摘要
As a kind of physical signals that could be easily acquired in daily life,photoplethysmography(PPG)signal becomes a promising solution to biometric identification for daily access management system(AMS).State-of-the-art PPG-based identification systems are susceptible to the form of motions and physical conditions of the subjects.In this work,to exploit the advantage of deep learning,we developed an improved deep convolutional neural network(CNN)architecture by using the Gram matrix(GM)technique to convert time-serial PPG signals to two-dimensional images with a temporal dependency to improve accuracy under different forms of motions.To ensure a fair evaluation,we have adopted cross-validation method and“training and testing”dataset splitting method on the TROIKA dataset collected in ambulatory conditions.As a result,the proposed GM-CNN method achieved accuracy improvement from 69.5%to 92.4%,which is the best result in terms of multi-class classification compared with state-of-the-art models.Based on average five-fold cross-validation,we achieved an accuracy of 99.2%,improved the accuracy by 3.3%compared with the best existing method for the binary-class.
作者
吴彩钰
SABOR Nabil
周世鸿
王敏
应亮
王国兴
Wu Caiyu;SABOR Nabil;Zhou Shihong;Wang Min;Ying Liang;Wang Guoxing(Department of Micro-Nano Electronics,Shanghai Jiao Tong University,Shanghai,200240,China;MoE Key Lab of Artificial Intelligence,Shanghai Jiao Tong University,Shanghai,200240,China;Electrical Engineering Department,Assiut University,Assiut,71516,Egypt;Department of Urology,Renji Hospital,Shanghai Jiao Tong University School of Medicine,Shanghai,200127,China)
基金
the National Key R&D Program of China(No.2019YFB2204500)
the Translational Medicine Cross Research Fund of Shanghai Jiao Tong University(No.ZH2018QNB22)。