@inbook{9a0ba326898b41e8a93ac001d2fd70f0,
title = "Fall Detection Through Thermal Vision Sensing",
abstract = "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.",
keywords = "Fall detection, Assistive technologies, Computer vision, Sensors, Thermal vision",
author = "Joseph Rafferty and Jonathan Synnott and CD Nugent and Gareth Morrison and Elena Tamburini",
year = "2016",
month = dec,
doi = "10.1007/978-3-319-48799-1_10",
language = "English",
isbn = "978-3-319-48798-4",
series = "Lecture Notes in Computer Science",
publisher = "Springer",
pages = "84--90",
booktitle = "Ubiquitous Computing and Ambient Intelligence",
address = "Switzerland",
}