基于表面肌电信号灰度图和多视野卷积神经网络的手势精确识别方法  

Gesture accuracy recognition based on grayscale image of surface electromyogram signal and multi-view convolutional neural network

作　　者：陈清正 陶庆[1] 张小栋[2] 胡学政 张天乐 CHEN Qingzheng;TAO Qing;ZHANG Xiaodong;HU Xuezheng;ZHANG Tianle(College of Intelligent Manufacturing Modern Industry(School of Mechanical Engineering),Xinjiang University,Urumchi 830017,P.R.China;School of Mechanical Engineering,Xi’an Jiaotong University,Xi’an 710049,P.R.China)

机构地区：[1]新疆大学智能制造现代产业学院(机械工程学院),乌鲁木齐830017 [2]西安交通大学机械工程学院,西安710049

出　　处：《生物医学工程学杂志》2024年第6期1153-1160,共8页Journal of Biomedical Engineering

基　　金：国家自然科学基金项目(51865056);自治区“天山英才”科技创新领军人才项目(2023TSYCLJ0051)。

摘　　要：针对表面肌电信号时域和频域特征提取识别手势的准确性易受影响及分类器识别率低的问题,本文提出一种将表面肌电信号处理为灰度图,并结合卷积神经网络作为分类器的手势精确识别方法。首先,使用能量阈值法截取肌电信号的活动段,通过线性和幂运算对时域电压值进行处理,生成灰度图作为卷积神经网络的输入。其次,搭建多视野卷积神经网络模型,使用1×n和3×n的异形卷积核,在同一卷积层内实现不同尺寸卷积核并行的结构,以优化对肌电信号的表达能力。实验结果表明,所提出的方法对13种手势和12种多指运动的识别准确率分别达到98.11%和98.75%,显著高于现有机器学习方法。本文提出的基于肌电信号灰度图与多视野卷积神经网络的手势识别方法,具有简单高效的特点,能够有效提升手势识别的准确性,具有较强的应用潜力。This study aims to address the limitations in gesture recognition caused by the susceptibility of temporal and frequency domain feature extraction from surface electromyography signals,as well as the low recognition rates of conventional classifiers.A novel gesture recognition approach was proposed,which transformed surface electromyography signals into grayscale images and employed convolutional neural networks as classifiers.The method began by segmenting the active portions of the surface electromyography signals using an energy threshold approach.Temporal voltage values were then processed through linear scaling and power transformations to generate grayscale images for convolutional neural network input.Subsequently,a multi-view convolutional neural network model was constructed,utilizing asymmetric convolutional kernels of sizes 1×n and 3×n within the same layer to enhance the representation capability of surface electromyography signals.Experimental results showed that the proposed method achieved recognition accuracies of 98.11%for 13 gestures and 98.75%for 12 multi-finger movements,significantly outperforming existing machine learning approaches.The proposed gesture recognition method,based on surface electromyography grayscale images and multi-view convolutional neural networks,demonstrates simplicity and efficiency,substantially improving recognition accuracy and exhibiting strong potential for practical applications.

关 键 词：卷积神经网络 表面肌电信号 灰度图 手势识别 

分 类 号：R318[医药卫生—生物医学工程] TP183[医药卫生—基础医学] TN911.7[自动化与计算机技术—控制理论与控制工程]