Cem O Yaldiz, Mark J Buller, Kristine L Richardson, Sungtae An, David J Lin, Aprameya Satish, Kyla Driver, Emma Atkinson, Timothy Mesite, Christopher King, Max Bursey, Meghan Galer, Mindy Millard Stafford, Michael N Sawka, Alessio Medda, Omer T Inan
We employed wearable multimodal sensing (heart rate and triaxial accelerometry) with machine learning to enable early prediction of impending exertional heat stroke (EHS). US Army Rangers and Combat Engineers (N = 2,102) were instrumented while participating in rigorous 7-mile and 12-mile loaded rucksack timed marches. There were three EHS cases, and data from 478 Rangers were analyzed for model building and controls. The data-driven machine learning approach incorporated estimates of physiological strain (heart rate) and physical stress (estimated metabolic rate) trajectories, followed by reconstruction to obtain compressed representations which then fed into anomaly detection for EHS prediction...
October 9, 2023: IEEE Journal of Biomedical and Health Informatics