Please use this identifier to cite or link to this item: http://dspace.unimap.edu.my:80/xmlui/handle/123456789/24220
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dc.contributor.authorNazrul H., Adnan-
dc.contributor.authorWan Khairunizam, Wan Ahmad, Dr.-
dc.contributor.authorShahriman, Abu Bakar, Dr.-
dc.contributor.authorJuliana Aida, Abu Bakar-
dc.date.accessioned2013-04-04T03:52:59Z-
dc.date.available2013-04-04T03:52:59Z-
dc.date.issued2013-03-
dc.identifier.citationInternational Journal of Computer Engineering and Technology, vol. 4 (2), 2013, pages 79-93en_US
dc.identifier.issn0976-6367-
dc.identifier.urihttp://www.iaeme.com/Journalcureentissue.asp-
dc.identifier.urihttp://dspace.unimap.edu.my/123456789/24220-
dc.descriptionLink to publisher's homepage at http://www.iaeme.com/en_US
dc.description.abstractNowadays, many classifier methods have been used to classify or categorizehuman body motions of human posture including the classification of fingers movement. Principal Component Analysis (PCA) is one of classical method that capable to be used to reduce the dimensional dataset of hand motion as well as to measure the capacity of the fingers movement of the hand grasping.The objective of this paper is to analyze thehuman grasping feature between thumbs, index and middle fingers while grasping an object using PCA- basedtechniques. The finger movement dataare measured using a low cost DataGlove“GloveMAP” which is based on fingers adapted postural movement (or EigenFingers) of the principal component. The fingers movement is estimated from the bending representative of proximal and intermediate phalanges of thumb, index and middlefingers. The effectiveness of the proposed assessment analysis is shown through the experimental study of three fingers motions. The experimental results showed that the use of the first and the second principal components allows for distinguishing between three fingers grasping and represent the features for an appropriate manipulation of the object grasping.en_US
dc.language.isoenen_US
dc.publisherInternational Association for Engineeering and Management Educationen_US
dc.subjectEigenFingeren_US
dc.subjectFinger movement classificationen_US
dc.subjectHand-graspingen_US
dc.subjectHuman-Computer-Interactionen_US
dc.subjectPrinciple Componenet Analysis (PCA)en_US
dc.titlePrinciple component analysis for the classification of fingers movement data using dataglove "glovemap"en_US
dc.typeArticleen_US
dc.contributor.urlkhairunizam@unimap.edu.myen_US
dc.contributor.urlshahriman@unimap.edu.myen_US
dc.contributor.urlliana@uum.edu.myen_US
Appears in Collections:School of Mechatronic Engineering (Articles)
Shahriman Abu Bakar, Assoc. Prof. Ir. Ts. Dr.
Wan Khairunizam Wan Ahmad, Assoc. Prof. Ir. Ts. Dr.

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