文献类型：期刊文献

英文题名：Mechanomyography signals pattern recognition in hand movements using swarm intelligence algorithm optimized support vector machine based on acceleration sensors

作者：Zhang, Yue[1];Cao, Gangsheng[2];Sun, Maoxun[3];Zhao, Baigan[1];Wu, Qing[2];Xia, Chunming[2,4]

机构：[1]Nantong Univ, Sch Mech Engn, Nantong 226019, Peoples R China;[2]East China Univ Sci & Technol, Sch Mech & Power Engn, Shanghai 200237, Peoples R China;[3]Univ Shanghai Sci & Technol, Sch Mech Engn, Shanghai 200093, Peoples R China;[4]Shanghai Univ Engn Sci, Sch Mech & Automot Engn, Shanghai 201620, Peoples R China

年份：2024

卷号：124

外文期刊名：MEDICAL ENGINEERING & PHYSICS

收录：;EI(收录号：20240215353328);WOS:【SCI-EXPANDED(收录号:WOS:001154617200001)】；

基金：Funding This work was supported by Shanghai University Youth Teacher Training Assistance Scheme (ZZ 202203047) .

语种：英文

外文关键词：Mechanomyography; Pattern recognition; Convolutional neural network; Bald eagle search; Grey wolf optimization; Sparrow search algorithm

摘要：On the basis of extracting mechanomyography (MMG) signal features, the classification of hand movements has certain application values in human -machine interaction systems and wearable devices. In this paper, pattern recognition of hand movements based on MMG signal is studied with swarm intelligence algorithms introduced to optimize support vector machine (SVM). Time domain (TD) features, wavelet packet node energy (WPNE) features, frequency domain (FD) features, convolution neural network (CNN) features were extracted from each channel to constitute different feature sets. Three novel swarm intelligence algorithms (i.e., bald eagle search (BES), sparrow search algorithm (SSA), grey wolf optimization (GWO)) optimized SVM is proposed to train the models and recognition of hand movements are tested for each MMG feature extraction method. Using GWO as the optimization algorithm, time consumption is less than using the other two swarm algorithms. Using GWO with TD+FD features can obtain the classification accuracy of 93.55 %, which is higher than other methods while using CNN to extract features can be independent of domain knowledge. The results confirm GWO-SVM with TD + FD features is superior to some other methods in the classification problem for tiny samples based on MMG.