Although the traditional physical password, fingerprint unlocking and facial features have improved the security to a certain extent, they have the characteristics of passive authentication and easiness to be stolen. The existing behavioral data collected based on mobile phone sensors is mainly used for human activity recognition and fall detection and health management. Prophet is a procedure for forecasting time series data based on an additive model where non-linear trends are fit with yearly, weekly, and daily seasonality, plus holiday effects. It works best with time series that have strong seasonal effects and several seasons of historical data. Prophet is robust to missing data and shifts in the trend, and typically handles outliers well. Based on the time series behavior data of mobile terminal users, this paper uses Prophet algorithm to decompose the time series of six kinds of daily behavior and strip off the singular value, to get the inherent cycle and trend of each behavior, and to verify the legitimacy of the behavior user at the next moment. The experimental results on the UniMiB SHAR public dataset show that the user only needs to do 2 cycles of specified actions to realize the prediction of the next time series. The main contribution of this paper is that we propose a new idea for smartphone user authentication. It is based on real-time data of smartphone user behavior, through Phophet algorithm for feature decomposition and time series prediction, and to find the inherent cycle and other characteristics, so as to perform user behavior recognition. This data-driven auxiliary authentication method can effectively solve the problem of easy forgery of static feature recognition such as password, fingerprint and face recognition.
A systematic review of smartphone-based human activity recognition for health research
Harvard T.H. Chan School of Public Health（哈佛大学陈曾熙公共卫生学院）；Harvard Medical School（哈佛医学院）；Harvard Pilgrim Health Care Institute（哈佛 pilgrim 健康护理研究所）
背景资料：智能手机现在几乎无处不在;其众多的内置传感器可以持续测量日常生活活动，使其特别适合健康研究。研究人员提出了各种人类活动识别（HAR）系统，旨在将智能手机的测量结果转换为各种类型的身体活动。在这篇综述中，我们总结了现有的方法，以智能手机为基础的HAR。研究方法：我们系统地检索了Scopus、PubMed和Web of Science，以查找截至2020年12月发表的关于使用智能手机进行HAR的同行评议文章。我们提取了有关智能手机身体位置、传感器和身体活动类型的信息，以及用于活动识别的数据转换技术和分类方案。结果如下：我们确定了108篇文章，并描述了用于数据采集、数据预处理、特征提取和活动分类的各种方法，确定了最常见的实践及其替代方案。结论：智能手机非常适合健康科学领域的HAR研究。对于人群水平的影响，未来的研究应侧重于提高收集的数据的质量，解决缺失的数据，纳入更多样化的参与者和活动，放宽对电话放置的要求，提供更完整的研究参与者文档，并共享实施方法和算法的源代码。
Physical Activity Recognition by Utilising Smartphone Sensor Signals
Centre for Security, Communications and Network Research, Plymouth University（普利茅斯大学安全、通信与网络研究中心）；Computer Sciences and Information Technology College, Majmaah University（迈季迈阿大学计算机科学与信息技术学院）；College of Education for Pure Science, University of Baghdad（巴格达大学纯科学教育学院）；Security Research Institute, Edith Cowan University（伊迪斯科文大学安全研究所）；School of Computing, University of Portsmouth（朴茨茅斯大学计算学院）
