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Construction of Human Digital Twin Model Based on Multimodal Data and Its Application in Locomotion Mode Identifcation

查看全文 作　　者：Ruirui [1,2]Zhong;Bingtao [1,2]Hu;Yixiong [1,2]Feng;Hao [3]Zheng;Zhaoxi [1,2]Hong;Shanhe [4]Lou;Jianrong [1,2]Tan 高影响力作者 机构地区：[1]State Key Laboratory of Fluid Power and Mechatronic Systems,Zhejiang University,Hangzhou 310027,China;[2]Engineering Research Center for Design Engineering and Digital Twin of Zhejiang Province,Hangzhou 310027,China;[3]Hangzhou Innovation Institute,Beihang University,Hangzhou 310052,China;[4]School of Mechanical and Aerospace Engineering,Nanyang Technological University,Singapore 637460,Singapore高影响力机构 出　　处：《Chinese Journal of Mechanical Engineering》索引2023年第36卷第5期,共13页高影响力期刊 基　　金：Supported by National Natural Science Foundation of China(Grant Nos.52205288,52130501,52075479);Zhejiang Provincial Key Research&Development Program(Grant No.2021C01110). 摘　　要：With the increasing attention to the state and role of people in intelligent manufacturing, there is a strong demand for human-cyber-physical systems (HCPS) that focus on human-robot interaction. The existing intelligent manufacturing system cannot satisfy efcient human-robot collaborative work. However, unlike machines equipped with sensors, human characteristic information is difcult to be perceived and digitized instantly. In view of the high complexity and uncertainty of the human body, this paper proposes a framework for building a human digital twin (HDT) model based on multimodal data and expounds on the key technologies. Data acquisition system is built to dynamically acquire and update the body state data and physiological data of the human body and realize the digital expression of multi-source heterogeneous human body information. A bidirectional long short-term memory and convolutional neural network (BiLSTM-CNN) based network is devised to fuse multimodal human data and extract the spatiotemporal features, and the human locomotion mode identifcation is taken as an application case. A series of optimization experiments are carried out to improve the performance of the proposed BiLSTM-CNN-based network model. The proposed model is compared with traditional locomotion mode identifcation models. The experimental results proved the superiority of the HDT framework for human locomotion mode identifcation. 关 键 词：Human digital twin Human-cyber-physical system Bidirectional long short-term memory Convolutional neural network Multimodal data
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