Tactile Sensing for Assistive Robotics

Student thesis: Doctoral Thesis


Humans perceive the world through information gathered by their five senses. Attempting to replicate some of these senses in intelligent systems has been a focus of research for many years. Due to high quality vision sensors such as cameras and laser scanners being readily available at a relatively low cost for some time now, vision sensing has been heavily researched for many decades enabling systems to distinguish a lot of information such as the size, shape and colour of objects or materials. However, there are attributes of objects, materials and the environment that cannot be determined by vision sensing alone such as compressibility, thermal properties or sub-surface vibration.

This thesis presents methods which demonstrate that tactile sensing can be used to assess a human’s current state of health by measuring their vital signs using a biomimetic fingertip, namely BioTAC. It involves three main contributions. The first contribution is a method for classifying materials from tactile sensing alone. Using machine learning approaches, the high sensitivity of the BioTAC tactile sensors is demonstrated via the ability to classify different (and similar) material with high accuracy based on surface texture and thermal properties. The second contribution focuses on the use of the BioTAC fingertip to accurately measure the vital signs of a human by mimicking medical professionals. Algorithms have been developed and evaluated for determining a human’s Beats Per Minute (BPM), Pulse to Pulse Interval (PPI), Respiratory Rate (RR), Breath to Breath Interval (BBI) via tactile sensing. Furthermore, algorithms have been developed to measure Capillary Refill Time (CRT) by using a combination of tactile sensing for the control of a robot fingertip and vision sensing to analyse changes in the subjects skin colour. The final contribution is a fuzzy classification algorithm capable of classifying the human’s health status based on their BPM, RR and CRT.

Significant contributions in the field of tactile sensing presented in this thesis demonstrate that a robotic system can determine a human’s current status of health. This could play a vital role in helping to rescue victims of a disaster or emergency by performing medical triage and determining an order of treatment. In turn, emergency personnel will be able to make a more informed decision on how they should allocate their valuable resources thus preventing unnecessary risk and reducing the further loss of human life.
Date of AwardMay 2018
Original languageEnglish
SupervisorMartin Mc Ginnity (Supervisor) & Sonya Coleman (Supervisor)


  • Tactile sensing
  • Assisstive living
  • Vital Sign Measurement
  • Neural Networks
  • Material Identification
  • Artificial Intelligence
  • Computational Learning Algorithms

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