The increasing popularity of wearable devices has brought great convenience to human life and business. As wearable devices have become widely used personal computing platforms, more and more private information gets accessed by them, which stresses an urgent need for feasible and reliable authentication mechanisms in the current mobile computing environment. However, traditional memory- based authentication methods like PINs have been proven easy to crack or steal. Based on the fact that hand-waving patterns vary among different users, we propose a novel hand-waving- based unlocking system using smartwatches, which consists of data acquisition, data preprocessing, feature extraction, and authentication modules. Furthermore, we established a 150-person-time hand-waving dataset with a smartwatch, and conducted a systematic performance evaluation, achieving an equal error rate of 4.27 percent in the zero-effort attacking scenario and 14.46 percent in the imitation-attack scenarios. Additional experiments on usability to operation length and sensitivity to sampling frequency are offered to explore the applicability and effectiveness.

中文翻译：

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移动环境中用户身份验证的挥手手势分析


可穿戴设备的日益普及给人类的生活和商业带来了极大的便利。随着可穿戴设备成为广泛使用的个人计算平台，越来越多的隐私信息被其访问，这迫切需要在当前移动计算环境中可行且可靠的身份验证机制。然而，事实证明，传统的基于内存的身份验证方法（例如 PIN）很容易被破解或窃取。基于不同用户的挥手模式不同的事实，我们提出了一种新型的基于挥手的智能手表解锁系统，该系统由数据采集、数据预处理、特征提取和身份验证模块组成。此外，我们通过智能手表建立了150人次的挥手数据集，并进行了系统的性能评估，在零努力攻击场景下达到了4.27%的错误率，在模仿攻击场景下达到了14.46%的错误率。还提供了关于操作长度的可用性和采样频率的敏感性的额外实验，以探索其适用性和有效性。