Deep Residual Shrinkage Networks for EMG-based Gesture Identification

02/07/2022
by   Yueying Ma, et al.
0

This work introduces a method for high-accuracy EMG based gesture identification. A newly developed deep learning method, namely, deep residual shrinkage network is applied to perform gesture identification. Based on the feature of EMG signal resulting from gestures, optimizations are made to improve the identification accuracy. Finally, three different algorithms are applied to compare the accuracy of EMG signal recognition with that of DRSN. The result shows that DRSN excel traditional neural networks in terms of EMG recognition accuracy. This paper provides a reliable way to classify EMG signals, as well as exploring possible applications of DRSN.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
01/04/2020

Res3ATN – Deep 3D Residual Attention Network for Hand Gesture Recognition in Videos

Hand gesture recognition is a strenuous task to solve in videos. In this...
research
11/30/2019

Interpreting Deep Learning Features for Myoelectric Control: A Comparison with Handcrafted Features

The research in myoelectric control systems primarily focuses on extract...
research
11/26/2021

WiFi-based Multi-task Sensing

WiFi-based sensing has aroused immense attention over recent years. The ...
research
06/22/2023

Russian assimilatory palatalization is incomplete neutralization

Incomplete neutralization refers to phonetic traces of underlying contra...
research
04/08/2020

Adversary Helps: Gradient-based Device-Free Domain-Independent Gesture Recognition

Wireless signal-based gesture recognition has promoted the developments ...
research
11/16/2022

Arbitrarily Accurate Classification Applied to Specific Emitter Identification

This article introduces a method of evaluating subsamples until any pres...
research
03/15/2019

GestureKeeper: Gesture Recognition for Controlling Devices in IoT Environments

This paper introduces and evaluates the GestureKeeper, a robust hand-ges...

Please sign up or login with your details

Forgot password? Click here to reset