Banos Oresti, Villalonga Claudia, Garcia Rafael, Saez Alejandro, Damas Miguel, Holgado-Terriza Juan A, Lee Sungyong, Pomares Hector, Rojas Ignacio
Biomed Eng Online. 2015;14 Suppl 2(Suppl 2):S6. doi: 10.1186/1475-925X-14-S2-S6. Epub 2015 Aug 13.
The delivery of healthcare services has experienced tremendous changes during the last years. Mobile health or mHealth is a key engine of advance in the forefront of this revolution. Although there exists a growing development of mobile health applications, there is a lack of tools specifically devised for their implementation. This work presents mHealthDroid, an open source Android implementation of a mHealth Framework designed to facilitate the rapid and easy development of mHealth and biomedical apps. The framework is particularly planned to leverage the potential of mobile devices such as smartphones or tablets, wearable sensors and portable biomedical systems. These devices are increasingly used for the monitoring and delivery of personal health care and wellbeing. The framework implements several functionalities to support resource and communication abstraction, biomedical data acquisition, health knowledge extraction, persistent data storage, adaptive visualization, system management and value-added services such as intelligent alerts, recommendations and guidelines. An exemplary application is also presented along this work to demonstrate the potential of mHealthDroid. This app is used to investigate on the analysis of human behavior, which is considered to be one of the most prominent areas in mHealth. An accurate activity recognition model is developed and successfully validated in both offline and online conditions.
在过去几年中,医疗服务的提供经历了巨大变革。移动健康(mHealth)是这场革命前沿进步的关键驱动力。尽管移动健康应用的发展日益蓬勃,但专门为其实施设计的工具却很匮乏。这项工作展示了mHealthDroid,它是一个mHealth框架的开源安卓实现,旨在促进移动健康和生物医学应用的快速简便开发。该框架特别规划用于利用智能手机或平板电脑、可穿戴传感器和便携式生物医学系统等移动设备的潜力。这些设备越来越多地用于个人医疗保健和福祉的监测与提供。该框架实现了多种功能,以支持资源和通信抽象、生物医学数据采集、健康知识提取、持久数据存储、自适应可视化、系统管理以及智能警报、建议和指南等增值服务。本文还展示了一个示例应用,以证明mHealthDroid的潜力。该应用用于研究人类行为分析,这被认为是移动健康中最突出的领域之一。开发了一个准确的活动识别模型,并在离线和在线条件下成功进行了验证。