Machine Learning and Signal Processing Based Analysis of sEMG Signals for Daily Action Classification

Abstract

The way of living of many individuals around the world endures because of mental and physical disability associated with the movement of limbs. The usage of the assistive technology and systems will enhance the quality of affected people. In this situation, you can pave the way for a solution by transforming the movement of physical activities into computer-assisted applications. Surface Electromyography (sEMG) introduced the non-intervention procedure that can transform physical activities into signals for classification purposes and then practice it in applications. In this study, we suggest a scheme based on machine learning for the identification of 20 physical movements. This scheme follows up on the distinct characteristics from numerous signatures that include time-domain features, frequency-domain features, and inter-channel statistics of an sEMG signal. Afterward, we performed a thorough comparative examination of the k-NN and SVM classifier by considering the group of features for multiple normal and aggressive activities. The impact of different arrangements of dimensionalities has been recorded as well. Eventually, the SVM classifier gives 100% accuracy for 10 normal actions whereas 1-NN for a subgroup of features achieves 98.91% accuracy for 10 aggressive actions respectively. Additionally, we combine both SVM and 1-NN to propose a hybrid approach to classify 20 physical actions. The hybrid classifier gives an accuracy of 98.97% respectively. These recommendations are valuable for algorithm designers to select the finest approach by considering the resources available for the execution of an algorithm.