用于健康跟踪IoT可穿戴设备的主动多模式用户认证
Opportunistic Multi-Modal User Authentication for Health-Tracking IoT Wearables
摘要：
随着技术的进步，市场上的可穿戴设备越来越受欢迎，提供一系列服务，包括访问银行账户、访问汽车、远程监控患者等。然而，这些可穿戴设备通常在没有认证或有限认证的情况下收集用户的各种敏感个人信息，例如，基于知识的外部身份验证技术，如PIN。虽然这些外部认证技术中的大多数都受到多重限制，包括召回负担、人为错误或偏见，但研究人员已经开始使用可穿戴设备收集的各种生理和行为数据，例如步态和心率，来隐式地认证可穿戴用户，由于可穿戴设备的感测和计算限制，准确性有限。在这项工作中，我们探讨了从血氧仪设备收集的血氧饱和度SpO2值的有用性，以区分用户与其他人。从25名受试者的队列中，我们发现92%的病例SpO2可以区分成对的用户。从详细的建模和性能分析中，我们观察到，虽然SpO2单独可以获得0.69的平均准确度和0.69的F1评分，但心率（HR）的加入可以将平均识别准确度提高15%，F1评分提高13%。这些结果表明，使用SpO2和其他生物识别技术为可穿戴设备开发隐式连续身份验证是有希望的。
英文摘要：
With the advancement of technologies, market wearables are becoming increasingly popular with a range of services, including providing access to bank accounts, accessing cars, monitoring patients remotely, among several others. However, often these wearables collect various sensitive personal information of a user with no to limited authentication, e.g., knowledge-based external authentication techniques, such as PINs. While most of these external authentication techniques suffer from multiple limitations, including recall burden, human errors, or biases, researchers have started using various physiological and behavioral data, such as gait and heart rate, collected by the wearables to authenticate a wearable user implicitly with a limited accuracy due to sensing and computing constraints of wearables. In this work, we explore the usefulness of blood oxygen saturation SpO2 values collected from the Oximeter device to distinguish a user from others. From a cohort of 25 subjects, we find that 92% of the cases SpO2 can distinguish pairs of users. From detailed modeling and performance analysis, we observe that while SpO2 alone can obtain an average accuracy of 0.69 and F1 score of 0.69, the addition of heart rate (HR) can improve the average identification accuracy by 15% and F1 score by 13%. These results show promise in using SpO2 along with other biometrics to develop implicit continuous authentications for wearables.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
