EAGER: Detecting and Addressing Adverse Dependencies Across Human-in-the-Loop In-Home Medical Apps

EAGER：检测并解决人在环家用医疗应用程序中的不良依赖性

• 批准号: 1527563
• 负责人: John Stankovic
• 金额: $ 20万
• 依托单位: University of Virginia Main Campus
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-06-15 至 2017-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1527563&HistoricalAwards=false
• 关键词: EAGER; Detecting; Addressing; Adverse; Dependencies

Millions of mobile applications (apps) are being developed in domains such as energy, health, security, and entertainment. The US FDA expects that there will be 500 million smart phone users downloading healthcare related apps by the end of 2015. Many of these apps will perform interventions to control human physiological parameters such as blood pressure and heart rate. The intervention aspects of the apps can cause dependency problems, e.g., multiple interventions of multiple apps can increase or decrease each other's effects, some of which can be harmful to the user. Detecting and resolving these dependencies are the main goals of this project. Success in this research can significantly improve the safety of home health care.This project will develop EyePhy, a completely new approach to primary and secondary dependency analysis for wellness and mobile medical apps based on smart phones. The approach offers personalized dependency analysis and accounts for time dependent interventions such as time interval for which a drug or other intervention is effective. To do that, EyePhy uses a physiological simulator called HumMod which was developed by the medical community to model the complex interactions of the human physiology using over 7800 variables. Among the goals of EyePhy are the reduction of app developers' effort in specifying dependency metadata compared to state of the art solutions, offering personalized dependency analysis for the user, and identifying problems in real time, as medical app products are being used. Such dependency problems occur mainly because (i) each app is developed independently without knowing how other apps work and (ii) when an app performs an intervention to control its target parameters (e.g., blood pressure), it may affect other physiological parameters (e.g., kidney) without even knowing it. A priori proofs that individual cyber-physical systems (CPS) app devices are safe cannot guarantee how it will be used and with which other (future) apps it may be run concurrently. It is becoming more common for people to use multiple apps. The average person will not understand how multiple apps might affect his health due to hidden dependencies among a large number of parameters. Consequently, a tool such as EyPhy is critical to future deployments of safe mobile medical apps.

能源、健康、安全和娱乐等领域正在开发数百万个移动应用程序 (app)。美国FDA预计，到2015年底，将有5亿智能手机用户下载医疗保健相关应用程序。其中许多应用程序将进行干预，以控制血压和心率等人体生理参数。应用程序的干预方面可能会导致依赖性问题，例如，多个应用程序的多次干预可能会增加或减少彼此的影响，其中一些可能对用户有害。检测并解决这些依赖关系是该项目的主要目标。这项研究的成功可以显着提高家庭医疗保健的安全性。该项目将开发 EyePhy，这是一种针对健康和基于智能手机的移动医疗应用程序进行主要和次要依赖性分析的全新方法。该方法提供个性化依赖性分析，并考虑时间依赖性干预措施，例如药物或其他干预措施有效的时间间隔。为此，EyePhy 使用名为 HumMod 的生理模拟器，该模拟器由医学界开发，使用 7800 多个变量来模拟人体生理学的复杂相互作用。 EyePhy 的目标之一是与最先进的解决方案相比，减少应用程序开发人员在指定依赖性元数据方面的工作量，为用户提供个性化的依赖性分析，并在使用医疗应用程序产品时实时识别问题。出现这种依赖性问题的主要原因是（i）每个应用程序都是独立开发的，不知道其他应用程序如何工作；（ii）当应用程序执行干预以控制其目标参数（例如血压）时，可能会影响其他生理参数（例如血压）。 ，肾脏）甚至不知道。单个网络物理系统 (CPS) 应用程序设备安全的先验证明不能保证它将如何使用以及它可以与哪些其他（未来）应用程序同时运行。人们使用多个应用程序变得越来越普遍。由于大量参数之间隐藏的依赖关系，普通人不会理解多个应用程序可能如何影响他的健康。因此，像 EyPhy 这样的工具对于安全移动医疗应用程序的未来部署至关重要。