The DOI matches its Crossref record; 26 of the 26 references it cites were found in a registry or an index.
Fall Detection With Multiple Cameras: An Occlusion-Resistant Method Based on 3-D Silhouette Vertical Distribution
Abstract
According to the demographic evolution in industrialized countries, more and more elderly people will experience falls at home and will require emergency services. The main problem comes from fall-prone elderly living alone at home. To resolve this lack of safety, we propose a new method to detect falls at home, based on a multiple-cameras network for reconstructing the 3-D shape of people. Fall events are detected by analyzing the volume distribution along the vertical axis, and an alarm is triggered when the major part of this distribution is abnormally near the floor during a predefined period of time, which implies that a person has fallen on the floor. This method was validated with videos of a healthy subject who performed 24 realistic scenarios showing 22 fall events and 24 cofounding events (11 crouching position, 9 sitting position, and 4 lying on a sofa position) under several camera configurations, and achieved 99.7% sensitivity and specificity or better with four cameras or more. A real-time implementation using a graphic processing unit (GPU) reached 10 frames per second (fps) with 8 cameras, and 16 fps with 3 cameras.
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1 retracted
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Fall detection - Principles and Methods
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Cited by
Elderly Fall Detection Systems: A Literature Survey
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Vision-Based Fall Detection with Convolutional Neural Networks
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Fall Detection Devices and Their Use With Older Adults: A Systematic Review
Chaudhuri, S., Thompson, H., & Demiris, G. (2014) · Journal of Geriatric Physical Therapy · doi:10.1519/jpt.0b013e3182abe779 · Cited by 215
Ambient Sensors for Elderly Care and Independent Living: A Survey
Uddin, M. Z., Khaksar, W., & Torresen, J. (2018) · Sensors · doi:10.3390/s18072027 · Cited by 166
Utilising LiDAR for fall detection
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Metadata
- Authors
- Auvinet, E., Multon, F., Saint-Arnaud, A., Rousseau, J., & Meunier, J.
- Venue
- IEEE Transactions on Information Technology in Biomedicine
- Published
- 2011-03
- Volume
- 15
- Issue
- 2
- Pages
- 290-300
- ISSN
- 1089-7771 (ISSN-L)
- 1558-0032
- OpenAlex
- W2150216628
- Cited by
- 256
- References
- 26
- FWCI
- 11.06
- Type
- journal article
- Language
- en
- Open access
- closed
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- Auvinet, E., Multon, F., Saint-Arnaud, A., Rousseau, J., & Meunier, J. (2011). Fall Detection With Multiple Cameras: An Occlusion-Resistant Method Based on 3-D Silhouette Vertical Distribution. IEEE Transactions on Information Technology in Biomedicine. https://doi.org/10.1109/titb.2010.2087385
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