  Falls prevention, especially in older people, becomes an increasingly
important topic in the times of aging societies. In this work, we present Gated
Recurrent Unit-based neural networks models designed for predicting falls
(syncope). The cardiovascular systems signals used in the study come from
Gravitational Physiology, Aging and Medicine Research Unit, Institute of
Physiology, Medical University of Graz. We used two of the collected signals,
heart rate, and mean blood pressure. By using bidirectional GRU model, it was
possible to predict the syncope occurrence approximately ten minutes before the
manual marker.
