Moment Invariant Features Extraction for Hand Gesture Recognition of Sign Language based on SIBI
Journal Title: EMITTER International Journal of Engineering Technology - Year 2017, Vol 5, Issue 1
Abstract
SIBI in Indonesian known as standard of Indonesian sign language. To help deaf people Myo Armband becomes an immersive technology for communication each other. The problem on Myo sensor is unstable clock rate. It causes different length data for the same period even on the same gesture. This paper proposes Moment Invariant Method to extract the feature of sensor data from Myo. This method reduces the amount of data as the result is same length of data. The implementation of this method is the user-dependent feature according to the characteristics of Myo Armband. We tested for gesture of alphabet A to Z based on SIBI (Indonesian Sign Language) with static and dynamic movements. There are 26 classes of alphabet and 10 variants. We use min-max normalization for guarantying the range of data and K-Nearest Neighbor method to classify dataset. Performance analysis with leave-one-out-validation method produced an accuracy of 82.31%.
Authors and Affiliations
Angga Rahagiyanto, Achmad Basuki, Riyanto Sigit
Keywords
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- EP ID EP312846
- DOI 10.24003/emitter.v5i1.173
- Views 65
- Downloads 0
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