Estimates of acausal joint impedance models

David T. Westwick*, Eric J. Perreault

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

9 Scopus citations


Estimates of joint or limb impedance are commonly used in the study of how the nervous system controls posture and movement, and how that control is altered by injury to the neural or musculoskeletal systems. Impedance characterizes the dynamic relationship between an imposed perturbation of joint position and the torques generated in response. While there are many practical reasons for estimating impedance rather than its inverse, admittance, it is an acausal representation of the limb mechanics that can lead to difficulties in interpretation or use. The purpose of this study was to explore the acausal nature of nonparametric estimates of joint impedance representations to determine how they are influenced by common experimental and computational choices. This was accomplished by deriving discrete-time realizations of first- and second-order derivatives to illustrate two key difficulties in the physical interpretation of impedance impulse response functions. These illustrations were provided using both simulated and experimental data. It was found that the shape of the impedance impulse response depends critically on the selected sampling rate, and on the bandwidth and noise characteristics of the position perturbation used during the estimation process. These results provide important guidelines for designing experiments in which nonparametric estimates of impedance will be obtained, especially when those estimates are to be used in a multistep identification process.

Original languageEnglish (US)
Article number29
Pages (from-to)2913-2921
Number of pages9
JournalIEEE Transactions on Biomedical Engineering
Issue number10
StatePublished - 2012


  • Discrete-time
  • joint dynamics
  • parametric model
  • sampling
  • system identification
  • time domain
  • two-sided impulse response

ASJC Scopus subject areas

  • Biomedical Engineering


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