Contrastive Domain Adaptation: A Self-Supervised Learning Framework for sEMG-Based Gesture Recognition

Zhiping Lai, Xiaoyang Kang*, Hongbo Wang*, Xueze Zhang, Weiqi Zhang, Fuhao Wang

*Corresponding author for this work

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

1 Scopus citations

Abstract

Gesture recognition using surface electromyography (sEMG) shows its great potential in the field of human-computer interaction (HCI). Previous works achieve relatively good performance based on the assumption of invariant statistic distribution. However, the practical application effect is unsatisfactory due to the problem of domain shift. Existing approaches need plenty of labeled sEMG samples from target scenarios for calibration, which is burdensome for experimenters and users. In this work, we present a contrastive self-supervised learning framework (ConSSL) for sEMG-based gesture recognition to realize domain adaptation in target domains. After pretraining on a bunch of unlabeled samples, only a small number of labeled samples are needed for calibration and domain adaptation. Experimental results indicate that the proposed framework out-performs other approaches even ifleq 50% labeled samples in target scenarios are available and achieves the state-of-the-art.

Original languageEnglish (US)
Title of host publication2022 IEEE International Joint Conference on Biometrics, IJCB 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665463942
DOIs
StatePublished - 2022
Event2022 IEEE International Joint Conference on Biometrics, IJCB 2022 - Abu Dhabi, United Arab Emirates
Duration: Oct 10 2022Oct 13 2022

Publication series

Name2022 IEEE International Joint Conference on Biometrics, IJCB 2022

Conference

Conference2022 IEEE International Joint Conference on Biometrics, IJCB 2022
Country/TerritoryUnited Arab Emirates
CityAbu Dhabi
Period10/10/2210/13/22

Bibliographical note

Publisher Copyright:
© 2022 IEEE.

ASJC Scopus subject areas

  • Agricultural and Biological Sciences (miscellaneous)
  • Computer Vision and Pattern Recognition
  • Health Informatics
  • Instrumentation

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