Classification of Electromyography Signal with Machine Learning

Abstract

The problem of classifying electromyography signals in each gesture occurs due to the use of a constant level of signal amplitude. This research presents an efficiency enhancement of electromyography signal classification in each gesture with machine learning. The performance efficiency of 5 models: SVM, RF, MLP, KNN, and Deep Leaning was compared. The signals were recorded by a low-cost signal sensor. Fist clenching and hand opening gestures were alternately performed every 5 second for 5 times each. Therefore, the total was 4,767 records divided into 3,274 records of hand opening gesture, 1,492 records of fist clenching gesture and 4,833 records of wrist rotating gesture. The results showed that the MLP model was found to have the highest accuracy at 81.45% for fist clenching and hand opening gestures. The Deep Learning model was found to have the highest accuracy at 89.03% for wrist rotating and hand opening gestures.