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Regularized nonlinear regression for simultaneously selecting and estimating key model parameters: Application to head-neck position tracking
DC Field | Value | Language |
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dc.contributor.author | Yoon, Kyubaek | - |
dc.contributor.author | You, Hojun | - |
dc.contributor.author | Wu, Wei-Ying | - |
dc.contributor.author | Lim, Chae Young | - |
dc.contributor.author | Choi, Jongeun | - |
dc.contributor.author | Boss, Connor | - |
dc.contributor.author | Ramadan, Ahmed | - |
dc.contributor.author | Popovich, John M. | - |
dc.contributor.author | Cholewicki, Jacek | - |
dc.contributor.author | Reeves, N. Peter | - |
dc.contributor.author | Radcliffe, Clark J. | - |
dc.date.accessioned | 2022-09-30T06:48:01Z | - |
dc.date.available | 2022-09-30T06:48:01Z | - |
dc.date.created | 2022-08-18 | - |
dc.date.issued | 2022-08 | - |
dc.identifier.citation | Engineering Applications of Artificial Intelligence, Vol.113, p. 104974 | - |
dc.identifier.issn | 0952-1976 | - |
dc.identifier.uri | https://hdl.handle.net/10371/185191 | - |
dc.description.abstract | In system identification, estimating parameters of a biomechanical model using limited observations results in poor identifiability. To cope with this issue, we propose a new method to simultaneously select and estimate sensitive parameters as key model parameters while fixing the remaining parameters to a set of typical values. The problem is formulated as a nonlinear least-squares estimator with L-1-regularization on the deviation of parameters from a set of typical values. In addition, a modified optimization approach is introduced to find the solution to the formulated problem. As a result, we provided consistency and oracle properties of the proposed estimator as a theoretical foundation. To show the effectiveness of the proposed method, we conducted simulation and experimental studies. In the simulation study, the proposed Lasso performed significantly better than the ordinary L-1-regularization methods in terms of the bias and variance of the parameter estimates. The experimental study presented an application identifying a biomechanical parametric model of a head position tracking task for ten human subjects from limited data. Compared with the variance of the parameter estimates from nonlinear ordinary least-squares regression, that of parameter estimates from the proposed Lasso decreased by 96% using the simulated data. Using the real-world data, the variance of estimated parameters decreased by 71%. In addition, the proposed method kept variance accounted for (VAF) at 83% and was 54 times faster than the ordinary Lasso using a standard simplex-based optimization algorithm. | - |
dc.language | 영어 | - |
dc.publisher | Pergamon Press Ltd. | - |
dc.title | Regularized nonlinear regression for simultaneously selecting and estimating key model parameters: Application to head-neck position tracking | - |
dc.type | Article | - |
dc.identifier.doi | 10.1016/j.engappai.2022.104974 | - |
dc.citation.journaltitle | Engineering Applications of Artificial Intelligence | - |
dc.identifier.wosid | 000833232700010 | - |
dc.identifier.scopusid | 2-s2.0-85131415310 | - |
dc.citation.startpage | 104974 | - |
dc.citation.volume | 113 | - |
dc.description.isOpenAccess | N | - |
dc.contributor.affiliatedAuthor | Lim, Chae Young | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
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