Faculty Sponsor: Dr. Deniz Gurkan, Dr. Driss Benhaddou

Motivation

With skyrocketing healthcare costs, it is critical to utilize current advanced technologies such as biosensors and wireless network technology to help improve the effectiveness and efficiency of chronic disease management by personalizing the disease management plan. Recent advances in miniature wireless sensors supported by ubiquitous computing have fostered a dramatic growth of interest for wellness and disease management based on distributed sensor networks. The miniature and noninvasive sensor nodes can be enclosed in patches or clothing (wearable sensors), or embedded in furniture and building structures (environmental sensors). Such a distributed sensor network will allow physicians to monitor, over extensive periods of time, individuals whose chronic condition includes a risk of sudden acute events. Pioneer research projects have set up small scale sensor network systems. However, these systems focus on wearable sensor technology and require manual intervention from the physician/nurse and/or individuals during the data acquisition process. In this project we will design and develop non-invasive biomedical monitoring sensors to be integrated into an affordable, wireless, and personalized chronic disease monitoring system.

Project Description

The team is developing a test bed of smart sensors that will be utilized to implement an individualized chronic disease management system. The project involves different components and expertise. First, the digital interface between the medical device and the sensor needs to be developed. Then, the communication between the sensor and the server through the Internet needs to be implemented. Last and not least, development of data processing and forecasting algorithms based on advanced data mining techniques need to be researched. Since healthcare applications deals with sensitive data and wireless technology is utilized, challenges such as reliability and security will be addressed.

Undergraduate Opportunities

REU undergraduate students will have multiple opportunities to be involved in the research. For example, they will be involved in digital interface design between the sensor and equipment, security issues and software applications that analyze the data. The undergraduate students will work with the graduate students to implement their specific tasks.