Double Authentication Model based on PPG and ECG Signals

Wearable devices in e-Health provide easy usage access as well as an information return to the user. In general, such devices possess a range of sensors that capture several information from both the environment and the user. The most popular information collected by smartwatches and bracelets is about the measurement of heartbeats, steps, oxygenation, and photoplethysmogram (PPG) and electrocardiogram (ECG) signals. These wearable devices rely on mobile devices for user authentication. If the user needs validation, he will resort to traditional methods on other equipment that possesses recognition sensors such as iris, face, or fingerprints. In this paper, we introduce a model for double authentication based on PPG and ECG signals for promoting another layer of security to the user, ensuring data security, and avoid weak dependence on a single biosignal for validation. The proposed model has a algorithm with two zones, namely the Algorithm for PPG and ECG Signals and Error Rate zones. The experimental results indicate that the proposed model presented up to 99.98% of accuracy.