Identification of time variant neuromuscular admittance using wavelets

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.