Design, Fabrication and Evaluation of a Stretchable High-Density Electromyography Array

Abstract

The adoption of high-density electrode systems for human-machine interfaces in real-life applications has been impeded by practical and technical challenges, including noise interference, motion artefacts and the lack of compact electrode interfaces. To overcome some of these challenges, we introduce a wearable and stretchable electromyography (EMG) array, and present its design, fabrication methodology, characterisation, and comprehensive evaluation. Our proposed solution comprises dry-electrodes on flexible printed circuit board (PCB) substrates, eliminating the need for time-consuming skin preparation. The proposed fabrication method allows the manufacturing of stretchable sleeves, with consistent and standardised coverage across subjects. We thoroughly tested our developed prototype, evaluating its potential for application in both research and real-world environments. The results of our study showed that the developed stretchable array matches or outperforms traditional EMG grids and holds promise in furthering the real-world translation of high-density EMG for human-machine interfaces.

Keywords: HD-EMG; human–machine interfacing; soft robotics and sensing; surface electromyography; wearable sensors.

Figure 1

A setup using the developed stretchable HD-EMG arrays with the MyoLink amplifier [19].

Figure 2

An overview of the fabrication methodology and evolution of the stretchable HD-EMG array. (A) The figure presents the fabrication and assembly of the different layers to realise the stretchable array from the flexible HD-EMG array. (B) The figure presents the evolution of the sleeve from a 32-channel array made up of only a single silicone layer to a 64-channel array. Version 3 benefits from a 4× electrode coverage due to improved electrode placement and benefits from superior stretchability and structural integrity due to construction reinforced with fabric and silicone.

Figure 3

(A) Stretchable array in wet (left) and dry (right) configurations. (B) Experimental setup for the proof of concept for the dynamic recordings (Section 4.2.1). (C) Experimental setup for comparing the wet vs. dry grids on the decomposition of surface EMG signals (Section 4.2.2).

Figure 4

CNN architecture used for gesture classification.

Figure 5

Characterisation and validation results: (A) Electrochemical characterisation results: (i). The impedance plot for gold-coated electrodes. (ii). The plot for the phase-angle for gold-plated electrodes. (iii). Cyclic voltammograms of gold-coated electrodes over eleven cycles. (B) Example EMG signal from a single electrode during (i). contraction of TA muscle for the decomposition task, and (ii) hand squeeze (both recorded using the dry-electrode configuration). (C) Confusion matrices from gesture classification validation experiments on subjects 1 and 2 using dry (i,ii) and wet (iii,iv) electrodes, respectively.