Falls Prediction in eldery people using Gated Recurrent Units
This work addresses falls prevention in aging populations, but it is incremental as it applies an existing method (GRU) to a specific medical dataset.
The paper tackled the problem of predicting falls in elderly people by developing a Gated Recurrent Unit-based neural network model using heart rate and mean blood pressure signals, achieving prediction approximately ten minutes before manual markers.
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.