In recent years, wrist-worn smart devices such as smart wrist band and smart watch have pervaded our everyday life. Under this trend, the security issue of these wearable devices has received considerable attention as these devices usually store various private information. Conventional methods, however, do not provide a good user experience because they either depend on a secret PIN number input or require an explicit user authentication process. In this paper, we present Gait-watch, a context-aware authentication system for smart watch based on gait recognition. We address the problem of recognizing the user under various walking activities (e.g., walking normally, walking upstairs and walking with calling the phone), and propose a feature extraction method from gait signals to improve recognition accuracy. Extensive evaluations show that Gait-watchimproves recognition accuracy by up to 30.2% by leveraging the activity information, and can achieve 3.5% Equal Error Rate (EER). We also report a user study to demonstrate that Gait-watchcan accurately authenticate the user in real-world scenarios and require low system cost.

近年来，腕带式智能设备（如智能腕带和智能手表）已经席卷了我们的日常生活。在这种趋势下，这些可穿戴设备的安全性问题受到了广泛的关注，因为这些设备通常会存储各种私人信息。但是，常规方法不能提供良好的用户体验，因为它们要么依赖于秘密PIN码输入，要么需要明确的用户身份验证过程。在本文中，我们介绍了Gait-watch，这是一种基于步态识别的上下文感知的智能手表身份验证系统。我们解决了在各种步行活动（例如，正常步行，上楼步行和打电话时步行）下识别用户的问题，并提出了一种从步态信号中提取特征的方法，以提高识别准确性。广泛的评估表明，步态监视通过利用活动信息将识别准确性提高了30.2％，并且可以实现3.5％的均等错误率（EER）。我们还报告了一项用户研究，以证明Gait-watch可以在真实场景中准确地验证用户身份，并且需要较低的系统成本。