Wrist Motion Classification Using Flexible sEMG Sensors in Different Feature Conditions Based on Machine Learning

Jiaqi Xue; Xiaoyang Zou; Ziqi Li; King Wai Chiu Lai

doi:10.1109/NANOMED59780.2023.10404762

Wrist Motion Classification Using Flexible sEMG Sensors in Different Feature Conditions Based on Machine Learning

Jiaqi Xue
, Xiaoyang Zou
, Ziqi Li
, King Wai Chiu Lai^*

^*Corresponding author for this work

Department of Biomedical Engineering

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

Abstract

Electromyography (EMG) is a bioelectrical signal to reflect human intention before actual motion occurs. EMG has been widely used in human-machine interaction such as robot control, rehabilitation and health monitoring. In this work, we have designed an intelligent approach for wrist motion classification based on EMG signals. Since commercial electrodes cannot maintain good contact with skin during deformation, we have utilized a new type of fabricated flexible electrodes. With these electrodes, high-quality signals can be acquired. And the machine learning methods have been utilized to classify the extracted feature sets. Four different feature conditions have been compared. In the condition of six EMG features, we have identified four wrist gestures including wrist flexion, extension, radial deviation, and ulnar deviation with the best accuracy of 92.26%. © 2023 IEEE.

Original language	English
Title of host publication	2023 IEEE 16th International Conference on Nano/Molecular Medicine & Engineering (NANOMED)
Publisher	IEEE
Pages	203-206
ISBN (Electronic)	9798350343700
ISBN (Print)	979-8-3503-4371-7
DOIs	https://doi.org/10.1109/NANOMED59780.2023.10404762
Publication status	Published - Dec 2023
Event	16th IEEE International Conference on Nano/Molecular Medicine and Engineering (NANOMED 2023) - Okinawa, Japan Duration: 5 Dec 2023 → 8 Dec 2023

Publication series

Name	IEEE International Conference on Nano/Molecular Medicine and Engineering, NANOMED
ISSN (Print)	2836-0249
ISSN (Electronic)	2836-0257

Conference

Conference	16th IEEE International Conference on Nano/Molecular Medicine and Engineering (NANOMED 2023)
Abbreviated title	IEEE-NANOMED 2023
Place	Japan
City	Okinawa
Period	5/12/23 → 8/12/23

Funding

The work is partially supported by grants from the Research Grant Council of the Hong Kong Special Administrative Region Government (TBRS Grant: T42-717/20-R and CRF Grant: C7174-20G).

RGC Funding Information

RGC-funded

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10.1109/NANOMED59780.2023.10404762

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Xue, J., Zou, X., Li, Z., & Lai, K. W. C. (2023). Wrist Motion Classification Using Flexible sEMG Sensors in Different Feature Conditions Based on Machine Learning. In 2023 IEEE 16th International Conference on Nano/Molecular Medicine & Engineering (NANOMED) (pp. 203-206). (IEEE International Conference on Nano/Molecular Medicine and Engineering, NANOMED). IEEE. https://doi.org/10.1109/NANOMED59780.2023.10404762

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title = "Wrist Motion Classification Using Flexible sEMG Sensors in Different Feature Conditions Based on Machine Learning",

abstract = "Electromyography (EMG) is a bioelectrical signal to reflect human intention before actual motion occurs. EMG has been widely used in human-machine interaction such as robot control, rehabilitation and health monitoring. In this work, we have designed an intelligent approach for wrist motion classification based on EMG signals. Since commercial electrodes cannot maintain good contact with skin during deformation, we have utilized a new type of fabricated flexible electrodes. With these electrodes, high-quality signals can be acquired. And the machine learning methods have been utilized to classify the extracted feature sets. Four different feature conditions have been compared. In the condition of six EMG features, we have identified four wrist gestures including wrist flexion, extension, radial deviation, and ulnar deviation with the best accuracy of 92.26\%. {\textcopyright} 2023 IEEE.",

author = "Jiaqi Xue and Xiaoyang Zou and Ziqi Li and Lai, \{King Wai Chiu\}",

year = "2023",

month = dec,

doi = "10.1109/NANOMED59780.2023.10404762",

language = "English",

isbn = "979-8-3503-4371-7",

series = "IEEE International Conference on Nano/Molecular Medicine and Engineering, NANOMED",

publisher = "IEEE",

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booktitle = "2023 IEEE 16th International Conference on Nano/Molecular Medicine \& Engineering (NANOMED)",

address = "United States",

note = "16th IEEE International Conference on Nano/Molecular Medicine and Engineering (NANOMED 2023), IEEE-NANOMED 2023 ; Conference date: 05-12-2023 Through 08-12-2023",

