Design, implementation and validation of a novel open framework for agile development of mobile health applications.
Design, implementation and validation of a novel open framework for agile development of mobile health applications.
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DOI：
10.1186/1475-925x-14-s2-s6
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发表时间：
2015
影响因子：
3.9
通讯作者：
Rojas I
中科院分区：
文献类型：
--
作者：
Banos O;Villalonga C;Garcia R;Saez A;Damas M;Holgado-Terriza JA;Lee S;Pomares H;Rojas I
在过去几年中，医疗保健服务的提供经历了巨大的变化。移动医疗或移动医疗是这场革命前沿的关键推动力。尽管移动健康应用程序不断发展，但缺乏专门为其实施设计的工具。这项工作提出了 mHealthDroid，它是 mHealth 框架的开源 Android 实现，旨在促进 mHealth 和生物医学应用程序的快速轻松开发。该框架特别计划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监测和提供个人医疗保健和福祉。该框架实现了多种功能来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、系统管理和增值服务，例如智能警报、建议和指南。这项工作还提供了一个示例应用程序来展示 mHealthDroid 的潜力。该应用程序用于研究人类行为分析，这被认为是移动医疗中最突出的领域之一。开发了准确的活动识别模型，并在离线和在线条件下成功验证。
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.