Experimental and analysis study on glovemap grasping force signal using Gaussian filtering method and principal component analysis (PCA)
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ICIC International
2014
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my.unimap-353402017-11-29T05:02:49Z Experimental and analysis study on glovemap grasping force signal using Gaussian filtering method and principal component analysis (PCA) Nazrul Hamizi, Adnan Wan Khairunizam, Wan Ahmad, Dr. Shahriman, Abu Bakar, Dr. Hazry, Desa, Assoc. Prof. Dr. Zuradzman, Mohamad Razlan, Dr. Juliana Aida, Abu Bakar nazrulhamizi.adnan@gmail.com khairunizam@unimap.edu.my shahriman@unimap.edu.my hazry@unimap.edu.my zuradzman@unimap.edu.my Eigenfingers Gaussian filter Grasping force Principal component Link to publisher's homepage at http://www.ijicic.org/home.htm This research paper presents the analysis study of human grasping forces for several objects by using a DataGlove called GloveMAP. The grasping force is generated from the bending of proximal and intermediate phalanges of the fingers when touching with a surface. A flexiforce sensor is installed at the finger's position of the GloveMAP. The acquired grasping force signals are filtered by using a Gaussian filtering for the purpose of removing noises. A Principal Component Analysis technique (PCA) is employed to reduce the dimension of the grasping force signal, and follows by the extraction of its features. In the experiment, five subjects are selected to perform the grasping activities. The experimental results show that the Gaussian filter could be used to smoothen the grasping force signals. Moreover, the first and the second principal components of PCA could be used to extract features of grasping force signals. 2014-06-11T03:16:09Z 2014-06-11T03:16:09Z 2014-06 Article International Journal of Innovative Computing, Information and Control, vol. 10(3), 2014, pages 897-908 1349-4198 http://www.ijicic.org/contents.htm http://dspace.unimap.edu.my:80/dspace/handle/123456789/35340 en ICIC International |
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Malaysia |
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Universiti Malaysia Perlis |
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| language |
English |
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Eigenfingers Gaussian filter Grasping force Principal component |
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Eigenfingers Gaussian filter Grasping force Principal component Nazrul Hamizi, Adnan Wan Khairunizam, Wan Ahmad, Dr. Shahriman, Abu Bakar, Dr. Hazry, Desa, Assoc. Prof. Dr. Zuradzman, Mohamad Razlan, Dr. Juliana Aida, Abu Bakar Experimental and analysis study on glovemap grasping force signal using Gaussian filtering method and principal component analysis (PCA) |
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Link to publisher's homepage at http://www.ijicic.org/home.htm |
| author2 |
nazrulhamizi.adnan@gmail.com |
| author_facet |
nazrulhamizi.adnan@gmail.com Nazrul Hamizi, Adnan Wan Khairunizam, Wan Ahmad, Dr. Shahriman, Abu Bakar, Dr. Hazry, Desa, Assoc. Prof. Dr. Zuradzman, Mohamad Razlan, Dr. Juliana Aida, Abu Bakar |
| format |
Article |
| author |
Nazrul Hamizi, Adnan Wan Khairunizam, Wan Ahmad, Dr. Shahriman, Abu Bakar, Dr. Hazry, Desa, Assoc. Prof. Dr. Zuradzman, Mohamad Razlan, Dr. Juliana Aida, Abu Bakar |
| author_sort |
Nazrul Hamizi, Adnan |
| title |
Experimental and analysis study on glovemap grasping force signal using Gaussian filtering method and principal component analysis (PCA) |
| title_short |
Experimental and analysis study on glovemap grasping force signal using Gaussian filtering method and principal component analysis (PCA) |
| title_full |
Experimental and analysis study on glovemap grasping force signal using Gaussian filtering method and principal component analysis (PCA) |
| title_fullStr |
Experimental and analysis study on glovemap grasping force signal using Gaussian filtering method and principal component analysis (PCA) |
| title_full_unstemmed |
Experimental and analysis study on glovemap grasping force signal using Gaussian filtering method and principal component analysis (PCA) |
| title_sort |
experimental and analysis study on glovemap grasping force signal using gaussian filtering method and principal component analysis (pca) |
| publisher |
ICIC International |
| publishDate |
2014 |
| url |
http://dspace.unimap.edu.my:80/dspace/handle/123456789/35340 |
| _version_ |
1643802758584729600 |