# Falls Prediction in eldery people using Gated Recurrent Units

## TL;DR

This paper introduces a bidirectional Gated Recurrent Unit neural network model that predicts falls in elderly individuals approximately ten minutes in advance using heart rate and blood pressure signals.

## Contribution

It is the first to apply bidirectional GRU models for elderly fall prediction using cardiovascular signals, achieving early detection of syncope.

## Key findings

- Predicts syncope about ten minutes before occurrence
- Uses cardiovascular signals like heart rate and blood pressure
- Demonstrates effectiveness of bidirectional GRU models

## Abstract

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.