Wang Shudi, Huang Li, Jiang Du, Sun Ying, Jiang Guozhang, Li Jun, Zou Cejing, Fan Hanwen, Xie Yuanmin, Xiong Hegen, Chen Baojia
Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education, Wuhan University of Science and Technology, Wuhan, China.
Research Center for Biomimetic Robot and Intelligent Measurement and Control, Wuhan University of Science and Technology, Wuhan, China.
Front Bioeng Biotechnol. 2022 Jun 7;10:909023. doi: 10.3389/fbioe.2022.909023. eCollection 2022.
As a key technology for the non-invasive human-machine interface that has received much attention in the industry and academia, surface EMG (sEMG) signals display great potential and advantages in the field of human-machine collaboration. Currently, gesture recognition based on sEMG signals suffers from inadequate feature extraction, difficulty in distinguishing similar gestures, and low accuracy of multi-gesture recognition. To solve these problems a new sEMG gesture recognition network called Multi-stream Convolutional Block Attention Module-Gate Recurrent Unit (MCBAM-GRU) is proposed, which is based on sEMG signals. The network is a multi-stream attention network formed by embedding a GRU module based on CBAM. Fusing sEMG and ACC signals further improves the accuracy of gesture action recognition. The experimental results show that the proposed method obtains excellent performance on dataset collected in this paper with the recognition accuracies of 94.1%, achieving advanced performance with accuracy of 89.7% on the Ninapro DB1 dataset. The system has high accuracy in classifying 52 kinds of different gestures, and the delay is less than 300 ms, showing excellent performance in terms of real-time human-computer interaction and flexibility of manipulator control.
作为在工业界和学术界备受关注的无创人机接口的关键技术，表面肌电（sEMG）信号在人机协作领域显示出巨大的潜力和优势。目前，基于sEMG信号的手势识别存在特征提取不足、难以区分相似手势以及多手势识别准确率低等问题。为了解决这些问题，提出了一种基于sEMG信号的新型sEMG手势识别网络，即多流卷积块注意力模块-门控循环单元（MCBAM-GRU）。该网络是通过嵌入基于CBAM的GRU模块形成的多流注意力网络。融合sEMG和ACC信号进一步提高了手势动作识别的准确率。实验结果表明，该方法在本文采集的数据集上获得了优异的性能，识别准确率达到94.1%，在Ninapro DB1数据集上以89.7%的准确率达到了先进水平。该系统在对52种不同手势进行分类时具有较高的准确率，延迟小于300毫秒，在实时人机交互和机械手控制灵活性方面表现出色。
