Data driven and optimization techniques for mobile health systems

A new research paradigm in healthcare applications investigates how to improve a patient's quality of care with wearable embedded systems that continuously monitor a patient's vital signs as he/she ubiquitously moves about the environment. While previous medical examinations could only extract localized symptoms through snap shots, now continuous monitoring can discretely analyze how a patient's lifestyle may affect his/her physiological conditions and whether additional symptoms occur under various stimuli. The Advanced Health and Disaster Aid Network (AID-N) used participatory design methods to develop an electronic triage system that replaced the paper triage system and changed how emergency personnel interact, collect, and process data at mass casualty incidents. My research investigated the design of an infrastructure that provided efficient resource allocation by continuously monitoring the vital signs and locations of patients. Medical embedded systems called electronic triage tags contained noninvasive, biomedical sensors (pulse oximeter, electrocardiogram, and blood pressure) that facilitated the seamless collection and dissemination of data from the incident site to key members of the emergency response community. In a mass casualty drill, paramedics were able to triple the number of times they reassessed patients and efficiently manage resources with the electronic triage system. This real world deployment uncovered numerous research challenges that arose from the complex interactions of the embedded systems with the dynamic environment that they were deployed in. I address the challenge of body attenuation by constructing a model of attenuation in body sensor networks from experimental data. I also use data driven methods to address the challenge of limited storage capacity in mobile embedded systems during network partitions. An algorithm models inter-arrival time, intra-arrival time, and body attenuation to achieve efficiency in storage capacity. My approach mitigates data loss and provides continuous data collection through a combination of optimization, statistical variance, and data driven modeling techniques. In addition, I also leverage statistical variance techniques to detect the physical tampering of portable systems. A data driven approach that uses quantitative information from experimental deployments is necessary when building realistic systems for medical applications where failure can result in the loss of a life.