A muscle architecture model offering control over motor unit fiber density distributions

Abstract

The aim of this study was to develop a muscle architecture model able to account for the observed distributions of innervation ratios and fiber densities of different types of motor units in a muscle. A model algorithm is proposed and mathematically analyzed in order to obtain an inverse procedure that allows, by modification of input parameters, control over the output distributions of motor unit fiber densities. The model's performance was tested with independent data from a glycogen depletion study of the medial gastrocnemius of the rat. Results show that the model accurately reproduces the observed physiological distributions of innervation ratios and fiber densities and their relationships. The reliability and accuracy of the new muscle architecture model developed here can provide more accurate models for the simulation of different electromyographic signals.

Effect of number of motor units and muscle fibre type on surface electromyogram

“…This study has simulated sEMG with the two neuromuscular parameters; (1) nMU, and (2) FFR, having values corresponding to the young and the elderly [4,6,8,18,19]. Baseline values of nMU and FFR for young were maintained at 110/0.45 (nMU/FFR), and the simulated and experimental results were compared.…”

Simulation of Motor Unit Action Potential Recordings From Intramuscular Multichannel Scanning Electrodes

“…We note that this property promotes the uniformity for distributions of the parameters that depend on MN size, e.g. muscle fiber diameters, a problem that was addressed in [13], [14]. Alternatively, motor neurons can be assigned in a randomized order, if the simulation strategy includes no specific assumptions on the innervation geometry.…”

“…Signal acquired by an array of point electrodes with consecutive differentiation can be represented as follows (see expression (14) Assuming that the electrode's trajectory is approximated by only two nodes, an EMG signal from a fine-wire electrode, before the shift (λ = 0) and after the shift (λ = 1) can be expressed as (see equation 15):…”

A Comprehensive Mathematical Model of Surface Electromyography and Force Generation

“…In summary, both the placement algorithms of Navallas et al (36) and the one proposed here represent viable modelling choices, with the former probably being preferrable if the particular influence of different aspects of MU geometry is of interest, and the latter being a much simpler algorithm.…”