title: Nonparametric identification of the elbow joint stiffness under compliant loads.
abstract: Several nonparametric system identification techniques have been used to estimate the dynamic joint stiffness of the human elbow. Most studies involved a very stiff environment, but some studies have also shown that stiffness is modified in response to environmental compliance. However, using the same identification technique used under very stiff conditions to do identification under compliant conditions leads to a biased estimate. This is due to the presence of feedback in the latter. In this paper, we use a nonparametric identification algorithm to demonstrate this problem. We then show how instrumental variables can be employed to obtain an unbiased estimate of the same. Both simulations as well as experimental data are used to this effect.
authors.affiliation: Department of Physical Medicine & Rehabilitation, Northwestern University, Chicago, IL, USA.
authors.forename: Vengateswaran J
authors.initials: VJ
authors.lastname: Ravichandran
authors.forename: Eric J
authors.initials: EJ
authors.lastname: Perreault
authors.forename: David T
authors.initials: DT
authors.lastname: Westwick
authors.forename: Nathan
authors.initials: N
authors.lastname: Cohen