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.
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Notes
- 1.
It should be noted that this detection process is designed to operate within a single occupant environment, when a fall is most dangerous and there is no immediate assistance available.
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Acknowledgments
Invest Northern Ireland is acknowledged for supporting this project under the Competence Centre Programs Grant RD0513853 – Connected Health Innovation Centre.
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Rafferty, J., Synnott, J., Nugent, C., Morrison, G., Tamburini, E. (2016). Fall Detection Through Thermal Vision Sensing. In: García, C., Caballero-Gil, P., Burmester, M., Quesada-Arencibia, A. (eds) Ubiquitous Computing and Ambient Intelligence. IWAAL AmIHEALTH UCAmI 2016 2016 2016. Lecture Notes in Computer Science(), vol 10070. Springer, Cham. https://doi.org/10.1007/978-3-319-48799-1_10
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DOI: https://doi.org/10.1007/978-3-319-48799-1_10
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