AI Article Synopsis

  • Researchers used wearable sensors to monitor heart rate and movement in US Army Rangers and Combat Engineers during intense marches, aiming to predict exertional heat stroke (EHS) early on.
  • Data from 478 participants were analyzed using machine learning to assess physical strain and stress, successfully predicting EHS up to 69 minutes before it occurred in three cases.
  • The study suggests that this predictive method can be adapted to other activities and improved with new sensor technology, potentially aiding in health intervention strategies.

Article Abstract

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. Impending EHS was predicted from 33 to 69 min before collapse. These findings demonstrate that low dimensional physiological stress to strain patterns with machine learning anomaly detection enables early prediction of impending EHS which will allow interventions that minimize or avoid pathophysiological sequelae. We describe how our approach can be expanded to other physical activities and enhanced with novel sensors.

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http://dx.doi.org/10.1109/JBHI.2023.3323014DOI Listing

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