Decoding Object Weight from Electromyography during Human Grasping

Abstract

Human urges, desires, and intentions manifest themselves in voluntary action. The final stages of such voluntary action are the muscle contractions that bring it about. Electromyography (EMG) signals measure such muscle contractions. Decoding action contents from EMG require advanced methods for detection, decomposition, processing, and classification and remains a challenge in neuroscience. This study presents a new, time-domain method of classifying EMG for grasping different types of objects. Our proposed method can classify objects with different weights with an accuracy of up to 90%. This progress in neuroscience affects other fields like physiology, brain-computer interfaces, robotics, and so on.