Personal wearable sensor platform for detecting and localizing WMDs
Current environmental monitors typically consist of bulky, intrusive stationary or portable systems that cannot be worn comfortably on the body during physical activity.
Through this NSRI IRAD project, funded in August 2021, a multidisciplinary research team of faculty and students from the University of Nebraska–Lincoln (UNL), University of Nebraska Medical Center (UNMC) and Texas A&M (A&M) is developing a sensitive surveillance system in the form of a wearable electronic nose that will automatically and passively monitor the air to detect previously defined environmental and chemical threats. This new small, adhesive device transforms each person into a probe while collecting more and better data that provides decision makers with detailed, real-time information to determine threat status.
"The NSRI IRAD project has provided us the opportunity to learn more about potential airborne hazards and threats, the human body's response and available sensing technologies," said NSRI Fellow Dr. Eric Markvicka, UNL assistant professor of mechanical and materials engineering. "We have also identified and addressed critical challenges related to the integration of polymer-based gas sensors with supporting electronic components and the intimate integration of these technologies with the human body."
- Principal Investigator: Eric Markvicka, NSRI fellow and assistant professor of mechanical and materials engineering at the University of Nebraska–Lincoln
- Stephen Rennard, NSRI fellow and Richard and Margaret Larson Professor of pulmonary research at the University of Nebraska Medical Center
- Jenna Yentes, associate professor of kinesiology and sport management at Texas A&M
Milestones (as of June 2022)
- Wireless Bluetooth capability
- Onboard local data storage
- Resistive and capacitive sensing
- Lab validation of the sensor
- Paper presented at April 2022 ASME Design of Medical Devices Conference
- Five-minute project overview video
The team is now working toward chemoresistive and chemocapacitive sensing capability. Their stretch goal is to validate the device in a controlled laboratory setting and train a supervised machine learning model using k-nearest neighbors, support vector machine and artificial neural networks.