Embedded Real-Time Fall Detection with Deep Learning on Wearable Devices
Emanuele Torti, Alessandro Fontanella, Mirto Musci, Nicola Blago, Danilo Pietro Pau, Francesco Leporati, Marco Piastra · 2018
Unintentional falls are the leading cause of fatal injuries and nonfatal trauma among older adults. An automated monitoring system that detects occurring falls and issues remote notifications will prove very valuable for improving the level of care that could be provided to people at higher risk. The work presented focuses on the design of embedded software for wearable devices that are connected in wireless mode to a remote monitoring system. The work focuses on the implementation of recurrent neural networks (RNNs) architectures of micro controller units (MCU) for fall detection with tri-axial accelerometers. A few general formulas for determining memory, computing power and power consumption for such architectures are presented. These formulas have been validated with an actual implementation for the SensorTile device by STMicroelectronics.