用智能手表心率传感器实现高可靠身份认证，抗运动干扰强。
Exploring Reliable PPG Authentication on Smartwatches in Daily Scenarios
- 多任务联合训练，同时评估信号质量与用户身份
- 在30人运动、32人时变实验中，最高AUC达99.2%，错误率仅3.5%
- 开源数据集与模型，助力可穿戴设备安全研究
光电容积脉搏波（PPG）传感器广泛部署于智能手表，为日常使用提供简单非侵入式身份认证。然而，身体活动引起的运动伪影及生理状态随时间变化，导致认证可靠性下降。为此，我们提出MTL-RAPID，一种高效可靠的PPG认证模型，采用多任务联合训练策略，同时评估信号质量与验证用户身份。该模型通过两项任务的联合优化，在参数更少的情况下性能优于单独训练的模型。在涵盖运动伪影（N=30）、时间变化（N=32）和用户偏好（N=16）的综合用户研究中，最佳AUC达到99.2%，等错误率（EER）为3.5%，显著优于现有基线。我们已在GitHub上开源了PPG认证数据集及MTL-RAPID模型，以促进未来相关研究。
原文摘要 · Abstract (English)
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.
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