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Proceedings 2018, 2(19), 1236;

Detection of Falls from Non-Invasive Thermal Vision Sensors Using Convolutional Neural Networks

Department of Computer Science, University of Jaén, Campus Las Lagunillas, 23071 Jaén, Spain
School of Computing, Ulster University, Newtownabbey, Co. Antrim, Northern Ireland BT15 1ED, UK
Ubiquitous Computing Lab in Kyung Hee University, Seoul 446-701, Korea
Author to whom correspondence should be addressed.
Presented at the 12th International Conference on Ubiquitous Computing and Ambient Intelligence (UCAmI 2018), Punta Cana, Dominican Republic, 4–7 December 2018.
Published: 24 October 2018
(This article belongs to the Proceedings of UCAmI 2018)
PDF [1695 KB, uploaded 24 October 2018]


In this work, we detail a methodology based on Convolutional Neural Networks (CNNs) to detect falls from non-invasive thermal vision sensors. First, we include an agile data collection to label images in order to create a dataset that describes several cases of single and multiple occupancy. These cases include standing inhabitants and target situations with a fallen inhabitant. Second, we provide data augmentation techniques to increase the learning capabilities of the classification and reduce the configuration time. Third, we have defined 3 types of CNN to evaluate the impact that the number of layers and kernel size have on the performance of the methodology. The results show an encouraging performance in single-occupancy contexts, with up to 92 % of accuracy, but a 10 % of reduction in accuracy in multiple-occupancy. The learning capabilities of CNNs have been highlighted due to the complex images obtained from the low-cost device. These images have strong noise as well as uncertain and blurred areas. The results highlight that the CNN based on 3-layers maintains a stable performance, as well as quick learning.
Keywords: thermal vision sensor; fall detection; convolutional neural networks thermal vision sensor; fall detection; convolutional neural networks
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Quero, J.M.; Burns, M.; Razzaq, M.A.; Nugent, C.; Espinilla, M. Detection of Falls from Non-Invasive Thermal Vision Sensors Using Convolutional Neural Networks. Proceedings 2018, 2, 1236.

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