Abstract
The force and position data used to construct models of limb impedance are often obtained from closed-loop experiments. If the system is tested in a stiff environment, it is possible to treat the data as if they were obtained in open loop. However, when limb impedance is studied in a compliant environment, the presence of feedback cannot be ignored. While unbiased estimates of a system can be obtained directly using the prediction error method, the same cannot be said when linear regression or correlation analysis is used to fit nonparametric time- or frequency-domain models. We develop a prediction error minimization-based identification method for a nonparametric time-domain model augmented with a parametric noise model. The identification algorithm is tested on a dynamic mass-spring-damper system and returns consistent estimates of the system's properties under both stiff and compliant feedback control. The algorithm is then used to estimate the impedance of a human elbow joint in both stiff and compliant environments.
Original language | English (US) |
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Pages (from-to) | 521-530 |
Number of pages | 10 |
Journal | IEEE Transactions on Biomedical Engineering |
Volume | 58 |
Issue number | 3 PART 1 |
DOIs | |
State | Published - Mar 2011 |
Funding
Manuscript received April 20, 2010; revised October 5, 2010; accepted November 7, 2010. Date of publication December 3, 2010; date of current version February 18, 2011. This work was supported in part by the Natural Sciences and Engineering Research Council of Canada under Grant RPGIN-238939-2005, and in part by the National Institutes of Health under Grants R01 NS053813 and R24 HD050821. Asterisk indicates corresponding author.
Keywords
- Autoregressive moving average (ARMA)
- joint dynamics
- limb impedance
- noise model
- separable least squares
- system identification
ASJC Scopus subject areas
- Biomedical Engineering