BiometricNet: Deep Learning based Biometric Identification using Wrist-Worn PPG

Luke Everson, Dwaipayan Biswas, Madhuri Panwar, Dimitrios Rodopoulos, Amit Acharyya, Chris H. Kim, Chris Van Hoof, Mario Konijnenburg, Nick Van Helleputte

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Scopus citations

Abstract

Rapid advances in semiconductor fabrication technology have enabled the proliferation of miniaturized body-worn sensors capable of long term pervasive biomedical signal monitoring. In this paper, we present a novel deep learning-based framework (BiometricNET) on biometric identification using data collected from wrist-worn Photoplethysmography (PPG) signals in ambulatory environments. We have formulated a completely personalized data-driven approach, using a four-layer deep neural network - employing two convolution neural network (CNN) layers in conjunction with two long short-term memory (LSTM) layers, followed by a dense output layer for modelling the temporal sequence inherent within the pulsatile signal representative of cardiac activity. The proposed network configuration was evaluated on the TROIKA dataset collected from 12 subjects involved in physical activity, achieved an average five-fold cross-validation accuracy of 96%.

Original languageEnglish (US)
Title of host publication2018 IEEE International Symposium on Circuits and Systems, ISCAS 2018 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781538648810
DOIs
StatePublished - Apr 26 2018
Event2018 IEEE International Symposium on Circuits and Systems, ISCAS 2018 - Florence, Italy
Duration: May 27 2018May 30 2018

Publication series

NameProceedings - IEEE International Symposium on Circuits and Systems
Volume2018-May
ISSN (Print)0271-4310

Other

Other2018 IEEE International Symposium on Circuits and Systems, ISCAS 2018
CountryItaly
CityFlorence
Period5/27/185/30/18

Keywords

  • PPG
  • biometric
  • convolutional neural network
  • deep learning
  • long short-term memory

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