EMG-based Classification of Forearm Muscles in Prehension Movements

Abstract

This paper aimed to classify two forearm muscles known as Flexor Carpi Ulnaris (FCU) and Extensor Carpi Radialis Longus (ECRL) using surface Electromyography (sEMG) signal during different hand prehension tasks, such as cylindrical, tip, spherical, palmar, lateral and hook while grasping any object. Thirteen Machine Learning (ML) algorithms were analyzed to compare their performance using a single EMG time domain feature called integrated EMG (IEMG). The tree-based methods have the top performance to classify the forearm muscles than other ML methods among all those 13 ML algorithms. Results showed that 4 out of 5 tree-based classifiers achieved more than 75% accuracies, where the random forest method showed maximum classification accuracy (85.07%). Additionally, these tree-based ML methods computed the variable importance in classification margin. The results showed that the lateral grasping was the most important moving variable for all those algorithms except AdaBoost where tipping was the most significant movement variable for this method. We hope, this ML- and EMG-based classification results presented in the paper may alleviate some of the problems in implementing advanced forearm prosthetics, rehabilitation devices and assistive biomedical robots.