Electromyogram (EMG) based fingers movement recognition using Neighborhood Preserving Analysis with QR-decomposition

Khushaba, RN; Kodagoda, S; Liu, D; Dissanayake, G

Electromyogram (EMG) based fingers movement recognition using Neighborhood Preserving Analysis with QR-decomposition

Khushaba, RN

Kodagoda, S

Liu, D

Dissanayake, G

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Publication Type:: Conference Proceeding
Citation:: Proceedings of the 2011 7th International Conference on Intelligent Sensors, Sensor Networks and Information Processing, ISSNIP 2011, 2011, pp. 1 - 6
Issue Date:: 2011-12-01

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Field	Value	Language
dc.contributor.author	Khushaba, RN https://orcid.org/0000-0001-8528-8979	en_US
dc.contributor.author	Kodagoda, S https://orcid.org/0000-0001-5175-9138	en_US
dc.contributor.author	Liu, D https://orcid.org/0000-0002-1581-5582	en_US
dc.contributor.author	Dissanayake, G https://orcid.org/0000-0002-7992-0680	en_US
dc.date.issued	2011-12-01	en_US
dc.identifier.citation	Proceedings of the 2011 7th International Conference on Intelligent Sensors, Sensor Networks and Information Processing, ISSNIP 2011, 2011, pp. 1 - 6	en_US
dc.identifier.isbn	9781457706738	en_US
dc.identifier.uri	http://hdl.handle.net/10453/19230
dc.description.abstract	Surface Electromyogram (EMG) signals recorded from an amputee's residual muscles have been investigated as a source of control for prosthetic devices for many years. Despite the extensive research focus on the EMG control of arm and gross hand movements, more dexterous individual and combined prosthetic fingers control has not received the same amount of attention. To facilitate such a control scheme, the first and the most significant step is the extraction of a set of highly discriminative feature set that can well separate between the different fingers movements and to do so in a computationally efficient manner. In this paper, an accurate and efficient feature projection method based on Fuzzy Neighborhood Preserving Analysis (FNPA) with QR-decomposition, is proposed and denoted as FNPA. Unlike existing attempts in fuzzy linear discriminant analysis, the objective of the proposed FNPA is to minimize the distance between samples that belong to the same class and maximize the distance between the centers of different classes, while taking into account the contribution of the samples to the different classes. The method also aims to efficiently overcome the singularity problems of classical LDA and Fuzzy LDA. The proposed FNPA is validated on EMG datasets collected from nine subjects performing 10 classes of individual and combined fingers movements. Practical results indicate the significance of FNPA in comparison to many other feature projection methods with an average accuracy of 91%, using only two EMG electrodes. © 2011 IEEE.	en_US
dc.relation.ispartof	Proceedings of the 2011 7th International Conference on Intelligent Sensors, Sensor Networks and Information Processing, ISSNIP 2011	en_US
dc.relation.isbasedon	10.1109/ISSNIP.2011.6146512	en_US
dc.title	Electromyogram (EMG) based fingers movement recognition using Neighborhood Preserving Analysis with QR-decomposition	en_US
dc.type	Conference Proceeding
utslib.for	090609 Signal Processing	en_US
utslib.for	090602 Control Systems, Robotics and Automation	en_US
utslib.for	080109 Pattern Recognition and Data Mining	en_US
dc.location.activity	Adelaide - Australia	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Biomedical Engineering
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Mechanical and Mechatronic Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CAS - Centre for Autonomous Systems
utslib.copyright.status	open_access
pubs.publication-status	Published	en_US

Abstract:

Surface Electromyogram (EMG) signals recorded from an amputee's residual muscles have been investigated as a source of control for prosthetic devices for many years. Despite the extensive research focus on the EMG control of arm and gross hand movements, more dexterous individual and combined prosthetic fingers control has not received the same amount of attention. To facilitate such a control scheme, the first and the most significant step is the extraction of a set of highly discriminative feature set that can well separate between the different fingers movements and to do so in a computationally efficient manner. In this paper, an accurate and efficient feature projection method based on Fuzzy Neighborhood Preserving Analysis (FNPA) with QR-decomposition, is proposed and denoted as FNPA. Unlike existing attempts in fuzzy linear discriminant analysis, the objective of the proposed FNPA is to minimize the distance between samples that belong to the same class and maximize the distance between the centers of different classes, while taking into account the contribution of the samples to the different classes. The method also aims to efficiently overcome the singularity problems of classical LDA and Fuzzy LDA. The proposed FNPA is validated on EMG datasets collected from nine subjects performing 10 classes of individual and combined fingers movements. Practical results indicate the significance of FNPA in comparison to many other feature projection methods with an average accuracy of 91%, using only two EMG electrodes. © 2011 IEEE.

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