Performance analysis of two ANN based classifiers for EMG signals to identify hand motions

The aim of this work is to develop an accurate method for pattern recognition of human hand motions. Eight surface EMG electrodes (dual type) were placed on the forearm of healthy subjects while performing individual wrist and finger motions. A total of 1080 signals that incorporated all the selected nine hand motions were acquired from 12 volunteers, preprocessed, and then time-domain features were extracted. Two ANN architectures were developed and their performance was compared. The first architecture used a single ANN to perform the classification of the nine hand movements. This architecture achieved an average accuracy of all classes of 83.43%. In an effort to improve the accuracy of the classification, a second ANN architecture was developed. The second architecture consisted of nine independent ANNs, each one designed and trained to detect a specific hand motion. The second architecture achieved an average accuracy of all classes of 91.85%. Although the second ANN architecture showed an improvement in the accuracy, more research has to be performed before this type of ANN architectures can be used in real-life applications.