Eager- Detecting and Addressing Adverse Dependencies Across Human-in-the-Loop In-Home Medical Apps poster.pdf
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.
EyePhy, a totally new approach to primary and secondary dependency analysis (conflict detection) is developed 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 reductions 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 CPS app devices are safe cannot guarantee how it will be used and with which other (future) apps it may be run concurrently with. Many apps provide textual advice and alarms. We have also developed techniques to identify conflicting textual information, thereby increasing safety for users of wellness and mobile health apps.
The principles of conflict detection and resolution investigated above can also be applied to smart city services. Extensions to the above work have been developed to address some of the unique issues for conflicts in smart cities that on-body networks do not exhibit.