XceptionTime: Independent Time-Window Xceptiontime Architecture for Hand Gesture Classification

Capitalizing on the goal of addressing identified shortcomings of recent solutions developed for recognition tasks via sparse multichannel surface Electromyography (sEMG) signals, the paper proposes a novel deep learning model, referred to as the XceptionTime architecture. The proposed innovative Xc...

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Bibliographic Details
Published inICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) pp. 1304 - 1308
Main Authors Rahimian, Elahe, Zabihi, Soheil, Atashzar, Seyed Farokh, Asif, Amir, Mohammadi, Arash
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.05.2020
Subjects
Adaptation models
Adaptive Average Pooling
Convolution
Depthwise Separable Convolution
Electromyography
Manuals
Speech processing
Surface Electromyography (sEMG)
Surface treatment
Task analysis
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Summary:Capitalizing on the goal of addressing identified shortcomings of recent solutions developed for recognition tasks via sparse multichannel surface Electromyography (sEMG) signals, the paper proposes a novel deep learning model, referred to as the XceptionTime architecture. The proposed innovative XceptionTime architecture is designed by integration of depthwise separable convolutions, adaptive average pooling, and a novel no-linear normalization technique. At the hearth of the proposed architecture is several XceptionTime modules concatenated in series fashion designed to captures both temporal and spatial information-bearing contents of the sparse multichannel sEMG signals without the need for data augmentation and manual design of feature extraction. In addition to instruction of the new XceptionTime module, by integration of adaptive average pooling, instead of fully connected layers, and utilization of a novel non-linear normalization approach, the proposed architecture is less prone to overfitting, more robust to temporal translation of the input, and more importantly is independent from the input window size, i.e., there is no need to change/reconfigure the architecture by changing the size of the input sequence. Finally, by utilizing the depthwise separable convolutions, the XceptionTime network has far less parameters resulting in less complex network.
ISSN:2379-190X
DOI:10.1109/ICASSP40776.2020.9054586