STCN-GR: Spatial-Temporal Convolutional Networks for Surface-Electromyography-Based Gesture Recognition

Zhiping Lai, Xiaoyang Kang*, Hongbo Wang, Weiqi Zhang, Xueze Zhang, Peixian Gong, Lan Niu, Huijie Huang

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

8 Scopus citations


Gesture recognition using surface electromyography (sEMG) is the technical core of muscle-computer interface (MCI) in human-computer interaction (HCI), which aims to classify gestures according to signals obtained from human hands. Since sEMG signals are characterized by spatial relevancy and temporal nonstationarity, sEMG-based gesture recognition is a challenging task. Previous works attempt to model this structured information and extract spatial and temporal features, but the results are not satisfactory. To tackle this problem, we proposed spatial-temporal convolutional networks for sEMG-based gesture recognition (STCN-GR). In this paper, the concept of the sEMG graph is first proposed by us to represent sEMG data instead of image and vector sequence adopted by previous works, which provides a new perspective for the research of sEMG-based tasks, not just gesture recognition. Graph convolutional networks (GCNs) and temporal convolutional networks (TCNs) are used in STCN-GR to capture spatial-temporal information. Additionally, the connectivity of the graph can be adjusted adaptively in different layers of networks, which increases the flexibility of networks compared with the fixed graph structure used by original GCNs. On two high-density sEMG (HD-sEMG) datasets and a sparse armband dataset, STCN-GR outperforms previous works and achieves the state-of-the-art, which shows superior performance and powerful generalization ability.

Original languageEnglish (US)
Title of host publicationNeural Information Processing - 28th International Conference, ICONIP 2021, Proceedings
EditorsTeddy Mantoro, Minho Lee, Media Anugerah Ayu, Kok Wai Wong, Achmad Nizar Hidayanto
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages13
ISBN (Print)9783030922375
StatePublished - 2021
Event28th International Conference on Neural Information Processing, ICONIP 2021 - Virtual, Online
Duration: Dec 8 2021Dec 12 2021

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13110 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference28th International Conference on Neural Information Processing, ICONIP 2021
CityVirtual, Online

Bibliographical note

Publisher Copyright:
© 2021, Springer Nature Switzerland AG.


  • Gesture recognition
  • Human-computer interaction
  • sEMG graph
  • Spatial-temporal convolutional networks
  • Surface electromyography

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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