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Kathy L Ryan1, Caroline A Rickards, Carmen Hinojosa-Laborde

  • 1US Army Institute of Surgical Research, Fort Sam Houston, TX, USA.

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New wearable physiological status monitors (PSMs) show promise for remote triage of combat injuries. Machine learning models using wearable data can distinguish physical activity from hypovolemia, improving battlefield readiness assessments.

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Area of Science:

  • Military Medicine
  • Biomedical Engineering
  • Physiological Monitoring

Background:

  • The Army requires "wear-and-forget" physiological status monitors (PSMs) for enhanced battlefield readiness and remote triage of injured soldiers.
  • Current PSM prototypes measuring electrocardiogram and respiration are insufficient for accurate combat injury triage.

Purpose of the Study:

  • To review the testing of remote triage system prototypes in laboratory and field settings.
  • To identify the need for a circulating blood volume status metric for effective remote triage.
  • To evaluate the potential of machine learning algorithms using wearable sensor data for triage.

Main Methods:

  • Testing of remote triage system prototypes in laboratory and field exercises.
  • Analysis of low-level physiological signals from wearable devices.
  • Application of machine-learning algorithms to physiological data.

Main Results:

  • Physiological data from electrocardiogram and respiration alone are inadequate for specific combat injury triage.
  • Machine learning models successfully modeled circulating blood volume status using wearable sensor data.
  • Algorithms can discriminate between physical activity and central hypovolemia.

Conclusions:

  • A metric for circulating blood volume status is essential for effective remote triage of combat injuries.
  • Machine learning applied to wearable physiological data shows significant promise for remote triage devices.
  • Wearable PSMs can enhance far-forward capabilities for assessing warrior readiness and managing injuries.