HAND GESTURE RECOGNITION USING DEEP LEARNING TECHNIQUES

Abstract

Deep learning algorithms have attracted more attention recently as a result of their unrivalled ability to automatically learn distinguishing features from enormous amounts of data. Surface Electromyography(sEMG) signals can be used to extract hidden characteristics, here seven dif ferent gestures are classified by building a deep neural network model. Analysis of the sEMG can be used to forecast the human’s intended motion. However, there are frequently a lot of parameters in the models that researchers have lately presented. As a result, the classifica tion accuracy of LSTM model is improved, compared to a simple Convolutional Neural Net work(CNN). The Myo Dataset were used to validate the proposed framework. The gesture recognition system produced high classification accuracy. The comparison study on numerous parameters conclude that LSTM model showed the best performance with overall accuracy of 94.78%