A MoliZoft System Identification Approach of the Just Walk Data

P. Lopes dos Santos, M. T. Freigoun, Daniel Rivera, E. B. Hekler, C. A. Martín, R. Romano, T. P. Perdicoúlis, J. A. Ramos

Research output: Contribution to journalArticle

3 Scopus citations

Abstract

A system identification approach is used estimate linear time invariant models from the data of physical activity gathered in the Just Walk intervention conducted by the Designing Health Lab and the Control Systems Laboratory at Arizona State University A class of identification algorithms proposed elsewhere by one of the authors, denoted as MoliZoft, was reformulated and adapted to estimate models from data gathered in this experience. In this paper, the identification algorithms are described and the best models estimated for a particular participant are analysed and used to improve the results in future experiments.

Original languageEnglish (US)
Pages (from-to)12508-12513
Number of pages6
JournalIFAC-PapersOnLine
Volume50
Issue number1
DOIs
StatePublished - Jul 2017

Keywords

  • Least squares identification
  • Output error identification
  • Prediction error methods
  • Social
  • System identification
  • behavioural sciences

ASJC Scopus subject areas

  • Control and Systems Engineering

Fingerprint Dive into the research topics of 'A MoliZoft System Identification Approach of the Just Walk Data'. Together they form a unique fingerprint.

  • Cite this

    dos Santos, P. L., Freigoun, M. T., Rivera, D., Hekler, E. B., Martín, C. A., Romano, R., Perdicoúlis, T. P., & Ramos, J. A. (2017). A MoliZoft System Identification Approach of the Just Walk Data. IFAC-PapersOnLine, 50(1), 12508-12513. https://doi.org/10.1016/j.ifacol.2017.08.2060