Development of gesture database for an adaptive gesture recognition system

Human gestural motion is one of the areas in studying human behaviour regardless the physical capability and intellectuality of each individual. In this research, the focus is to investigate human physical characteristics which contribute to the performance of gestural motions. Every person has dif...

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Format:	Thesis
Language:	English
Subjects:	Gesture Computer science > Mathematics Human physical characteristics
Online Access:	http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/78204/1/Page%201-24.pdf http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/78204/2/Full%20text.pdf http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/78204/4/Mohd%20Azri.pdf
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spelling	my-unimap-782042023-03-23T03:36:48Z Development of gesture database for an adaptive gesture recognition system Wan Khairunizam, Wan Ahmad, Assoc. Prof. Dr. Human gestural motion is one of the areas in studying human behaviour regardless the physical capability and intellectuality of each individual. In this research, the focus is to investigate human physical characteristics which contribute to the performance of gestural motions. Every person has different body structure and physical distinctive that can be determined by calculating the person’s body mass index (BMI) and measuring the size represented by the weight an geometrical gestures. The geometrical gesture databases are developed based on human body characteristic features. These gesture databases are utilized to recognize and identify an unknown gesture by gathering some information of human features for further analysis. A motion capture system was used to capture gestural motions. Three dimensional data obtained from motion capture system are analysed, classified and stored in the gesture database. The resampling algorithm is developed to diminish the excessive movement information which to be used in the represented form. Principal Component Analysis (PCA) is used to reduce dimension of data and classify the gesture data. PCA classifies three groups of people based on gestural motions of subjects. For further clarification, data inside the group database were tested for similarity and dissimilarity measured using Jaccard Similarity Measure; the result of total average is 90.8% dissimilarity of all five geometrical gestures between group #1, group #2 and group #3 for all the three axes: X-axis, Y-axis and Z-axis. Consequently, adaptive gesture recognition is introduced to select the suitable database especially for identifying unknown gestures inserted into the system. The result of recognition shows that recognition of individual database is 86.5%, group database 83.7% and the lowest is recognition of universal database which is 82.8%. The experimental result shows that the group database is preferable for an adaptive gesture recognition system. Universiti Malaysia Perlis (UniMAP) Thesis en http://dspace.unimap.edu.my:80/xmlui/handle/123456789/78204 http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/78204/3/license.txt 8a4605be74aa9ea9d79846c1fba20a33 http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/78204/1/Page%201-24.pdf 4aaa7e12fc4ce93493791e91f8a694a9 http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/78204/2/Full%20text.pdf f2b78843abcb4ff375416a6cbb2b7fb7 http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/78204/4/Mohd%20Azri.pdf 03e70d677e990b88d529ab46a81751bd Universiti Malaysia Perlis (UniMAP) Gesture Computer science -- Mathematics Human physical characteristics School of Mechatronic Engineering
institution	Universiti Malaysia Perlis
collection	UniMAP Institutional Repository
language	English
advisor	Wan Khairunizam, Wan Ahmad, Assoc. Prof. Dr.
topic	Gesture Computer science -- Mathematics Human physical characteristics
spellingShingle	Gesture Computer science -- Mathematics Human physical characteristics Development of gesture database for an adaptive gesture recognition system
description	Human gestural motion is one of the areas in studying human behaviour regardless the physical capability and intellectuality of each individual. In this research, the focus is to investigate human physical characteristics which contribute to the performance of gestural motions. Every person has different body structure and physical distinctive that can be determined by calculating the person’s body mass index (BMI) and measuring the size represented by the weight an geometrical gestures. The geometrical gesture databases are developed based on human body characteristic features. These gesture databases are utilized to recognize and identify an unknown gesture by gathering some information of human features for further analysis. A motion capture system was used to capture gestural motions. Three dimensional data obtained from motion capture system are analysed, classified and stored in the gesture database. The resampling algorithm is developed to diminish the excessive movement information which to be used in the represented form. Principal Component Analysis (PCA) is used to reduce dimension of data and classify the gesture data. PCA classifies three groups of people based on gestural motions of subjects. For further clarification, data inside the group database were tested for similarity and dissimilarity measured using Jaccard Similarity Measure; the result of total average is 90.8% dissimilarity of all five geometrical gestures between group #1, group #2 and group #3 for all the three axes: X-axis, Y-axis and Z-axis. Consequently, adaptive gesture recognition is introduced to select the suitable database especially for identifying unknown gestures inserted into the system. The result of recognition shows that recognition of individual database is 86.5%, group database 83.7% and the lowest is recognition of universal database which is 82.8%. The experimental result shows that the group database is preferable for an adaptive gesture recognition system.
format	Thesis
title	Development of gesture database for an adaptive gesture recognition system
title_short	Development of gesture database for an adaptive gesture recognition system
title_full	Development of gesture database for an adaptive gesture recognition system
title_fullStr	Development of gesture database for an adaptive gesture recognition system
title_full_unstemmed	Development of gesture database for an adaptive gesture recognition system
title_sort	development of gesture database for an adaptive gesture recognition system
granting_institution	Universiti Malaysia Perlis (UniMAP)
granting_department	School of Mechatronic Engineering
url	http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/78204/1/Page%201-24.pdf http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/78204/2/Full%20text.pdf http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/78204/4/Mohd%20Azri.pdf
_version_	1776104266719559680

Development of gesture database for an adaptive gesture recognition system

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