Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.advisor | 이재홍 | - |
| dc.contributor.author | Seonggyu Lee | - |
| dc.date.accessioned | 2023-03-22T19:56:47Z | - |
| dc.date.available | 2023-03-22T19:56:47Z | - |
| dc.date.issued | 2023 | - |
| dc.identifier.uri | http://hdl.handle.net/20.500.11750/45716 | - |
| dc.identifier.uri | http://dgist.dcollection.net/common/orgView/200000658736 | - |
| dc.description | Textile electrode, Multi-channel, EMG, Deep learning, Human-machine interface | - |
| dc.description.tableofcontents | 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 |
- |
| dc.format.extent | 27 | - |
| dc.language | eng | - |
| dc.publisher | DGIST | - |
| dc.title | Multi-Channel Wearable Textile Electrode Array With Deep Learning for EMG-Based Human-Machine Interfaces | - |
| dc.type | Thesis | - |
| dc.identifier.doi | 10.22677/THESIS.200000658736 | - |
| dc.description.degree | Master | - |
| dc.contributor.department | Department of Robotics and Mechatronics Engineering | - |
| dc.contributor.coadvisor | Sanghoon Lee | - |
| dc.date.awarded | 2023-02-01 | - |
| dc.publisher.location | Daegu | - |
| dc.description.database | dCollection | - |
| dc.citation | XT.RM 이54 202302 | - |
| dc.date.accepted | 2023-03-21 | - |
| dc.contributor.alternativeDepartment | 로봇및기계전자공학과 | - |
| dc.subject.keyword | Textile electrode | - |
| dc.subject.keyword | Multi-channel | - |
| dc.subject.keyword | EMG | - |
| dc.subject.keyword | Deep learning | - |
| dc.subject.keyword | Human-machine interface | - |
| dc.contributor.affiliatedAuthor | Seonggyu Lee | - |
| dc.contributor.affiliatedAuthor | Jaehong Lee | - |
| dc.contributor.affiliatedAuthor | Sanghoon Lee | - |
| dc.contributor.alternativeName | 이성규 | - |
| dc.contributor.alternativeName | Jaehong Lee| | - |
| dc.contributor.alternativeName | 이상훈 | - |
| dc.rights.embargoReleaseDate | 2028-02-28 | - |
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