Next Article in Journal
Efficiency Assessment of Inbound Tourist Service Using Data Envelopment Analysis
Previous Article in Journal
Cycle Tourism as a Driver for the Sustainable Development of Little-Known or Remote Territories: The Experience of the Apennine Regions of Northern Italy
Previous Article in Special Issue
Sustainable Situation-Aware Recommendation Services with Collective Intelligence
Article Menu
Issue 6 (June) cover image

Export Article

Open AccessArticle
Sustainability 2018, 10(6), 1865;

sEMG-Based Gesture Recognition with Convolution Neural Networks

School of Mechatronics Engineering, Harbin Institute of Technology, Harbin 150000, China
Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou 510000, China
Institute of Computer Application, China Academy of Engineer Physics, Mianyang 621000, China
School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150000, China
Author to whom correspondence should be addressed.
Received: 20 March 2018 / Revised: 28 May 2018 / Accepted: 28 May 2018 / Published: 4 June 2018
Full-Text   |   PDF [1293 KB, uploaded 4 June 2018]   |  


The traditional classification methods for limb motion recognition based on sEMG have been deeply researched and shown promising results. However, information loss during feature extraction reduces the recognition accuracy. To obtain higher accuracy, the deep learning method was introduced. In this paper, we propose a parallel multiple-scale convolution architecture. Compared with the state-of-art methods, the proposed architecture fully considers the characteristics of the sEMG signal. Larger sizes of kernel filter than commonly used in other CNN-based hand recognition methods are adopted. Meanwhile, the characteristics of the sEMG signal, that is, muscle independence, is considered when designing the architecture. All the classification methods were evaluated on the NinaPro database. The results show that the proposed architecture has the highest recognition accuracy. Furthermore, the results indicate that parallel multiple-scale convolution architecture with larger size of kernel filter and considering muscle independence can significantly increase the classification accuracy. View Full-Text
Keywords: gesture recognition; convolution neural network; surface electromyographic gesture recognition; convolution neural network; surface electromyographic

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Share & Cite This Article

MDPI and ACS Style

Ding, Z.; Yang, C.; Tian, Z.; Yi, C.; Fu, Y.; Jiang, F. sEMG-Based Gesture Recognition with Convolution Neural Networks. Sustainability 2018, 10, 1865.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Sustainability EISSN 2071-1050 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top