Smart Textiles for Electromyography: A Novel Approach Using Conductive Fabrics

This study investigates the application of conductive fabrics in wearable technologies as transformational materials with specific emphasis on monitoring electromyography (EMG) signals, which forms the crux of the test for muscle activity and neuromuscular function. Unlike conventional EMG electrodes, these conductive fabrics are soft, moldable and, therefore, promote comfort, which may even prolong time durations of wear time with little irritation. By integrating them into a daily garment, it is possible to open the possibility for continuous physiological monitoring. However, signal noise and electrode-skin contact optimization are still not overcome because these two factors have large influences in EMG signals' quality. Likewise, material suitability can also be long-term due to durability after being repeatedly worn and laundered. We therefore conclude that the conductive fabrics will most probably provide a viable alternative for use in EMG monitoring applications, whether the latter be experimental or field-based. To this end, there already is a safer alternative than what is being used today. This work is the groundwork for developing wearable health technologies that are more user-friendly whereby there is continuous health and fitness monitoring with further enhancement of comfort and performance.

Authors

Publication Details

Published
2024-12-12
DOI
https://doi.org/10.1109/icuis64676.2024.10866574
Primary Topic
Advanced Sensor and Energy Harvesting Materials
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Smart Textiles for Electromyography: A Novel Approach Using Conductive Fabrics

Neha Arora, K Manikandan, S Prabakar, K Mothishwaran et al.
2 links
Advanced Sensor and Energy Harvesting Materials
article

Smart Textiles for Electromyography: A Novel Approach Using Conductive Fabrics

Neha Arora, K Manikandan, S Prabakar, K Mothishwaran, M S L Niranjanaa, V Madhanbalaji
article en

Abstract

This study investigates the application of conductive fabrics in wearable technologies as transformational materials with specific emphasis on monitoring electromyography (EMG) signals, which forms the crux of the test for muscle activity and neuromuscular function. Unlike conventional EMG electrodes, these conductive fabrics are soft, moldable and, therefore, promote comfort, which may even prolong time durations of wear time with little irritation. By integrating them into a daily garment, it is possible to open the possibility for continuous physiological monitoring. However, signal noise and electrode-skin contact optimization are still not overcome because these two factors have large influences in EMG signals' quality. Likewise, material suitability can also be long-term due to durability after being repeatedly worn and laundered. We therefore conclude that the conductive fabrics will most probably provide a viable alternative for use in EMG monitoring applications, whether the latter be experimental or field-based. To this end, there already is a safer alternative than what is being used today. This work is the groundwork for developing wearable health technologies that are more user-friendly whereby there is continuous health and fitness monitoring with further enhancement of comfort and performance.

Openalex Percentile: Top 76%
Advanced Sensor and Energy Harvesting Materials
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Smart Textiles for Electromyography: A Novel Approach Using Conductive Fabrics — Neha Arora, K Manikandan, et al. · (2024) | TGRS Research Map | TGRS