Human Vital Sign Determination using Tactile Sensing and Fuzzy Triage System

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The ability to quickly and accurately triage a person’s medical condition in an emergency situation or other critical scenarios could mean the difference between life and death. Endowing a robotic system with vision and tactile capabilities, similar to those of medical professionals, and thus enabling robots to assess a patient’s status in an emergency is a highly sought after characteristic in healthcare robotics. This paper presents a novel fuzzy triage system exploiting visual and tactile sensing, to equip a robot with the skills to accurately determine key vital signs in humans. There are three key signs of human health: respiratory rate, pulse rate (Beats Per Minute (BPM)) and capillary refill time. Using ground truth from a medical professional, the fuzzy triage system is trained and validated initially with informed synthetic data and then further evaluated using vital signs data collected from subjects in a pilot study. Results from this pilot study indicate that the fuzzy triage system is capable of classifying a patient’s health using the the novel approaches for collecting BPM,
Respiratory Rate (RR) and Capillary Refill Time (CRT) which replicate, to some extent, the approaches used by medical professionals for measuring vital signs. Furthermore, the intelligent system proved capable of determining whether a pulse was regular or arrhythmic, whether respiratory rate was regular or irregular, and determining the subject’s capillary refill time. Such results imply that this system could ultimately be used, for example, in a home assistance robot for elderly or disabled persons, or as a first responder robot. Ultimately the aim would be that these methods could be utilised by robotic systems in emergency scenarios or disaster zones.
Original languageEnglish
Article number114781
JournalExpert Systems with Applications
Early online date4 Mar 2021
Publication statusPublished online - 4 Mar 2021

Bibliographical note

Funding Information:
Sonya Coleman has a BSc (Hons) in Mathematics, Statistics and Computing from Ulster University in 1999 and a PhD in Image Processing from Ulster University in 2003. She is also the Cognitive Robotics team leader within the Intelligent Systems Research Centre at Ulster. She has 180+ publications primarily in the fields of mathematical image processing, robotics, computational neuroscience and capital markets engineering. Prof. Coleman’s research has been supported by funding from EPSRC award EP/C006283/11, the Nuffeld Foundation, and the Leverhulme Trust. Additionally she was co-investigator on the EU FP7 funded project RUBICON, VISUALISE and SLANDIAL. In 2016 Prof Coleman was appointed as secretary of the Irish Pattern Recognition and Classification Society and in 2018 was appointed as a Visiting Professor at North Eastern University, Shenyang, China. In 2009 was awarded the Distinguished Research Fellowship by Ulster University in recognition of her research contribution.

Publisher Copyright:
© 2021 The Authors


  • Fuzzy Systems
  • Automated Triage
  • signal processing
  • Tactile Sensing
  • artificial intelligence (AI)
  • classification
  • Human Vital Sign Detection
  • Tactile sensing
  • Human vital sign detection
  • Classification
  • Signal processing
  • Artificial intelligence
  • Automated triage
  • Fuzzy systems


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