Fall Detection With UWB Radars and CNN-LSTM Architecture

Fall detection is a major challenge for researchers. Indeed, a fall can cause injuries such as femoral neck fracture, brain hemorrhage, or skin burns, leading to significant pain. However, in some cases, trauma caused by an undetected fall can get worse with the time and conducts to painful end of l...

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Bibliographic Details
Published in:IEEE journal of biomedical and health informatics 2021-04, Vol.25 (4), p.1273-1283
Main Authors: Maitre, Julien, Bouchard, Kevin, Gaboury, Sebastien
Format: Article
Language:English
Subjects:
Apartments
Artificial neural networks
Brain injury
Burns
Cameras
Classification
CNN-LSTM
Detection
End of life
Fall
Fall detection
Falls
Feature extraction
Hemorrhage
Informatics
Injuries
Leave-one-subject-out
Long short-term memory
Model testing
Neural networks
Pain
Short term
Three-dimensional displays
Trauma
Ultra wideband radar
Ultrawideband
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Summary:Fall detection is a major challenge for researchers. Indeed, a fall can cause injuries such as femoral neck fracture, brain hemorrhage, or skin burns, leading to significant pain. However, in some cases, trauma caused by an undetected fall can get worse with the time and conducts to painful end of life or even death. One solution is to detect falls efficiently to alert somebody (e.g., nurses) as quickly as possible. To respond to this need, we propose to detect falls in a real apartment of 40 square meters by exploiting three ultra-wideband radars and a deep neural network model. The deep neural network is composed of a convolutional neural network stacked with a long-short term memory network and a fully connected neural network to identify falls. In other words, the problem addressed in this paper is a binary classification attempting to differentiate fall and non-fall events. As it can be noticed in real cases, the falls can have different forms. Hence, the data to train and test the classification model have been generated with falls (four types) simulated by 10 participants in three locations in the apartment. Finally, the train and test stages have been achieved according to three strategies, including the leave-one-subject-out method. This latter method allows for obtaining the performances of the proposed system in a generalization context. The results are very promising since we reach almost 90% of accuracy.
ISSN:2168-2194
2168-2208
DOI:10.1109/JBHI.2020.3027967