文献类型：期刊文献

英文题名：sEMG-Based Drawing Trace Reconstruction: A Novel Hybrid Algorithm Fusing Gene Expression Programming into Kalman Filter

作者：Yang, Zhongliang[1];Wen, Yangliang[1];Chen, Yumiao[2]

机构：[1]Donghua Univ, Coll Mech Engn, Shanghai 201620, Peoples R China;[2]East China Univ Sci & Technol, Sch Art Design & Media, Shanghai 200237, Peoples R China

年份：2018

卷号：18

期号：10

外文期刊名：SENSORS

收录：;EI(收录号：20184105929145);WOS:【SCI-EXPANDED(收录号:WOS:000448661500124)】；

基金：This study was partly supported by the Fundamental Research Funds for the Central Universities (No. 2232018D3-27), the Zhejiang Provincial Key Laboratory of Integration of Healthy Smart Kitchen System (No. 2014E10014) and the Shanghai Summit Discipline in Design (No. DB18304).

语种：英文

外文关键词：drawing trace; electromyography; gene expression programming; Kalman Filter; muscle-computer interface

摘要：How to reconstruct drawing and handwriting traces from surface electromyography (sEMG) signals accurately has attracted a number of researchers recently. An effective algorithm is crucial to reliable reconstruction. Previously, nonlinear regression methods have been utilized successfully to some extent. In the quest to improve the accuracy of transient myoelectric signal decoding, a novel hybrid algorithm KF-GEP fusing Gene Expression Programming (GEP) into Kalman Filter (KF) framework is proposed for sEMG-based drawing trace reconstruction. In this work, the KF-GEP was applied to reconstruct fourteen drawn shapes and ten numeric characters from sEMG signals across five participants. Then the reconstruction performance of KF-GEP, KF and GEP were compared. The experimental results show that the KF-GEP algorithm performs best because it combines the advantages of KF and GEP. The findings add to the literature on the muscle-computer interface and can be introduced to many practical fields.