Real-Time Electromyographic Hand Gesture Signal Classification Using Machine Learning Algorithm Based on Bispectrum Feature

A real-time hand gesture classification model based on a robust feature extraction and machine learning algorithm is proposed in this paper. The Delsys Trigno Wireless EMG System is used to collect the EMG database. To improve the performance of EMG pattern recognition system, a new feature set based on bispectrum feature extraction is used as it can detect the non-Gaussian and nonlinear characteristics of EMG, unlike conventional feature set. The feature extracted is then fed to the classifiers to classify two hand gestures, hand close and hand open. The performance of the proposed pattern recognition system is measured in terms of their classification accuracies. The classifier algorithms used in this work are set of base classifiers such as Naive Bayes, support vector machine (SVM) and decision tree. Further to achieve increased classification accuracy ensemble classifiers such as random forest, gradient boost and AdaBoost are employed. From the experimental results, it is observed that the Adaboost performs better with high classification accuracy of 99% as compared to other classifiers such as SVM, decision tree, Naive Bayes, random forest and gradient boost with accuracies of 81%, 75%, 81%, 75% and 75%, respectively. Due to its adaptiveness in correcting the weak learners (base classifiers) in favor of reducing misclassification by the previous classifiers, AdaBoost resulted in higher accuracy compared to other classifiers.