PCA-based Finger Movement and Grasping Classification using Data Glove “Glove MAP”
Nazrul H. Adnan1, Khairunizam wan2, Shariman Ab3, Juliana A. Abu Bakar4, Azri A. Aziz5
1Nazrul H. Adnan, Advanced Intelligent Computing and Sustainability Research Group, School of Mechatronic, Universiti Malaysia Perlis Kampus Pauh Putra, 02600 Arau, Perlis, MALAYSIA.
2Khairunizam WAN, Advanced Intelligent Computing and Sustainability Research Group, School of Mechatronic, Universiti Malaysia Perlis Kampus Pauh Putra, 02600 Arau, Perlis, MALAYSIA.
3Shahriman AB, Advanced Intelligent Computing and Sustainability Research Group, School of Mechatronic, Universiti Malaysia Perlis Kampus Pauh Putra, 02600 Arau, Perlis, MALAYSIA.
4Juliana Aida Abu Bakar, Department of Multimedia School of Multimedia Tech & Communication College of Arts and Sciences Universiti Utara Malaysia 06010 Sintok, Kedah, MALAYSIA.
5Azri A. Aziz, Advanced Intelligent Computing and Sustainability Research Group, School of Mechatronic, Universiti Malaysia Perlis KampusPauh Putra, 02600 Arau, Perlis, MALAYSIA.
Manuscript received on 07 February 2013 | Revised Manuscript received on 21 February 2013 | Manuscript Published on 28 February 2013 | PP: 66-71 | Volume-2 Issue-3, February 2013 | Retrieval Number: C0423022313/2013©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Nowadays, fingers movement and hand gestures can be used as main activities in translating by naturally and convenient way to the human computer interaction. The purpose of this paper is to analyze in depth the thumb, index and middle fingers on the hand grasping movement against an object. The classification of the fingers activities is analyzed using the statistical analysis method. Principal Component Analysis (PCA) is one of the methods that able to reduce the dimensional dataset of hand motion as well as measure the capacity of the fingers movement. The fingers movement is estimated from the bending representative of proximal and intermediate phalanges of thumb, index and middle fingers. The effectiveness of the propose assessment analysis were shown through the experiments of three fingers motions. Preliminary results of this experiment showed that the use of the first and second principal components can allow distinguishing between three fingers grasping movements.
Keywords: finger movement; finger activities; hand grasping; Human Computer Interaction; Principle Component Analysis (PCA)
Scope of the Article: Human Computer Interaction