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Electromyographic Signal Based Dynamic Hand Gesture Recognition Using Transfer Learning

Song, Shouan; Yang, Lei; Huo, Benyan; Wu, Man; Liu, Yanhong; Yu, Hongnian

Authors

Shouan Song

Lei Yang

Benyan Huo

Man Wu

Yanhong Liu



Abstract

Recent years, the research of gesture recognition based on surface EMG signal has become an active topic. The conventional methods mainly focus on feature engineering, but the sEMG signal is non-stationary temporally, which makes proper feature design and selection very complicated. To tackle this problem, this paper proposes a novel dynamic gesture recognition method by employing Convolutional Neural Networks. Short-time Fourier transform (STFT) and continuous wavelet transform (CWT) are introduced to model the time-frequency features of a single channel and the relationship between different channels. Meanwhile, the performance of deep learning-based methods relies on a large amount of training data. In the context of sEMG based gesture recognition, one user cannot be expected to generate tens of thousands of examples at a time. Hence, transfer learning (TL) techniques is utilized to alleviate the data generation burden imposed on a single individual and enhance the performance of the Convolutional Neural Networks. Experiment results on the sEMG data set recorded with Myo Armband indicate that the transfer learning augmented ConvNet can achieve an accuracy of 99.41%

Citation

Song, S., Yang, L., Huo, B., Wu, M., Liu, Y., & Yu, H. (2022). Electromyographic Signal Based Dynamic Hand Gesture Recognition Using Transfer Learning. In Proceedings of 2021 Chinese Intelligent Automation Conference (389-397). https://doi.org/10.1007/978-981-16-6372-7_44

Conference Name 2021 Chinese Intelligent Automation Conference
Conference Location Zhanjiang, China
Online Publication Date Oct 8, 2021
Publication Date 2022
Deposit Date Jun 15, 2022
Publisher Springer
Pages 389-397
Series Title Lecture Notes in Electrical Engineering
Series Number 801
Series ISSN 1876-1100
Book Title Proceedings of 2021 Chinese Intelligent Automation Conference
ISBN 978-981-16-6371-0
DOI https://doi.org/10.1007/978-981-16-6372-7_44
Public URL http://researchrepository.napier.ac.uk/Output/2879042