Nonparametric identification of the elbow joint stiffness under compliant loads

Vengateswaran J. Ravichandran*, Eric J. Perreault, David T. Westwick, Nathan Cohen

*Corresponding author for this work

Research output: Contribution to journalConference article

3 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)4706-4709
Number of pages4
JournalAnnual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings
Volume26 VII
StatePublished - Dec 1 2004
EventConference Proceedings - 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2004 - San Francisco, CA, United States
Duration: Sep 1 2004Sep 5 2004



  • Compliant loads
  • Dynamic endpoint stiffness
  • Instrumental variables
  • Nonparametric system identification

ASJC Scopus subject areas

  • Signal Processing
  • Biomedical Engineering
  • Computer Vision and Pattern Recognition
  • Health Informatics

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