Citation

Hader GK, Ben Ismail MM, Bchir O. Int. J. Inj. Control Safe. Promot. 2020; ePub(ePub): ePub.

Copyright

(Copyright © 2020, Informa - Taylor and Francis Group)

DOI

10.1080/17457300.2020.1819341

PMID

32930063

Abstract

The common classifiers usually used to detect fall incidents depend on building and maintaining complex feature extraction for accurate machine learning tasks. However, these efforts have not succeeded in determining an ideal classifier or feature extraction for fall detection. In this research, we address the feature extraction problem along with the choice of the most appropriate classifier by using deep learning where the most prominent features are learned over the numerous layers of the network. More specifically, a general framework that relies on a faster region-based convolutional neural network was designed and developped to recognize the fall incidents. In particular, we designed three custom architectures and exploited transfer learning by using pre-trained networks such as the VGG-16 and AlexNet to overcome the fall detection challenge. The performance of the proposed networks showed the advantage of the pre-trained networks, where VGG-16 scored highest in those measures followed by AlexNet, the custom networks showed impressive results that were close to the pre-trained networks.

Language: en

Keywords

fall detection; Machine learning; digital image processing; neural networks; transfer learning