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. 2022 Feb 13;12(2):117.
doi: 10.3390/bios12020117.

Hammerstein-Wiener Multimodel Approach for Fast and Efficient Muscle Force Estimation from EMG Signals

Ines Chihi  1 , Lilia Sidhom  2 , Ernest Nlandu Kamavuako  3   4
Affiliations

Hammerstein-Wiener Multimodel Approach for Fast and Efficient Muscle Force Estimation from EMG Signals

Ines Chihi et al. Biosensors (Basel). .

Abstract

This paper develops a novel approach to characterise muscle force from electromyography (EMG) signals, which are the electric activities generated by muscles. Based on the nonlinear Hammerstein-Wiener model, the first part of this study outlines the estimation of different sub-models to mimic diverse force profiles. The second part fixes the appropriate sub-models of a multimodel library and computes the contribution of sub-models to estimate the desired force. Based on a pre-existing dataset, the obtained results show the effectiveness of the proposed approach to estimate muscle force from EMG signals with reasonable accuracy. The coefficient of determination ranges from 0.6568 to 0.9754 using the proposed method compared with a range of 0.5060 to 0.9329 using an artificial neural network (ANN), generating significantly different accuracy (p < 0.03). Results imply that the use of multimodel approach can improve the accuracy in proportional control of prostheses.

Keywords: Hammerstein–Wiener model; artificial neural network; electromyography (EMG) signals; multimodel; muscle force.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 7
Figure 7
The architecture of the applied ANN algorithm.
Figure 1
Figure 1
Magnetisation principal of multimodel approach.
Figure 2
Figure 2
Multimodel structure to estimate two muscle forces Fm1 and Fm2 from two EMG signals EMGm1 and EMGm2.
Figure 3
Figure 3
Responses of sub-models based on Hammerstein–Weiner structure.
Figure 4
Figure 4
The general structure of the Hammerstein–Weiner model.
Figure 5
Figure 5
The considered experimental approach.
Figure 6
Figure 6
Data analysis (box plots and density profiles) of EMG signals for the movement “Two linear ramps.” (a) Box plot of the EMG signal i (b) density of EMG signal i.
Figure 8
Figure 8
The Representative performance of the multimodel approach compared with ANN.
Figure 9
Figure 9
Performance (for ten subjects) of force estimation evaluated with training computing time for three scenarios (multimode approach is represented by the red line and ANN the blue one).
See this image and copyright information in PMC

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