Abstract
| Language | English |
|---|---|
| Title of host publication | Ubiquitous Computing and Ambient Intelligence |
| Subtitle of host publication | IWAAL 2016, AmIHEALTH 2016, UCAmI 2016: Ubiquitous Computing and Ambient Intelligence |
| Pages | 84-90 |
| ISBN (Electronic) | 978-3-319-48799-1 |
| DOIs | |
| Publication status | Published - Dec 2016 |
Publication series
| Name | Lecture Notes in Computer Science |
|---|---|
| Publisher | Springer Verlag (Germany): Computer Proceedings |
| ISSN (Print) | 0302-9743 |
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Keywords
- Fall detection
- Assistive technologies
- Computer vision
- Sensors
- Thermal vision
Cite this
}
Fall Detection Through Thermal Vision Sensing. / Rafferty, Joseph; Synnott, Jonathan; Nugent, CD; Morrison, Gareth; Tamburini, Elena.
Ubiquitous Computing and Ambient Intelligence : IWAAL 2016, AmIHEALTH 2016, UCAmI 2016: Ubiquitous Computing and Ambient Intelligence. 2016. p. 84-90 (Lecture Notes in Computer Science).Research output: Chapter in Book/Report/Conference proceeding › Chapter
TY - CHAP
T1 - Fall Detection Through Thermal Vision Sensing
AU - Rafferty, Joseph
AU - Synnott, Jonathan
AU - Nugent, CD
AU - Morrison, Gareth
AU - Tamburini, Elena
PY - 2016/12
Y1 - 2016/12
N2 - 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.
AB - 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.
KW - Fall detection
KW - Assistive technologies
KW - Computer vision
KW - Sensors
KW - Thermal vision
U2 - 10.1007/978-3-319-48799-1_10
DO - 10.1007/978-3-319-48799-1_10
M3 - Chapter
SN - 978-3-319-48798-4
T3 - Lecture Notes in Computer Science
SP - 84
EP - 90
BT - Ubiquitous Computing and Ambient Intelligence
ER -