Hand Gesture Recognition Using Compact CNN via Surface Electromyography Signals

There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

Abstract

By training the deep neural network model, the hidden features in Surface Electromyography(sEMG) signals can be extracted. The motion intention of the human can be predicted by analysis of sEMG. However, the models recently proposed by researchers often have a large number of parameters. Therefore, we designed a compact Convolution Neural Network (CNN) model, which not only improves the classification accuracy but also reduces the number of parameters in the model. Our proposed model was validated on the Ninapro DB5 Dataset and the Myo Dataset. The classification accuracy of gesture recognition achieved good results.

Related collections

Most cited references 26

Record: found
Abstract: found
Article: found

Is Open Access

Very Deep Convolutional Networks for Large-Scale Image Recognition

, (2014)

In this work we investigate the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting. Our main contribution is a thorough evaluation of networks of increasing depth using an architecture with very small (3x3) convolution filters, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 weight layers. These findings were the basis of our ImageNet Challenge 2014 submission, where our team secured the first and the second places in the localisation and classification tracks respectively. We also show that our representations generalise well to other datasets, where they achieve state-of-the-art results. We have made our two best-performing ConvNet models publicly available to facilitate further research on the use of deep visual representations in computer vision.

0 comments Cited 509 times – based on 0 reviews

Preprint

     Review now

Bookmark

Record: found
Abstract: found
Article: found

Is Open Access

Adam: A Method for Stochastic Optimization

, (2015)

We introduce Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments. The method is straightforward to implement, is computationally efficient, has little memory requirements, is invariant to diagonal rescaling of the gradients, and is well suited for problems that are large in terms of data and/or parameters. The method is also appropriate for non-stationary objectives and problems with very noisy and/or sparse gradients. The hyper-parameters have intuitive interpretations and typically require little tuning. Some connections to related algorithms, on which Adam was inspired, are discussed. We also analyze the theoretical convergence properties of the algorithm and provide a regret bound on the convergence rate that is comparable to the best known results under the online convex optimization framework. Empirical results demonstrate that Adam works well in practice and compares favorably to other stochastic optimization methods. Finally, we discuss AdaMax, a variant of Adam based on the infinity norm.

0 comments Cited 456 times – based on 0 reviews

Preprint

     Review now

Bookmark

Record: found
Abstract: found
Article: not found

A robust, real-time control scheme for multifunction myoelectric control.

K Englehart, B Hudgins (2003)

This paper represents an ongoing investigation of dexterous and natural control of upper extremity prostheses using the myoelectric signal (MES). The scheme described within uses pattern recognition to process four channels of MES, with the task of discriminating multiple classes of limb movement. The method does not require segmentation of the MES data, allowing a continuous stream of class decisions to be delivered to a prosthetic device. It is shown in this paper that, by exploiting the processing power inherent in current computing systems, substantial gains in classifier accuracy and response time are possible. Other important characteristics for prosthetic control systems are met as well. Due to the fact that the classifier learns the muscle activation patterns for each desired class for each individual, a natural control actuation results. The continuous decision stream allows complex sequences of manipulation involving multiple joints to be performed without interruption. Finally, minimal storage capacity is required, which is an important factor in embedded control systems.

0 comments Cited 282 times – based on 0 reviews      Review now

Bookmark

All references

Author and article information

Journal

Journal ID (nlm-ta): Sensors (Basel)

Journal ID (iso-abbrev): Sensors (Basel)

Journal ID (publisher-id): sensors

Title: Sensors (Basel, Switzerland)

Publisher: MDPI

ISSN (Electronic): 1424-8220

Publication date (Electronic): 26 January 2020

Publication date Collection: February 2020

Volume: 20

Issue: 3

Electronic Location Identifier: 672

Affiliations

[1 ]Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing 400700, China; chenlin18@ 123456cigit.ac.cn (L.C.); jiantingfu@ 123456cigit.ac.cn (J.F.); wuyuheng21@ 123456163.com (Y.W.); lihaochen@ 123456csu.ac.cn (H.L.)

[2 ]School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing 100049, China

[3 ]School of Mechatronical Engineering, Changchun University of Science and Technology, Changchun 130022, China

Author notes

[* ]Correspondence: zhengbin@ 123456cigit.ac.cn

[†]

These authors contributed equally.

Author information

Jianting Fu https://orcid.org/0000-0001-7163-1318

Article

Publisher ID: sensors-20-00672

DOI: 10.3390/s20030672

PMC ID: 7039218

PubMed ID: 31991849

SO-VID: ad125951-4b6f-41cf-b9f3-4dc8c9747ff7

License:

Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

History

Date received : 25 December 2019

Date accepted : 22 January 2020

Comments

Comment on this article

scite_

Smart Citations

Citing PublicationsSupportingMentioningContrasting

View Citations

See how this article has been cited at scite.ai

scite shows how a scientific paper has been cited by providing the context of the citation, a classification describing whether it supports, mentions, or contrasts the cited claim, and a label indicating in which section the citation was made.

Cited by 39

See all cited by

Most referenced authors 306

See all reference authors

Hand Gesture Recognition Using Compact CNN via Surface Electromyography Signals

Read this article at

Abstract

Related collections

Journal of Disability Research

Most cited references 26

Very Deep Convolutional Networks for Large-Scale Image Recognition

Adam: A Method for Stochastic Optimization

A robust, real-time control scheme for multifunction myoelectric control.

Author and article information

Journal

Affiliations

Author notes

Author information

Article

History

Categories

Comments

Comment on this article

Similar content 446

Cited by 39

Most referenced authors 306