Heterogeneous Hand Guise Classification Based on Surface Electromyographic Signals Using Multichannel Convolutional Neural Network

TL;DR

This paper introduces a novel multichannel CNN approach for classifying surface EMG signals to accurately recognize hand gestures, aiding prosthetic development.

Contribution

It presents a new multichannel CNN method that interprets EMG signals in the power domain, achieving high classification accuracy for hand gesture recognition.

Findings

High classification accuracy on EMG dataset

Effective interpretation of EMG signals in the power domain

Potential application in prosthetic hand control

Abstract

Electromyography (EMG) is a way of measuring the bioelectric activities that take place inside the muscles. EMG is usually performed to detect abnormalities within the nerves or muscles of a target area. The recent developments in the field of Machine Learning allow us to use EMG signals to teach machines the complex properties of human movements. Modern machines are capable of detecting numerous human activities and distinguishing among them solely based on the EMG signals produced by those activities. However, success in accomplishing this task mostly depends on the learning technique used by the machine to analyze EMG signals; and even the latest algorithms do not result in flawless classification. In this study, a novel classification method has been described employing a multichannel Convolutional Neural Network (CNN) that interprets surface EMG signals by the properties they exhibit in the power domain. The proposed method was tested on a well-established EMG dataset, and the result yields very high classification accuracy. This learning model will help researchers to develop prosthetic arms capable of detecting various hand gestures to mimic them afterwards.