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- Guto Leoni Santos
- Centro de Informática, Universidade Federal de Pernambuco, Recife 50670-901, Brazil
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- Patricia Takako Endo
- Universidade de Pernambuco, Recife 50100-010, Brazil
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- Kayo Henrique de Carvalho Monteiro
- Universidade de Pernambuco, Recife 50100-010, Brazil
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- Elisson da Silva Rocha
- Universidade de Pernambuco, Recife 50100-010, Brazil
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- Ivanovitch Silva
- Universidade Federal do Rio Grande do Norte, Natal 59078-970, Brazil
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- Theo Lynn
- Business School, Dublin City University, Dublin 9, Ireland
説明
<jats:p>Human falls are a global public health issue resulting in over 37.3 million severe injuries and 646,000 deaths yearly. Falls result in direct financial cost to health systems and indirectly to society productivity. Unsurprisingly, human fall detection and prevention are a major focus of health research. In this article, we consider deep learning for fall detection in an IoT and fog computing environment. We propose a Convolutional Neural Network composed of three convolutional layers, two maxpool, and three fully-connected layers as our deep learning model. We evaluate its performance using three open data sets and against extant research. Our approach for resolving dimensionality and modelling simplicity issues is outlined. Accuracy, precision, sensitivity, specificity, and the Matthews Correlation Coefficient are used to evaluate performance. The best results are achieved when using data augmentation during the training process. The paper concludes with a discussion of challenges and future directions for research in this domain.</jats:p>
収録刊行物
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- Sensors
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Sensors 19 (7), 1644-, 2019-04-06
MDPI AG
