Resumen de Time Series Data Augmentation and Dropout Roles in Deep Learning Applied to Fall Detection

Enol García González, José Ramón Villar Flecha , Enrique Antonio de la Cal Marín

• Fall Detection is one of the most interesting and challenging research topics in the world today because of its implications in society and also because the complexity of processing Time Series (TS). Plenty of research has been published in the literature, several of them introducing Deep Learning (DL) Neural Network (NN) as the modelling element. In this study we analyse one of these contributions and address several enhancement using TS data augmentation and dropout. Moreover, the possibility of reducing the NN to make it lighter has been studied. The NN has been implemented using Keras in Python and the experimentation includes an staged fall publicly available data set. Results show the TS data augmentation together with dropout helped in learning a more robust and precise model. Future work includes introducing different types of cross-validation as well as introducing other types of DL models more suitable for TS.