TY  - JOUR
PY  - 2019//
TI  - Accelerometer-based human fall detection using convolutional neural networks
JO  - Sensors (Basel)
A1  - Santos, Guto Leoni
A1  - Endo, Patricia Takako
A1  - Monteiro, Kayo Henrique de Carvalho
A1  - Rocha, Elisson da Silva
A1  - Silva, Ivanovitch
A1  - Lynn, Theo
SP  - s19071644
EP  - s19071644
VL  - 19
IS  - 7
N2  - 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.<p />  <p>Language: en</p>
LA  - en
SN  - 1424-8220
UR  - http://dx.doi.org/10.3390/s19071644
ID  - ref1
ER  -