Secure Wearable Technology for Early Illness Detection and Analysis | RTI Tech Talk Webinar
The use of wearable devices for a deeper understanding of a person’s overall health and performance capabilities continues to grow, giving us more data for analysis and better informed decision-making.
Join the virtual Secure Wearable Technology for Early Illness Detection and Analysis webinar to learn more about the SIGMA+ Health platform—an end-to-end data collection and analytics framework for wearable physiological sensors for the U.S. Department of Defense (DoD) and Homeland Security applications. We will discuss how wearable sensor technology aids in early illness detection, ensuring optimal physical and mental health for active-duty Service members.
Contact our wearable tech experts to learn more about our offerings, services, and capabilities
Meet the Presenters
Moderator: Dennis McGurk, DoD Strategic Account Executive, RTI International
Dennis McGurk is an expert in military medical research and development with over 15 years performing research and leading research programs. He is dedicated to expanding RTI’s work with the DoD. He leads all DoD business development efforts for RTI’s Social, Statistical, and Environmental Sciences division and works across all of RTI. With an academic background in research and health psychology, he focuses on managing medical research and development programs that serve the health and performance needs of the military population. Dr. McGurk joined RTI in 2020 after retiring from the Army following a 25+ year career, predominantly as a Research Psychologist.
Meghan Hegarty-Craver, Research Biomedical Engineer, RTI International
Meghan Hegarty-Craver is a biomedical research engineer specializing in signal processing for wearable sensors, extracting heart rate and heart rate variability metrics from electrocardiogram (ECG) and photoplethysmogram (PPG)-based sensors, and engineering for machine learning model development. Currently, Dr. Hegarty-Craver is working on the Human-Based Sensor System for Presymptomatic Biological Exposure Detection project, funded by the Defense Advanced Research Projects Agency. In this initiative, RTI leverages data from wearable sensors like smartwatches to identify changes in health statuses related to viral infections.
David Dausch, Senior Director of Technology Advancement & Commercialization, RTI International
David Dausch is the senior director of RTI’s Technology Advancement & Commercialization (TAC) business unit. He leads TAC’s multi-disciplinary team of over 60 staff, including scientists and engineers who perform research in advanced materials, biomedical technologies, decarbonization sciences, environmental exposure and protection, and sustainable energy solutions. Dr. Dausch manages RTI’s SIGMA+ Health wearable sensor research platform, an end-to-end data collection and analytics solution for improving military Service members health, readiness, and performance. He serves as Principal Investigator for the Human-Based Sensor System for Presymptomatic Biological Exposure Detection research project for the SIGMA+ program sponsored by the Defense Advanced Research Projects Agency (DARPA).
Dorota Temple, Distinguished Fellow, Electronics and Applied Physics, RTI International
Dorota Temple, PhD, has spent much of her professional life leading scientists and engineers in developing new technology capabilities. As Technical Director at RTI International, she continues her work in semiconductors through research and development of three-dimensional microsystem integration, electronic circuits manufacture on flexible substrates, and advanced infrared focal plane arrays. These programs result in technologies that are now practiced by commercial defense companies and RTI spinouts. In recent years, Dr. Temple has applied her expertise in semiconductor sensors, imaging devices, and advanced data analytics to the development of automated systems to detect threats posed by weapons of mass destruction. These systems use chemical and biological sensors and wearable physiological devices and incorporate artificial intelligence/machine learning algorithms operating on the sensor signals to provide presymptomatic warning of exposure to respiratory pathogens.