Falls prediction in elderly people using Gated Recurrent Units
Falls prevention, especially in older people, becomes an increasingly important topic in the times of aging societies. In this short concept paper, based on Marcin Radzio’s master’s thesis, 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.
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