Modeling and analysis of motion data from dynamic soldier state estimation to enable situational understanding

Michael Lee; Andrew Tweedell; Mark Dennison Jr.; Paul Sabbagh; Joseph Conroy; Theron Trout; Jade Freeman; Brent Lance

doi:10.1117/12.2618634

31 May 2022 Modeling and analysis of motion data from dynamic soldier state estimation to enable situational understanding

Michael Lee, Andrew Tweedell, Mark Dennison Jr., Paul Sabbagh, Joseph Conroy, Theron Trout, Jade Freeman, Brent Lance

Author Affiliations +

Proceedings Volume 12125, Virtual, Augmented, and Mixed Reality (XR) Technology for Multi-Domain Operations III; 121250E (2022) https://doi.org/10.1117/12.2618634
Event: SPIE Defense + Commercial Sensing, 2022, Orlando, Florida, United States

Abstract

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. The Soldier states that were classified include whether the Soldier is resting, tracking a target, transitioning between potential targets, or firing a shot at the target. We implemented Multivariate Time Series Classification (TSC) using the SKTime toolkit to perform this task and discuss the performance from various classification methods. We also discuss a framework for efficient transference of this information to other tactical information systems on the network.

Conference Presentation

Citation Download Citation

Michael Lee, Andrew Tweedell, Mark Dennison Jr., Paul Sabbagh, Joseph Conroy, Theron Trout, Jade Freeman, and Brent Lance "Modeling and analysis of motion data from dynamic soldier state estimation to enable situational understanding", Proc. SPIE 12125, Virtual, Augmented, and Mixed Reality (XR) Technology for Multi-Domain Operations III, 121250E (31 May 2022); https://doi.org/10.1117/12.2618634

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