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

doi:10.1109/ICSMC.2011.6083879

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 proceeding › Conference contribution › Academic › peer-review

14 Citations (Scopus)

Abstract

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 language	English
Title of host publication	2011 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2011 - Conference Digest
Pages	1474-1480
Number of pages	7
DOIs	https://doi.org/10.1109/ICSMC.2011.6083879
Publication status	Published - 23 Dec 2011
Externally published	Yes
Event	IEEE International Conference on Systems, Man, and Cybernetics, SMC 2011 - Hilton Anchorage, Anchorage, United States Duration: 9 Oct 2011 → 12 Oct 2011

Conference

Conference	IEEE International Conference on Systems, Man, and Cybernetics, SMC 2011
Abbreviated title	SMC
Country/Territory	United States
City	Anchorage
Period	9/10/11 → 12/10/11

Keywords

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

Access to Document

10.1109/ICSMC.2011.6083879

Cite this

Mulder, M., Verspecht, T., Abbink, D. A., Van Paassen, M. M., Balderas S., D. C., Schouten, A., De Vlugt, E., & Mulder, M. (2011). Identification of time variant neuromuscular admittance using wavelets. In 2011 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2011 - Conference Digest (pp. 1474-1480). Article 6083879 https://doi.org/10.1109/ICSMC.2011.6083879

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title = "Identification of time variant neuromuscular admittance using wavelets",

abstract = "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.",

keywords = "Admittance, Frequency Response Functions, Human Machine Interaction, Neuromuscular System, Time Variant System Identification, Wavelets",

author = "Mark Mulder and Tom Verspecht and Abbink, {David A.} and {Van Paassen}, {Marinus M.} and {Balderas S.}, {David C.} and Alfred Schouten and {De Vlugt}, Erwin and Max Mulder",

year = "2011",

month = dec,

day = "23",

doi = "10.1109/ICSMC.2011.6083879",

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isbn = "9781457706523",

pages = "1474--1480",

booktitle = "2011 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2011 - Conference Digest",

note = "IEEE International Conference on Systems, Man, and Cybernetics, SMC 2011, SMC ; Conference date: 09-10-2011 Through 12-10-2011",

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Mulder, M, Verspecht, T, Abbink, DA, Van Paassen, MM, Balderas S., DC, Schouten, A, De Vlugt, E & Mulder, M 2011, Identification of time variant neuromuscular admittance using wavelets. in 2011 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2011 - Conference Digest., 6083879, pp. 1474-1480, IEEE International Conference on Systems, Man, and Cybernetics, SMC 2011, Anchorage, Alaska, United States, 9/10/11. https://doi.org/10.1109/ICSMC.2011.6083879

Identification of time variant neuromuscular admittance using wavelets. / Mulder, Mark; Verspecht, Tom; Abbink, David A. et al.
2011 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2011 - Conference Digest. 2011. p. 1474-1480 6083879.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

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AU - Verspecht, Tom

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AU - Van Paassen, Marinus M.

AU - Balderas S., David C.

AU - Schouten, Alfred

AU - De Vlugt, Erwin

AU - Mulder, Max

PY - 2011/12/23

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N2 - 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.

AB - 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.

KW - Admittance

KW - Frequency Response Functions

KW - Human Machine Interaction

KW - Neuromuscular System

KW - Time Variant System Identification

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T2 - IEEE International Conference on Systems, Man, and Cybernetics, SMC 2011

Y2 - 9 October 2011 through 12 October 2011

ER -

Identification of time variant neuromuscular admittance using wavelets

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Keywords

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