Accelerometer-Based Human Fall Detection Using Convolutional Neural Networks

doi:10.3390/s19071644

Accelerometer-Based Human Fall Detection Using Convolutional Neural Networks

2019-04-06

160

OA

AI

G

Guto Leoni Santos

P

Patrícia Takako Endo *

K

Kayo Henrique de Carvalho Monteiro

É

Élisson da Silva Rocha

DOI：10.3390/s19071644

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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.

Keywords:

deep learning

human fall detection

sensor

accelerometer

convolutional neural networks

Accelerometer-Based Human Fall Detection Using Convolutional Neural Networks

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