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Sensors 2019, 19(7), 1644; https://doi.org/10.3390/s19071644

Accelerometer-Based Human Fall Detection Using Convolutional Neural Networks

1
Centro de Inform√°tica, Universidade Federal de Pernambuco, Recife 50670-901, Brazil
2
Universidade de Pernambuco, Recife 50100-010, Brazil
3
Business School, Dublin City University, Dublin 9, Ireland
4
Universidade Federal do Rio Grande do Norte, Natal 59078-970, Brazil
*
Author to whom correspondence should be addressed.
Received: 9 March 2019 / Revised: 2 April 2019 / Accepted: 3 April 2019 / Published: 6 April 2019
(This article belongs to the Special Issue Internet of Things in Healthcare Applications)
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Abstract

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. View Full-Text
Keywords: deep learning; human fall detection; sensor; accelerometer; convolutional neural networks deep learning; human fall detection; sensor; accelerometer; convolutional neural networks
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Santos, G.L.; Endo, P.T.; Monteiro, K.H.C.; Rocha, E.S.; Silva, I.; Lynn, T. Accelerometer-Based Human Fall Detection Using Convolutional Neural Networks. Sensors 2019, 19, 1644.

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