文献类型：期刊文献

英文题名：Design on a wireless mechanomyography acquisition equipment and feature selection for lower limb motion recognition

作者：Zhang, Hanyang[1];Wang, Xinping[1];Zhang, Yue[1];Cao, Gangsheng[1];Xia, Chunming[1,2]

机构：[1]East China Univ Sci & Technol, Dept Mech Engn, Shanghai 200237, Peoples R China;[2]Shanghai Univ Engn Sci, Sch Mech & Automot Engn, Shanghai 201620, Peoples R China

年份：2022

卷号：77

外文期刊名：BIOMEDICAL SIGNAL PROCESSING AND CONTROL

收录：;EI(收录号：20222412211192);WOS:【SCI-EXPANDED(收录号:WOS:000814367300004)】；

语种：英文

外文关键词：Mechanomyography; Wireless acquisition; Feature selection; Swarm intelligence algorithms; Pattern recognition

摘要：Mechanomyography (MMG) is a kind of biomedical signal with great research value. This paper designed a novel wireless MMG signal acquisition system composed of modular nodes and core board. The modular nodes were worn on different muscle of the lower limbs, collecting movement data of the corresponding part, and transmitted it to the core board wirelessly. The core board was connected to the computer through the USB to achieve the wireless collection and real-time display of MMG signals. In order to improve the real-time performance, this paper adopted four swarm intelligence algorithms (GA, PSO, WOA, SSA) and three improved algorithms (IPSO, IWOA, ISSA) for feature selection to reduce the redundancy of information. In this study, the different effects of different feature selection methods on the recognition of eight types of lower limb movements based on MMG signals were discussed by comparing the classification accuracy, number of iterations and calculation time. The results show that swarm intelligence algorithms have merits in this type of feature selection problem, and GA has the most obvious effect in improving classification accuracy, but it requires more iterations and calculation time, while the classification accuracy of IPSO is close to that of GA, and it has advantages in time consumption.