重庆理工大学学报2024，Vol.38Issue(21)：119-128,10.DOI:10.3969/j.issn.1674-8425(z).2024.11.015
特征图组合的双流CNN手指关节角度连续运动预测方法研究
Research on the continuous motion prediction method of finger joint angles using dual-stream CNN based on feature map combination
摘要
Abstract
To address the insufficient extraction of timing information and low accuracy in predicting continuous motion of finger joint angles based on surface electromyographic(sEMG)signals,we propose a two-stream convolutional neural network prediction method based on feature map combination(FMC).First,the feature information of the sEMG signal is extracted.Then,the feature information is integrated into feature maps(FMC)by employing a sliding window method to express the temporal coherence of the features and extract the temporal information of the sEMG signal.Finally,the dual stream convolutional neural network(DCNN)network is used to extract deep features from the combined feature maps in the temporal and spatial dimensions to improve the prediction of finger joint angles continuous motion.Experiments are conducted on the NinaPro-DB8 dataset,and our results show,compared with three different degrees of freedom(18,5,3),the R2 values of healthy subjects increase by 7.9％,16.8％,and 17.8％respectively,while the R2 values of amputees increase by 9.6％,14.3％,and 10.3％respectively.关键词
sEMG/连续运动预测/特征图组合/双流卷积神经网络Key words
sEMG/continuous motion prediction/feature map combination/dual stream convolutional neural network分类
信息技术与安全科学引用本文复制引用
武岩,曹崇莉,李奇,姬鹏辉,张航..特征图组合的双流CNN手指关节角度连续运动预测方法研究[J].重庆理工大学学报,2024,38(21):119-128,10.基金项目
吉林省科技发展计划国际科技合作项目(20200801035GH) （20200801035GH）
吉林省科技发展计划国际联合研究中心建设项目(20200802004GH) （20200802004GH）
