Recognition of combined arm motions using support vector machine

Yanjuan Geng; Dandan Tao; Liang Chen; Guanglin Li

doi:10.1007/978-3-642-25992-0_108

Recognition of combined arm motions using support vector machine

Yanjuan Geng^*
, Dandan Tao
, Liang Chen
, Guanglin Li

^*Corresponding author for this work

Shenzhen Institute of Advanced Technology
Harbin Institute of Technology Shenzhen

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

9 Scopus citations

Abstract

To investigate the classification performance of combined arm motions only using surface electromyography (EMG) signal, six different feature sets were adopted to match support vector machine (SVM) classifier respectively. Four unilateral transradial amputees participated in multi-channel surface EMG signal collection. The results show that the wavelet features outperforms others with average classification accuracy 98%±2% for intact arm and 89%±6% for amputated arm across all subjects. And the classification performance of intact arm motions was significantly better than that of amputated arm motions.

Original language	English
Title of host publication	Informatics in Control, Automation and Robotics
Pages	807-814
Number of pages	8
Edition	VOL. 2
DOIs	https://doi.org/10.1007/978-3-642-25992-0_108
State	Published - 2011
Externally published	Yes
Event	2011 3rd International Asia Conference on Informatics in Control, Automation and Robotics, CAR 2011 - Shenzhen, China Duration: 24 Dec 2011 → 25 Dec 2011

Publication series

Name	Lecture Notes in Electrical Engineering
Number	VOL. 2
Volume	133 LNEE
ISSN (Print)	1876-1100
ISSN (Electronic)	1876-1119

Conference

Conference	2011 3rd International Asia Conference on Informatics in Control, Automation and Robotics, CAR 2011
Country/Territory	China
City	Shenzhen
Period	24/12/11 → 25/12/11

Keywords

Combined motion
SVM
pattern recognition
surface electromyography

Access to Document

10.1007/978-3-642-25992-0_108

Cite this

@inproceedings{4a87bffcfc8b4c45b188f033aef08602,

title = "Recognition of combined arm motions using support vector machine",

abstract = "To investigate the classification performance of combined arm motions only using surface electromyography (EMG) signal, six different feature sets were adopted to match support vector machine (SVM) classifier respectively. Four unilateral transradial amputees participated in multi-channel surface EMG signal collection. The results show that the wavelet features outperforms others with average classification accuracy 98\%±2\% for intact arm and 89\%±6\% for amputated arm across all subjects. And the classification performance of intact arm motions was significantly better than that of amputated arm motions.",

keywords = "Combined motion, SVM, pattern recognition, surface electromyography",

author = "Yanjuan Geng and Dandan Tao and Liang Chen and Guanglin Li",

year = "2011",

doi = "10.1007/978-3-642-25992-0\_108",

language = "英语",

isbn = "9783642259913",

series = "Lecture Notes in Electrical Engineering",

number = "VOL. 2",

pages = "807--814",

booktitle = "Informatics in Control, Automation and Robotics",

edition = "VOL. 2",

note = "2011 3rd International Asia Conference on Informatics in Control, Automation and Robotics, CAR 2011 ; Conference date: 24-12-2011 Through 25-12-2011",

}

Geng, Y, Tao, D, Chen, L & Li, G 2011, Recognition of combined arm motions using support vector machine. in Informatics in Control, Automation and Robotics. VOL. 2 edn, Lecture Notes in Electrical Engineering, no. VOL. 2, vol. 133 LNEE, pp. 807-814, 2011 3rd International Asia Conference on Informatics in Control, Automation and Robotics, CAR 2011, Shenzhen, China, 24/12/11. https://doi.org/10.1007/978-3-642-25992-0_108

TY - GEN

T1 - Recognition of combined arm motions using support vector machine

AU - Geng, Yanjuan

AU - Tao, Dandan

AU - Chen, Liang

AU - Li, Guanglin

PY - 2011

Y1 - 2011

N2 - To investigate the classification performance of combined arm motions only using surface electromyography (EMG) signal, six different feature sets were adopted to match support vector machine (SVM) classifier respectively. Four unilateral transradial amputees participated in multi-channel surface EMG signal collection. The results show that the wavelet features outperforms others with average classification accuracy 98%±2% for intact arm and 89%±6% for amputated arm across all subjects. And the classification performance of intact arm motions was significantly better than that of amputated arm motions.

AB - To investigate the classification performance of combined arm motions only using surface electromyography (EMG) signal, six different feature sets were adopted to match support vector machine (SVM) classifier respectively. Four unilateral transradial amputees participated in multi-channel surface EMG signal collection. The results show that the wavelet features outperforms others with average classification accuracy 98%±2% for intact arm and 89%±6% for amputated arm across all subjects. And the classification performance of intact arm motions was significantly better than that of amputated arm motions.

KW - Combined motion

KW - SVM

KW - pattern recognition

KW - surface electromyography

UR - https://www.scopus.com/pages/publications/84863147226

U2 - 10.1007/978-3-642-25992-0_108

DO - 10.1007/978-3-642-25992-0_108

M3 - 会议稿件

AN - SCOPUS:84863147226

SN - 9783642259913

T3 - Lecture Notes in Electrical Engineering

SP - 807

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BT - Informatics in Control, Automation and Robotics

T2 - 2011 3rd International Asia Conference on Informatics in Control, Automation and Robotics, CAR 2011

Y2 - 24 December 2011 through 25 December 2011

ER -

Recognition of combined arm motions using support vector machine

Abstract

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Keywords

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