Surface EMG signal classification using ensemble algorithm, PCA and DWT for robot control

Abstract

This paper presents a framework of surface electromyography signals based robotic arm prototype control using discrete wavelet transform, principle component analysis, ensemble algorithms and Arduino Uno controller. In this context, the sequential floating forward selection algorithm is used for sorting out the features based on their relevance. The performance of different ensemble algorithms is evaluated with various parameters like classification accuracy, sensitivity, specificity, false descriptive rate, positive predictive rate and speed. Among the all ensemble algorithm, the subspace discriminate ensemble was found the best method with the 100% accuracy, specificity, and sensitivity using 35 base classifiers. Subspace ensemble algorithm with principle component analysis and 4th scaling daubechies 4 wavelet filters produced the best performance. The main contribution of this work is that method has the potency of best classification of sEMG signal for elbow movement which can be beneficial for assistive robotic device development.