EMG Signals Classification on Human Activity Recognition using Machine Learning Algorithm

Abstract

In Human activity recognition (HAR) research, it is a common practice to use wearable sensors to acquire the signals for human daily activities. In this study, an experimental data from electromyography (EMG) wireless sensors is analysed for six different activities recognition. This paper aims to compare EMG signals of left and right of upper leg muscles by using Random Forest (RF) Machine Learning Classifier. The HAR processing comprises of data filtering and segmentation, data feature extraction, feature selection of the data, and classification. Model evaluation of holdout method is implemented for classification assessment. The performance of all human daily activities is evaluated according to the comparison of precision and recall for each activity. The results show combined muscles obtained the highest precision and recall on running activity with 89.2% and 88.3%. The highest overall accuracy of classification was 82.08% on the bicep femoris left and right (BF-Left & Right).