Undergrad Research Project - Monitoring Wellness for Chronic Disease Management

Spring 2016

Steve Sroba
Asim Smailagic
Project description

The goal of this research is to develop a system of instrumentation and data analysis software that alerts clinicians to congestive heart failure patient decline well before they reach the point that hospitalization is called for. This includes analytics with predictive power using the inputs from external sensors in combination with implantable sensors to provide better and sooner indication of changes in both physiologic and functional parameters in patients, allowing the medical team to make clinical decisions earlier and reduce the utilization of care by the patient; clinician-facing data management and visualization interfaces that are flexible and well-matched to clinical workflow and thought processes; and a monitoring system that augments telehealth technology with continuous measurements by external (wearable) sensors. My research will focus on using machine learning techniques to recognize and track different types of movement using an Apple Watch. We will test different methods of machine learning and feature extraction, and analyze accuracy, memory, and power usage to determine the best method for movement recognition and tracking. Using this movement data combined with the data from the other sensors being used, we expect to give a medical team a better picture of a patient's progress, as well as a predictive tool for when a patient is in danger of being re-hospitalized.

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