A novel two-stage deep learning technique using Deep Belief Networks and Restricted Boltzmann Machines for PPG-based biometric identification achieved an accuracy of 96.1% on the TROIKA dataset.
A novel deep learning approach using PPG signals demonstrates high accuracy for biometric identification, potentially improving robustness in clinical and fitness monitoring.
Wearable biosensors have become increasingly popular in healthcare due to their capabilities for low cost and long term biosignal monitoring. This paper presents a novel two-stage technique to offer biometric identification using these biosensors through Deep Belief Networks and Restricted Boltzman Machines. Our identification approach improves robustness in current monitoring procedures within clinical, e-health and fitness environments using Photoplethysmography (PPG) signals through deep learning classification models. The approach is tested on TROIKA dataset using 10-fold cross validation and achieved an accuracy of 96.1%.
Jindal et al. (Mon,) conducted a other in Biometric identification. Two-stage deep learning technique (Deep Belief Networks and Restricted Boltzman Machines) was evaluated on Accuracy of biometric identification. A novel two-stage deep learning technique using Deep Belief Networks and Restricted Boltzmann Machines for PPG-based biometric identification achieved an accuracy of 96.1% on the TROIKA dataset.