• Skip to primary navigation
  • Skip to main content
  • Skip to primary sidebar
  • ART Lab
  • Research
  • Publications
  • ART Studio
  • People
  • News
  • Contact Us

Adaptive Robotics & Technology Lab

ART Lab

Texas A&M University College of Engineering

Design and Evaluation of a Multi-Sensor Unit for Measuring Physiological Stressors of Medical Transport

Georgios Kaloutsakis, Andrew Reimer, Donghwa Jeong, and Kiju Lee

International Mechanical Engineering Congress and Exposition (IMECE)

November 15-21, 2013

DOI: 10.1115/IMECE2013-65435

Patients who undergo inter-hospital transfer experience increased relative mortality, ranging from 10 to 100% higher than non-transferred patients. The high-cost, increased risk of complications and poor outcomes of transferred patients warrant the critical examination of potential causes. One of the major causes may be the external stressors that patients are exposed to during medical transport. To realize simultaneous measurements of external stressors, we developed a multi-sensor unit for measuring vibration, noise, ambient temperature, and barometric pressure. For preliminary evaluation, the sensor unit was tested on 29 medical transports, 11 air transports by a helicopter and 18 ground missions by an ambulance. The average whole-body vibration for each air and ground transport was calculated at 0.3510m/s2 and 0.5871m/s2 respectively. Air transports produced much higher level of noise than the ground transports. We found no significant difference between two modes in terms of average temperature and the temperature changes. Barometric pressure drops significantly during air transport, indicating potential use of this data for automatic mode classification.

Recent Posts

  • Comparison of GPS Collars and Solar-Powered GPS Ear Tags for Animal Movement Studies September 29, 2025
  • Low-Cost, Compact Mobile Robot for Autonomous Soil Monitoring in Crop Fields September 29, 2025
  • Hardware Prototype and System Apparatus of an Autonomous Robotic Harvesting Cell September 29, 2025
  • Multi-Robot Shepherding: A CLF-CBF Approach September 29, 2025
  • Unmanned aerial system and machine learning driven Digital-Twin framework for in-season cotton growth forecasting September 29, 2025

© 2016–2026 Adaptive Robotics & Technology Lab Log in

Texas A&M Engineering Experiment Station Logo
  • State of Texas
  • Open Records
  • Risk, Fraud & Misconduct Hotline
  • Statewide Search
  • Site Links & Policies
  • Accommodations
  • Environmental Health, Safety & Security
  • Employment