Author and Presenter: Fatima Amjad

Background:
Electromyogram (EMG) is the electrical measure of the muscular activity which can vary depending upon what motion is being performed. This phenomenon forms the basis of this research which is the detection of various hand gestures that are performed during sign language communication. Pakistani sign language (PSL) data were acquired using the BIOPAC system. A total of 550 signals were collected. The signals were preprocessed using Empirical mode decomposition, which is succeeded by feature extraction giving feature vector consisting of statistical, spectral, time domain, and Local Ternary Pattern (LTP). These features were fed to support vector machines, which gives optimized results of 85.4% of accuracy, 85.36% of sensitivity, and 85.81 of specificity.


#MachineLearningProject
#PhrasalSignLanguageInterpreter
#SignLanguageInterpreter
#PakistaniSignLanguageInterpreter
#EMGbasedSignLanguageInterpretation
#PhrasalSignLanguageClassification
#MachineLearningResearch
#PakistaniSignLanguageInterpreter
#IntellCity

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