Assessing the Soldier Survivability Tradespace Using a Single IMU

Abstract

Soldier burden is influenced by the environment, metabolic demands, equipment properties, and psychological stressors; however, much of our knowledge of soldier burden is in the context of body-borne load mass in controlled laboratory environments. Thus, to further our understanding of how all aspects of soldier burden affect the survivability tradespace (i.e., performance, health, and susceptibility to enemy action), field-based motion capture methods are needed. We developed a human activity recognition method using the deep convolutional long short-term memory neural network architecture, trained using a single inertial measurement unit on the upper back, to identify eleven tactical movement patterns commonly performed by soldiers. Using a two-step logical algorithm, real-world constraints are forced, and class labels are expanded to 19 movements. Presented are three models based on Indoor, Section Attack (outdoors), and a General approach. Across all three approaches, we obtained an average accuracy of 90.0%. Further, we used these predictions to calculate meaningful tradespace metrics, which had an excellent agreement with calculations using the true labels. Military leaders and defence scientists can use this approach to quantify tradespace metrics in the field, as a preprocessing tool to supplement other technology, and make data-driven decisions that can help improve performance, decrease susceptibility, and increase overall mission success.

Keywords

• Activity recognition
• performance
• LSTM
• military
• wearables
• DNN