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

doi:10.1109/ISCAS.2018.8350983

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

Electrical and Computer Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

48 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 language	English (US)
Title of host publication	2018 IEEE International Symposium on Circuits and Systems, ISCAS 2018 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781538648810
DOIs	https://doi.org/10.1109/ISCAS.2018.8350983
State	Published - Apr 26 2018
Event	2018 IEEE International Symposium on Circuits and Systems, ISCAS 2018 - Florence, Italy Duration: May 27 2018 → May 30 2018

Publication series

Name	Proceedings - IEEE International Symposium on Circuits and Systems
Volume	2018-May
ISSN (Print)	0271-4310

Other

Other	2018 IEEE International Symposium on Circuits and Systems, ISCAS 2018
Country/Territory	Italy
City	Florence
Period	5/27/18 → 5/30/18

Bibliographical note

Funding Information:
This research was supported in part by NSF IGERT grant DGE-1069104.

Publisher Copyright:
© 2018 IEEE.

Keywords

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

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10.1109/ISCAS.2018.8350983

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Everson, L., Biswas, D., Panwar, M., Rodopoulos, D., Acharyya, A., Kim, C. H., Van Hoof, C., Konijnenburg, M., & Van Helleputte, N. (2018). BiometricNet: Deep Learning based Biometric Identification using Wrist-Worn PPG. In 2018 IEEE International Symposium on Circuits and Systems, ISCAS 2018 - Proceedings Article 8350983 (Proceedings - IEEE International Symposium on Circuits and Systems; Vol. 2018-May). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ISCAS.2018.8350983

BiometricNet: Deep Learning based Biometric Identification using Wrist-Worn PPG. / Everson, Luke; Biswas, Dwaipayan; Panwar, Madhuri et al.
2018 IEEE International Symposium on Circuits and Systems, ISCAS 2018 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2018. 8350983 (Proceedings - IEEE International Symposium on Circuits and Systems; Vol. 2018-May).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Everson, L, Biswas, D, Panwar, M, Rodopoulos, D, Acharyya, A, Kim, CH, Van Hoof, C, Konijnenburg, M & Van Helleputte, N 2018, BiometricNet: Deep Learning based Biometric Identification using Wrist-Worn PPG. in 2018 IEEE International Symposium on Circuits and Systems, ISCAS 2018 - Proceedings., 8350983, Proceedings - IEEE International Symposium on Circuits and Systems, vol. 2018-May, Institute of Electrical and Electronics Engineers Inc., 2018 IEEE International Symposium on Circuits and Systems, ISCAS 2018, Florence, Italy, 5/27/18. https://doi.org/10.1109/ISCAS.2018.8350983

Everson L, Biswas D, Panwar M, Rodopoulos D, Acharyya A, Kim CH et al. BiometricNet: Deep Learning based Biometric Identification using Wrist-Worn PPG. In 2018 IEEE International Symposium on Circuits and Systems, ISCAS 2018 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2018. 8350983. (Proceedings - IEEE International Symposium on Circuits and Systems). doi: 10.1109/ISCAS.2018.8350983

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note = "Funding Information: This research was supported in part by NSF IGERT grant DGE-1069104. Publisher Copyright: {\textcopyright} 2018 IEEE.; 2018 IEEE International Symposium on Circuits and Systems, ISCAS 2018 ; Conference date: 27-05-2018 Through 30-05-2018",

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

Abstract

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Keywords

Access

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