Automatic fall detection using region-based convolutional neural network
| Autor: | Mohamed Maher Ben Ismail, Ouiem Bchir, Ghada Khaled Hader |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Computer science
Feature extraction Monitoring Ambulatory Poison control Convolutional neural network Machine Learning 0502 economics and business Classifier (linguistics) Digital image processing Image Processing Computer-Assisted Humans 0501 psychology and cognitive sciences 050107 human factors 050210 logistics & transportation Artificial neural network business.industry Deep learning 05 social sciences Public Health Environmental and Occupational Health Pattern recognition Accidental Falls Neural Networks Computer Artificial intelligence business Transfer of learning Safety Research |
| Zdroj: | International Journal of Injury Control and Safety Promotion. 27:546-557 |
| ISSN: | 1745-7319 1745-7300 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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