Abstract

Electromyography signal processing approaches have traditionally been validated through computer simulations. Electromyography electrodes and systems are often not validated or have been validated on human subjects where there is no clear ground truth signal for comparison. We sought to develop a physical limb phantom for validation of electromyography hardware and signal processing approaches. We embedded pairs of wires within a conductive gelatin surrounding an artificial bone such that the antennae could broadcast identified ground truth signals. The ground truth signals can be simple sinusoids or more complex representations of muscle activity. With the phantom and surface electromyography electrodes, we were able to show varying levels of crosstalk between nearby recording electrodes as we altered the amplitude of the antennae signals. We were also able to induce motion artifacts in our recordings by lightly dropping the phantom on a surface while antennae broadcast signals. High-density electromyography recordings of the trials showed that traditional filtering techniques fail to fully eliminate relatively small motion artifacts. The results suggest that the electrical limb phantom could be a valuable tool for testing potential effects of muscle crosstalk and motion artifacts on different electromyography systems and signal processing approaches.

Highlights

  • S URFACE electromyography (EMG) is a non-invasive method for quantifying the electrical activity of muscles with technical limitations [1], [2]

  • Signal crosstalk between neighboring muscles and motion artifacts are especially relevant when recording EMG during dynamic conditions, such as walking and running [3]

  • We developed a phantom consisting of antennae embedded in a conductive gelatin material and broadcast signals from these antennae to be recorded with EMG sensors at the phantom surface

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Summary

Introduction

S URFACE electromyography (EMG) is a non-invasive method for quantifying the electrical activity of muscles with technical limitations [1], [2]. Signal crosstalk between neighboring muscles and motion artifacts are especially relevant when recording EMG during dynamic conditions, such as walking and running [3]. Crosstalk occurs when a surface electrode on one muscle records a signal from both the target muscle and one or more nearby muscles [3], [4]. The level of crosstalk depends on the size and location of the target muscles [5]. Surface EMG measurements from a target muscle may contain up to 30% of a non-target muscle’s signal [6]. Motion artifacts can result from disturbance of the electrode

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