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Technical Brief

Selecting Sensitive Parameter Subsets in Dynamical Models with Application to Biomechanical System Identification

[+] Author and Article Information
Ahmed Ramadan

ASME Student Member, Dept. of Mechanical Engineering, Michigan State University, 428 S. Shaw Ln, East Lansing, Michigan 48824
ramadana@msu.edu

Connor Boss

Dept. of Electrical and Computer Engineering, Michigan State University
bossconn@egr.msu.edu

Jongeun Choi

ASME Member, School of Mechanical Engineering, Yonsei University, Seoul, Republic of Korea
jongeunchoi@yonsei.ac.kr

N. Peter Reeves

Sumaq Life LLC
reevesn@icloud.com

Jacek Cholewicki

Dept. of Osteopathic Surg. Specialties, Michigan State University
cholewic@msu.edu

John Popovich, Jr.

Dept. of Osteopathic Surg. Specialties, Michigan State University
popovi16@msu.edu

Clark J. Radcliffe

Dept. of Mechanical Engineering, Michigan State University
radcliffe@egr.msu.edu

1Corresponding author.

ASME doi:10.1115/1.4039677 History: Received October 05, 2017; Revised March 02, 2018

Abstract

Estimating many parameters of biomechanical systems with limited data may achieve good fit, but there could be multiple parameter solutions that yield very similar model response. This results in lack of identifiability in the estimation problem. Therefore, we propose two systematic methods to select a sensitive parameter subset to be estimated, while fixing the remaining parameters to values obtained from preliminary estimation. The selection approach relies on identifying the parameter subset to which the measurement output is most sensitive. The proposed methods are based on the Fisher Information Matrix (FIM) and the Least Absolute Shrinkage and Selection Operator (LASSO). 95% confidence intervals of the parameter estimates were computed using model-based bootstrap. We presented an application of identifying a parametric model of a head rotational position-tracking test for human subjects. Our methods led to reduced model complexity (less number of parameters to be estimated), narrow confidence intervals of the parameter estimates, and accepted goodness of fit.

Copyright (c) 2018 by ASME
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