Exploring Reliable PPG Authentication on Smartwatches in Daily Scenarios
This addresses authentication problems for smartwatch users in daily scenarios, but it is incremental as it builds on existing PPG methods with multitask learning.
The paper tackled reliability issues in PPG authentication on smartwatches due to motion artifacts and physiological variability, proposing MTL-RAPID, which achieved a best AUC of 99.2% and an EER of 3.5% in user studies.
Photoplethysmography (PPG) Sensors, widely deployed in smartwatches, offer a simple and non-invasive authentication approach for daily use. However, PPG authentication faces reliability issues due to motion artifacts from physical activity and physiological variability over time. To address these challenges, we propose MTL-RAPID, an efficient and reliable PPG authentication model, that employs a multitask joint training strategy, simultaneously assessing signal quality and verifying user identity. The joint optimization of these two tasks in MTL-RAPID results in a structure that outperforms models trained on individual tasks separately, achieving stronger performance with fewer parameters. In our comprehensive user studies regarding motion artifacts (N = 30), time variations (N = 32), and user preferences (N = 16), MTL-RAPID achieves a best AUC of 99.2\% and an EER of 3.5\%, outperforming existing baselines. We opensource our PPG authentication dataset along with the MTL-RAPID model to facilitate future research on GitHub.