A New CNN Approach for Hand Gesture Classification using sEMG Data

Aysun Tutak Erözen

doi:10.38088/jise.730957

EN

A New CNN Approach for Hand Gesture Classification using sEMG Data

Abstract

In this paper, a new CNN architecture is introduced for classification of six different hand gestures using surface electromyography (EMG) data collected from the forearm. At first, two different deep neural networks produced based on Slow Fusion and Inception models separately. Then, the average of accuracy values and standard deviations were calculated for each type of network. The average accuracy was 80.88% and standard deviation was 0.030 for the Slow Fusion based network. For the Inception based network, average accuracy was 82.64% and standard deviation was 0.028. In addition to these two networks, a new CNN architecture is introduced using Slow fusion and Inception models in combination. The architecture has two parallel Inception modules in parallel. Each parallel module is fed by the half of the 3D feature map. The proposed model slowly fuses the information of the parallel modules throughout the network as in Slow-Fusion architecture. The average accuracy achieved with this model was 83.97% and the standard deviation was 0.027. Despite the small data set, the accuracy had increased with the proposed hybrid model. The smaller standard deviation indicates that it is less affected by variations in the training dataset. Our experimental results show that the proposed method gives the best results among the Slow Fusion based and Inception based models.

Keywords

EMG,CNN,Deep Learning,Slow Fusion,Gesture Recognition,Inception

References

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Details

Primary Language

English

Subjects

Engineering

Journal Section

Research Article

Authors

Aysun Tutak Erözen ^*
0000-0002-6551-4008
Türkiye

Publication Date

June 15, 2020

Submission Date

May 2, 2020

Acceptance Date

May 28, 2020

Published in Issue

Year 2020 Volume: 4 Number: 1

DOI

https://doi.org/10.38088/jise.730957

IZ

https://izlik.org/JA28CM35SF

Cite

RIS / Bibtex

APA

Tutak Erözen, A. (2020). A New CNN Approach for Hand Gesture Classification using sEMG Data. Journal of Innovative Science and Engineering, 4(1), 44-55. https://doi.org/10.38088/jise.730957

AMA

1.Tutak Erözen A. A New CNN Approach for Hand Gesture Classification using sEMG Data. JISE. 2020;4(1):44-55. doi:10.38088/jise.730957

Chicago

Tutak Erözen, Aysun. 2020. “A New CNN Approach for Hand Gesture Classification Using SEMG Data”. Journal of Innovative Science and Engineering 4 (1): 44-55. https://doi.org/10.38088/jise.730957.

EndNote

Tutak Erözen A (June 1, 2020) A New CNN Approach for Hand Gesture Classification using sEMG Data. Journal of Innovative Science and Engineering 4 1 44–55.

IEEE

[1]A. Tutak Erözen, “A New CNN Approach for Hand Gesture Classification using sEMG Data”, JISE, vol. 4, no. 1, pp. 44–55, June 2020, doi: 10.38088/jise.730957.

ISNAD

Tutak Erözen, Aysun. “A New CNN Approach for Hand Gesture Classification Using SEMG Data”. Journal of Innovative Science and Engineering 4/1 (June 1, 2020): 44-55. https://doi.org/10.38088/jise.730957.

JAMA

1.Tutak Erözen A. A New CNN Approach for Hand Gesture Classification using sEMG Data. JISE. 2020;4:44–55.

MLA

Tutak Erözen, Aysun. “A New CNN Approach for Hand Gesture Classification Using SEMG Data”. Journal of Innovative Science and Engineering, vol. 4, no. 1, June 2020, pp. 44-55, doi:10.38088/jise.730957.

Vancouver

1.Aysun Tutak Erözen. A New CNN Approach for Hand Gesture Classification using sEMG Data. JISE. 2020 Jun. 1;4(1):44-55. doi:10.38088/jise.730957