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The research presented in this paper addresses the exploitation of Deep Learning methods on wearable devices. We propose a hardware architecture capable of analyzing time series signals through a Recurrent Neural Network implemented on FPGA technology. This architecture has been validated using a real dataset, which includes three-axial accelerometer data acquired by a wearable device used for fall detection. The experiments have been conducted considering different devices and demonstrates that the proposed hardware architecture outperforms the state of the art solutions both in terms of processing time and power consumption. Indeed, the proposed architecture is real-time compliant in the elaboration of the fall detection dataset adopted for the validation. The power consumption is in the order of dozens μW. Finally, futher functionalities could be added in the same chip since the resource usage is low. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]