手势
计算机科学
块(置换群论)
手势识别
人工智能
语音识别
卷积神经网络
特征提取
模式识别(心理学)
特征(语言学)
接口(物质)
嵌入
计算机视觉
语言学
哲学
几何学
数学
气泡
最大气泡压力法
并行计算
作者
Shudi Wang,Li Huang,Jiang Du,Ying Sun,Guozhang Jiang,Jun Li,Cejing Zou,Hanwen Fan,Yuanmin Xie,Hao Xiong,Baojia Chen
标识
DOI:10.3389/fbioe.2022.909023
摘要
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.
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