Accidental falls are the preminent cause of fatal injuries and the most common cause of nonfatal trauma-related hospital admissions among elderly adults. An automated monitoring system that detects occurring falls and issues remote notifications will prove very valuable to improve the level of care that could be provided to vulnerable people. The paper focuses on the wearable-device approach to real-time fall detection, and presents the design of an embedded software for wearable devices that are connected in wireless mode to a remote monitoring system. In particular, the work proposes the embedding of a recurrent neural network (RNN) architecture on a micro controller unit (MCU) fed by tri-axial accelerometers data recorded by onboard sensors. To address the feasibility of such resource-costrained deep learning approach, the paper presents a few general formulas to determine memory occupation, computational load and power consumption. The formulas have been validated with the implementation of the run-time detection module for the SensorTile® device by STMicroelectronics.

Embedding Recurrent Neural Networks in Wearable Systems for Real-Time Fall Detection

Emanuele Torti
;
Alessandro Fontanella;Mirto Musci;Nicola Blago;Francesco Leporati;Marco Piastra
2019-01-01

Abstract

Accidental falls are the preminent cause of fatal injuries and the most common cause of nonfatal trauma-related hospital admissions among elderly adults. An automated monitoring system that detects occurring falls and issues remote notifications will prove very valuable to improve the level of care that could be provided to vulnerable people. The paper focuses on the wearable-device approach to real-time fall detection, and presents the design of an embedded software for wearable devices that are connected in wireless mode to a remote monitoring system. In particular, the work proposes the embedding of a recurrent neural network (RNN) architecture on a micro controller unit (MCU) fed by tri-axial accelerometers data recorded by onboard sensors. To address the feasibility of such resource-costrained deep learning approach, the paper presents a few general formulas to determine memory occupation, computational load and power consumption. The formulas have been validated with the implementation of the run-time detection module for the SensorTile® device by STMicroelectronics.
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11571/1298270
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 33
  • ???jsp.display-item.citation.isi??? 28
social impact