Open AccessReal-Time Classification of Hand Motions Using Electromyography Collected from Minimal Electrodes for Robotic Control
Author(s) -
Richard Byfield,
Richard Weng,
Morgan Miller,
Yunchao Xie,
JhengWun Su,
Jian Lin
Publication year - 2021
Publication title -
international journal of robotics and control
Language(s) - English
Resource type - Journals
eISSN - 2577-7769
pISSN - 2577-7742
DOI - 10.5430/ijrc.v3n1p13
Subject(s) - electromyography , artificial intelligence , computer science , robot , task (project management) , naive bayes classifier , bayes' theorem , pattern recognition (psychology) , bayesian probability , engineering , support vector machine , physical medicine and rehabilitation , medicine , systems engineering
In recent years, advances in human robot interaction (HRI) has shown massive potential for universal control of robots. Among them, electromyography (EMG) signals generated by motions of muscles have been identified as an important and useful source. Powered by recently emerged machine learning algorithms, real-time classification has been proved applicable to control robots. However, collecting EMG signals with minimum number of electrodes for real-time classification and robotic control is still a challenge. In this paper, we demonstrate that twenty five robotic commands in a robotic arm can be controlled in real time by using the EMG signals collected from only two pairs of active surface electrodes on each forearm of human subjects. To achieve this task, a variety of tested ML models for this classification were tested. Among them, the Gaussian Naïve Bayes (GNB) achieved an accuracy of >96%. This unprecedented level of classification accuracy of the EMG signals collected from the least number of active electrodes suggest that by combination of optimized electrode configuration and a suitable ML model, the capability of robotic control can be maximized.