WearableMil: An End-to-End Framework for Military Activity Recognition and Performance Monitoring

TL;DR

This paper presents WearableMil, an end-to-end deep learning framework for military activity recognition and performance monitoring using wearable data, addressing challenges like continuous data processing, missing data, and diverse activity recognition.

Contribution

It introduces a hierarchical deep learning approach with physiologically-informed missing data handling and a real-time visualization system for military activity monitoring.

Findings

Achieved 93.8% accuracy in temporal splits and 83.8% in cross-user evaluation.

Reduced unknown sleep states from 40.38% to 3.66% with physiologically-informed methods.

Longer time windows improve basic state classification but affect fine-grained activity detection.

Abstract

Musculoskeletal injuries during military training significantly impact readiness, making prevention through activity monitoring crucial. While Human Activity Recognition (HAR) using wearable devices offers promising solutions, it faces challenges in processing continuous data streams and recognizing diverse activities without predefined sessions. This paper introduces an end-to-end framework for preprocessing, analyzing, and recognizing activities from wearable data in military training contexts. Using data from 135 soldiers wearing \textit{Garmin--55} smartwatches over six months with over 15 million minutes. We develop a hierarchical deep learning approach that achieves 93.8% accuracy in temporal splits and 83.8% in cross-user evaluation. Our framework addresses missing data through physiologically-informed methods, reducing unknown sleep states from 40.38% to 3.66%. We demonstrate that while longer time windows (45-60 minutes) improve basic state classification, they present trade-offs in detecting fine-grained activities. Additionally, we introduce an intuitive visualization system that enables real-time comparison of individual performance against group metrics across multiple physiological indicators. This approach to activity recognition and performance monitoring provides military trainers with actionable insights for optimizing training programs and preventing injuries.