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Xue, J , Zou, X , Li, Z & Lai, KWC 2023, Wrist Motion Classification Using Flexible sEMG Sensors in Different Feature Conditions Based on Machine Learning. in 2023 IEEE 16th International Conference on Nano/Molecular Medicine & Engineering (NANOMED). IEEE International Conference on Nano/Molecular Medicine and Engineering, NANOMED, IEEE, pp. 203-206, 16th IEEE International Conference on Nano/Molecular Medicine and Engineering (NANOMED 2023), Okinawa, Japan, 5/12/23. https://doi.org/10.1109/NANOMED59780.2023.10404762

Wrist Motion Classification Using Flexible sEMG Sensors in Different Feature Conditions Based on Machine Learning. / Xue, Jiaqi ; Zou, Xiaoyang ; Li, Ziqi et al.
2023 IEEE 16th International Conference on Nano/Molecular Medicine & Engineering (NANOMED). IEEE, 2023. p. 203-206 (IEEE International Conference on Nano/Molecular Medicine and Engineering, NANOMED).

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

TY - GEN

T1 - Wrist Motion Classification Using Flexible sEMG Sensors in Different Feature Conditions Based on Machine Learning

AU - Xue, Jiaqi

AU - Zou, Xiaoyang

AU - Li, Ziqi

AU - Lai, King Wai Chiu

PY - 2023/12

Y1 - 2023/12

N2 - Electromyography (EMG) is a bioelectrical signal to reflect human intention before actual motion occurs. EMG has been widely used in human-machine interaction such as robot control, rehabilitation and health monitoring. In this work, we have designed an intelligent approach for wrist motion classification based on EMG signals. Since commercial electrodes cannot maintain good contact with skin during deformation, we have utilized a new type of fabricated flexible electrodes. With these electrodes, high-quality signals can be acquired. And the machine learning methods have been utilized to classify the extracted feature sets. Four different feature conditions have been compared. In the condition of six EMG features, we have identified four wrist gestures including wrist flexion, extension, radial deviation, and ulnar deviation with the best accuracy of 92.26%. © 2023 IEEE.

AB - Electromyography (EMG) is a bioelectrical signal to reflect human intention before actual motion occurs. EMG has been widely used in human-machine interaction such as robot control, rehabilitation and health monitoring. In this work, we have designed an intelligent approach for wrist motion classification based on EMG signals. Since commercial electrodes cannot maintain good contact with skin during deformation, we have utilized a new type of fabricated flexible electrodes. With these electrodes, high-quality signals can be acquired. And the machine learning methods have been utilized to classify the extracted feature sets. Four different feature conditions have been compared. In the condition of six EMG features, we have identified four wrist gestures including wrist flexion, extension, radial deviation, and ulnar deviation with the best accuracy of 92.26%. © 2023 IEEE.

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Xue J , Zou X , Li Z , Lai KWC. Wrist Motion Classification Using Flexible sEMG Sensors in Different Feature Conditions Based on Machine Learning. In 2023 IEEE 16th International Conference on Nano/Molecular Medicine & Engineering (NANOMED). IEEE. 2023. p. 203-206. (IEEE International Conference on Nano/Molecular Medicine and Engineering, NANOMED). doi: 10.1109/NANOMED59780.2023.10404762

Wrist Motion Classification Using Flexible sEMG Sensors in Different Feature Conditions Based on Machine Learning

Abstract

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Conference

Funding

RGC Funding Information

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TBRS-ExtU-Lead: Intelligent Robotics for Elderly Assistance in Hong Kong

CRF-ExtU-Lead: Super Reality for Hands-on Online Education

Cite this

Wrist Motion Classification Using Flexible sEMG Sensors in Different Feature Conditions Based on Machine Learning

Abstract

Publication series

Conference

Funding

RGC Funding Information

Access Output

Other Access Options

Permanent Link

Other Files and Links

Fingerprint

Projects

TBRS-ExtU-Lead: Intelligent Robotics for Elderly Assistance in Hong Kong

CRF-ExtU-Lead: Super Reality for Hands-on Online Education

Cite this