A New CNN Approach for Hand Gesture Classification using sEMG Data

Aysun TUTAK ERÖZEN

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

Article Details

A New CNN Approach for Hand Gesture Classification using sEMG Data

Authors:

Aysun TUTAK ERÖZEN

Main Topics:

Applied physics

Journal:

Journal of Innovative Science and Engineering 2020 Vol.4 Issue 1, pp.44-55

Document Type:

Article

Publication Date:

6/2020

Document Language:

English

Document ID (Org No.):

2000483216

DOI:

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

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.

Article Details

A New CNN Approach for Hand Gesture Classification using sEMG Data

Authors: Aysun TUTAK ERÖZEN

Main Topics: Applied physics

Journal: Journal of Innovative Science and Engineering 2020 Vol.4 Issue 1, pp.44-55

Document Type: Article

Publication Date: 6/2020

Document Language: English

Document ID (Org No.): 2000483216

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

Issues

Authors:

Aysun TUTAK ERÖZEN

Main Topics:

Applied physics

Journal:

Journal of Innovative Science and Engineering 2020 Vol.4 Issue 1, pp.44-55

Document Type:

Article

Publication Date:

6/2020

Document Language:

English

Document ID (Org No.):

2000483216

DOI:

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