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.|
|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
|Name||Proceedings - IEEE International Symposium on Circuits and Systems|
|Other||2018 IEEE International Symposium on Circuits and Systems, ISCAS 2018|
|Period||5/27/18 → 5/30/18|
Bibliographical noteFunding Information:
This research was supported in part by NSF IGERT grant DGE-1069104.
- convolutional neural network
- deep learning
- long short-term memory