Washable garment-embedded textile electrodes can measure high-quality surface EMG data across a range of motor tasks

(Waschbare, in die Kleidung eingebettete Textilelektroden können hochwertige Oberflächen-EMG-Daten für eine Reihe von motorischen Aufgaben messen)

Measuring surface electromyography (sEMG) outside the laboratory environment is pivotal for numerous clinical and performance applications. The development of long-term wearable smart garments embedded with textile electrodes provides a promising solution to facilitate the recording of sEMG in ecologically valid settings. Inexpensive conductive textiles are commercially available, but their performance in recording sEMG during dynamic motor tasks remains unexplored. In addition, the washability of smart garments is a practical, yet essential, requirement, which should be addressed when evaluating the performance of textile electrodes. This study investigated the ability to measure sEMG with electrodes manufactured using different commercially available textiles. The performance characteristics of seven different textiles were assessed before and after washing. This included resistance degradation tests after 30 washing cycles and skin-impedance measurements. Furthermore, in vivo testing of the electrodes was performed while recording sEMG on five participants. Data were acquired from the medial gastrocnemius muscle during walking, jogging, heel raise, and countermovement jump (CMJ) of five participants, using both unwashed and washed textile electrodes and without skin preparation. Comparison with traditional Ag/AgCl gel electrodes revealed nickel/copper plated polyester textile as the best performer (linear envelope: R2=0.88 and frequency spectrum: R2=0.80 ). Overall, electrodes manufactured from commercially available nickel/copper plated polyester textiles could be readily embedded in garments for measuring sEMG data, attaining signal quality comparable to that achieved from Ag/AgCl gel electrodes.
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Bibliographische Detailangaben
Schlagworte:
Notationen:Naturwissenschaften und Technik
Veröffentlicht in:IEEE Sensors Journal
Sprache:Englisch
Veröffentlicht: 2023
Online-Zugang:https://doi.org/10.1109/JSEN.2023.3295773
Jahrgang:23
Heft:17
Seiten:20150-20158
Dokumentenarten:Artikel
Level:hoch