文献类型：期刊文献

英文题名：Optimization of HD-sEMG-Based Cross-Day Hand Gesture Classification by Optimal Feature Extraction and Data Augmentation

作者：Jiang, Xinyu[1];Liu, Xiangyu[2];Fan, Jiahao[1];Ye, Xinming[3];Dai, Chenyun[1];Clancy, Edward A.[4];Farina, Dario[5];Chen, Wei[1]

机构：[1]Fudan Univ, Ctr Intelligent Med Elect, Sch Informat Sci & Technol, Shanghai 200433, Peoples R China;[2]Univ Shanghai Sci & Technol, Coll Commun & Art Design, Shanghai 200093, Peoples R China;[3]East China Univ Sci & Technol, Sch Sports Sci & Engn, Shanghai 200237, Peoples R China;[4]Worcester Polytech Inst, Dept Elect & Comp Engn, Worcester, MA 01609 USA;[5]Imperial Coll London, Dept Bioengn, London SW7 2AZ, England

年份：2022

卷号：52

期号：6

起止页码：1281

外文期刊名：IEEE TRANSACTIONS ON HUMAN-MACHINE SYSTEMS

收录：;EI(收录号：20224913215381);WOS:【SCI-EXPANDED(收录号:WOS:000800783600001)】；

基金：This work was supported in part by the National Natural Science Foundation of China under Grant 62001122, in part by the Shanghai Municipal Science and Technology Project under Grant 20510710500, and in part by the Natural Science Foundation of Shanghai under Grant 20ZR1403400.

语种：英文

外文关键词：Feature extraction; Task analysis; Electrodes; Training; Robustness; Optimization; Indexes; Hand gesture classification; high-density sEMG (HD-sEMG); human-machine interactions (HMIs); pattern recognition

摘要：Human-machine interaction requires accurate recognition of human intentions (e.g., via hand gestures). Here, we assessed the cross-day robustness of widely used hand gesture classification techniques applied to high-density surface electromyogram (HD-sEMG) signals (256 channels). Our evaluation covered techniques in each stage of the classification framework: first, 50 temporal-spectral-spatial domain features, second, 15 feature optimization techniques, and third, seven classifiers. Moreover, although HD-sEMG provides sufficient neuromuscular information, some of the channels may present low signal-to-noise ratio and should therefore be treated as outliers. Accordingly, we performed our evaluation with, first, all outlier channels retained, and second, removal of the features corresponding to poor-quality channels and substitution with interpolated values from neighbor channels. The impact of sliding window and data augmentation was also investigated. We examined the results on a 35-gesture classification task using HD-sEMG acquired from 20 subjects on two sessions in separate days. The results showed that interpolation of features from outlier channels significantly improved the performance in most cases. Use of a sliding window and of data augmentation contributed to a higher classification accuracy. For the classification of 11 selected gestures of common daily use, the support vector machine classifier achieved the highest classification accuracy of 91.9% in a cross-day validation protocol using an optimal combination of 13 features (each extracted from sliding windows), feature optimization by linear discriminant analysis, and data augmentation. Our work can serve as a technique-screening tool on cross-day applications of human-machine interactions.