@inproceedings{78a24f3161d3423aa63af3d4ac4b8a0f,
title = "A Lightweight Spatial Channel Reconstruction Network for sEMG Gesture Recognition",
abstract = "At present, gesture recognition models based on surface electromyography (sEMG) have difficulty achieving a balance between computational efficiency and high accuracy. To address this issue, we propose a spatial channel reconstruction convolutional neural network (SCR-CNN). The model comprises two primary units: the spatial reconstruct unit (SRU) and the channel reconstruct unit (CRU). The SRU uses a separation reconstruction methodology to effectively alleviate spatial redundancy, whereas the CRU adopts a separation transformation fusion strategy aimed at reducing channel redundancy and the computational cost. The sEMG signal is converted into a twodimensional image through the Gramian angular field and input into the SCR-CNN for feature extraction and classification. Evaluation across NinaPro dataset demonstrates that SCR-CNN exhibits strong performance in both computational efficiency and accuracy.",
keywords = "CRU, Gesture recognition, SCR-CNN, SRU",
author = "Jiamin Zhong and Shuo Zhai and Xiaoliang Guo",
note = "Publisher Copyright: {\textcopyright} 2025 IEEE.; 6th International Conference on Computer Vision, Image and Deep Learning, CVIDL 2025 ; Conference date: 23-05-2025 Through 25-05-2025",
year = "2025",
doi = "10.1109/CVIDL65390.2025.11085494",
language = "English",
series = "2025 6th International Conference on Computer Vision, Image and Deep Learning, CVIDL 2025",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1085--1090",
booktitle = "2025 6th International Conference on Computer Vision, Image and Deep Learning, CVIDL 2025",
address = "United States",
}