A MoliZoft System Identification Approach of the Just Walk Data

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

doi:10.1016/j.ifacol.2017.08.2060

A MoliZoft System Identification Approach of the Just Walk Data

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

Nova Southeastern University

Research output: Contribution to journal › Article › peer-review

2 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 language	English
Pages (from-to)	12508-12513
Number of pages	6
Journal	IFAC Proceedings Volumes
Volume	50
Issue number	1
DOIs	https://doi.org/10.1016/j.ifacol.2017.08.2060
State	Published - Jul 2017

Bibliographical note

Publisher Copyright:
© 2017

ASJC Scopus Subject Areas

Control and Systems Engineering

Keywords

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

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10.1016/j.ifacol.2017.08.2060

Cite this

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title = "A MoliZoft System Identification Approach of the Just Walk Data",

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. ",

keywords = "behavioural sciences, Least squares identification, Output error identification, Prediction error methods, Social, System identification",

author = "\{dos Santos\}, \{P. Lopes\} and Freigoun, \{M. T.\} and Rivera, \{D. E.\} and Hekler, \{E. B.\} and Mart{\'i}n, \{C. A.\} and R. Romano and Perdico{\'u}lis, \{T. P.\} and Ramos, \{J. A.\}",

note = "Publisher Copyright: {\textcopyright} 2017",

year = "2017",

month = jul,

doi = "10.1016/j.ifacol.2017.08.2060",

language = "English",

volume = "50",

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AU - dos Santos, P. Lopes

AU - Freigoun, M. T.

AU - Rivera, D. E.

AU - Hekler, E. B.

AU - Martín, C. A.

AU - Romano, R.

AU - Perdicoúlis, T. P.

AU - Ramos, J. A.

PY - 2017/7

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N2 - 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.

AB - 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.

KW - behavioural sciences

KW - Least squares identification

KW - Output error identification

KW - Prediction error methods

KW - Social

KW - System identification

U2 - 10.1016/j.ifacol.2017.08.2060

DO - 10.1016/j.ifacol.2017.08.2060

M3 - Article

AN - SCOPUS:85044864674

SN - 2405-8971

VL - 50

SP - 12508

EP - 12513

JO - IFAC Proceedings Volumes

JF - IFAC Proceedings Volumes

IS - 1

ER -

A MoliZoft System Identification Approach of the Just Walk Data

Abstract

Bibliographical note

ASJC Scopus Subject Areas

Keywords

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