WD-EEMD based Voting Classifier for hand gestures classification using sEMG signals

In the biomedical field, there are many applications available based on surface EMG (sEMG) signal classification such as human-machine interaction, diagnosis of kinesiological studies and neuromuscular diseases. However, These signals are complicated because noise is generated during the recording of the sEMG signal. In this study, a hybridization of two signal pre-processing techniques, Wavelet Decomposition and Ensemble Empirical Mode Decomposition, called WD-EEMD with Voting classifier, is introduced to classify hand gestures based on sEMG signals. A study of different Decision Tree ensembles has been done for the classification process. Signals are preprocessed, segmented and then classified after extracting relevant features from them. The final prediction of the signal's class is done via a voting mechanism. Different studied pre-processing techniques, similar to that of the proposed methodology with different classifiers have been compared. A new performance metric called confidence has been introduced to analyze the classification procedure. The models have been evaluated and compared on performance criteria like accuracy and overall confidence (gross and true confidence). It has been observed that Gradient Tree Boosting along with WD-EEMD gives the best classification accuracy with high confidence.