Estimation of Motor Unit Parameters from Surface Electromyograms using an Informed Autoencoder

TL;DR

This paper introduces an informed autoencoder approach to non-invasively estimate multiple motor unit parameters from surface EMG recordings, improving accuracy and reducing manual effort.

Contribution

It presents a novel machine learning method that simultaneously estimates several motor unit parameters from EMG data, integrating physical laws into the autoencoder.

Findings

Innervation zone centers estimated with a mean absolute error of 2.5989 mm.

Conduction velocities estimated with a mean absolute error of 0.1697 m/s.

Demonstrates plausibility of the approach on synthetic data.

Abstract

Motor unit parameters such as the innervation zone centre or the conduction velocity of the electrical potential harbour the potential to improve the fidelity of neuromechanical models used for movement and force prediction. Determining these parameters in a non-invasive way is challenging, as they are subject-specific and may vary with muscle contraction. Existing work on the estimation of motor unit parameters mainly relies on white-box modelling and therefore requires substantial manual modelling effort. This work targets the simultaneous estimation of multiple subject-specific motor unit parameters from electromyography (EMG) recordings measured non-invasively at the skin surface. This results in an inverse problem with a nonlinear loss function. To address this problem, an informed autoencoder is developed. This autoencoder reconstructs the surface EMG recordings while learning the parameters in its latent space and adhering to physical laws that relate the parameters to the EMG signals. In experiments on synthetic data, innervation zone centres are estimated with a mean absolute error of 2.5989 $\mathrm{mm}$, and conduction velocities of the electric potential are estimated with a mean absolute error of 0.1697 $\mathrm{m}\mathrm{s}^{-1}$. These results demonstrate the plausibility of this novel approach, which enables the simultaneous estimation of several motor unit parameters while reducing manual modelling effort through the integration of data-driven machine learning.