计算机工程与应用 ›› 2024, Vol. 60 ›› Issue (20): 124-132.DOI: 10.3778/j.issn.1002-8331.2307-0046

• 模式识别与人工智能 • 上一篇    下一篇

基于迁移学习和表面肌电信号的上肢动作识别

张恒玮,徐林森,陈根,汪志焕,眭翔   

  1. 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
  • 出版日期:2024-10-15 发布日期:2024-10-15

Upper Limb Action Recognition Based on Transfer Learning and sEMG

ZHANG Hengwei, XU Linsen, CHEN Gen, WANG Zhihuan, SUI Xiang   

  1. 1.College of Mechanical and Electrical Engineering, Hohai University, Changzhou, Jiangsu 213022, China
    2.Science Island Branch of Graduate School, University of Science and Technology of China, Hefei 230026, China
  • Online:2024-10-15 Published:2024-10-15

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。

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)

Abstract: 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.

Key words: upper limb action recognition, surface electromyography (sEMG), short-time fourier transform (STFT), pre-trained models, support vector machine (SVM)