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

数以百万计的移动应用程序（app）正在能源、健康、安全和娱乐等领域开发。美国食品药品监督管理局预计，到2015年底，将有5亿智能手机用户下载医疗相关应用程序。其中许多应用程序将进行干预，以控制人体的生理参数，如血压和心率。应用程序的干预方面可能会导致依赖问题，例如，多个应用程序的多个干预可能会增加或减少彼此的效果，其中一些可能对用户有害。检测和解决这些依赖关系是这个项目的主要目标。本研究的成功可以显著提高家庭保健的安全性。该项目将开发EyePhy，这是一种全新的方法，用于健康和基于智能手机的移动医疗应用程序的主要和次要依赖分析。该方法提供了个性化的依赖性分析，并解释了时间依赖性干预措施，如药物或其他干预措施有效的时间间隔。为了做到这一点，EyePhy使用了一个名为HumMod的生理模拟器，该模拟器是由医学界开发的，用于模拟人类生理的复杂相互作用，使用超过7800个变量。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元数据方面的努力，为用户提供个性化的依赖分析，并在使用医疗应用产品时实时识别问题。这种依赖问题的发生主要是因为(i)每个应用程序都是独立开发的，而不知道其他应用程序是如何工作的；（ii）当一个应用程序执行干预来控制其目标参数（例如血压）时，它可能会在不知情的情况下影响其他生理参数（例如肾脏）。单独的网络物理系统（CPS）应用程序设备是安全的先验证明不能保证它将如何使用，以及它可能与其他（未来）应用程序同时运行。人们使用多个应用程序正变得越来越普遍。由于大量参数之间隐藏的依赖关系，普通人无法理解多个应用程序如何影响他的健康。因此，像EyPhy这样的工具对于未来部署安全的移动医疗应用程序至关重要。

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.