sEMG-Based Gesture Recognition with Convolution Neural Networks

Ding, Zhen; Yang, Chifu; Tian, Zhihong; Yi, Chunzhi; Fu, Yunsheng; Jiang, Feng

doi:10.3390/su10061865

Open AccessArticle

sEMG-Based Gesture Recognition with Convolution Neural Networks

by

Zhen Ding

¹,

Chifu Yang

¹,

Zhihong Tian

²

,

Chunzhi Yi

¹,

Yunsheng Fu

³ and

Feng Jiang

^4,*

¹

School of Mechatronics Engineering, Harbin Institute of Technology, Harbin 150000, China

²

Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou 510000, China

³

Institute of Computer Application, China Academy of Engineer Physics, Mianyang 621000, China

⁴

School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150000, China

^*

Author to whom correspondence should be addressed.

Sustainability 2018, 10(6), 1865; https://doi.org/10.3390/su10061865

Submission received: 20 March 2018 / Revised: 28 May 2018 / Accepted: 28 May 2018 / Published: 4 June 2018

(This article belongs to the Special Issue Autonomous and Sustainable Computing for preparing the Internet of Things Environment)

Download

Browse Figures

Versions Notes

Abstract

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.

Keywords: gesture recognition; convolution neural network; surface electromyographic

Share and Cite

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. https://doi.org/10.3390/su10061865

AMA Style

Ding Z, Yang C, Tian Z, Yi C, Fu Y, Jiang F. sEMG-Based Gesture Recognition with Convolution Neural Networks. Sustainability. 2018; 10(6):1865. https://doi.org/10.3390/su10061865

Chicago/Turabian Style

Ding, Zhen, Chifu Yang, Zhihong Tian, Chunzhi Yi, Yunsheng Fu, and Feng Jiang. 2018. "sEMG-Based Gesture Recognition with Convolution Neural Networks" Sustainability 10, no. 6: 1865. https://doi.org/10.3390/su10061865

APA Style

Ding, Z., Yang, C., Tian, Z., Yi, C., Fu, Y., & Jiang, F. (2018). sEMG-Based Gesture Recognition with Convolution Neural Networks. Sustainability, 10(6), 1865. https://doi.org/10.3390/su10061865

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Menu

sEMG-Based Gesture Recognition with Convolution Neural Networks

Abstract

Share and Cite

Article Metrics

Article Access Statistics

Further Information

Guidelines

MDPI Initiatives

Follow MDPI