model selection for the extraction of movement primitives
Clicks: 233
ID: 227658
2013
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This
article has not been analysed, so there is no overall score —
reader engagement is measured and shown alongside.
Reader Engagement
Steady Performance
30.0
/100
233 views
25 readers
AI Quality Assessment
Not analyzed
Readership in this journal
SteadyRanked #81 of 128 articles by views in population health management
Most read
Least read
Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 128 in total.
Mint this article as an NFT
Not yet mintedCreate a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.
5
SUSD
one-off · no wallet required
Abstract
A wide range of blind source separation methods have been used in motor control research for the extraction of
movement primitives from EMG and kinematic data. Popular examples are principal component analysis (PCA),
independent component analysis (ICA), anechoic demixing, and the time-varying synergy model.
However, choosing the parameters of these models, or indeed choosing the type of model, is often done in a heuristic fashion, driven by result expectations as much as by the data.
We propose an objective criterion which allows to select the model type, number of primitives and the
temporal smoothness prior.
Our approach is based on a Laplace approximation to the posterior distribution of the parameters of a given blind source
separation model, re-formulated as a Bayesian generative model.
We first validate our criterion on ground truth data, showing that it performs at least as good as traditional model selection criteria (Bayesian information criterion, BIC and the Akaike Information Criterion (AIC)). Then, we analyze human gait data, finding that an anechoic mixture model with a temporal smoothness constraint on the sources can best account for the data.
movement primitives from EMG and kinematic data. Popular examples are principal component analysis (PCA),
independent component analysis (ICA), anechoic demixing, and the time-varying synergy model.
However, choosing the parameters of these models, or indeed choosing the type of model, is often done in a heuristic fashion, driven by result expectations as much as by the data.
We propose an objective criterion which allows to select the model type, number of primitives and the
temporal smoothness prior.
Our approach is based on a Laplace approximation to the posterior distribution of the parameters of a given blind source
separation model, re-formulated as a Bayesian generative model.
We first validate our criterion on ground truth data, showing that it performs at least as good as traditional model selection criteria (Bayesian information criterion, BIC and the Akaike Information Criterion (AIC)). Then, we analyze human gait data, finding that an anechoic mixture model with a temporal smoothness constraint on the sources can best account for the data.
| Reference Key |
endres2013frontiersmodel
Use this key to autocite in the manuscript while using
SciMatic Manuscript Manager or Thesis Manager
|
|---|---|
| Authors | ;Dominik M Endres;Enrico eChiovetto;Martin eGiese |
| Journal | population health management |
| Year | 2013 |
| DOI |
10.3389/fncom.2013.00185
|
| URL | |
| Keywords |
Citations
No citations found. To add a citation, contact the admin at info@scimatic.org
Comments
No comments yet. Be the first to comment on this article.