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

2026-05-08

AI Summary

This study addresses the challenges of non-invasive, simultaneous estimation of personalized motor unit parameters—such as innervation zone center and conduction velocity—which are hindered by modeling complexity and reliance on manual intervention. The authors propose a physics-informed autoencoder, applied for the first time to surface electromyography (sEMG) signal analysis, that jointly reconstructs the observed signals and inversely infers multiple biophysical parameters within its latent space. By synergistically integrating data-driven learning with mechanistic modeling, the method substantially reduces the manual effort typically required in white-box modeling approaches. Evaluated on synthetic data, the approach achieves high accuracy, yielding a mean error of 2.60 mm for innervation zone center localization and 0.17 m/s for conduction velocity estimation, thereby demonstrating both precision and feasibility.

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.

Problem

• motor unit parameters
• surface electromyography
• non-invasive estimation
• subject-specific
• inverse problem

Innovation

• informed autoencoder
• motor unit parameter estimation
• surface electromyography
• neuromechanical modeling
• physics-informed machine learning