semantics/openAccess en
dc.title System Identification of Just Walk: Using Matchable-Observable Linear Parametrizations en
dc.type Publication en
dc.type article en
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