Key result
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.
May enable robust PPG biometrics in wearables; leaves open prospective clinical validation before adoption.
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%.
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Jindal et al. (2016) studied 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.
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