Fall Detection Through Thermal Vision Sensing

Joseph Rafferty, Jonathan Synnott, CD Nugent, Gareth Morrison, Elena Tamburini

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

23 Citations (Scopus)
456 Downloads (Pure)


Accidental falls can cause serious injury to at risk individuals. This is especially true in the elderly community where falls are the leading cause of hospitalization, injury-related deaths and loss of independence. Detecting and rapidly responding to falls has shown to reduce the long-term impact of and risks associated with falls. A number of real time fall detection solutions exist, however, these have some deficiencies relating to privacy, maintenance, and correct usage. This study introduces a novel fall detection approach that aims to address some of these deficiencies through use of computer vision processes and ceiling mounted thermal vision sensors. A preliminary evaluation has been performed on this process showing promising results, with an accuracy of 68 %, however, highlighting a number of issues related to false positives. Future work will improve this approach and provide extended evaluation.
Original languageEnglish
Title of host publicationUbiquitous Computing and Ambient Intelligence
Subtitle of host publicationIWAAL 2016, AmIHEALTH 2016, UCAmI 2016: Ubiquitous Computing and Ambient Intelligence
ISBN (Electronic)978-3-319-48799-1
ISBN (Print)978-3-319-48798-4
Publication statusPublished (in print/issue) - Dec 2016

Publication series

NameLecture Notes in Computer Science
PublisherSpringer Verlag (Germany): Computer Proceedings
ISSN (Print)0302-9743


  • Fall detection
  • Assistive technologies
  • Computer vision
  • Sensors
  • Thermal vision


Dive into the research topics of 'Fall Detection Through Thermal Vision Sensing'. Together they form a unique fingerprint.

Cite this