Classification of multi-channels SEMG signals using wavelet and neural networks on assistive robot

2.50
Hdl Handle:
http://hdl.handle.net/10547/270613
Title:
Classification of multi-channels SEMG signals using wavelet and neural networks on assistive robot
Authors:
Gu, Shuang; Yue, Yong; Wu, Chengdong; Maple, Carsten; Liu, Beisheng
Abstract:
Recently, the robot technology research is changing from manufacturing industry to non-manufacturing industry, especially the service industry related to the human life. Assistive robot is a kind of novel service robot. It can not only help the elder and disabled people to rehabilitate their impaired musculoskeletal functions, but also help healthy people to perform tasks requiring large forces. This kind of robot has a broad application prospect in many areas, such as medical rehabilitation, special military operations, special/high intensity physical labour, space, sports, and entertainment. SEMG (Surface Electromyography) of Palmaris longus, brachioradialis, flexor carpiulnaris and biceps brachii are analysed with a wavelet transform method. The absolute variance of 3-layer wavelet coefficients is distilled and regarded as signal characteristics to compose eigenvectors. The eigenvectors are input data of a neural network classifier used to identify 5 different kinds of movement patterns including wrist flexor, wrist extensor, elbow flexion, forearm pronation and forearm rotation. Experiments verify the effectiveness of the proposed method.
Affiliation:
University of Bedfordshire, UK
Citation:
Shuang Gu; Yong Yue; Maple, C.; Beisheng Liu; Chengdong Wu; , "Classification of multi-channels SEMG signals using wavelet and neural networks on assistive robot," Industrial Informatics (INDIN), 2012 10th IEEE International Conference on , pp.1158-1163, 25-27 July 2012
Publisher:
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Issue Date:
2012
URI:
http://hdl.handle.net/10547/270613
DOI:
10.1109/INDIN.2012.6301140
Additional Links:
http://ieeexplore.ieee.org/xpls/abs_all.jsp?tp=&arnumber=6301140
Type:
Conference papers, meetings and proceedings
Language:
en
ISBN:
9781467303125
Appears in Collections:
Centre for Research in Distributed Technologies (CREDIT)

Full metadata record

DC FieldValue Language
dc.contributor.authorGu, Shuangen_GB
dc.contributor.authorYue, Yongen_GB
dc.contributor.authorWu, Chengdongen_GB
dc.contributor.authorMaple, Carstenen_GB
dc.contributor.authorLiu, Beishengen_GB
dc.date.accessioned2013-02-28T10:04:28Z-
dc.date.available2013-02-28T10:04:28Z-
dc.date.issued2012-
dc.identifier.citationShuang Gu; Yong Yue; Maple, C.; Beisheng Liu; Chengdong Wu; , "Classification of multi-channels SEMG signals using wavelet and neural networks on assistive robot," Industrial Informatics (INDIN), 2012 10th IEEE International Conference on , pp.1158-1163, 25-27 July 2012en_GB
dc.identifier.isbn9781467303125-
dc.identifier.doi10.1109/INDIN.2012.6301140-
dc.identifier.urihttp://hdl.handle.net/10547/270613-
dc.description.abstractRecently, the robot technology research is changing from manufacturing industry to non-manufacturing industry, especially the service industry related to the human life. Assistive robot is a kind of novel service robot. It can not only help the elder and disabled people to rehabilitate their impaired musculoskeletal functions, but also help healthy people to perform tasks requiring large forces. This kind of robot has a broad application prospect in many areas, such as medical rehabilitation, special military operations, special/high intensity physical labour, space, sports, and entertainment. SEMG (Surface Electromyography) of Palmaris longus, brachioradialis, flexor carpiulnaris and biceps brachii are analysed with a wavelet transform method. The absolute variance of 3-layer wavelet coefficients is distilled and regarded as signal characteristics to compose eigenvectors. The eigenvectors are input data of a neural network classifier used to identify 5 different kinds of movement patterns including wrist flexor, wrist extensor, elbow flexion, forearm pronation and forearm rotation. Experiments verify the effectiveness of the proposed method.en_GB
dc.language.isoenen
dc.publisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INCen_GB
dc.relation.urlhttp://ieeexplore.ieee.org/xpls/abs_all.jsp?tp=&arnumber=6301140en_GB
dc.subjectassistive roboten_GB
dc.subjectneural networksen_GB
dc.subjectsurface electromyographyen_GB
dc.subjectwaveleten_GB
dc.titleClassification of multi-channels SEMG signals using wavelet and neural networks on assistive roboten
dc.typeConference papers, meetings and proceedingsen
dc.contributor.departmentUniversity of Bedfordshire, UKen_GB
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