Open AccessMoment Invariant Features Extraction for Hand Gesture Recognition of Sign Language based on SIBI
Author(s) -
Angga Rahagiyanto,
Achmad Basuki,
Riyanto Sigit
Publication year - 2017
Publication title -
emitter international journal of engineering technology
Language(s) - English
Resource type - Journals
eISSN - 2443-1168
pISSN - 2355-391X
DOI - 10.24003/emitter.v5i1.173
Subject(s) - gesture , sign language , computer science , invariant (physics) , normalization (sociology) , gesture recognition , speech recognition , feature extraction , pattern recognition (psychology) , artificial intelligence , mathematics , philosophy , linguistics , mathematical physics , sociology , anthropology
Myo Armband became an immersive technology to help deaf people for communication each other. The problem on Myo sensor is unstable clock rate. It causes the different length data for the same period even on the same gesture. This research proposes Moment Invariant Method to extract the feature of sensor data from Myo. This method reduces the amount of data and makes the same length of data. This research is user-dependent, according to the characteristics of Myo Armband. The testing process was performed by using alphabet A to Z on SIBI, Indonesian Sign Language, with static and dynamic finger movements. There are 26 class of alphabets and 10 variants in each class. We use min-max normalization for guarantying the range of data. We use K-Nearest Neighbor method to classify dataset. Performance analysis with leave-one-out-validation method produced an accuracy of 82.31%. It requires a more advanced method of classification to improve the performance on the detection results.