Penalized nonlinear regression with application to head-neck position tracking

Kyubaek Yoon, Jongeun Choi

Research output: Chapter in Book/Report/Conference proceedingConference contribution


In this paper, we propose a novel approach which selects and estimates sensitive parameters of a nonlinear model using L1-regularization. A biomechanical model have many parameters to be estimated for accurate human body simulation. However, when we have insufficient data for estimation, it occurs the overfitting problem. Therefore, we reformulate the parameter update process of the Levenberg-Marquardt (LM) optimization in order to apply the least absolute shrinkage and selection operator (LASSO) to a nonlinear least squares problem. To show the effectiveness of our method, we compare our method with other methods from application of head-neck position tracking task. As a result, our method selects sensitive parameters with much shorter computation time than other method. In addition, our method maintains goodness of fit measured by Variance accounted for (VAF) at 82.45% although reducing the number of estimated parameters.

Original languageEnglish
Title of host publicationAdvanced Driver Assistance and Autonomous Technologies; Advances in Control Design Methods; Advances in Robotics; Automotive Systems; Design, Modeling, Analysis, and Control of Assistive and Rehabilitation Devices; Diagnostics and Detection; Dynamics and Control of Human-Robot Systems; Energy Optimization for Intelligent Vehicle Systems; Estimation and Identification; Manufacturing
PublisherAmerican Society of Mechanical Engineers (ASME)
ISBN (Electronic)9780791859148
Publication statusPublished - 2019
EventASME 2019 Dynamic Systems and Control Conference, DSCC 2019 - Park City, United States
Duration: 2019 Oct 82019 Oct 11

Publication series

NameASME 2019 Dynamic Systems and Control Conference, DSCC 2019


ConferenceASME 2019 Dynamic Systems and Control Conference, DSCC 2019
CountryUnited States
CityPark City

Bibliographical note

Funding Information:
This work was supported by the Mid-career Research Program through the National Research Foundation of Ko- rea (NRF) funded by the Ministry of Science and ICT (NRF-2018R1A2B6008063).

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Mechanical Engineering
  • Industrial and Manufacturing Engineering

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