Falls are one of the most frequent causes of injuries in elderly people. Wearable Fall Detection Systems
provided a ubiquitous tool for monitoring and alert when these events happen. Recurrent Neural Networks
(RNN) are algorithms that demonstrates a great accuracy in some problems analyzing sequential inputs, such
as temporal signal values. However, their computational complexity are an obstacle for the implementation
in IoT devices. This work shows a performance analysis of a set of RNN architectures when trained with
data obtained using different sampling frequencies. These architectures were trained to detect both fall and
fall hazards by using accelerometers and were tested with 10-fold cross validation, using the F1-score metric.
The results obtained show that sampling with a frequency of 25Hz does not affect the effectiveness, based
on the F1-score, which implies a substantial increase in the performance in terms of computational cost. The
architectures with two RNN layers and without a first dense layer had slightly better results than the smallest
architectures. In future works, the best architectures obtained will be integrated in an IoT solution to determine
the effectiveness empirically.Ministerio de Economía y Competitividad TEC2016-77785-
