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The aim of this study was to evaluate a surface electromyography (sEMG) signal and force model for the biceps brachii muscle during isotonic isometric contractions for an experimental set-up as well as for a simulation. The proposed model includes a new rate coding scheme and a new analytical formulation of the muscle force generation. The proposed rate coding scheme supposes varying minimum and peak firing frequencies according to motor unit (MU) type (I or II). Practically, the proposed analytical mechanogram allows us to tune the force contribution of each active MU according to its type and instantaneous firing rate. A subsequent sensitivity analysis using a Monte Carlo simulation allows deducing optimised input parameter ranges that guarantee a realistic behaviour of the proposed model according to two existing criteria and an additional one. In fact, this proposed new criterion evaluates the force generation efficiency according to neural intent. Experiments and simulations, at varying force levels and using the optimised parameter ranges, were performed to evaluate the proposed model. As a result, our study showed that the proposed sEMG-force modelling can emulate the biceps brachii behaviour during isotonic isometric contractions.

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In this work, some techniques are proposed to improve the usual Newton–Raphson Method (NRM) used in the numerical analysis of large strain viscoplastic problems. These techniques, based on the first‐order perturbation technique, allow to define an adaptive step strategy and to improve the trial solution (guessed solution) for the first iteration at each step. To assess the efficiency of the proposed techniques, a number of numerical examples are presented: necking of a circular bar, plane strain bending, and an axisymmetric hydrostatic bulging process. Compared with the classical Newton–Raphson method, the proposed scheme requires less matrix inversions, less time steps and less CPU time. For a typical axisymmetric hydrostatic bulging process (606 degrees of freedom), the proposed method needs 5 times less matrix inversions, 1·7 times less time steps, and 4 times less CPU time. Copyright © 1999 John Wiley & Sons, Ltd.

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In an electromyographic and muscle force (EMG-Force) model, the variability and uncertainty of the input muscle parameters increase the difficulty of assessing this type of model. In this study, a Monte Carlo method is used to evaluate the robustness and the sensitivity of an EMG-Force model, recently developed by our team, for two groups of simulations (constant and sinusoidal force contractions). Two existing criteria (EMG/force and force/force-variability relations) and a new criterion derived from this model (Root Mean Square error, Error(RMS), between the force command and the generated force) are used to extract relevant simulations and obtain the optimized parameter ranges in constant force contractions, while only the new criterion could be valuable in sinusoidal force contractions. The comparison of obtained results from the two groups of simulations has shown that the new criterion can replace the two existing criteria in constant and sinusoidal force contractions to give rise to stable optimized input parameter ranges for the studied EMG-Force model.