Modeling and analysis of motion data from dynamic soldier state estimation to enable situational understanding
4 April 2022 • 3:00 PM - 3:20 PM EDT
Enabling leaders with the ability to make decisive actions in high operational tempo environments is key to achieving decision-superiority. Under stressful battlefield conditions with little to no time for communication, it is critical to acquire relevant tactical information quickly to inform decision-making. A potential augmentation to tactical information systems is access to real-time analytics on a unit's operating status and emergent behaviors inferred from soldier-worn or embedded sensors on their kit. Automatic human activity recognition (HAR) has been greatly achievable in recent years thanks to advancements in algorithms and ubiquitous low-cost, yet powerful processors, hardware and sensors. In this paper, we present weapon-born sensor measurement acquisition, processing, and HAR approaches to demonstrate soldier state estimation in a target acquisition and tracking experiment. We also discuss a framework for sending this data to cross-reality information systems.
DEVCOM Army Research Lab. (United States)
Michael Lee received his B.S. degree in Computer Science from the University of Maryland at College Park in 2000 and M.S. degree in Information Systems from Johns Hopkins University in 2009. He is currently a Computer Scientist at the U.S. Army Research Laboratory (ARL). In 2003, he joined the Machine Translation Branch to help develop end-to-end systems (in cooperation with the USAF, the NSA, and the CIA) that integrate Machine Translation technology with Optical Character Recognition technology. Since 2007, he has been part of the Battlefield Information Systems Branch where he developed applications to exploit data (e.g. images and videos) generated from SEDD sensors.