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A Biodynamic Feedthrough Model Based on Neuromuscular Principles

Authors
Venrooij, JoostAbbink, David A.Mulder, Markvan Paassen, Marinus M.Mulder, Maxvan der Helm, Frans C. T.Buelthoff, Heinrich H.
Issue Date
Jul-2014
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Keywords
Biodynamic feedthrough (BDFT); force disturbance feedthrough; manual control; neuromuscular admittance
Citation
IEEE TRANSACTIONS ON CYBERNETICS, v.44, no.7, pp.1141 - 1154
Indexed
SCIE
SCOPUS
Journal Title
IEEE TRANSACTIONS ON CYBERNETICS
Volume
44
Number
7
Start Page
1141
End Page
1154
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/98078
DOI
10.1109/TCYB.2013.2280028
ISSN
2168-2267
Abstract
A biodynamic feedthrough (BDFT) model is proposed that describes how vehicle accelerations feed through the human body, causing involuntary limb motions and so involuntary control inputs. BDFT dynamics strongly depend on limb dynamics, which can vary between persons (between-subject variability), but also within one person over time, e.g., due to the control task performed (within-subject variability). The proposed BDFT model is based on physical neuromuscular principles and is derived from an established admittance model-describing limb dynamics-which was extended to include control device dynamics and account for acceleration effects. The resulting BDFT model serves primarily the purpose of increasing the understanding of the relationship between neuromuscular admittance and biodynamic feedthrough. An added advantage of the proposed model is that its parameters can be estimated using a two-stage approach, making the parameter estimation more robust, as the procedure is largely based on the well documented procedure required for the admittance model. To estimate the parameter values of the BDFT model, data are used from an experiment in which both neuromuscular admittance and biodynamic feedthrough are measured. The quality of the BDFT model is evaluated in the frequency and time domain. Results provide strong evidence that the BDFT model and the proposed method of parameter estimation put forward in this paper allows for accurate BDFT modeling across different subjects (accounting for between-subject variability) and across control tasks (accounting for within-subject variability).
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