Multi-Stream CNN-Based Personal Recognition Method Using Surface Electromyogram for 5G Security  

作　　者：Jin Su Kim Min-Gu Kim Jae Myung Kim Sung Bum Pan 

机构地区：[1]IT Research Institute,Chosun University,Gwangju,61452,Korea [2]Interdisciplinary Program in IT-Bio Convergence System,Chosun University,Gwangju,61452,Korea

出　　处：《Computers, Materials & Continua》2022年第8期2997-3007,共11页计算机、材料和连续体（英文）

基　　金：This research was supported by Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(No.2017R1A6A1A03015496);the National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(No.NRF-2021R1A2C1014033).

摘　　要：As fifth generation technology standard(5G)technology develops,the possibility of being exposed to the risk of cyber-attacks that exploits vulnerabilities in the 5G environment is increasing.The existing personal recognitionmethod used for granting permission is a password-basedmethod,which causes security problems.Therefore,personal recognition studies using bio-signals are being conducted as a method to access control to devices.Among bio-signal,surface electromyogram(sEMG)can solve the existing personal recognition problem that was unable to the modification of registered information owing to the characteristic changes in its signal according to the performed operation.Furthermore,as an advantage,sEMG can be conveniently measured from arms and legs.This paper proposes a personal recognition method using sEMG,based on a multi-stream convolutional neural network(CNN).The proposed method decomposes sEMG signals into intrinsic mode functions(IMF)using empirical mode decomposition(EMD)and transforms each IMF into a spectrogram.Personal recognition is performed by analyzing time–frequency features from the spectrogram transformed intomulti-streamCNN.The database(DB)adopted in this paper is the Ninapro DB,which is a benchmark EMG DB.The experimental results indicate that the personal recognition performance of the multi-stream CNN using the IMF spectrogram improved by 1.91%,compared with the singlestream CNN using the spectrogram of raw sEMG.

关 键 词：Personal recognition electromyogram signal multi-stream network empirical mode decomposition 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]