Xceptiontime: Independent time-window xceptiontime architecture for hand gesture classification

Elahe Rahimian, Soheil Zabihi, Seyed Farokh Atashzar, Amir Asif, Arash Mohammadi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish (US)
Title of host publication2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1304-1308
Number of pages5
ISBN (Electronic)9781509066315
DOIs
StatePublished - May 2020
Event2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Barcelona, Spain
Duration: May 4 2020May 8 2020

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume2020-May
ISSN (Print)1520-6149

Conference

Conference2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020
CountrySpain
CityBarcelona
Period5/4/205/8/20

Keywords

  • Adaptive Average Pooling
  • Depthwise Separable Convolution
  • Surface Electromyography (sEMG)

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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