DARPA asks industry for ways to diagnose and treat mental health issues using machine learning and sensors
ARLINGTON, Va. – U.S. military researchers are asking industry for new ways to analyze and treat depression and other mental health issues in U.S. military personnel to help stressed warfighters say in the fight.
Officials of the U.S. Defense Advanced Research Projects Agency (DARPA) in Arlington, Va., issued a broad agency announcement (HR001122S0032) on Friday for the Neural Evidence Aggregation Tool (NEAT) in an effort to diagnose and treat mental health problems.
NEAT aims to bring together advances in cognitive science, neuroscience, physiological sensors, data science, and machine learning to develop processes that can measure what a person believes to be true.
Downward trends in mental health and mental fitness were alarming before the COVID-19 pandemic, yet have worsened during the pandemic, with rates of depression and anxiety rising precipitously. These findings particularly are harmful for U.S. military personnel who face the additional strains of combat, long deployments, and more than two decades of war.
Veterans between the ages of 18 and 34 are almost three times more likely to commit suicide than their civilian peers. Current methods to detect early signs of behavioral health risk factors like anxiety, depression, or substance abuse that can lead to suicide rely on self- reporting and screening questionnaires, which are inadequate.
Worse, a warfighter’s commitment to stay in the fight combined with the persistent stigma of seeking behavioral health assistance make current screening methods particularly difficult in military personnel.
NEAT seeks to use preconscious signals, sensors, and machine learning to identify what someone believes to be true about their own behavioral health risk factors -- especially when what he or she believe to be true can be difficult to acknowledge.
The use of preconscious signals will eliminate the possibility explaining dangerous mental conditions away because the new methods will collect signals before someone has the ability to formulate their responses consciously.
NEAT will revolutionize behavioral health screening to assist clinicians, minimize long term vulnerabilities, and make the most of warfighter readiness.
The NEAT program will develop processes that can measure what a person believes to be true by presenting carefully crafted stimuli designed to evoke specific preconscious mental processes; detecting preconscious processes using physiological sensors, digital signal processing, and neural analytics; and using machine learning to aggregate preconscious responses into a final measurement that quantifies what a person believes to be true for a specific topic.
NEAT proposers should leverage existing, commercial-off-the-shelf (COTS) sensor technologies to support development of NEAT processes. NEAT is a 42-month, two phase effort divided into two technical areas: research and development, and independent validation and verification.
The program's first two-year phase is to demonstrate efficacy, and the second 18-month phase is to develop a system. Demonstrate efficacy will demonstrate essential proof of principle and show basic feasibility of the NEAT process.
Developing a system will build on work in phase-one by refining models and stimuli, improving overall performance, assessing the possible sensitivity of the NEAT process to confounding variables, and testing the NEAT process outside of laboratory settings.
Companies interested should submit abstracts by 29 March 2022, and full proposals by 23 May 2022 to the DARPA BAA website at https://baa.darpa.mil/.
Email questions or concerns to DARPA's Gregory Witkop, the NEAT program manager, at [email protected]. More information is online at https://sam.gov/opp/f72c8f446c8144359478c07b97bd275b/view.
John Keller | Editor-in-Chief
John Keller is the Editor-in-Chief, Military & Aerospace Electronics Magazine--provides extensive coverage and analysis of enabling electronics and optoelectronic technologies in military, space and commercial aviation applications. John has been a member of the Military & Aerospace Electronics staff since 1989 and chief editor since 1995.
