计算机工程与应用2024，Vol.60Issue(20)：124-132,9.DOI:10.3778/j.issn.1002-8331.2307-0046
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
摘要
Abstract
Accurate recognition of upper limb action intention in stroke patients is a key step towards efficient rehabilita-tion 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 corre-sponding 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)Key words
upper limb action recognition/surface electromyography(sEMG)/short-time fourier transform(STFT)/pre-trained models/support vector machine(SVM)分类
计算机与自动化引用本文复制引用
张恒玮,徐林森,陈根,汪志焕,眭翔..基于迁移学习和表面肌电信号的上肢动作识别[J].计算机工程与应用,2024,60(20):124-132,9.基金项目
江苏省前沿引领技术基础研究专项(BK20191004) （BK20191004）
常州市科技计划项目(重点实验室)(CM20223014) （重点实验室）
江苏省高等学校基础科学(自然科学)研究项目(23KJD460001). （自然科学）
