基于TiCNN-DRSN模型的sEMG手势识别算法的研究  

Research on sEMG gesture recognition algorithm based on TiCNN-DRSN model

作　　者：周国良 张道辉 郭小萍[1] Zhou Guoliang;Zhang Daohui;Guo Xiaoping(Shenyang University of Chemical Technology,Shenyang 110000,China)

机构地区：[1]沈阳化工大学,沈阳110000

出　　处：《电子测量技术》2024年第6期190-196,共7页Electronic Measurement Technology

基　　金：国家自然科学基金(62273336,92048302,U20A20197);国家重点研发计划(2022YPP1202500,2022YPP1202502)项目资助。

摘　　要：基于表面肌电信号和模式识别的手势识别方法在康复手领域中具有广阔的应用前景。提出一种基于表面肌电信号的手部姿势识别方法,以预测手部的52种动作。为解决表面肌电信号易受干扰的问题,提高对表面肌电信号的分类效果,提出了TiCNN-DRSN网络,主要作用是在拥有噪声的情况下能够更好的识别率,减少滤除噪声的时间。TiCNN网络使用卷积核Dropout和极小批量训练,为卷积神经网络引入训练干扰并且增加了模型的泛化性;DRSN网络可以有效的剔除sEMG信号中的冗余信号,减少信号噪声干扰。TiCNN-DRSN网络在不需要任何降噪预处理的前提下,取得了很高的抗噪与自适应性能。本模型在Ninapro数据库上的识别率达到97.43%±0.8%。The gesture recognition method based on surface electromyography and pattern recognition has a broad application prospect in the field of rehabilitation hand.In this paper,a hand gesture recognition method based on surface electromyography(sEMG)is proposed to predict 52 hand movements.In order to solve the problem that surface EMG signals are easily disturbed and improve the classification effect of surface EMG signals,TiCNN-DRSN network is proposed,whose main function is to better identify the noise and reduce the time for filtering the noise.Ti is a TiCNN network,in which convolutional kernel Dropout and minimal batch training are used to introduce training interference to the convolutional neural network and increase the generalization of the model;DRSN is a deep residual shrinkage network,which can effectively eliminate redundant signals in sEMG signals and reduce signal noise interference.TiCNN-DRSN has achieved high anti-noise and adaptive performance without any noise reduction pretreatment.The recognition rate of this model on Ninapro database reaches 97.43%0.8%.

关 键 词：表面肌电信号 抗噪声 TiCNN-DRSN网络 

分 类 号：TP242[自动化与计算机技术—检测技术与自动化装置]