Removing motion artifacts from mechanomyographic signals: an innovative filtering method applied to human movement analysis

TL;DR

This paper introduces an adaptive filtering method based on empirical mode decomposition and spectral fuzzy entropy to effectively remove motion artifacts from mechanomyographic signals during dynamic movements, outperforming traditional filters.

Contribution

The study presents a novel adaptive filtering technique for MMG signals that improves artifact removal during movement, enabling more accurate muscle activity analysis.

Findings

Better motion artifact removal compared to traditional filters (R^2 = 0.907 and 0.842)

Effective filtering in the 5-20 Hz bandwidth during dynamic activities

Caution advised in interpreting signals from trunk and lower-limb muscles due to residual impact accelerations

Abstract

Mechanomyography (MMG) is a promising tool for measuring muscle activity in the field but its sensitivity to motion artifacts limits its application. In this study, we proposed an adaptative filtering method for MMG accelerometers based on the complete ensemble empirical mode decomposition, with adaptative noise and spectral fuzzy entropy, to isolate motions artefacts from the MMG signal in dynamic conditions. We compared our method with the traditional band-pass filtering technique, demonstrating better results concerning motion recomposition for deltoid and erector spinae muscles (R${}^2$ = 0.907 and 0.842). Thus, this innovative method allows the filtering of motion artifacts dynamically in the 5-20 Hz bandwidth, which is not achievable with traditional method. However, the interpretation of accelerometric MMG signals from the trunk and lower-limb muscles during walking or running should be approached with great caution as impact-related accelerations are still present, though their exact quantity still needs to be quantified.