Identification of time variant neuromuscular admittance using wavelets

Mark Mulder, Tom Verspecht, David A. Abbink, Marinus M. Van Paassen, David C. Balderas S., Alfred Schouten, Erwin De Vlugt, Max Mulder

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

14 Citations (Scopus)


Driver control behaviour is highly time variant. When studying the neuromuscular system of drivers in interaction with the steering wheel, the common Fourier system identification techniques are only applicable when time-invariant behaviour is assumed. This paper describes how wavelets can be used to identify time-variant neuromuscular admittance. Using the Morlet wavelet transformation, time domain signals are transformed to a time-frequency representation. A non-parametric, time-variant frequency response function can be estimated using the transformed signals. A model of the neuromuscular system of a driver controlling a steering wheel was used to generate time-variant data. This paper shows that the Morlet wavelet transformation is a valid tool for estimating accurate time-variant frequency responses of neuromuscular arm dynamics. The results of this article give us confidence that wavelet analysis can be used on experimental data, with lower signal-to-noise ratio, too. This will allow us to identify how drivers adjust their neuromuscular system during driving.

Original languageEnglish
Title of host publication2011 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2011 - Conference Digest
Number of pages7
Publication statusPublished - 23 Dec 2011
Externally publishedYes
EventIEEE International Conference on Systems, Man, and Cybernetics, SMC 2011 - Hilton Anchorage, Anchorage, United States
Duration: 9 Oct 201112 Oct 2011


ConferenceIEEE International Conference on Systems, Man, and Cybernetics, SMC 2011
Abbreviated titleSMC
Country/TerritoryUnited States


  • Admittance
  • Frequency Response Functions
  • Human Machine Interaction
  • Neuromuscular System
  • Time Variant System Identification
  • Wavelets


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