Abstract
sEMG (surface electromyography) signal control of bionic prostheses has been widely studied over the past few years. In particular, sparse sEMG signals are rapidly developing in the field of gesture recognition for their convenience, noninvasiveness, and ease of access. However, compared with high-density EMG signals, sparse EMG signals lack rich feature information, which in turn affects gesture recognition accuracy. In order to reduce the loss of feature information of sparse EMG signals in the spatio-temporal dimension, this paper proposes a hybrid neural network Res-LSTM combining residual network and long short-term memory network. The ordinary convolutional blocks in the CNN network are replaced with residual blocks, and the final fully connected layer is removed and a constant mapping layer is added to adequate extraction of spatial feature information of the data. The Res layer’s output is utilized as the input for the long and short-term memory (LSTM) network, which further extracts the features of the data in the temporal dimension, and finally completes the categorization output through a fully connected layer. The average accuracy of 91.11% for gesture-motion classification was verified by training on a homebrew dataset; and tested on a public dataset, NinaPro DB1, with an average accuracy of 91.03%. The experimental findings indicate that the proposed Res-LSTM network framework contributes to solving the problem of lack of feature information of sparse sEMG signals and improves the pattern recognition accuracy.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Farina, D., Aszmann, O.: Bionic limbs: clinical reality and academic promises. Sci. Transl. Med. 6, 257 (2014)
Sun, Y., Tang, H., et al.: Review of recent progress in robotic knee prosthesis related techniques: structure, actuation and control. J. Bionic Eng. 18(4), 764–785 (2021)
Mendez, V., Iberite, F., Shokur, S., et al.: Current solutions and future trends for robotic prosthetic hands. Annu. Rev. Control Robot. Auton. Syst. 4, 595–627 (2021)
Krasoulis, A., Vijayakumar, S., Nazarpour, K.: Multi-grip classification-based prosthesis control with two EMG-IMU sensors. IEEE Trans. Neural Syst. Rehabil. Eng. 28(2), 508–518 (2019)
Purushothaman, G., Ray, K.K.: EMG based man–machine interaction – a pattern recognition research platform. Robot. Auton. Syst. 62(6), 864–870 (2014)
Song, G., Huang, R., Guo, Y., et al.: An EEG-EMG-based motor intention recognition for walking assistive exoskeletons. In: International Conference on Intelligent Robotics and Applications, pp. 769–781. Springer International Publishing, Cham (2022)
Atzori, M., Gijsberts, A., Castellini, C., et al.: Electromyography data for non-invasive naturally-controlled robotic hand prostheses. Sci. Data 1(1), 1–13 (2014)
Castellini, C., Fiorilla, A.E., Sandini, G.: Multi-subject/daily-life activity EMG-based control of mechanical hands. J. Neuroeng. Rehabil. 6, 1–11 (2009)
Khokhar, Z.O., Xiao, Z.G., Menon, C.: Surface EMG pattern recognition for real-time control of a wrist exoskeleton. Biomed. Eng. Online 9, 1–17 (2010)
Atzori, M., Cognolato, M., Müller, H.: Deep learning with convolutional neural networks applied to electromyography data: a resource for the classification of movements for prosthetic hands. Front. Neurorobot. 10, 9 (2016)
Zhang, Z., He, C., Yang, K.: A novel surface electromyographic signal-based hand gesture prediction using a recurrent neural network. Sensors 20(14), 3994 (2020)
Xing, K., Ding, Z., Jiang, S., et al.: Hand gesture recognition based on deep learning method. In: 2018 IEEE Third International Conference on Data Science in Cyberspace (DSC), pp. 542–546. IEEE, Guangzhou, China (2018)
Ma, Y., Liu, Q., Liu, H., et al.: sEMG-based estimation of human arm endpoint stiffness using long short-term memory neural networks and autoencoders. In: International Conference on Intelligent Robotics and Applications, pp. 699–710. Springer International Publishing, Cham (2022)
Yao, H., Tang, X., Wei, H., et al.: Modeling spatial-temporal dynamics for traffic prediction. arXiv preprint 1(9) (2018)
Wu, Y., Zheng, B., Zhao, Y.: Dynamic gesture recognition based on LSTM-CNN. In: 2018 Chinese Automation Congress (CAC), pp. 2446–2450. IEEE, Xi’an, China (2018)
Bao, T., Zaidi, S.A.R., Xie, S., et al.: A CNN-LSTM hybrid model for wrist kinematics estimation using surface electromyography. IEEE Trans. Instrum. Meas. 70, 1–9 (2020)
Yu, M., Li, G., Jiang, D., et al.: Hand medical monitoring system based on machine learning and optimal EMG feature set. Pers. Ubiquit. Comput. 1–17 (2019)
Li, G., Zou, C., Jiang, G., et al.: Multi-view fusion network-based gesture recognition using sEMG data. IEEE J. Biomed. Health Inform. 1–13 (2023)
Geng, W., Du, Y., et al.: Gesture recognition by instantaneous surface EMG images. Sci. Rep. 6(1), 36571 (2016)
Du, Y., Wei, W., et al.: Surface EMG-based inter-session gesture recognition enhanced by deep domain adaptation. Sensors 17(3), 458 (2017)
Cene, H.V., Tosin, M., Machado, J., et al.: Open database for accurate upper-limb intent detection using electromyography and reliable extreme learning machines. Sensors 19(8), 1864 (2019)
Hu, Y., Wong, Y., Wei, W., et al.: A novel attention-based hybrid CNN-RNN architecture for sEMG-based gesture recognition. PLoS ONE 13(10), 0206049 (2018)
Acknowledgements
The study acknowledge the support of the National Natural Science Foundation of China (Grant Nos. 51575407 and 51505349). ‘The 14th Five Year Plan’ Hubei Provincial advantaged characteristic disciplines (groups) project of Wuhan University of Science and Technology (2023C0401).
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2025 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Zhao, Y. et al. (2025). Gesture Recognition of sEMG Based on Res-LSTM. In: Lan, X., Mei, X., Jiang, C., Zhao, F., Tian, Z. (eds) Intelligent Robotics and Applications. ICIRA 2024. Lecture Notes in Computer Science(), vol 15205. Springer, Singapore. https://doi.org/10.1007/978-981-96-0777-8_14
Download citation
DOI: https://doi.org/10.1007/978-981-96-0777-8_14
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-96-0776-1
Online ISBN: 978-981-96-0777-8
eBook Packages: Computer ScienceComputer Science (R0)