Title

Multi-Channel Wearable Textile Electrode Array With Deep Learning for EMG-Based Human-Machine Interfaces

DGIST Authors

Seonggyu Lee ; Jaehong Lee ; Sanghoon Lee

Advisor

이재홍

Co-Advisor(s)

Sanghoon Lee

Issued Date

2023

Awarded Date

2023-02-01

Citation

Seonggyu Lee. (2023). Multi-Channel Wearable Textile Electrode Array With Deep Learning for EMG-Based Human-Machine Interfaces. doi: 10.22677/THESIS.200000658736

Type

Thesis

Description

Textile electrode, Multi-channel, EMG, Deep learning, Human-machine interface

Table Of Contents

Ⅰ. Introduction 1
1.1 Electromyography for Human-machine interface 1
1.2 Limitations of existing EMG electrodes 3
Ⅱ. Methods 5
2.1 Fabrication process of textile electrodes 5
2.1.1 Materials for fabrication of textile electrodes 5
2.1.2 Fabrication of highly conductive fibers 6
2.1.3 Fabrication of textile electrodes 7
2.2 Mechanical & electrical properties 8
2.2.1 Stretching test 8
2.2.2 Strain-resistance measurement 9
2.2.3 Skin-electrode contact impedance measurement 10
2.3 Performance evaluation 11
2.3.1 Recording quality test 11
2.3.2 Long-term signal monitoring test 11
2.3.3 Motion artifacts measurement 12
2.4 Robot hand control 12
Ⅲ. Results 14
3.1 Optimization of fabrication conditions for textile electrodes 14
3.2 Mechanical & electrical properties of textile electrodes 16
3.2.1 Mechanical properties of textile electrodes 16
3.2.2 Electrical properties of textile electrodes 18
3.2.3 Skin-electrode contact impedance of textile electrodes 18
3.3 Performance evaluation 20
3.3.1 High-quality recording 20
3.3.2 Long-term signal recording 20
3.3.3 Motion artifact-tolerant recording 21
3.4 Results of robot hand control 22
Ⅳ. Conclusion 24
Reference 25

URI

http://hdl.handle.net/20.500.11750/45716
http://dgist.dcollection.net/common/orgView/200000658736

DOI

10.22677/THESIS.200000658736

Degree

Master

Department

Department of Robotics and Mechatronics Engineering

Publisher

DGIST

Show Full Item Record