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SURFACE EMG-BASED HAND GESTURE RECOGNITION VIA DILATED CONVOLUTIONAL NEURAL NETWORKS

Abstract: 

The recent evolution of Artificial Intelligence (AI) and deep learning models coupled with advancements of assistive robotic systems have shown great potential in significantly improving myoelectric control of prosthetic devices. In this regard, the paper proposes a novel deep-learning-based architecture for processing surface Electromyography (sEMG) signals to classify and recognize upper-limb hand gestures via incorporation of dilated causal convolutions. The proposed approach has the potential to significantly improve the overall recognition accuracy due to the specific design of the convolutional layers. By using dilated causal convolutions which gradually increases the receptive field of the network, and by applying Conv1D, the proposed architecture eliminates the need for readjustment of the input sequences and inherently takes into account the hidden temporal correlations existing among the available set of sEMG sequences. Contrary to recent hybrid (RNN-CNN) solutions, the proposed architecture neither uses recurrent units nor 2-dimensional convolutions. The publically-accessible NinaPro DB2 dataset is utilized for training and evaluation of the proposed network. Through an extensive set of experiments, it is observed that the proposed architecture significantly outperforms its state-of-the-art counterparts and provides an average 8.71% performance improvement.

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Hand Gesture Recognition

Paper Details

Authors:
Elahe Rahimian, Soheil Zabihi, Seyed Farokh Atashzar, Amir Asif, Arash Mohammadi$
Submitted On:
7 November 2019 - 10:35am
Short Link:
Type:
Presentation Slides
Event:
Presenter's Name:
Elahe Rahimian
Paper Code:
1570567928
Document Year:
2019
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Hand Gesture Recognition via Dilated Causal Convolutions

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[1] Elahe Rahimian, Soheil Zabihi, Seyed Farokh Atashzar, Amir Asif, Arash Mohammadi$, "SURFACE EMG-BASED HAND GESTURE RECOGNITION VIA DILATED CONVOLUTIONAL NEURAL NETWORKS", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4924. Accessed: Nov. 11, 2019.
@article{4924-19,
url = {http://sigport.org/4924},
author = {Elahe Rahimian; Soheil Zabihi; Seyed Farokh Atashzar; Amir Asif; Arash Mohammadi$ },
publisher = {IEEE SigPort},
title = {SURFACE EMG-BASED HAND GESTURE RECOGNITION VIA DILATED CONVOLUTIONAL NEURAL NETWORKS},
year = {2019} }
TY - EJOUR
T1 - SURFACE EMG-BASED HAND GESTURE RECOGNITION VIA DILATED CONVOLUTIONAL NEURAL NETWORKS
AU - Elahe Rahimian; Soheil Zabihi; Seyed Farokh Atashzar; Amir Asif; Arash Mohammadi$
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4924
ER -
Elahe Rahimian, Soheil Zabihi, Seyed Farokh Atashzar, Amir Asif, Arash Mohammadi$. (2019). SURFACE EMG-BASED HAND GESTURE RECOGNITION VIA DILATED CONVOLUTIONAL NEURAL NETWORKS. IEEE SigPort. http://sigport.org/4924
Elahe Rahimian, Soheil Zabihi, Seyed Farokh Atashzar, Amir Asif, Arash Mohammadi$, 2019. SURFACE EMG-BASED HAND GESTURE RECOGNITION VIA DILATED CONVOLUTIONAL NEURAL NETWORKS. Available at: http://sigport.org/4924.
Elahe Rahimian, Soheil Zabihi, Seyed Farokh Atashzar, Amir Asif, Arash Mohammadi$. (2019). "SURFACE EMG-BASED HAND GESTURE RECOGNITION VIA DILATED CONVOLUTIONAL NEURAL NETWORKS." Web.
1. Elahe Rahimian, Soheil Zabihi, Seyed Farokh Atashzar, Amir Asif, Arash Mohammadi$. SURFACE EMG-BASED HAND GESTURE RECOGNITION VIA DILATED CONVOLUTIONAL NEURAL NETWORKS [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4924