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 |
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| Language: | English |
| Subjects: | |
| 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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| Summary: | 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. |
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