Enhanced feature extraction method for hand gesture recognition using support vector machine

2013 ◽  
Author(s):  
E. M. P. S. Edirisinghe ◽  
P. W. G. D. Shaminda ◽  
I. D. T. Prabash ◽  
N. S. Hettiarachchige ◽  
L. Seneviratne ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Feature Extraction ◽  
Gesture Recognition ◽  
Extraction Method ◽  
Hand Gesture Recognition ◽  
Support Vector ◽  
Hand Gesture ◽  
Feature Extraction Method

scholarly journals HAND GESTURE RECOGNITION FOR INDONESIAN SIGN LANGUAGE INTERPRETER SYSTEM WITH MYO ARMBAND USING SUPPORT VECTOR MACHINE

2020 ◽  
Vol 7 (2) ◽  
pp. 164
Author(s):  
Aditiya Anwar ◽  
Achmad Basuki ◽  
Riyanto Sigit
Keyword(s):  
Support Vector Machine ◽  
Sign Language ◽  
Gesture Recognition ◽  
Hand Gesture Recognition ◽  
Support Vector ◽  
Hand Gesture ◽  
Deaf People ◽  
Hand Gestures ◽  
Moment Invariant ◽  
Myo Armband

<p><em>Hand gestures are the communication ways for the deaf people and the other. Each hand gesture has a different meaning.  In order to better communicate, we need an automatic translator who can recognize hand movements as a word or sentence in communicating with deaf people. </em><em>This paper proposes a system to recognize hand gestures based on Indonesian Sign Language Standard. This system uses Myo Armband as hand gesture sensors. Myo Armband has 21 sensors to express the hand gesture data. Recognition process uses a Support Vector Machine (SVM) to classify the hand gesture based on the dataset of Indonesian Sign Language Standard. SVM yields the accuracy of 86.59% to recognize hand gestures as sign language.</em></p><p><em><strong>Keywords</strong></em><em>: </em><em>Hand Gesture Recognition, Feature Extraction, Indonesian Sign Language, Myo Armband, Moment Invariant</em></p>

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