Department of Computing, Universiti Pendidikan Sultan Idris, Tanjong Malim, Perak, Malaysia.
J Med Syst. 2019 Jan 6;43(2):33. doi: 10.1007/s10916-018-1149-5.
The new and groundbreaking real-time remote healthcare monitoring system on sensor-based mobile health (mHealth) authentication in telemedicine has considerably bounded and dispersed communication components. mHealth, an attractive part in telemedicine architecture, plays an imperative role in patient security and privacy and adapts different sensing technologies through many built-in sensors. This study aims to improve sensor-based defence and attack mechanisms to ensure patient privacy in client side when using mHealth. Thus, a multilayer taxonomy was conducted to attain the goal of this study. Within the first layer, real-time remote monitoring studies based on sensor technology for telemedicine application were reviewed and analysed to examine these technologies and provide researchers with a clear vision of security- and privacy-based sensors in the telemedicine area. An extensive search was conducted to find articles about security and privacy issues, review related applications comprehensively and establish the coherent taxonomy of these articles. ScienceDirect, IEEE Xplore and Web of Science databases were investigated for articles on mHealth in telemedicine-based sensor. A total of 3064 papers were collected from 2007 to 2017. The retrieved articles were filtered according to the security and privacy of sensor-based telemedicine applications. A total of 19 articles were selected and classified into two categories. The first category, 57.89% (n = 11/19), included survey on telemedicine articles and their applications. The second category, 42.1% (n = 8/19), included articles contributed to the three-tiered architecture of telemedicine. The collected studies improved the essential need to add another taxonomy layer and review the sensor-based smartphone authentication studies. This map matching for both taxonomies was developed for this study to investigate sensor field comprehensively and gain access to novel risks and benefits of the mHealth security in telemedicine application. The literature on sensor-based smartphones in the second layer of our taxonomy was analysed and reviewed. A total of 599 papers were collected from 2007 to 2017. In this layer, we obtained a final set of 81 articles classified into three categories. The first category of the articles [86.41% (n = 70/81)], where sensor-based smartphones were examined by utilising orientation sensors for user authentication, was used. The second category [7.40% (n = 6/81)] included attack articles, which were not intensively included in our literature analysis. The third category [8.64% (n = 7/81)] included 'other' articles. Factors were considered to understand fully the various contextual aspects of the field in published studies. The characteristics included the motivation and challenges related to sensor-based authentication of smartphones encountered by researchers and the recommendations to strengthen this critical area of research. Finally, many studies on the sensor-based smartphone in the second layer have focused on enhancing accurate authentication because sensor-based smartphones require sensors that could authentically secure mHealth.
基于传感器的移动健康 (mHealth) 在远程医疗中的身份验证的实时远程医疗监测系统是一个具有开创性的新系统,它大大限制和分散了通信组件。mHealth 是远程医疗架构中的一个有吸引力的部分,它在患者安全和隐私方面发挥着重要作用,并通过许多内置传感器适应不同的传感技术。本研究旨在改进基于传感器的防御和攻击机制,以确保在使用 mHealth 时客户端的患者隐私。因此,进行了多层分类法以实现本研究的目标。在第一层中,回顾和分析了基于传感器技术的实时远程监测研究,以检查这些技术,并为研究人员提供远程医疗领域基于传感器的安全和隐私的清晰愿景。进行了广泛的搜索以查找有关安全和隐私问题的文章,全面审查相关应用程序,并建立这些文章的连贯分类法。调查了 ScienceDirect、IEEE Xplore 和 Web of Science 数据库中基于传感器的远程医疗中的 mHealth 文章。从 2007 年到 2017 年共收集了 3064 篇论文。根据基于传感器的远程医疗应用程序的安全性和隐私性,对检索到的文章进行了过滤。共选择了 19 篇文章,并将其分为两类。第一类,57.89%(n=11/19),包括对远程医疗文章及其应用的调查。第二类,42.1%(n=8/19),包括对远程医疗三层架构做出贡献的文章。所收集的研究增加了添加另一个分类法层的必要,并审查了基于传感器的智能手机认证研究。为此研究开发了此映射匹配,以全面研究传感器领域,并了解 mHealth 在远程医疗应用中的安全性的新风险和收益。分析和审查了我们分类法第二层中基于传感器的智能手机的文献。从 2007 年到 2017 年共收集了 599 篇论文。在这一层,我们获得了最终的 81 篇文章,分为三类。文章的第一类[86.41%(n=70/81)],利用方向传感器对基于传感器的智能手机进行了检查,用于用户认证。第二类[7.40%(n=6/81)]包括攻击文章,这些文章没有被我们的文献分析广泛收录。第三类[8.64%(n=7/81)]包括“其他”文章。考虑了各种因素,以充分了解发表研究中该领域的各种上下文方面。这些特征包括研究人员在基于传感器的智能手机认证方面遇到的动机和挑战,以及加强这一关键研究领域的建议。最后,第二层中基于传感器的智能手机的许多研究都集中在提高准确认证上,因为基于传感器的智能手机需要能够真实保护 mHealth 的传感器。