Traumatic brain injuries (TBIs) are unfortunate occurrences during military training and deployment. Because mild TBIs can be experienced without presenting obvious signs of head trauma or facial lacerations, they are the most difficult type to diagnose at the time of the injury and patients themselves may perceive the impact as mild or harmless. TBIs are cumulative, so treating a patient within the “golden hour” – the first 60 minutes after being injured – is crucial for improved long-term recovery.
Dr. Jie Huang, assistant professor of electrical and computer engineering at Missouri S&T, is working to meet the need for recognizing TBIs by developing technology that enables autonomous collection and processing of data – pertaining to trauma-inducing actions – in a reliable and “smart” manner for prompt identification.
Huang received a $2.3 million grant from the U.S. Army Research Laboratory through the Leonard Wood Institute (LWI), supported by the Acute Effects of Neurotrama Consortium (AENC), to develop the technology to create a “smart helmet.” By embedding military helmets with sensors and other data-transmission technologies, Huang aims to help accurately diagnose and administer aid to mild TBI victims. Huang assembled a team from Missouri S&T to assist in the project consisting of Dr. Rex E. Gerald, visiting professor and a senior research scientist, and Dr. Aditya Kumar, assistant professor of materials science and engineering at S&T.
“Our aim is to develop a fundamental understanding of acute TBIs through large-scale data acquisition of blast lab impact events from pressure-sensor-equipped helmets processed through machine learning,” says Huang. “Military-related TBIs come primarily from repeated exposures to explosive blasts during planned training activities. Blast TBIs account for approximately 60 percent of all military-related TBIs, of which 80 percent are categorized as mild.”
Huang’s research places an emphasis on understanding the origins of acute and mild forms of TBIs. He does this by having data relayed wirelessly in real-time via the “smart” helmets and integrating machine-learning based on a decision-making framework that can detect the severity of an impact level.
Huang and his research team are developing a football smart-helmet prototype, which will be equipped with fiber optic micro interferometer sensors. The sensors will be activated by blunt-force impacts that range from 3-15 on the Glasgow Coma Scale. Once it is developed, Huang will correlate laboratory testing data with field data and improve the overall configuration of the helmets.
“Our research project will use advanced optical fiber sensors, embedded in smart helmets, to instantly warn soldiers of the severity of a concussive event in the field so that treatment can be sought immediately,” says Huang. “Such a framework, with the ability to yield highly accurate predictions, will mitigate a soldier’s suffering and save medical personnel’s time.”
A total of 14 projects valued at $9.4 million were awarded based on the existence of the AENC, which was formed as a multicenter collaboration linking science, translational and clinical neuroscience researchers from a regional medical center, military and academia to address the acute aspect mTBI.
Huang praises the support he has received from the consortium and its foundation of experience.
“Col. Sidney Hinds of the U.S. Army supported the research, Dr. Don James and Barry White of Phelps Health provided a vision and determination to build the consortium group, and Kent Thomas, executive director of the LWI, ensured that the projects proceed smoothly,” says Huang. “This all combined to provide a supportive relationship and strong focus. Without these four and the AENC, the opportunity for this research would not exist.”