Literature DB >> 32106451

Strain-Insensitive Elastic Surface Electromyographic (sEMG) Electrode for Efficient Recognition of Exercise Intensities.

Daxiu Tang1,2,3, Zhe Yu2,3,4, Yong He2,3, Waqas Asghar2,3,5, Ya-Nan Zheng2,3,4, Fali Li2,3,4, Changcheng Shi2,3, Roozbeh Zarei2,3,6, Yiwei Liu2,3,4, Jie Shang2,3,4, Xiang Liu1, Run-Wei Li2,3,4.   

Abstract

Surface electromyography (sEMG) sensors are widely used in the fields of ergonomics, sports science, and medical research. However, current sEMG sensors cannot recognize the various exercise intensities efficiently because of the strain interference, low conductivity, and poor skin-conformability of their electrodes. Here, we present a highly conductive, strain-insensitive, and low electrode-skin impedance elastic sEMG electrode, which consists of a three-layered structure (polydimethylsiloxane/galinstan + polydimethylsiloxane/silver-coated nickel + polydimethylsiloxane). The bottom layer of the electrode consists of vertically conductive magnetic particle paths, which are insensitive to stretching strain, collect sEMG charge from human skin, and finally transfer it to processing circuits via an intermediate layer. Our skin-friendly electrode exhibits high conductivity (0.237 and 1.635 mΩ.cm resistivities in transverse and longitudinal directions, respectively), low electrode-skin impedance (47.23 kΩ at 150 Hz), excellent strain-insensitivity (10% change of electrode-skin impedance within the 0%-25% strain range), high fatigue resistance (>1500 cycles), and good conformability with skin. During various exercise intensities, the signal-to-noise ratio (SNR) of our electrode increased by 22.53 dB, which is 206% and 330% more than that of traditional Ag/AgCl and copper electrode, respectively. The ability of our electrode to efficiently recognize various exercise intensities confirms its great application potential for the field of sports health.

Entities:  

Keywords:  elastic sEMG electrode; electrode–skin impedance; signal-to-noise ratio; skin-conformability; strain-insensitivity

Year:  2020        PMID: 32106451     DOI: 10.3390/mi11030239

Source DB:  PubMed          Journal:  Micromachines (Basel)        ISSN: 2072-666X            Impact factor:   2.891


  4 in total

1.  Development and Characterization of Embroidery-Based Textile Electrodes for Surface EMG Detection.

Authors:  Hyelim Kim; Siyeon Kim; Daeyoung Lim; Wonyoung Jeong
Journal:  Sensors (Basel)       Date:  2022-06-23       Impact factor: 3.847

Review 2.  Liquid Metal Based Nano-Composites for Printable Stretchable Electronics.

Authors:  Dan Xu; Jinwei Cao; Fei Liu; Shengbo Zou; Wenjuan Lei; Yuanzhao Wu; Yiwei Liu; Jie Shang; Run-Wei Li
Journal:  Sensors (Basel)       Date:  2022-03-25       Impact factor: 3.576

3.  Exercise fatigue diagnosis method based on short-time Fourier transform and convolutional neural network.

Authors:  Haiyan Zhu; Yuelong Ji; Baiyang Wang; Yuyun Kang
Journal:  Front Physiol       Date:  2022-08-30       Impact factor: 4.755

Review 4.  Application of Surface Electromyography in Exercise Fatigue: A Review.

Authors:  Jiaqi Sun; Guangda Liu; Yubing Sun; Kai Lin; Zijian Zhou; Jing Cai
Journal:  Front Syst Neurosci       Date:  2022-08-11
  4 in total

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