Year 2020, Volume 4 , Issue 1, Pages 44 - 55 2020-06-15

A New CNN Approach for Hand Gesture Classification using sEMG Data

Aysun TUTAK ERÖZEN [1]


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.
EMG, CNN, Deep Learning, Slow Fusion, Gesture Recognition, Inception
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Primary Language en
Subjects Engineering
Journal Section Research Articles
Authors

Orcid: 0000-0002-6551-4008
Author: Aysun TUTAK ERÖZEN (Primary Author)
Institution: TOFAŞ Türk Otomobil Fabrikası
Country: Turkey


Dates

Publication Date : June 15, 2020

Bibtex @research article { jise730957, journal = {Journal of Innovative Science and Engineering (JISE)}, issn = {}, eissn = {2602-4217}, address = {ursa Technical University, Mimar Sinan Campus, Mimar Sinan Mah. Mimar Sinan Blv. Eflak Cad. No:177 16310 Yıldırım, Bursa / Turkey}, publisher = {Bursa Technical University}, year = {2020}, volume = {4}, pages = {44 - 55}, doi = {10.38088/jise.730957}, title = {A New CNN Approach for Hand Gesture Classification using sEMG Data}, key = {cite}, author = {Tutak Erözen, Aysun} }
APA Tutak Erözen, A . (2020). A New CNN Approach for Hand Gesture Classification using sEMG Data . Journal of Innovative Science and Engineering (JISE) , 4 (1) , 44-55 . DOI: 10.38088/jise.730957
MLA Tutak Erözen, A . "A New CNN Approach for Hand Gesture Classification using sEMG Data" . Journal of Innovative Science and Engineering (JISE) 4 (2020 ): 44-55 <http://jise.btu.edu.tr/en/pub/issue/53898/730957>
Chicago Tutak Erözen, A . "A New CNN Approach for Hand Gesture Classification using sEMG Data". Journal of Innovative Science and Engineering (JISE) 4 (2020 ): 44-55
RIS TY - JOUR T1 - A New CNN Approach for Hand Gesture Classification using sEMG Data AU - Aysun Tutak Erözen Y1 - 2020 PY - 2020 N1 - doi: 10.38088/jise.730957 DO - 10.38088/jise.730957 T2 - Journal of Innovative Science and Engineering (JISE) JF - Journal JO - JOR SP - 44 EP - 55 VL - 4 IS - 1 SN - -2602-4217 M3 - doi: 10.38088/jise.730957 UR - https://doi.org/10.38088/jise.730957 Y2 - 2020 ER -
EndNote %0 Journal of Innovative Science and Engineering (JISE) A New CNN Approach for Hand Gesture Classification using sEMG Data %A Aysun Tutak Erözen %T A New CNN Approach for Hand Gesture Classification using sEMG Data %D 2020 %J Journal of Innovative Science and Engineering (JISE) %P -2602-4217 %V 4 %N 1 %R doi: 10.38088/jise.730957 %U 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 (JISE) 4 / 1 (June 2020): 44-55 . https://doi.org/10.38088/jise.730957
AMA Tutak Erözen A . A New CNN Approach for Hand Gesture Classification using sEMG Data. JISE. 2020; 4(1): 44-55.
Vancouver Tutak Erözen A . A New CNN Approach for Hand Gesture Classification using sEMG Data. Journal of Innovative Science and Engineering (JISE). 2020; 4(1): 44-55.