基于迁移学习和表面肌电信号的上肢动作识别  

Upper Limb Action Recognition Based on Transfer Learning and sEMG

作　　者：张恒玮 徐林森 陈根 汪志焕 眭翔 ZHANG Hengwei;XU Linsen;CHEN Gen;WANG Zhihuan;SUI Xiang(College of Mechanical and Electrical Engineering,Hohai University,Changzhou,Jiangsu 213022,China;Science Island Branch of Graduate School,University of Science and Technology of China,Hefei 230026,China)

机构地区：[1]河海大学机电工程学院,江苏常州213022 [2]中国科学技术大学研究生院科学岛分院,合肥230026

出　　处：《计算机工程与应用》2024年第20期124-132,共9页Computer Engineering and Applications

基　　金：江苏省前沿引领技术基础研究专项(BK20191004);常州市科技计划项目(重点实验室)(CM20223014);江苏省高等学校基础科学(自然科学)研究项目(23KJD460001)。

摘　　要：准确识别脑卒中患者上肢运动意图是实现高效康复训练的关键步骤。为了提高基于表面肌电信号(surface electromyography,sEMG)的上肢动作识别精度,提出了一种结合预训练模型和支持向量机(support vector machine,SVM)的肌电动作识别方法。该方法充分考虑通道之间的关联性,将预处理后的时域信号通过短时傅里叶变换(short-time Fourier transform,STFT)转换为对应频谱图,并将所有通道的频谱图沿竖直方向拼接。利用两种微调的预训练模型VGG16和Resnet50对肌电图像提取特征,分别考虑三种上肢动作识别方案:仅使用微调的预训练模型进行识别、单个微调预训练模型提取特征后使用SVM进行识别、两个微调预训练模型提取特征拼接后使用SVM进行识别。实验结果表明,所提出的方法在采集的受试者肌电信号数据集上均达到90%以上的识别精度,可有效区分不同的上肢动作。Accurate recognition of upper limb action intention in stroke patients is a key step towards efficient rehabilitation training.In order to improve the accuracy of upper limb action recognition based on surface electromyography(sEMG),a method is proposed that combines pre-trained models and support vector machine(SVM)classification.This method fully considers the correlation between channels and converts the preprocessed time-domain signal into corresponding spectrograms through short time Fourier transform(STFT),and concatenates the spectrograms of all channels in the vertical direction.Two fine-tuning pre-training models,VGG16 and Resnet50,are used to extract features from the EMG images.Three upper limb action recognition schemes are considered separately:using only fine-tuning pre-trained models for recognition,a single fine-tuning pre-trained model extracts features and uses SVM for recognition,and two fine-tuning pre-trained models extract feature concatenation and use SVM for recognition.The experimental results show that the proposed method achieves a recognition accuracy of over 90%on the collected subject EMG signal dataset,which can effectively differentiate between different upper limb action.

关 键 词：上肢动作识别 表面肌电信号(sEMG) 短时傅里叶变换(STFT) 预训练模型 支持向量机(SVM) 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]